hypothesis examples sleep

August 1, 2013

12 min read

New Hypothesis Explains Why We Sleep

During sleep, the brain weakens the connections among nerve cells, apparently conserving energy and, paradoxically, aiding memory

By Giulio Tononi & Chiara Cirelli

Every night, while we lie asleep, blind, dumb and almost paralyzed, our brains are hard at work. Neurons in the sleeping brain fire nearly as often as they do in a waking state, and they consume almost as much energy. What is the point of this unceasing activity at a time when we are supposedly resting? Why does the conscious mind disconnect so completely from the external environment while the brain keeps nattering on?

The brain's activity during rest likely serves some essential function. The evidence for this importance starts with sleep's ubiquity. All animals apparently sleep even though being unconscious and unresponsive greatly raises the risk of becoming another creature's lunch. Birds do it, bees do it, iguanas and cockroaches do it, even fruit flies do it, as we and others demonstrated more than a decade ago.

Furthermore, evolution has devised a few extraordinary adaptations to accommodate sleep: dolphins and some other marine mammals that must surface often to breathe, for example, sleep by alternately switching off one hemisphere of their brain while the other remains in a waking state.

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Like many scientists and nonscientists, the two of us have long wondered what benefit sleep provides that makes it so crucial to living creatures. More than 20 years ago, when we worked together at the Sant'Anna School of Advanced Studies in Pisa, Italy, we began to suspect that the brain's activity during slumber may somehow restore to a baseline state the billions of neural connections that get modified every day by the events of waking life. Sleep, in this telling, would preserve the ability of the brain's circuitry to form new memories continually over the course of an individual's lifetime without becoming oversaturated or obliterating older memories.

We also have an idea of why awareness of the external environment must be shut off during sleep. It seems to us that conscious experience of the here and now has to be interrupted for the brain to gain the chance to integrate new and old memories; sleep provides that respite.

Our hypothesis is somewhat controversial among our fellow neuroscientists who study sleep's role in learning and memory because we suggest that the return to baseline results from a weakening of the links among the neurons that fire during sleep. Conventional wisdom holds, instead, that brain activity during sleep strengthens the neural connections involved in storing newly formed memories. Yet years of research with organisms ranging from flies to people lend support to our notions.

School of Nod

Scientists first proposed the idea that sleep is important to memory nearly a century ago, and plenty of experiments since then have shown that after a night of sleep, and sometimes just a nap, newly formed memories “stick” better than they would if one had spent the same amount of time awake. This pattern holds for declarative memories, such as lists of words and associations between pictures and places, as well as for procedural memories, which underlie perceptual and motor skills, such as playing a musical instrument.

The evidence that sleep benefits memory led scientists to look for signs that the brain rehashes newly learned material at night. They found them: studies performed over the past 20 years, first in rodents and then in humans, show that patterns of neural activity during sleep sometimes do resemble those recorded while subjects are awake. For example, when a rat learns to navigate a maze, certain neurons in a part of the brain called the hippocampus fire in specific sequences. During subsequent sleep, rats “replay” these sequences more often than predicted by chance.

Because of such findings, many researchers came to assume that sleep “replay” consolidates memories by further reinforcing synapses—the contact points between neurons—that have been strengthened when an individual is awake. The idea is that, as linked neurons fire repeatedly, the synapses connecting them more readily convey signals from one neuron to another, helping neuronal circuits to encode memories in the brain. This process of selective strengthening is known as synaptic potentiation, and it is the favored mechanism by which the brain is thought to accomplish learning and remembering.

Yet while replay and potentiation are known to occur during waking activities, scientists have so far found no direct evidence that the synapses in replayed circuits get strengthened during sleep. This lack of evidence hardly surprises us. It is consistent with our suspicion that while the sleeper lies unaware, all that brain activity—the “replay” as well as other, seemingly random firings—might actually be weakening neural connections, not strengthening them.

The Price of Plasticity

There are many good reasons to propose that synapses must become weakened as well as strengthened for the brain to function properly. For one thing, strong synapses consume more energy than weak ones, and the brain does not have infinite stores of energy. In humans the brain accounts for almost 20 percent of the body's energy budget—more than any other organ by weight—and at least two thirds of that portion goes to supporting synaptic activity. Building and bolstering synapses is also a major source of cellular stress, requiring cells to synthesize and deliver components ranging from mitochondria (the cell's power plants), to synaptic vesicles (which ferry signaling molecules), to various proteins and lipids that are needed for communication across synapses.

It seems clear to us that this strain on resources is unsustainable. The brain cannot go on strengthening and maintaining revved-up synapses both day and night for the whole of an individual's lifetime. We do not doubt that learning occurs mainly through synaptic potentiation. We simply doubt that strengthening continues to happen during sleep.

In contrast, synaptic weakening during sleep would restore brain circuitry to a baseline level of strength, thereby avoiding excessive energy consumption and cellular stress. We refer to this baseline-restoring function of sleep as preserving synaptic homeostasis, and we call our overall hypothesis about the role of sleep the synaptic homeostasis hypothesis, or SHY. In principle, SHY explains the essential, universal purpose of sleep for all organisms that do it: sleep restores the brain to a state where it can learn and adapt when we are awake. The risk we take by becoming disconnected from the environment for hours at a time is the price we pay for this neural recalibration. Most generally, sleep is the price we pay for the brain's plasticity—its ability to modify its wiring in response to experience.

But how does SHY explain sleep's salutary effects on learning and memory? How can weakened synapses improve the overall retention of skills and facts? Consider that, over the course of a typical day, almost everything you experience leaves a neural trace in the brain and that the significant events, like meeting a new person or learning a piece of music on the guitar, make up just a trifling portion of that neural encoding. To improve memory, the sleeping brain must somehow distinguish the “noise” of irrelevant information from the “signal” of significant happenings.

We suggest that in sleep, the spontaneous firing of neurons in the brain activates many different circuits in many different combinations, encompassing both new memory traces and old networks of learned associations. (You get a glimpse of this neural free-for-all in dreams.) The spontaneous activity lets the brain try out which new memories fit better with stored memories of proved significance and weakens those synapses that do not fit well in the grand scheme of memory. We and other investigators are exploring possible mechanisms by which brain activity could selectively weaken synapses that encode the “noise” while preserving those that correspond to the “signal.”

While the brain tries out these imaginary scenarios and enacts weakening where appropriate, we had best be unaware of the surrounding environment and be incapable of acting in it; that is, we had best be asleep. Likewise, restoring synaptic homeostasis should not take place while we are awake because the events of the day would dominate the process, giving salience to them rather than to all the knowledge the brain has accumulated over a lifetime. The profound disconnection of sleep frees our brain from the tyranny of the present, creating an ideal circumstance for integrating and consolidating memories.

A Weak Connection

Our proposal that the brain uses neuronal firing during sleep to weaken rather than strengthen synapses is supported in part by close analyses of data from a standard workhorse of sleep research: the electroencephalogram, or EEG. EEGs record patterns of electrical activity in the cerebral cortex via electrodes attached to the scalp. Decades ago EEG recordings of the sleeping brain revealed two main categories of sleep, called rapid eye movement (REM) and non-REM (NREM), that alternate throughout the night. Each has distinctive brain-wave patterns. In addition to the jittering of eyeballs underneath closed lids that gives REM sleep its name, that stage is dominated by relatively fast oscillations—quick ups and downs in the curves of the EEG readout, resembling EEG recordings of the waking state. In contrast, slow oscillations—with frequencies of about one cycle per second—are the most prominent feature of NREM sleep.

A decade ago the late Mircea Steriade of Laval University in Quebec discovered that the slow oscillations of NREM sleep arise when groups of neurons fire together for a little while (so-called on periods), then fall silent for about a fraction of a second (off periods) and then resume their synchronized firing. This was one of the fundamental discoveries in sleep research. Since then, scientists have also discovered that in birds and mammals, the slow waves are large if preceded by a long period of wakefulness and become smaller as sleep goes on.

We reasoned that if synapses are strong, neurons will synchronize their firing more, producing larger slow waves. If synapses are weak, neurons will be less synchronized and the resulting slow waves will be smaller. Results of computer simulations and experiments in humans and animals led us to conclude that the big, steep slow waves early in the night indicate that synapses have been strengthened by prior wakefulness, whereas the small, shallow slow waves early in the morning indicate that synapses have become weaker during sleep.

Direct support for the idea that synapses become weaker during sleep, and may even be pruned away, comes from studies in animals. In fruit flies, for instance, we find that sleep reverses a progressive increase in the number and size of synapses that occurs during the day, especially when the flies are exposed to stimulating environments. Synaptic spines are specialized protrusions on a neuron's signal-detecting arm. When fruit flies spend the day interacting with other flies, neurons throughout their brain sprout more synaptic spines by evening than were present in the morning.

Just as remarkably, the number of spines goes back to the baseline level by the following morning if—and only if—the flies are allowed to sleep. We saw a similar phenomenon in the cerebral cortex of adolescent mice: the number of synaptic spines tended to rise when the animals were awake and to fall when they slept. In adult rodents, the upshot is the same, although it is not the number of synaptic spines that changes with wakefulness and sleep but rather the abundance of certain spine molecules, known as AMPA receptors, that determine the strength of a synapse. When we monitored these AMPA receptors, we found that their number per synapse increases after wakefulness and decreases after sleep. More receptors make for stronger synapses; fewer mean the synapses have weakened.

Synaptic strength can be gauged directly by using an electrical probe to stimulate neural fibers in the cortex. The neurons respond with an induced electrical discharge that is larger when synapses are strong and smaller when the connections are weak. We showed that in rats, stimulated neurons fire more strongly after a few hours of wakefulness and less strongly after sleep. Marcello Massimini of the University of Milan in Italy and Reto Huber, now at the University of Zurich, performed a similar experiment in humans. Instead of an electrical probe, they turned to transcranial magnetic stimulation—a short magnetic pulse applied to the scalp—to stimulate the underlying neurons. They then recorded the strength of the cortical responses with high-density EEG. The results were clear: the longer a subject was awake, the larger the EEG responses. It took a night of sleep for cortical responses to return to the baseline.

Less Is More

The common conclusion of these experiments, which we performed over two decades, is that spontaneous cortical activity in sleep does indeed weaken the synaptic connections in neural circuits, whether by damping their ability to send electrical impulses or by erasing them outright.

This process, which we call down selection, would ensure the survival of the circuits that are “fittest,” either because they were activated strongly and consistently during wakefulness (say, by playing the right notes on a guitar while trying to master a new piece) or because they were better integrated with previous, older memories (as would be the case for a new word encountered in a known language). Meanwhile synapses in circuits that were only mildly enhanced during wakefulness (such as fumbled notes on the guitar) or that fit less with old memories (such as a new word presented in an unknown language) would be depressed.

Down selection would ensure that insignificant events would leave no lasting trace in our neural circuitry, whereas memories of note would be preserved. As an additional bonus, down selection would also make room for another cycle of synaptic strengthening during wakefulness. Indeed, some findings imply that among its many other benefits for learning and memory, sleep aids the subsequent acquisition of new memories (material encountered before the next bout of sleep). Quite a few studies have shown that after a night of sleep, you can learn new material much better than you can after having been awake all day. (Students, take note.)

Although we have no direct evidence for a mechanism that would produce selective weakening of activated synapses as yet, we have a notion of how synaptic weakening could occur. We suspect the slow waves of mammalian NREM sleep somehow play a role. In lab studies of rat brain tissue, nerve cells became less effective at passing signals to one another when stimulated in ways that mimic the synchronized on/off cycles of slow-wave sleep.

The chemistry of the brain also changes in NREM sleep in a way that could lead to synaptic weakening. In the awake individual, a concentrated soup of signaling chemicals, or neuromodulators—including acetylcholine, norepinephrine, dopamine, serotonin, histamine and hypocretin—bathe the brain and bias synapses toward strengthening when signals pass through them. During sleep—especially NREM sleep—the soup becomes much less concentrated. This diluted milieu of neuromodulators may bias the neural circuitry so that synapses become weakened, rather than strengthened, when signals flow across them. The process might also involve a substance called brain-derived neurotrophic factor (BDNF), which is known to promote synaptic strengthening and to be involved in memory acquisition. BDNF levels are high in neurons during wakefulness and minimal during sleep.

Local Sleep

Regardless of specific mechanisms and selective processes, the evidence is strong in several species that overall synaptic strength goes up during wakefulness and down during sleep: the core prediction of SHY. We can test SHY further by examining some of its intriguing corollaries.

For example, if the hypothesis is correct, then the more plasticity a part of the brain undergoes during wakefulness, the more that part should need to sleep. “Sleep need” can, in turn, be indicated by an increase in the size and duration of NREM slow waves. To explore this prediction, we asked human subjects to learn a novel task: how to reach a target on a computer screen while the cursor (controlled by a mouse) is systematically rotated. The part of the brain that engages in this kind of learning is the right parietal cortex. Sure enough, when our subjects slept, the slow waves over their right parietal cortex were larger, relative to waves from the same area on the night before learning occurred. These large waves did flatten out in the course of the night, as such oscillations do. But those large, localized waves at the start of the night tell us that particular part of the brain had been exhausted by the task we assigned.

Many other experiments by the two of us and others have since confirmed that learning, and more generally the activation of synapses in circuits, produces a local increase in sleep need. Recently we have even found that prolonged or intense use of certain circuits can make local groups of neurons “fall asleep” even though the rest of the brain (and the organism itself) remains awake. Thus, if a rat stays awake longer than usual, some cortical neurons show brief periods of silence that are basically indistinguishable from the off periods observed during slow-wave sleep. Meanwhile the rat is running around, its eyes open, tending to its business, as any awake rat would do.

This phenomenon is called local sleep, and it is attracting scrutiny from other investigators. Our latest studies indicate that localized off periods also occur in the brains of sleep-deprived humans and that those periods become more frequent after intense learning. It seems that when we have been awake for too long or have overexerted certain circuits, small chunks of the brain may take quick naps without giving notice. One wonders how many errors of judgment, silly mistakes, irritable responses and foul moods result from local sleep in the brains of exhausted people who believe they are fully awake and in complete control.

SHY also predicts that sleep is especially important in childhood and adolescence, times of concentrated learning and of intense synaptic remodeling, as many studies have shown. In youth, synapses are formed, strengthened and pruned at an explosive rate never approached in adulthood. It makes sense that down selection during sleep would be crucial to minimize the energy costs of this frenzied synaptic remodeling and to favor the survival of adaptive neural circuits in these stages of life. One can only wonder what happens when sleep is disrupted or insufficient during critical periods in development. Might the deficit corrupt the proper refinement of neural circuits? In that case, the effect of sleep loss would not merely be occasional forgetfulness or misjudgment but a lasting change in the way the brain is wired.

We look forward to testing SHY's predictions and exploring its implications further. For example, we hope to discover whether sleep deprivation during neural development leads to changes in the organization of brain circuitry. We would also like to learn more about the effect of sleep on deep-brain areas, such as the thalamus, cerebellum, hypothalamus and brain stem, and about the role of REM sleep in synaptic homeostasis. Perhaps we would then learn if sleep is indeed the price of waking plasticity, a price that every brain and every neuron must pay.

Why We Sleep: A Hypothesis for an Ultimate or Evolutionary Origin for Sleep and Other Physiological Rhythms

Affiliation.

• 1 Penn State Health, Department of Pediatrics, Penn State Hershey Medical Center, Hershey, PA, US.
• PMID: 32269596
• PMCID: PMC7120898
• DOI: 10.5334/jcr.189

Although sleep is ubiquitous, its evolutionary purpose remains elusive. Though every species of animal, as well as many plants sleep, theories of its origin are purely physiological, e.g. to conserve energy, make repairs or to consolidate learning. An evolutionary reason for sleep would answer one of biology's fundamental unanswered questions. When environmental conditions change on a periodic basis (winter/summer, day/night) organisms must somehow confront the change or else be less able to compete in either niche. Seasonal adaptation includes the migration of birds, changes in honeybee physiology and winter abscission in plants. Diurnal adaptation must be more rapid, forcing changes in behavior in addition to physiology. Since organisms must exist in both environments, evolution has created a way to force a change in behavior, in effect creating "different" organisms (one awake, one asleep) adapted separately to two distinct niches. We sleep to allow evolving into two competing niches. The physiology of sleep forces a change to a different state for the second niche. The physiological needs for sleep are mechanisms that have evolved to achieve this goal.

Keywords: adaptive theory; evolution; physiology; proximate cause; restorative theory; sleep; ultimate cause.

Copyright: © 2020 The Author(s).

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Restorative Theory and More Ideas About Why We Sleep

Restorative theory, adaptive theory.

• Energy Conservation
• Brain Plasticity

Frequently Asked Questions

Even though it's something you have to do every day, why we sleep remains a mystery. Many sleep theories abound, yet scientists are far from universal agreement about how to answer the question, "Why do we sleep at night?" Only in the last few decades have they even begun to unravel sleep's true secrets. At least four common theories are in play, but it remains to be seen which—if any—are correct.

You may look at these theories and the scientific evidence that supports them and wonder why they all can't, at least in part, be right. They could be, but researchers are continuing to search for "the" core reason for sleep, and many subscribe to a belief expressed in a 1998 paper:  

Allen Rechtschaffen

Sleep can be understood as fulfilling many different functions but intuition suggests there is one essential function. The discovery of this function will open an important door to the understanding of biological processes.

The sleep cycle involves several stages, but these theories generally focus on rapid-eye movement (REM) sleep —which is when you dream —and the other stages lumped together as non-REM sleep.

The restorative theory of sleep, first proposed in 2006, is among the more accepted explanations for why people need sleep. It suggests that the purpose of sleep is to store memory and restore our brains and bodies for the next day.

• Organizing and storing memories : Memories are believed to be converted from short-term to long-term storage, while information deemed unimportant is removed. This primarily occurs during REM sleep, which is when the brain cells most involved in memory, attention, and learning are least active.
• Clearing out and replenishing brain chemicals : Many brain chemicals build up while you're awake, including adenosine , which makes you sleepy when it accumulates. Meanwhile, during sleep, the brain "restocks" the chemicals it uses for sending signals and other purposes, so you have enough for the next day.
• Clearing waste toxins from the brain : Similar to brain chemicals, the waste products of energy metabolism build up during the day and are cleared out at night. (However, the primary evidence for this comes from studies of mice, not humans.)
• Repairing tissues throughout the body : During sleep, your body increases cellular division, growth hormone levels, and the production of proteins necessary for proper function. This is believed to be why strenuous physical activity during the day increases your time in REM sleep.
• Rest for the brain : The inactivity during non-REM sleep appears to provide a period of rest for the brain.

Some of the brain chemicals that accumulate during the day are associated with the plaques that characterize Alzheimer's disease , so it's theorized that the brain-clearing activities of sleep may protect you against Alzheimer's.

Also called the evolution theory or preservation theory, the original version of this early theory suggested that while humans evolved, hiding out overnight increased the ability to survive.  

For our early ancestors, nighttime was dangerous—especially because the predators who hunt at night function better in the dark than humans do—so it made sense to seek a safe refuge. Also, because they couldn't be out finding food themselves, their bodies slowed down to conserve energy for when they could be active. People who avoided dangers in this way, the theory argued, lived longer and were more likely to reproduce.

Thus, sleep became an adaptive or evolutionary advantage and became part of the neurochemistry of the species. However, most experts reject this idea because sleeping leaves animals (including humans) vulnerable and defenseless, which doesn't support the idea that sleeping made our ancestors safer.

A flaw in this theory, some experts say, is that sleeping limits productivity, such as finding food and reproducing, so staying awake longer would be an evolutionary benefit.   However, no species evolved without a need for sleep, which calls the adaptive benefit into question.

According to some sleep researchers, the daily need for sleep combined with the incentive not to be out in the dark caused us to adapt to function best during daylight, which prevented us from adapting to the dark.  

Energy Conservation Theory

In a similar vein to aspects of other theories, some experts theorize the primary purpose of sleep is conserving energy. By sleeping, they say, you're able to spend part of your time functioning at a lower metabolism.

That lowers the number of calories you need to eat. For early humans, that extra food requirement could have been the difference between life and death, or survival of the species versus extinction. It was harder to gather food at night, so it made sense to stay hidden then. They also point to the brain's need to replenish its reserve of glycogen, which is an important fuel.

However, while it's true that metabolism slows during non-REM, the brain is extremely active during REM sleep, which some say is a strike against the energy conservation theory.

Brain Plasticity Theory

Among the more recent theories deals with brain plasticity (also called neuroplasticity), which is the brain's ability to change and adapt in response to experience. It can change both functional aspects (such as re-learning skills in a new area after damage) and structural aspects (such as forming new pathways due to learning).

The brain plasticity theory says that sleep is necessary for the brain to make structural changes. Support for this theory comes from many places.

As in restorative theory, this concept deals with information processing and memory formation. Research suggests that sleep loss leads to less structural plasticity, which can have a negative effect on alertness, cognition, and mood. Sleep deprivation also compromises memory formation, which is related to learning and plasticity.  

It's believed that the plasticity theory explains why babies and young children require a lot of sleep—they're learning so much about the world that their brains need more time to process it. Researchers are even trying to promote less sleep interruption for preterm babies in neonatal intensive care, citing studies about sleep's long-term impact on brain development and plasticity.  

Some researchers have even theorized that sleep is the price we pay for brain plasticity. That concept is based on the importance of the processes that occur during sleep to the brain's ability to adapt and change.  

Declining Sleep Needs

Newborns need between 14 and 17 hours of sleep per day. The recommended amount of sleep declines throughout childhood, with teenagers needing between 8 and 10 hours a day.

Why do we close our eyes when we sleep?

There are several reasons why most people sleep with eyes closed, though some people do actually sleep with their eyes open. Closed eyes stay moist and are protected by the eyelids during sleep. Eyelids also block out light during sleep, and light signals to the brain that it is time to wake up.

At what stage of sleep can you experience sleep paralysis?

Sleep paralysis , or the feeling that you are conscious and yet unable to control your body, is very common and typically occurs during the transition from REM to being awake.

Why do we dream?

There are a lot of theories explaining why we dream, such as that dreaming aids in memory processing and that it allows us to process emotions. Sigmund Freud believed that dreams express our unconscious desires and deepest wishes.

A Word From Verywell

While it's a phenomenon we don't fully understand, sleep is critical to our daily health. Not only is it necessary for restoration and repair, learning and memory, growth and development, and brain plasticity, sleep also helps with problem-solving, a healthy metabolism, blood-sugar and hormone regulation, heart health, and strengthening immunity. With how crucial it is to our survival, it's no wonder many of us long for more of it.

Freiberg AS. Why we sleep: A hypothesis for an ultimate or evolutionary origin for sleep and other physiological rhythms .  J Circadian Rhythms . 2020;18:2. Published 2020 Mar 30. doi:10.5334/jcr.189

Ezenwanne E. Current concepts in the neurophysiologic basis of sleep; a review .  Ann Med Health Sci Res ; 1(2):173-179.

Xie L, Kang H, Xu Q, et al.  Sleep drives metabolite clearance from the adult brain .  Science . 2013;342(6156):373–377. doi:10.1126/science.1241224

Raven F, Van der Zee EA, Meerlo P, Havekes R. The role of sleep in regulating structural plasticity and synaptic strength: Implications for memory and cognitive function .  Sleep Med Rev . 2018;39:3-11. doi:10.1016/j.smrv.2017.05.002

Park J. Sleep promotion for preterm infants in the NICU .  Nurs Womens Health . 2020;24(1):24-35. doi:10.1016/j.nwh.2019.11.004

Tononi G, Cirelli C. Sleep and the price of plasticity: From synaptic and cellular homeostasis to memory consolidation and integration .  Neuron . 2014;81(1):12-34. doi:10.1016/j.neuron.2013.12.025

U.S. Centers for Disease Control and Prevention. About sleep .

National Sleep Foundation. Can you really sleep with your eyes open ?

Rasch B, Born J.  About sleep's role in memory .  Physiol Rev . 2013;93(2):681-766. doi:10.1152/physrev.00032.2012

Zhang W, Guo B.  Freud's dream interpretation: A different perspective based on the self-organization theory of dreaming .  Front Psychol . 2018;9:1553. doi:10.3389/fpsyg.2018.01553

National Institutes of Health, National Heart, Lung, and Blood Institute. Sleep deprivation and deficiency .

By Brandon Peters, MD Dr. Peters is a board-certified neurologist and sleep medicine specialist and is a fellow of the American Academy of Sleep Medicine.

4.2 Sleep and Why We Sleep

Learning objectives.

By the end of this section, you will be able to:

• Describe areas of the brain involved in sleep
• Understand hormone secretions associated with sleep
• Describe several theories aimed at explaining the function of sleep
• Name and describe three theories about why we dream

We spend approximately one-third of our lives sleeping. Given the average life expectancy for U.S. citizens falls between 73 and 79 years old (Singh & Siahpush, 2006), we can expect to spend approximately 25 years of our lives sleeping. Some animals never sleep (e.g., some fish and amphibian species); other animals sleep very little without apparent negative consequences (e.g., giraffes); yet some animals (e.g., rats) die after two weeks of sleep deprivation (Siegel, 2008). Why do we devote so much time to sleeping? Is it absolutely essential that we sleep? This section will consider these questions and explore various explanations for why we sleep.

What is Sleep?

You have read that sleep is distinguished by low levels of physical activity and reduced sensory awareness. As discussed by Siegel (2008), a definition of sleep must also include mention of the interplay of the circadian and homeostatic mechanisms that regulate sleep. Homeostatic regulation of sleep is evidenced by sleep rebound following sleep deprivation. Sleep rebound refers to the fact that a sleep-deprived individual will fall asleep more quickly during subsequent opportunities for sleep. Sleep is characterized by certain patterns of activity of the brain that can be visualized using electroencephalography (EEG), and different phases of sleep can be differentiated using EEG as well.

Sleep-wake cycles seem to be controlled by multiple brain areas acting in conjunction with one another. Some of these areas include the thalamus, the hypothalamus, and the pons. As already mentioned, the hypothalamus contains the SCN—the biological clock of the body—in addition to other nuclei that, in conjunction with the thalamus, regulate slow-wave sleep. The pons is important for regulating rapid eye movement (REM) sleep (National Institutes of Health, n.d.).

Sleep is also associated with the secretion and regulation of a number of hormones from several endocrine glands including: melatonin, follicle stimulating hormone (FSH), luteinizing hormone (LH), and growth hormone (National Institutes of Health, n.d.). You have read that the pineal gland releases melatonin during sleep ( Figure 4.6 ). Melatonin is thought to be involved in the regulation of various biological rhythms and the immune system (Hardeland et al., 2006). During sleep, the pituitary gland secretes both FSH and LH which are important in regulating the reproductive system (Christensen et al., 2012; Sofikitis et al., 2008). The pituitary gland also secretes growth hormone, during sleep, which plays a role in physical growth and maturation as well as other metabolic processes (Bartke, Sun, & Longo, 2013).

Why Do We Sleep?

Given the central role that sleep plays in our lives and the number of adverse consequences that have been associated with sleep deprivation, one would think that we would have a clear understanding of why it is that we sleep. Unfortunately, this is not the case; however, several hypotheses have been proposed to explain the function of sleep.

Adaptive Function of Sleep

One popular hypothesis of sleep incorporates the perspective of evolutionary psychology. Evolutionary psychology is a discipline that studies how universal patterns of behavior and cognitive processes have evolved over time as a result of natural selection . Variations and adaptations in cognition and behavior make individuals more or less successful in reproducing and passing their genes to their offspring. One hypothesis from this perspective might argue that sleep is essential to restore resources that are expended during the day. Just as bears hibernate in the winter when resources are scarce, perhaps people sleep at night to reduce their energy expenditures. While this is an intuitive explanation of sleep, there is little research that supports this explanation. In fact, it has been suggested that there is no reason to think that energetic demands could not be addressed with periods of rest and inactivity (Frank, 2006; Rial et al., 2007), and some research has actually found a negative correlation between energetic demands and the amount of time spent sleeping (Capellini, Barton, McNamara, Preston, & Nunn, 2008).

Another evolutionary hypothesis of sleep holds that our sleep patterns evolved as an adaptive response to predatory risks, which increase in darkness. Thus we sleep in safe areas to reduce the chance of harm. Again, this is an intuitive and appealing explanation for why we sleep. Perhaps our ancestors spent extended periods of time asleep to reduce attention to themselves from potential predators. Comparative research indicates, however, that the relationship that exists between predatory risk and sleep is very complex and equivocal. Some research suggests that species that face higher predatory risks sleep fewer hours than other species (Capellini et al., 2008), while other researchers suggest there is no relationship between the amount of time a given species spends in deep sleep and its predation risk (Lesku, Roth, Amlaner, & Lima, 2006).

It is quite possible that sleep serves no single universally adaptive function, and different species have evolved different patterns of sleep in response to their unique evolutionary pressures. While we have discussed the negative outcomes associated with sleep deprivation, it should be pointed out that there are many benefits that are associated with adequate amounts of sleep. A few such benefits listed by the National Sleep Foundation (n.d.) include maintaining health, lowering stress levels, improving mood, and increasing motor coordination, as well as a number of benefits related to cognition and memory formation.

Cognitive Function of Sleep

Another theory regarding why we sleep involves sleep’s importance for cognitive function and memory formation (Rattenborg, Lesku, Martinez-Gonzalez, & Lima, 2007). Indeed, we know sleep deprivation results in disruptions in cognition and memory deficits (Brown, 2012), leading to impairments in our abilities to maintain attention, make decisions, and recall long-term memories. Moreover, these impairments become more severe as the amount of sleep deprivation increases (Alhola & Polo-Kantola, 2007). Furthermore, slow-wave sleep after learning a new task can improve resultant performance on that task (Huber, Ghilardi, Massimini, & Tononi, 2004) and seems essential for effective memory formation (Stickgold, 2005). Understanding the impact of sleep on cognitive function should help you understand that cramming all night for a test may not be effective and can even prove counterproductive.

Link to Learning

Watch this brief video that gives sleep tips for college students to learn more.

Getting the optimal amount of sleep has also been associated with other cognitive benefits. Research indicates that included among these possible benefits are increased capacities for creative thinking (Cai, Mednick, Harrison, Kanady, & Mednick, 2009; Wagner, Gais, Haider, Verleger, & Born, 2004), language learning (Fenn, Nusbaum, & Margoliash, 2003; Gómez, Bootzin, & Nadel, 2006), and inferential judgments (Ellenbogen, Hu, Payne, Titone, & Walker, 2007). It is possible that even the processing of emotional information is influenced by certain aspects of sleep (Walker, 2009).

Watch this brief video about the relationship between sleep and memory to learn more.

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4.2 Sleep & Why We Sleep

Learning objectives.

By the end of this section, you will be able to:

• Describe areas of the brain involved in sleep
• Understand hormone secretions associated with sleep
• Describe several theories aimed at explaining the function of sleep

   We spend approximately one-third of our lives sleeping. Given the average life expectancy for U.S. citizens falls between 73 and 79 years old (Singh & Siahpush, 2006), we can expect to spend approximately 25 years of our lives sleeping. Some animals never sleep (e.g., several fish and amphibian species); other animals can go extended periods of time without sleep and without apparent negative consequences (e.g., dolphins); yet some animals (e.g., rats) die after two weeks of sleep deprivation (Siegel, 2008). Why do we devote so much time to sleeping? Is it absolutely essential that we sleep? This section will consider these questions and explore various explanations for why we sleep.

WHAT IS SLEEP?

   You have read that sleep is distinguished by low levels of physical activity and reduced sensory awareness. As discussed by Siegel (2008), a definition of sleep must also include mention of the interplay of the circadian and homeostatic mechanisms that regulate sleep. Homeostatic regulation of sleep is evidenced by sleep rebound following sleep deprivation. Sleep rebound refers to the fact that a sleep-deprived individual will tend to take a shorter time to fall asleep during subsequent opportunities for sleep. Sleep is characterized by certain patterns of activity of the brain that can be visualized using electroencephalography (EEG), and different phases of sleep can be differentiated using EEG as well (figure below).

This is a segment of a polysonograph (PSG), a recording of several physical variables during sleep. The x -axis shows passage of time in seconds; this record includes 30 seconds of data. The location of the sets of electrode that produced each signal is labeled on the y -axis. The red box encompasses EEG output, and the waveforms are characteristic of a specific stage of sleep. Other curves show other sleep-related data, such as body temperature, muscle activity, and heartbeat.

   Sleep-wake cycles seem to be controlled by multiple brain areas acting in conjunction with one another. Some of these areas include the thalamus, the hypothalamus, and the pons. As already mentioned, the hypothalamus contains the SCN—the biological clock of the body—in addition to other nuclei that, in conjunction with the thalamus, regulate slow-wave sleep. The pons is important for regulating rapid eye movement (REM) sleep (National Institutes of Health, n.d.).

Sleep is also associated with the secretion and regulation of a number of hormones from several endocrine glands including: melatonin, follicle stimulating hormone (FSH), luteinizing hormone (LH), and growth hormone (National Institutes of Health, n.d.). You have read that the pineal gland releases melatonin during sleep (figure below). Melatonin is thought to be involved in the regulation of various biological rhythms and the immune system (Hardeland et al., 2006). During sleep, the pituitary gland secretes both FSH and LH which are important in regulating the reproductive system (Christensen et al., 2012; Sofikitis et al., 2008). The pituitary gland also secretes growth hormone, during sleep, which plays a role in physical growth and maturation as well as other metabolic processes (Bartke, Sun, & Longo, 2013).

Although it is still unclear exactly which hormones cause one to sleep, new studies have shown that melanin-concentrating hormone (MCH) neurons promote sleep in the brain. They discharge action potentials during both NREM and REM sleep to regulate these sleep states. Recent studies have also shown that sex hormones may have an effect on sleep-wake cycles. One study found that males may be more prone to sleep apnea due to their lower levels of progesterone (Empson & Purdie, 1999). Yet another theory for why we sleep is that it is important for information consolidation (i.e., solidifying information in long-term memory). One study examined this theory by dividing 28 participants into two groups and having them learn 12 new words at either 10:00 am or 10:00 pm. They hypothesized that the participants learning the words at 10:00 pm would have better recollection based on the assumption that they were closer to going to sleep (and therefore closer to entering a state in which they could consolidate the new information). This is not, however, what they found. They found that there was no significant difference in the recollection of words based on the time those words were learned (Bengino, 2006).

The pineal and pituitary glands secrete a number of hormones during sleep.

Why do we sleep.

   Given the central role that sleep plays in our lives and the number of adverse consequences that have been associated with sleep deprivation, one would think that we would have a clear understanding of why it is that we sleep. Unfortunately, this is not the case; however, several hypotheses have been proposed to explain the function of sleep.

Adaptive Function of Sleep

One popular hypothesis of sleep incorporates the perspective of evolutionary psychology. Evolutionary psychology is a discipline that studies how universal patterns of behavior and cognitive processes have evolved over time as a result of natural selection . Variations and adaptations in cognition and behavior make individuals more or less successful in reproducing and passing their genes to their offspring. One hypothesis from this perspective might argue that sleep is essential to restore resources that are expended during the day. Just as bears hibernate in the winter when resources are scarce, perhaps people sleep at night to reduce their energy expenditures. While this is an intuitive explanation of sleep, there is little research that supports this explanation. In fact, it has been suggested that there is no reason to think that energetic demands could not be addressed with periods of rest and inactivity (Frank, 2006; Rial et al., 2007), and some research has actually found a negative correlation between energetic demands and the amount of time spent sleeping (Capellini, Barton, McNamara, Preston, & Nunn, 2008).

Another evolutionary hypothesis of sleep holds that our sleep patterns evolved as an adaptive response to predatory risks, which increase in darkness. Thus we sleep in safe areas to reduce the chance of harm. Again, this is an intuitive and appealing explanation for why we sleep. Perhaps our ancestors spent extended periods of time asleep to reduce attention to themselves from potential predators. Comparative research indicates, however, that the relationship that exists between predatory risk and sleep is very complex and equivocal. Some research suggests that species that face higher predatory risks sleep fewer hours than other species (Capellini et al., 2008), while other researchers suggest there is no relationship between the amount of time a given species spends in deep sleep and its predation risk (Lesku, Roth, Amlaner, & Lima, 2006).

It is quite possible that sleep serves no single universally adaptive function, and different species have evolved different patterns of sleep in response to their unique evolutionary pressures. While we have discussed the negative outcomes associated with sleep deprivation, it should be pointed out that there are many benefits that are associated with adequate amounts of sleep. A few such benefits listed by the National Sleep Foundation (n.d.) include maintaining healthy weight, lowering stress levels, improving mood, and increasing motor coordination, as well as a number of benefits related to cognition and memory formation.

Cognitive Function of Sleep

   Another theory regarding why we sleep involves sleep’s importance for cognitive function and memory formation (Rattenborg, Lesku, Martinez-Gonzalez, & Lima, 2007). Indeed, we know sleep deprivation results in disruptions in cognition and memory deficits (Brown, 2012), leading to impairments in our abilities to maintain attention, make decisions, and recall long-term memories. Moreover, these impairments become more severe as the amount of sleep deprivation increases (Alhola & Polo-Kantola, 2007). Furthermore, slow-wave sleep after learning a new task can improve resultant performance on that task (Huber, Ghilardi, Massimini, & Tononi, 2004) and seems essential for effective memory formation (Stickgold, 2005). Understanding the impact of sleep on cognitive function should help you understand that cramming all night for a test may be not effective and can even prove counterproductive.

   Sleep has also been associated with other cognitive benefits. Research indicates that included among these possible benefits are increased capacities for creative thinking (Cai, Mednick, Harrison, Kanady, & Mednick, 2009; Wagner, Gais, Haider, Verleger, & Born, 2004), language learning (Fenn, Nusbaum, & Margoliash, 2003; Gómez, Bootzin, & Nadel, 2006), and inferential judgments (Ellenbogen, Hu, Payne, Titone, & Walker, 2007). It is possible that even the processing of emotional information is influenced by certain aspects of sleep (Walker, 2009).

   We devote a very large portion of time to sleep, and our brains have complex systems that control various aspects of sleep. Several hormones important for physical growth and maturation are secreted during sleep. While the reason we sleep remains something of a mystery, there is some evidence to suggest that sleep is very important to learning and memory.

References:

Openstax Psychology text by Kathryn Dumper, William Jenkins, Arlene Lacombe, Marilyn Lovett and Marion Perlmutter licensed under CC BY v4.0. https://openstax.org/details/books/psychology

Review Questions: 

1. Growth hormone is secreted by the ________ while we sleep.

a. pineal gland

c. pituitary gland

d. pancreas

2. The ________ plays a role in controlling slow-wave sleep.

a. hypothalamus

b. thalamus

d. both a and b

3. ________ is a hormone secreted by the pineal gland that plays a role in regulating biological rhythms and immune function.

a. growth hormone

b. melatonin

4. ________ appears to be especially important for enhanced performance on recently learned tasks.

a. melatonin

b. slow-wave sleep

c. sleep deprivation

d. growth hormone

5. What does the acronym ‘REM’ stand for?

a. Restless Eye Movement

b. Rapid Evolutionary Moments

c. Rapid Eye Movement

d. Recurring Evolutionary Minds

6. What is ‘manifest content’?

a. Being able to recall the events in your dreams

b. The hidden meaning of a dream

c. The act of dreaming while in deep sleep

d. The actual content of a dream

Critical Thinking Questions:

1. If theories that assert sleep is necessary for restoration and recovery from daily energetic demands are correct, what do you predict about the relationship that would exist between individuals’ total sleep duration and their level of activity?

2. How could researchers determine if given areas of the brain are involved in the regulation of sleep?

3. Differentiate the evolutionary theories of sleep and make a case for the one with the most compelling evidence.

Personal Application Question:

1. Have you (or someone you know) ever experienced significant periods of sleep deprivation because of simple insomnia, high levels of stress, or as a side effect from a medication? What were the consequences of missing out on sleep?

evolutionary psychology

sleep rebound 

Answers to Exercises

1.  Those individuals (or species) that expend the greatest amounts of energy would require the longest periods of sleep.

2.  Researchers could use lesion or brain stimulation techniques to determine how deactivation or activation of a given brain region affects behavior. Furthermore, researchers could use any number of brain imaging techniques like fMRI or CT scans to come to these conclusions.

3.  One evolutionary theory of sleep holds that sleep is essential for restoration of resources that are expended during the demands of day-to-day life. A second theory proposes that our sleep patterns evolved as an adaptive response to predatory risks, which increase in darkness. The first theory has little or no empirical support, and the second theory is supported by some, though not all, research.

evolutionary psychology:  discipline that studies how universal patterns of behavior and cognitive processes have evolved over time as a result of natural selection

sleep rebound:  sleep-deprived individuals will experience shorter sleep latencies during subsequent opportunities for sleep

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• Published: 09 April 2020

The interrelation of sleep and mental and physical health is anchored in grey-matter neuroanatomy and under genetic control

• Masoud Tahmasian   ORCID: orcid.org/0000-0003-3999-3807 1 ,
• Fateme Samea 1 ,
• Habibolah Khazaie 2 ,
• Mojtaba Zarei 1 ,
• Shahrzad Kharabian Masouleh   ORCID: orcid.org/0000-0003-4810-9542 3 , 4 ,
• Felix Hoffstaedter 3 , 4 ,
• Julia Camilleri 3 , 4 ,
• Peter Kochunov 5 ,
• B. T. Thomas Yeo 6 , 7 , 8 , 9 ,
• Simon Bodo Eickhoff 3 , 4 &
• Sofie Louise Valk 3 , 4  

Communications Biology volume  3 , Article number:  171 ( 2020 ) Cite this article

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• Circadian rhythms and sleep
• Genetics of the nervous system

An Author Correction to this article was published on 02 June 2020

This article has been updated

Humans need about seven to nine hours of sleep per night. Sleep habits are heritable, associated with brain function and structure, and intrinsically related to well-being, mental, and physical health. However, the biological basis of the interplay of sleep and health is incompletely understood. Here we show, by combining neuroimaging and behavioral genetic approaches in two independent large-scale datasets (HCP ( n  = 1106), age range: 22–37, eNKI ( n  = 783), age range: 12–85), that sleep, mental, and physical health have a shared neurobiological basis in grey matter anatomy; and that these relationships are driven by shared genetic factors. Though local associations between sleep and cortical thickness were inconsistent across samples, we identified two robust latent components, highlighting the multivariate interdigitation of sleep, intelligence, BMI, depression, and macroscale cortical structure. Our observations provide a system-level perspective on the interrelation of sleep, mental, and physical conditions, anchored in grey-matter neuroanatomy.

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Introduction.

Sleep plays an active role in providing adaptive physiological functions 1 , consolidating and retaining new memories 2 , metabolite clearance 3 , hormones’ secretion 4 , and synaptic hemostasis 5 . The National Sleep Foundation suggests 7–9 h of sleep per night for adults (18–64) and 7–8 h for older adults (65+). For school aged children (6–13 years) this is 9–11 h, and for teenagers 8–10 h 6 . However, people in modern societies are suffering from inadequate sleep and its consequences 6 . Sleep loss is associated with impairment in cognitive performance, motor vehicle accidents and poor quality of life 7 , 8 ; and contributes to heightened socioeconomic burden 9 , 10 . Beyond the quantity of sleep (sleep duration), quality of sleep includes sleep onset latency (i.e., time between going to bed and falling asleep), sleep efficiency (i.e., the percentage of time in bed during which someone is asleep), sleep disturbances, use of sleeping medication, and daytime dysfunction, all interacting with individual health and well-being 11 , 12 . Of note, it has been revealed that poor sleep quality is associated with higher rate of depressive symptoms in healthy subjects 13 , 14 ; and sleep disturbances are common in mood (e.g., major depression) and cognitive disorders 15 , 16 .

Individual differences in sleep behaviors are heritable 17 , 18 , 19 ; and various genetic, metabolic, behavioral, and psychological risk factors have been suggested for the development and maintenance of poor sleep quality and sleep disorders 20 , 21 , 22 . For example, genome wide association studies have associated insomnia disorder to structure of the striatum, hypothalamus, and claustrum, where gene expression profiles show association with the genetic risk profile of such individuals 23 , 24 . Moreover, sleep can have a bidirectional relation with health. Not only is sleep disturbance linked with hypertension, diabetes, and obesity 25 , 26 , but also depressive symptoms, physical illness, and fatigue were reported as associated factors for both poor sleep quality and short sleep duration 27 , 28 . A meta-analysis reported that insomnia disorder is associated with alterations in widespread brain structure and function 29 . In addition, other neuroimaging meta-analyses have implicated structural and functional abnormalities in the hippocampus, amygdala, and insula in patients with sleep apnea 30 and have indicated convergent functional brain alterations in the inferior parietal cortex and superior parietal lobule, following acute sleep deprivation 31 . Moreover, white matter integrity underlying prefrontal areas has been associated with sleep duration and sleep quality 32 , 33 , 34 . Lastly, lower prefrontal gray matter volume has been associated with greater sleep fragmentation in older individuals 35 .

Importantly, it has been demonstrated that macroscale gray matter neuroanatomy is heritable 36 , 37 , 38 , indicating part of the variance in brain structure can be related to additive genetic effects. Indeed, genetic factors influence cortical thickness in a systematic fashion where both functional and geometric constraints influence genetic correlation between and within brain systems 39 , 40 . Recent studies have indicated that phenotypic correlation between cortical thickness and intelligence, as well as BMI, is driven by additive genetic factors 41 , 42 , 43 suggesting a shared genetic basis of cortical thickness and non-brain traits. This raises the question whether the interrelation of sleep, mental, and physical health can be linked to the shared neurobiological mechanisms; and whether these relationships are driven by shared genetic factors.

To answer this question, we combined structural neuroimaging data from two independent samples: the Human Connectome Project (HCP unrelated sample n  = 424) and the enhanced NKI Rockland sample (eNKI: n  = 783) to explore whether the interrelation of sleep, mental, and physical health can be linked to a shared macroscale neurobiological signature. The HCP sample consists of young adults only, whereas the eNKI sample consists of adolescents, younger and older adults, enabling us to evaluate the generalizability of the interrelation of sleep, health and local brain structure. We conducted genetic correlation analysis in the complete HCP sample ( n  = 1105), which included twins and siblings. Sleep variation was assessed using the Pittsburg Sleep Quality Index (PSQI) 11 , a widely used questionnaire summarizing self-reported indices of sleep. Our main measures of interest were sleep quantity (self-reported sleep duration) and global sleep quality (total PSQI) score, as previous work has associated these factors with brain structure 44 , 45 and genetic variation 46 . Based on previous literature 7 , 8 , 25 , 26 , 27 , 28 and data-driven phenotypic correlations in the HCP sample, we selected BMI, intelligence and depression scores to evaluate the potential existence of a shared neuroanatomical basis of sleep and mental and physical aptitudes. In the HCP sample, intelligence was summarized as Total Cognitive Score, based on the NIH Toolbox Cognition 47 , whereas in the eNKI sample, intelligence was measured using the Wechsler Abbreviated Scale of Intelligence (WASI-II) 48 . Depression was measured using the ASR depression DSM-oriented scale for Ages 18–59 49 in the HCP sample. In the eNKI sample the Beck Depression Inventory (BDI–II) was used. BMI was calculated at weight/squared (height) in both datasets. Based on previous knowledge, we expected to observe phenotypic relationships between sleep duration/quality and markers of mental and physical health. Moreover, we expected to observe a phenotypic relation between sleep and local gray matter structure.

Our analyses revealed a phenotypic relationship between sleep and depression, BMI, and intelligence in both the HCP and the eNKI sample. Subsequently, we demonstrated our sleep markers, depression, BMI, and intelligence were heritable and we observed a genetic correlation between sleep quantity and quality, BMI, and intelligence in the twin-based HCP sample, indicating that sleep hygiene displays pleiotropy with these factors in the mentioned sample. Analysis of heritability and genetic correlation were performed with maximum likelihood variance-decomposition methods using Sequential Oligogenic Linkage Analysis Routines ( www.solar-eclipse-genetics.org ; Solar Eclipse 8.4.0.). Heritability (h 2 ) is the total additive genetic variance and genetic (ρ g ) correlations were estimated using bivariate polygenic analysis. Using an atlas-based approach to summarize cortical thickness 50 , we observed local associations between sleep duration and cortical structure in both samples which were, in part, driven by additive genetic factors. Post-hoc analysis indicated that variance in intelligence and BMI also related to thickness in areas associated with sleep duration. Subsequently, based on our observation that sleep relates to BMI, intelligence, and depression, as well as to cortical thickness, we performed partial least squares (PLS) analysis, in order to identify latent relationships between these factors. PLS is a multivariate data-driven approach, enabling simultaneous linking of behavioral measures to brain structure. We identified two robust latent factors, spanning distinct neurocognitive dimensions. Using the twin-structure of HCP, we observed these factors were heritable and their relation driven by shared genetic effects. Taken together, the current study highlights the interrelation of sleep, mental and physical health, which is reflected by shared neurobiological signatures.

Data samples

We studied two independent samples from openly-shared neuroimaging repositories: HCP and eNKI. HCP ( http://www.humanconnectome.org/ ) comprised data from 1105 individuals (599 females), 285 MZ twins, 170 DZ twins, and 650 singletons, with mean age 28.8 years (SD = 3.7, range = 22–37). For phenotypic analysis, we selected unrelated individuals, resulting in a sample of 424 (228 females) individuals with a mean age of 28.6 years (SD = 3.7, range = 22–36). Our second sample was based on the eNKI sample, made available by the Nathan-Kline Institute (NKY, NY, USA) 51 . This sample consisted of 783 (487 females) individuals with mean age of 41.2 years (SD = 20.3, range = 12–85), enabling us to identify life-span relations between sleep, brain structure and behavior. Details on the sample characteristics can be found in the Methods section.

Relation between sleep, mental and physical health

First, we sought to evaluate whether our measures of mental and physical status are related to sleep quantity and quality. Here, we correlated sleep duration and global sleep quality to phenotypic variation in cognition, mental, and physical health (for selection of markers see Supplementary Table  1 ). This data-driven analysis in the HCP phenotypic data revealed that cognitive, mental and physical phenotypic variation have a strong relation to variation in sleep (Supplementary Table  2 and Supplementary Table  3 ). Given the marked role of both depression and BMI on both sleep duration and global sleep quality, we selected these as phenotypes of interest for further analyses. As several cognitive factors were related to sleep duration and sleep quality, we selected general intelligence in this study, as this marker has been shown to be highly heritable and consistently relates to brain structure 42 . Next, we demonstrated that depression, IQ, and BMI have moderate phenotypic inter-correlations in unrelated HCP, eNKI, as well as full HCP samples (Supplementary Table  4 ). Evaluating the relation between sleep and our selected markers in eNKI, in addition to HCP, we observed that, sleep duration had a consistent negative phenotypic relation to both BMI and depression, and a positive relation to IQ (Table  1 ).

Taking advantage of the pedigree-structure of the full HCP sample, we observed that depression, IQ, and BMI were all heritable (Table  1 ); and we observed a negative genetic correlation between BMI and IQ (ρ g  = −0.27, p  < 0.0001) (Supplementary Table  4 ). Moreover, sleep duration was heritable (h2 = 0.24, p  < 0.001), and phenotypic correlations were mirrored by genetic correlations. We observed sleep duration to show a positive genetic correlation with IQ (ρ g  = 0.42, p  < 0.0001), but negative with BMI (ρ g  = −0.33, p  < 0.005) (Table  1 ). Depression showed a weak environmental correlation with sleep duration (ρ e  = −0.16, p  < 0.01).

Global sleep quality showed comparable relations to depression, IQ and BMI, with strong phenotypic correlation between poor sleep quality (higher total PSQI score) and higher depression and BMI scores, as well as between poor sleep quality and lower IQ across samples (Table  1 ). Global sleep quaity was also influenced by additive genetic effects (h2 = 0.12, p  < 0.05), but less so than sleep duration. Phenotypic correlations were paralleled by genetic correlations, were poor sleep quality were genetically correlated with lower IQ (ρ g  = −0.59, p  < 0.0001) and higher BMI (ρ g  = 0.41, p  < 0.025). Again, depression only showed environmental correlation with global sleep quality (ρ e  = 0.38, p  < 0.0001) (Table  1 ).

Phenotypic correlation between sleep and brain structure in two independent samples

Next, we evaluated the phenotypic relation between sleep indices (global sleep quality and sleep duration) and cortical thickness in both the unrelated subsample from HCP ( n  = 424) and eNKI ( n  = 783). Behaviorally, we observed a strong negative correlation (Spearman r  = −0.51 [−0.59 −0.44], p  < 0.0001) between global sleep quality and sleep duration (Fig.  1a ). Correlation of sleep indices with brain structure demonstrated a negative link between left superior frontal thickness (area 6d2 and pre-supplementary motor area) and sleep duration (Spearman r  = −0.1, FDR q  < 0.02, Fig.  1b ), that remained significant when controlling for self-reported depressive symptoms, as well as intake of sleep medications, intelligence, and BMI. Global sleep quality did not relate to local variations in cortical thickness (Fig.  1c ). When evaluating the relationship in the complete HCP sample, including twins and siblings, we observed only a trending relation between sleep duration and cortical thickness (Supplementary Fig.  1 ).

a Distribution of variables in the unrelated HCP subsample; b , c . Phenotypic correlation of sleep duration/global sleep quality and cortical thickness; d Distribution of variables in the eNKI sample, as well as the correlation between sleep duration and global sleep quality score ; e , f Phenotypic correlation of sleep duration/global sleep quality and cortical thickness. Red indicates a positive relationship, whereas blue indicates a negative phonotypical relationship between sleep and brain structure. Whole-brain findings were corrected for multiple comparisons using FDR correction ( q  < 0.05, black outline). Significant associations between sleep indices and brain structure have black outline, whereas trends ( p  < 0.01) were visualized at 60% transparency.

In eNKI, we replicated the negative behavioral correlation between sleep duration and global sleep quality (Spearman r  = −0.53 [−0.58 −0.47], p  < 0.0001) (Fig.  1d ). Though we again found no relation between global sleep quality and cortical brain structure (Fig.  1f ), sleep duration showed a positive link between bilateral inferior temporal regions (left: Spearman r  = 0.13, FDR q  < 0.02, right: Spearman r  = 0.12, FDR q  < 0.02) and right occipital cortex (Spearman r  = 0.14, FDR q  < 0.02) (Fig.  1e ). Findings remained significant when controlling for self-reported depressive symptoms, as well as intake of sleep medications, intelligence, and BMI.

In both samples, most individuals (>65%) slept less than the recommended 7–9 h (Supplementary Table  5 ) and only a small proportion of both samples (<9%) slept 9 h or more. Post-hoc analysis evaluating the linear relationship between short and long sleep duration and local brain structure replicated overall effects between sleep duration and local brain structure in individuals who reported to sleep less than 9h per night (short-to-normal sleep duration), but not in individuals sleeping more than 7h per night (normal-to-long sleep duration) (Supplementary Fig.  2 ). As the eNKI sample had a broad age range from 12 to 85 years of age, we performed several stability analyses to evaluate the relationship between sleep duration and brain structure in youths, adults and elderly populations (Supplementary Table  6 ). Here, we did not observe differential sleep duration effects in each sub-group, as well as differences between age-groups (Supplementary Fig.  3 ).

Replication analysis of correspondence between sleep duration and cortical thickness

As we found divergent local phenotypic correlations between sleep duration and cortical thickness in two large-scale independent samples, we evaluated the inconsistencies across samples more precisely. Indeed, post-hoc analysis indicated that local effects of phenotypic correlations varied strongly in magnitude across samples in phenotypic analysis (Table  2 ). At the same time, we observed a high overlap between spatial distribution of phenotypic correlations between sleep duration, but not global sleep quality, and cortical thickness across samples and sub-samples, indicating that the direction of sleep thickness associations is similar across both samples (Supplementary Table  7 ). This suggests that the relation between sleep and cortical thickness might be robust at the inter-regional level rather than in local effects only. In addition, we observed that both intelligence and BMI related to local thickness associated with sleep duration (Supplementary Table  8 ), suggesting that sleep, intelligence and BMI are dependent on overlapping macro-anatomical structures.

Phenotypic correlations between sleep and cortical thickness are driven by additive genetic effects

Next, we explored whether phenotypic correlations between sleep duration and cortical thickness were mirrored by additive genetic effects using the twin-structure of the HCP dataset. First, we confirmed that cortical thickness was heritable in this sample (Supplementary Fig.  4 , Supplementary Table  9 ). Second, we assessed whether phenotypic correlations observed in Fig.  1 were driven by shared additive genetic effects. We found that both frontal cortex (based on HCP), as well as right occipital cortex and right inferior temporal cortex (based on eNKI) showed a trend-level genetic correlation ( p  < 0.05) with sleep duration (Table  3 ). Using a whole-brain approach, we identified a negative genetic correlation between sleep duration and bilateral frontal cortices thickness, mainly in the bilateral superior frontal gyrus and frontal pole, areas p32 and Fp2 (left: ρ e  = 0.12, p  < 0.06, ρ g  = −0.46, FDR q  < 0.025; right: ρ e  = 0.15, p  < 0.01, ρ g  = −0.46, FDR q  < 0.025) (Supplementary Fig.  1 ). Findings were robust when controlling for intelligence, BMI or depression score (Supplementary Table  10 ). Frontal regions showing genetic correlation with sleep in the HCP sample did not show an association with sleep in the eNKI sample (Supplementary Table  11 ). At the same time, we observed patterns of genetic correlation to reflect phenotypic correlation at the whole brain level within the HCP sample and sub-sample, and in the eNKI sample (Supplementary Table  7 ). Last, though we did not observe a genetic correlation between global sleep quality and brain structure, we identified an environmental relation between global sleep quality and left precentral thickness (Spearman r  = 0.01, ρ e  = 0.22, p  < 0.0002, ρ g  = −0.64, p  < 0.0003) (Supplementary Fig.  5 ).

Latent relation between sleep, brain and behavior

As we observed (1) phenotypic and (2) genetic correlations between sleep, intelligence, BMI, and, in part, depression, as well as (3) an inconsistent relation between sleep duration and cortical thickness, we utilized a multivariate data-driven approach to evaluate the latent relationship between sleep, intelligence, BMI and depression on the one hand, and cortical thickness on the other (Fig.  2 ). Indeed, it has been suggested multiple comparison corrections in mass univariate analysis may result in missing effects and thus inconsistencies in the results and a more comprehensive picture of the associations could be gained by a multivariate approach 52 . Here, our primary analysis sample is the eNKI sample, as this enables us to replicate and evaluate phenotypic and genetic correlations between latent structures using the full HCP sample.

a Bootstrap ratio of the first brain saliency that showed significant robustness, where parcel-wise saliencies of BSR > 2 are highlighted. Red indicates a positive association whereas blue indicates a negative association; Loadings of the individual traits (SD: Sleep duration, D: Depression, B: BMI, I: Intelligence, SQ: Sleep quality); Relative distribution of positive(P) and negative (N) -2>BSR > 2 scores per functional networks 102 , and average BSR in functional networks 102 (V = visual, SM = sensorimotor, Da = dorsal-attention, Va = ventral attention, L = limbic, FP = frontopolar, DMN = default mode network); Replication of brain–behavior saliency association in the HCP sample; and b Relation between brain and behavioral saliencies in HCP sample of the second brain saliency; Loadings of the individual traits; Relation to functional networks 102 and; Relation between brain and behavioral saliencies of second factor in the HCP sample.

In the eNKI sample, we observed two latent relations between our behavioral phenotypes and cortical thickness, controlling for effects of age, sex and global thickness, explaining, respectively, 41% of the shared variance (first latent component; p  < 0.001, association between behavior and brain saliencies: Spearman r  = 0.38), and 25% of the shared variance (second latent component; p  < 0.01, association between behavior and brain saliencies: Spearman r  = 0.29). The first component had a positive relation with both sleep duration (Spearman r  = 0.49) and intelligence (Spearman r  = 0.83), and a negative relation with sleep quality (Spearman r  = −0.43), BMI (Spearman r  = −0.46) and depression (Spearman r  = −0.21). The brain saliency loadings were positively (bootstrap ratio > 2) associated sensory-motor areas, as well as superior temporal areas, and parahippocampal structures, and negatively with lateral and medial frontal cortex, as well as inferior temporal lobe, precuneus, and posterior parietal cortex. Further qualifying the brain saliency, we observed that positive relations were mainly in visual, sensorimotor and limbic functional networks, whereas negative relations were predominantly located in the dorsal-attention, fronto-parietal and default-mode networks. Replicating the association in HCP using the behavioral and brain loadings, we identified a relation between latent brain and behavioral factors in this sample as well (component 1: Spearman r  = 0.25, p  < 0.001). Moreover, both brain and behavioral latent factors were heritable (brain saliency: h2 ± std = 0.63 ± 0.04; behavior saliency: h2 ± std 0.76 ± 0.03) and showed genetic correlation (ρ e  = −0.07 ± 0.07, p  = ns, ρ g  = 0.38 ± 0.05, p  < 0.0001). The first brain-behavior component seems to reflect a positive-negative axis of behavior, relating high sleep quantity to positive behaviors whereas low sleep quality related negatively to this factor (Fig.  2a ).

The second component related positively to depression (Spearman r  = 0.70), BMI (Spearman r  = 0.50), intelligence (Spearman r = 0.16), and reduced sleep quality (Spearman r  = 0.69), and negatively to sleep duration (Spearman r  = −0.43). Positive brain loadings (bootstrap ratio>2) were located in the left sensorimotor areas, right precuneus, and right parietal areas. Negative loadings (bootstrap ratio < −2) were located in left dorsolateral areas, left mid-cingulate, right dorsolateral frontal cortex, and left anterior-mid cingulate. Qualitative analysis revealed positive loading were predominantly in sensorimotor, visual, dorsal attention and default networks, whereas negative loadings were associated with the fronto-parietal, ventral attention, limbic and default networks. Again, we replicated this association in HCP using the behavioral and brain loadings (Spearman r  = 0.10, p  < 0.002). Both brain and behavioral saliency of the second component were heritable (brain saliency: h2 ± std = 0.72 ± 0.03; behavior saliency: h2 ± std 0.51 ± 0.05) and showed genetic correlation (ρ e  = −0.11 ± 0.07, p  = ns, ρ g  = 0.22 ± 0.07, p  < 0.0001). This time, sleep quality, depression, BMI, and intelligence showed positive latent relations, but duration had a negative relation to the behavioral saliency, suggesting that sleep quality has both positive and negative relationships to intelligence (Fig.  2b ).

Sleep is key for normal human functioning and associated with brain structure and function. At the same time, individual differences in sleeping behavior are heritable and have substantial overlap with cognition, physical, and mental health. This raises the question whether shared variance in sleep, intelligence, BMI, and depression could be due to a shared relationship to macroscale gray-matter anatomy. Here, we combined computational approaches from behavioral genetics and big-data neuroimaging to evaluate the interrelation between sleep, macroscale brain structure, and mental and physical health. Indeed, in two large-scale independent samples, we observed that sleep duration, as well as global sleep quality, had a phenotypic relation with intelligence and BMI, which was mirrored by additive genetic effects. Depression showed only a phenotypic correlation with sleep. Following, we demonstrated that sleep duration, but not global sleep quality, had a (inconsistent) relation with local variance in cortical thickness in two samples. Three out of four phenotypic relations between sleep duration and local cortical thickness were driven by additive genetic factors. At the same time, both intelligence and BMI related to variance in cortical thickness in these regions, suggesting that these factors might have an overlapping neuroanatomical basis. Consistent with these results, a comprehensive multivariate analysis revealed two robust and heritable signatures, highlighting shared relationships between macroscale anatomy and sleep, intelligence, BMI and depression. Both components featured brain structures in both unimodal and heteromodal association areas, and underlined the embedding of nocturnal behavior in daytime functioning. Collectively, our multi-sample approach provides evidence that sleep is intrinsically interrelated with macroscale gray matter structure, mental, and physical health.

Our observations highlight the key relation between intelligence, mental and physical health and sleep profile in healthy subjects. Previous work has implicated the important role of sleep on life functioning, such as cognitive performance and quality of life 7 , 8 , as well as higher rate of depressive symptoms 13 , 14 , hypertension, diabetes, and obesity 25 , 26 . Indeed, clear associations of sleep, cognitive performance and behavioral problems have been observed in children 53 , adults 54 , and elderly 44 . It has been revealed that short-term sleep deprivation has a deleterious effect on a broad range of cognitive domains 54 and short sleep duration is associated with poor overall IQ /cognitive performance in healthy children 55 .

There are various hypotheses on the biological processes underlying the important role of sleep in the neuronal processing of information and consequently mental processing. The trace reactivation or replay hypothesis 56 , 57 suggests that sleep helps memory consolidation through reactivation of traces of neuronal activity patterns, encoding information. The synaptic homeostasis hypothesis proposes that sleep is necessary to counterbalance the increase of synaptic connectivity 5 . Converging evidence suggests a role of sleep in maintaining functional integrity of the frontoparietal networks, that support sustained attention 58 , 59 , as well as default mode network 60 , which is a brain network, implicated in task-unrelated thought. Indeed, in our multivariate analysis, we observed a shared relation of intelligence and sleep with cortical thickness in these networks. Importantly, we observed a positive phenotypic relationship between amount of sleep and domain-general cognitive skill in both the HCP sample, consisting of young healthy adults, and in the eNKI sample, which included a broad age-range with children, adults and elderly. Of note, though the measurement of domain-general cognition was not consistent across two samples, as we used NIH Toolbox Cognition in HCP and the WASI-II in eNKI, both tests have been validated for different age-ranges 47 , 48 . Further studies are needed to uncover the causal and longitudinal relationship between sleep and cognitive skill across the life-span.

At the same time, our work highlights that inadequate sleep is linked with increased BMI. Previously, it has been shown that high BMI is associated with abnormal sleep duration and vice versa 61 . Short term sleep restriction is associated with impaired glucose metabolism, dysregulation of appetite, and increased blood pressure, and prospective studies found increased risk of weight gain associated with inadequate sleep 62 , 63 . In the same vein, various studies have related BMI to brain structure and function 41 , suggestive of a bidirectional relation between sleep, BMI, and the brain.

Last, we observed a relation between sleep and depressive symptoms. A recent meta-analysis implicated both long and short sleep to be associated with increased risk of depression in adults 64 . Though the mechanisms underlying this association are not fully understood, daytime tiredness, resulting in increased negative events and emotions, has been shown to be predictive of poor outcome of depression. Next to this, sleep abnormalities relate also to low physical activity, which in turn modulates risk of depression. Importantly, sleep factors can predispose, precipitate, and perpetuate depression and in our multivariate model, we observed both neutral and positive associations between depression and unhealthy sleep behaviors, highlighting the complex relation between sleep and mental health.

Though we could establish phenotypic and genetic correlations between sleep duration and local cortical thickness in two independent samples, findings were inconsistent. In the HCP sample, but not in the eNKI sample, sleep duration was linked to thickness in the frontal areas. The important role of frontal cortex in sleep is previously well-documented. For example, sleep deprivation influences frontal executive functions in both healthy individuals and patients with insomnia disorder 65 , 66 , 67 . In addition, sleep deprivation leads to lower metabolism in the frontal cortex, while sleep recovery moderately restores frontal lobe functions 68 . Function abnormalities are also mirrored by abnormalities in macro-anatomical structure, where cortical thinning in bilateral precentral cortex and the superior/mid frontal cortex related to insomnia symptoms 69 and patients with insomnia disorder showed gray matter abnormalities in the frontal cortices 70 , 71 . On the other hand, phenotypic analyses in the eNKI sample demonstrated that sleep duration had a positive link with thickness in bilateral inferior temporal regions and right occipital cortex. Also function and structure of temporal and occipital areas has been associated with sleep patterns. For instance, older adults with short or long sleep duration had higher rates of cortical thinning in the frontal and temporal regions, as well as the inferior occipital gyrus 72 relative to older adults with normal sleep duration. Also, insomnia disorder has been related to functional abnormalities in the temporal and occipital areas, beside the frontal regions 73 , 74 . These activations have been associated with excessive hyperarousal, impaired alertness, auditory-related and vision-related inattention, and experiencing negative moods in such patients. Possible causes for divergence could be sample characteristics, as well as confounding effects. However, even when controlling for age, intelligence, BMI, and depression, findings remained dissimilar between samples. Only when evaluating spatial patterns of relationships between sleep duration and cortical thickness, we observed cross-sample consistency, suggesting that the degree of impact of sleep duration on local brain structure varied across samples, but that the direction of the relation between sleep and cortical thickness was comparable across the cortex. Of note, though we observed diverging phenotypic relationships across samples, three out of four local relationships between sleep and cortical thickness were observed driven by additive genetic factors, suggestive of a system-level impact of sleep on brain structure, with modest but robust underlying local genetic associations. Nevertheless, local, univariate, associations between sleep and cortical thickness should be interpreted with caution, as they were not consistent across samples. It is of note that detrimental effects of both short and long sleep have been reported previously 46 , 75 and in the current study, the large majority of individuals (>65% for all samples) reported less than 7h of sleep whereas only a small proportion (<9%) slept 9 h or more in both samples. Indeed, post-hoc analysis indicated associations between local brain structure and sleep duration reflected patterns of short-to-normal sleep duration. Further studies in samples reporting long sleep duration are needed to evaluate the differential effects of short and long sleep duration on local brain structure.

Univariate relationships between sleep, brain structure, and behavior in two independent samples were further corroborated by our multivariate analysis. We could identify multivariate, latent, relationships between cortical thickness on the one hand and sleep, intelligence, BMI, and depression on the other one. In the first factor, reflecting a positive-negative axis of behavior, where sleep duration, together with intelligence, low BMI, and high sleep quality, showed a negative relation to thickness in the frontal and parietal areas, but a positive relation to the sensory-motor and parahippocampal areas. These latent relationships were robust across samples and driven by shared additive genetic effects. Behaviorally, the observed latent factor mirrors the previously reported positive-negative axis of behavior previously defined in a sub-sample of the HCP using functional connectivity. Here, the axis related to increased functional connectivity of the default mode network and negative associations in the sensorimotor networks 76 . We observed a second axis with a positive link between low sleep quality, high intelligence, high depression, and high BMI score and thickness of dorsolateral frontal cortex, accompanied by negative relation to thickness in sensorimotor areas. This axis related positively to intelligence and depression.

Our multivariate observations are broadly in line with our univariate results. Indeed, the first brain factor again highlights a negative relation between frontal thickness and sleep duration, whereas temporal-occipital regions show a positive relation with duration of sleep, reconciling divergent findings in the two independent samples. However, our latent model also provided a system-level perspective on the relation between sleep, behavior, and brain structure. Here, unimodal and heteromodal association cortices revealed an inverse relation to sleep and behavioral variability. A previous body of literature have put forward a so-called hierarchical model of brain function stretching from unimodal to transmodal cortices, enabling both externally, as well as internally oriented processing 77 , 78 . Indeed, it is likely that sleep, intelligence, BMI, and depression do not only relate to internally oriented processes, but also functional processes focused on the external world supported by the somatosensory cortices. For example, previous work has implicated sleep deprivation in sensorimotor coupling, reporting that sleep deprived individuals showed difficulties standing upright 79 . Likewise, memory consolidation processes during sleep have been linked to primary and secondary sensorimotor cortices. For example, in mice, inhibition of projecting axons from motor cortex to somatosensory cortex impaired sleep-dependent reactivation of sensorimotor neurons and memory consolidation 80 . Similarly, other studies applying multivariate methods to understand the relation between system-level brain function and complex behavior also have implicated alterations of inter-network relationships between somatosensory and heteromodal association cortices in mental function and dysfunction 81 , 82 . It is possible that such disruptions are due to dissociable neurodevelopmental as well as genetic effects affecting the hierarchical interrelation of these brain systems 77 . Future research on the neurobiology of sleep requires to be conducted with functional and structural connectivity data enabling more direct analysis of the relation between system-level connectivity, sleep, and behavior.

In addition to providing evidence for a shared neurobiological basis of sleep, mental and physical health, we observed that, in line with previous literature 17 , 18 , 46 , variance in global sleep quality and sleep duration was in part driven by additive genetic effects. A recent GWAS study using 446,118 adults from UK Biobank identified 78 loci, mainly PAX8 locus, for self-reported habitual sleep duration 46 . Moreover, Dashti et al. observed, similar to our observations, genetic overlap between sleep, markers of mental and physical health, as well as education attainment. It is likely that the observed genetic correlation within our sample between cortical thickness, intelligence, BMI, and sleep is due to mediated pleiotropy (a gene affects A, which affects B). Thus, it could be that a genetic mechanism affects gray matter macrostructure and associated function and, as a consequence, sleep duration. Alternatively, genetic variation might affect brain function, which in turn modulates both macroscale structure and sleep duration, or a genetic mechanism affects sleeping behaviors through non-brain processes and in turn affects brain function and structure. However, it is worthy to mention that our genetic correlations analysis does not provide causal mechanisms on the relation between brain structure, sleep, and behavior. Indeed, there is genetic evidence for a bidirectional relationship between sleep duration and schizophrenia 46 , as well as smoking behavior 83 , highlighting the complex interplay between and behavior. Next to this, though there is a negative genetic correlation between short sleep duration and long sleep duration 46 , suggestive of shared biological mechanisms, it is possible that both relate to partly distinct underlying biological mechanisms. Further studies will be needed to investigate whether shorter and longer sleep duration differentially affect brain and behavior, and investigate the relation between sleep, health, and brain in longitudinal datasets with imaging and deep phenotyping to further disentangle causal relationships between sleep, brain structure, and function.

Our integrative perspective on sleep, behavior, and brain structure may be relevant for future work targeting the relation between sleep and neurodevelopment. For example, studies on development have indicated a close relation between sleep, behavioral problems, and school performance in children 53 . As childhood is an essential time for neurodevelopment 84 , 85 combining these two lines of research might help to understand how healthy and abnormal sleep patterns relate to neurodevelopment in youth. At the same time, sleep disturbances have been related to neurodegenerative conditions, and may drive early-onset pathogenesis. For example, sleep disruption has been observed to upregulate neuronal activity, which increases the production of amyloid-beta proteins resulting in exacerbated tau pathology in various mouse models 86 and sleep disturbances in ageing might directly influence synaptic homeostasis and cognitive function. By providing system-level evidence integrating cortical thickness with sleep and behavior, follow-up research could further disseminate how brain anatomy relates to sleep and general functioning during development and ageing and identify functional and structural mechanisms that explain the interrelation between sleep, development and ageing.

Notably, we refrain from interpreting environmental correlations, as the environmental component includes environmental factors, but also measurement errors. In a previous work 87 , we have shown that a model based on genetic and environmental factors only was more parsimonious compared to a model including common household effects in extended family samples such as the HCP sample. Nevertheless, as individual variance in sleep duration and quality was only in part explained by genetic factors, future longitudinal models might help uncover relevant familial and non-familial environmental effects relating sleep to mental and physical health.

Taken together, our study on the interrelation between sleep, mental, and physical health and brain structure was made possible by the open HCP and eNKI neuroimaging repositories. These initiatives offer cognitive neuroimaging communities a unique access to large datasets for the investigation of the brain basis of individual difference. The use of multiple datasets has enabled us to highlight variability across samples, and allowed us to preform validation experiments to verify stability of our observations. Given that reproducibility is increasingly important nowadays, our study illustrates the advantages of open data to increase our understanding of complex traits.

Participants and study design: human connectome project

We studied the publicly available Human Connectome Project dataset (HCP; http://www.humanconnectome.org/ ), which included data from 1206 individuals (656 females), 298 monozygotic twins (MZ), 188 dizygotic twins (DZ), and 720 singletons, with mean age 28.8 years (SD = 3.7, range = 22–37). Participants for whom the images and data had been released (humanconnectome.org) after passing the HCP quality control and assurance standards were included. The full set of inclusion and exclusion criteria are described elsewhere 88 . All participants signed an informed consent document at the beginning of day 1 of testing.

For our phenotypic analyses, we selected an unrelated subsample with complete behavioral data ( n  = 457). After removing individuals with missing structural imaging our sample for phenotypic correlations consisted of 424 (228 females) individuals with mean age of 28.6 years (SD = 3.7, range = 22–36), see further Table  4 . For our twin-based genetic analyses, we used the complete sample of individuals with complete structural imaging for structural gray matter and behavioral data for sleep genetic correlation analyses including 1105 individuals (599 females), 285 MZ twins, 170 DZ twins, and 650 singletons, with mean age 28.8 years (SD = 3.7, range = 22–37), see further Table  5 . Environmental correlations were also derived in this sample as a by-product of analysis of genetic correlation analysis.

Structural imaging processing: human connectome project

MRI protocols of the HCP are previously described 89 , 90 . In particular, the applied pipeline to obtain the FreeSurfer-segm entation is described earlier 89 and is recommended for the HCP data. The pre-processing steps included co-registration of T1 and T2 images, B1 (bias field) correction, and segmentation and surface reconstruction using FreeSurfer version 5.3-HCP to estimate cortical thickness 89 .

Participants and study design: eNKI sample

To evaluate the cross-sample reproducibility of observations, we additionally investigated correspondence between sleep and cortical brain structure in the enhanced Nathan Kline Institute-Rockland Sample (NKI). The sample was made available by the Nathan-Kline Institute (NKY, NY, USA), as part of the ‘enhanced NKI-Rockland sample’ ( https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3472598/ ). All approvals regarding human subjects’ studies were sought following NKI procedures. Images were acquired from the International Neuroimaging Data Sharing Initiative (INDI) online database https://fcon_1000.projects.nitrc.org/indi/enhanced/studies.html.

For our phenotypic analyses, we selected individuals with complete sleep and imaging data. Our sample for phenotypic correlations consisted of 783 (487 females) individuals with mean age of 41.2 years (SD = 20.3, range = 12–85). See Table  6 for demographic characteristics.

Structural imaging processing: NKI Rockland sample

3D magnetization-prepared rapid gradient-echo imaging (3D MP-RAGE) structural images 91 were acquired using a 3.0 T Siemens Trio scanner with TR = 2500 ms, TE = 3.5 ms, Bandwidth = 190 Hz/Px, field of view = 256 × 256 mm, flip angle = 8°, voxel size = 1.0 × 1.0 × 1.0 mm. More details on image acquisition are available at https://fcon_1000.projects.nitrc.org/indi/enhanced/studies.html. All T1 images were visually inspected to ensure the absence of gross artefacts and subsequently pre-processed using the FreeSurfer software library ( http://surfer.nmr.mgh.harvard.edu/ ) Version 5.3.0 92 .

Parcellation-summaries of cortical thickness

We used a parcellation scheme 50 based on the combination of a local gradient approach and a global similarity approach using a gradient-weighted Markov Random models. The parcellation has been comprehensively evaluated with regards to stability and convergence with histological mapping and alternative parcellations. In the context of the current study, we focused on the granularity of 200 parcels. In order to improve signal-to-noise and improve analysis speed, we opted to average unsmoothed structural data within each parcel and cortical thickness of each region of interest (ROI) was estimated as the trimmed mean (10 percent trim).

Selection of behavioral markers based on HCP phenotypic traits

First, to constrain analyses, we selected primary markers for cognition, mental and physical health based on the relation of sleep to these traits in HCP. The selected traits include 38 emotional, cognitive, NEO-FFI personality, as well as the 7 PSQI sleep markers for reference, based on the unrestricted phenotypic data, as well as 46 mental and physical health markers based on the restricted phenotypic data. For more information on available phenotypes, see: https://wiki.humanconnectome.org/display/PublicData .

Behavioral markers: HCP

Inter-individual difference in sleep quality was derived from information of the self-reported Pittsburg Sleep Questionnaire (PSQI) 11 , which is a common measure of sleep quality with significant item-level reliability and validity.

For markers of life function, we used BMI (703 × weight/(height) 2 ) and the ASR depression DSM-oriented scale for ages 18–59 49 ( https://aseba.org/ ). The ASR is a self-administered test examining diverse aspects of adaptive functioning and problems. Scales are based on 2020 referred adults and normed on 1767 non-referred adults. The test-retest reliability of the ASR was supported by 1-week test-retest that were all above 0.71. The ASR also has good internal consistency (0.83), and in the current study we focused on depression sub-score.

As a proxy for intelligence, we used the NIH Toolbox Cognition 47 , ‘total composite score’. The Cognitive Function Composite score is derived by averaging the normalized scores of each of the Fluid and Crystallized cognition measures, then deriving scale scores based on this new distribution. Higher scores indicate higher levels of cognitive functioning. Participant score is normed to those in the entire NIH Toolbox Normative Sample (18 and older), regardless of age or any other variable, where a score of 100 indicates performance that was at the national average and a score of 115 or 85, indicates performance 1 SD above or below the national average.

Behavioral markers: NKI

Sleep markers were derived from the Pittsburg Sleep Questionnaire (see further the section on this question in the HCP sample).

Depression was measured using the Beck Depression Inventory (BDI–II). The BDI-II is a 21-item self-report questionnaire assessing the current severity of depression symptoms in adolescents and adults (ages 13 and up). It is not designed to serve as an instrument of diagnosis, but rather to identify the presence and severity of symptoms consistent with the criteria of the DSM-IV. Questions assess the typical symptoms of depression such as mood, pessimism, sense of failure, self-dissatisfaction, guilt, punishment, self-dislike, self-accusation, suicidal ideas, crying, irritability, social withdrawal, insomnia, fatigue, appetite, and loss of libido. Participants are asked to pick a statement on a 4-point scale that best describes the way they have been feeling during the past two weeks 93 . Body-mass-index was calculated using weight and height. These vitals are obtained and recorded by study staff. Height was recorded in centimeters. Weight was recorded in kilograms. Body Mass Index (BMI) was automatically calculated.

Intelligence was measured using the Wechsler Abbreviated Scale of Intelligence (WASI-II). The WASI is a general intelligence, or IQ test designed to assess specific and overall cognitive capabilities and is individually administered to children, adolescents and adults (ages 6-89). It is a battery of four subtests: Vocabulary (31-item), Block Design (13-item), Similarities (24-item) and Matrix Reasoning (30-item). In addition to assessing general, or Full Scale, intelligence, the WASI is also designed to provide estimates of Verbal and Performance intelligence consistent with other Wechsler tests. Specifically, the four subtests comprise the full scale and yield the Full-Scale IQ (FSIQ-4). The Vocabulary and Similarities subtests are combined to form the Verbal Scale and yield a Verbal IQ (VIQ) score, and the Block Design and Matrix Reasoning subtests form the Performance Scale and yield a Performance IQ (PIQ) score 48 .

Statistics and reproducibility

Phenotypic analysis.

For our phenotypic analysis in the HCP sample, we selected an unrelated subsample to overcome possible bias due to genetic similarity of individuals. In eNKI the complete sample with available data was used. To assess phenotypic relationships between sleep parameters and behavior/brain structure, we used Spearman’s correlation test to account for outliers, while controlling for age, sex, age × sex interaction, age 2 , age 2 × sex interaction. In our structural whole-brain analysis, we additionally controlled for global thickness. Findings were similar when additionally controlling for depression, BMI or intelligence. We controlled for multiple comparisons at FDR q  < 0.05, per analysis step of univariate behavior and univariate brain analysis, and report FDR q thresholds for reference. We used the Robust Correlation Toolbox for Matlab to define confidence intervals in our post-hoc phenotypic correlations 94 .

Heritability and genetic correlation analysis

To investigate the heritability and genetic correlation of sleep parameters and brain structure, we analyzed sleep parameters and 200 parcels of cortical thickness of each subject in a twin-based heritability analysis. As previously described 95 , the quantitative genetic analyses were conducted using Sequential Oligogenic Linkage Analysis Routines (SOLAR) 96 . SOLAR uses maximum likelihood variance-decomposition methods to evaluate the relative importance of familial and environmental influences on a phenotype by modeling the covariance among family members as a function of genetic proximity. Coefficient of relationship (genetic proximity) between individuals in the HCP sample was computed based on self-reported data and was highly similar to other approaches based on genotyped data of the HC 97 . This approach can handle pedigrees of arbitrary size and complexity and thus is optimally efficient with regard to extracting maximal genetic information. To ensure that neuroimaging traits, parcels of cortical thickness, conform to the assumptions of normality, an inverse normal transformation was applied 95 .

Heritability (h 2 ) represents the portion of the phenotypic variance ( \({\mathrm{\sigma}} _{\mathrm{p}}^2\) ) accounted for by the total additive genetic variance ( \({\mathrm{\sigma}} _{\mathrm{g}}^2\) ), i.e., \({\mathrm{h}}^2\,=\,{\mathrm{\sigma}} _{\mathrm{g}}^2/{\mathrm{\sigma}}_{\mathrm{p}}^2\) . Phenotypes exhibiting stronger covariances between genetically more similar individuals than between genetically less similar individuals have higher heritability. Within SOLAR, this is assessed by contrasting the observed covariance matrices for a phenotypic (neuroimaging or behavioral) measure with the structure of the covariance matrix predicted by kinship. Heritability analyses were conducted with simultaneous estimation for the effects of potential covariates. For this study, we included covariates of age, sex, age × sex interaction, age 2 , age 2  × sex interaction. When investigating cortical thickness, we additionally controlled for global thickness effects, as well as depression score, BMI, and intelligence in post-hoc tests. Heritability estimates were corrected for multiple comparisons at FDR q  < 0.05, controlling for the number of parcels in case of analysis of brain structure.

We performed genetic correlation analysis to determine if variations in sleep and cortical thickness were influenced by the same genetic factors. Specifically, bivariate polygenic analyses were conducted to estimate genetic (ρ g ) and environmental (ρ e ) correlations, based on the phenotypic correlation (ρ p ), between brain structure and sleep with the following formula: \({\uprho} _{\mathrm{p}}\,=\,{\uprho} _{\mathrm{g}}\surd \left( {{\mathrm{h}}_1^2{\mathrm{h}}_2^2} \right) + {\uprho} _{\mathrm{e}}\surd [(1 - {\mathrm{h}}_1^2)(1 - {\mathrm{h}}_2^2)]\) , where \({\mathrm{h}}_1^2\) and \({\mathrm{h}}_2^2\) are the heritability’s of the parcel-based cortical thickness and the sleep parameters. The significance of these correlations was tested by comparing the log likelihood for two restricted models (with either ρ g or ρ e constrained to be equal to 0) against the log likelihood for the model in which these parameters were estimated. A significant genetic correlation (using a FDR q  < 0.05) is evidence suggesting that both phenotypes are influenced by a gene or set of genes 98 .

Partial least squares

PLS is a multivariate data-driven statistical technique that aims to maximize the covariance between two matrices by deriving latent components (LCs), which are optimal linear combinations of the original matrices 99 , 100 . We applied PLS to the cortical thickness and sleep, BMI, depression and IQ measures of all participants. In short, PLS performs data normalization, cross-covariance, and singular value decomposition. Following, brain and behavioral scores are created and permutation testing is performed to assess significance of each latent factor solution. Last, bootstrapping is performed to test the stability of the brain saliencies.

Each LC has a distinct cortical thickness pattern (called brain saliences) and a distinct behavioral profile (called behavioral saliences). By linearly projecting the cortical thickness and behavioral measures of each participant onto their corresponding saliences, we obtain individual-specific brain and behavioral composite scores for each LC. PLS seeks to find saliences that maximize across-participant covariance between the brain and behavioral composite scores. The number of significant LCs was determined by a permutation (1000 permutations). The p -values (from the permutation test) for the LCs were corrected for multiple comparisons using a false discovery rate (FDR) of p  < 0.05. For the brain saliencies, though all regions contributed to the latent brain score, we highlighted regions with a bootstrap ratio > 2, approximately p  < 0.05. Findings where summarized at the level of macroscale function networks 101 , by averaging the BSR score per network, as well as summarizing the relative contribution of each functional network to positive (BSR > 2), as well as negative (BSR < −2) relations. Here we controlled for the size of the network.

Functional decoding

All significant parcels were functionally characterized, using the Behavioral Domain meta-data from the BrainMap database using forward inference ( www.brainmap.org ) 102 , 103 . To do so, volumetric counterparts of the surface-based parcels were identified. In particular, we identified those meta-data labels (describing the computed contrast [behavioral domain]) that were significantly more likely than chance to result in activation of a given parcel 104 , 105 , 106 . That is, functions were attributed to the identified effects by quantitatively determining which types of experiments are associated with activation in the respective parcellation region. Significance was established using a binomial test ( p  < 0.05, corrected for multiple comparisons using false discovery rate (FDR)) (Supplementary Fig.  6 ).

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this article.

Data availability

All data, analyzed in this manuscript, were obtained from the open-access HCP young adult sample ( http://www.humanconnectome.org/ ) 90 and enhanced NKI-Rockland sample ( https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3472598/ ) 51 . Brain images were acquired from the International Neuroimaging Data Sharing Initiative (INDI) online database http://fcon_1000.projects.nitrc.org/indi/enhanced/studies.html. The raw data may not be shared by third parties due to ethics requirements, but can be downloaded directly via the above weblinks. Spearman correlations and confidence intervals were computed using the Robust Correlation toolbox https://github.com/CPernet/robustcorrtool 94 .

Genetic analyses were performed using solar eclipse 8.4.0 ( http://www.solar-eclipse-genetics.org ), and data on the KING pedigree analysis is available here: https://www.nitrc.org/projects/se_linux/ 96 , 97 . We performed partial least square analysis using https://miplab.epfl.ch/index.php/software/PLS . 99 , 100 . BrainMap analysis were performed using http://www.brainmap.org 102 , 103 .

Code availability

Main analysis scripts and genetic correlation tables are available at https://github.com/sofievalk/projects/tree/master/Tahmasian_Sleep .

Change history

17 february 2021.

A Correction to this paper has been published: https://doi.org/10.1038/s42003-020-1017-y

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Acknowledgements

We would like to thank the various contributors to the open access databases that our data was downloaded from. Specifically; HCP data were provided by the Human Connectome Project, Washington University, the University of Minnesota, and Oxford University Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil;1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. For enhanced NKI, we would like to thank the principal support for the enhanced NKI-RS project is provided by the NIMH BRAINS R01MH094639-01 (PI Milham). Funding for key personnel was also provided in part by the New York State Office of Mental Health and Research Foundation for Mental Hygiene. Funding for the decompression and augmentation of administrative and phenotypic protocols provided by a grant from the Child Mind Institute (1FDN2012-1). Additional personnel support provided by the Center for the Developing Brain at the Child Mind Institute, as well as NIMH R01MH081218, R01MH083246, and R21MH084126. Project support also provided by the NKI Center for Advanced Brain Imaging (CABI), the Brain Research Foundation (Chicago, IL), and the Stavros Niarchos Foundation. This study was supported by the Deutsche Forschungsgemeinschaft (DFG, EI 816/21-1), the National Institute of Mental Health (R01-MH074457), the Helmholtz Portfolio Theme “Supercomputing and Modeling for the Human Brain” and the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No. 785907 (HBP SGA2). B.T.T.Y. is supported by the Singapore National Research Foundation (NRF) Fellowship (Class of 2017). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Singapore NRF. This study was also supported by NIH grants R01EB015611 and S10OD023696 to P.K.

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S.L.V., M.T. and F.S. designed the experiments and wrote the paper. S.L.V. performed analyses. F.H. processed imaging data. J.A.C. performed the functional decoding analysis and approved the paper. P.K. created the twin-pedigree for genetic analysis. H.K., M.Z., S.K., B.T.T.Y. and S.B.E. revised and approved the paper.

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Tahmasian, M., Samea, F., Khazaie, H. et al. The interrelation of sleep and mental and physical health is anchored in grey-matter neuroanatomy and under genetic control. Commun Biol 3 , 171 (2020). https://doi.org/10.1038/s42003-020-0892-6

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Journal of Circadian Rhythms

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Why We Sleep: A Hypothesis for an Ultimate or Evolutionary Origin for Sleep and Other Physiological Rhythms

• Andrew S. Freiberg
• proximate cause
• ultimate cause
• adaptive theory
• restorative theory

It is ‘one of the last great biological mysteries’ [ 1 ] that although sleep is ubiquitous and essential [ 2 ], we have yet to determine its true evolutionary purpose [ 3 ]. Even though every species of animal, as well as many plants, go to sleep every day, a satisfactory answer to one of biology’s fundamental questions is lacking [ 4 , 5 , 6 , 7 ].

We know a lot about the physiology of sleep, with more being learned every day. We know what happens during sleep, and what happens when we or other animals are deprived of it [ 1 , 4 , 8 , 9 ]. What we still do not know is why sleep exists in the first place.

What Allen Rechtschaffen wrote in 1998 is still true [ 10 ]:

A number of sleep theories have been put forth and fluctuations in biological patterns have been measured during sleep, but the function of sleep is not yet understood. Sleep can be understood as fulfilling many different functions but intuition suggests there is one essential function. The discovery of this function will open an important door to the understanding of biological processes.

From a biological and evolutionary standpoint, sleep theories can be categorized into those that consider proximate or ultimate causes [ 11 ]. Proximate causes explore sleep as a process – the physiology of sleep and sleep deprivation, which is the focus of nearly all current research. Ultimate or evolutionary causes have been proposed, broadly categorized as restorative or adaptive theories [ 12 ].

Restorative, or recuperative theories hypothesize that sleep serves one or more of the following functions: to rest and repair [ 13 , 14 ]; to consolidate what we have learned while we were awake [ 7 , 15 , 16 , 17 , 18 ]; to dream [ 19 ]; to enhance the immune response [ 15 , 19 , 20 , 21 ]; to avoid the serious detrimental effects of deprivation [ 1 , 4 , 9 , 22 ]; to detoxify [ 23 ].

In fact, all of these do occur during sleep, and have been found to be important, even essential functions of the complicated sleep process, i.e. proximate causes. However, as evolutionary causes, for the most part these theories have fallen apart under scrutiny [ 10 ]. For example, energy expenditure is not significantly lower during sleep, and if sleep were important to learning, why is it present in lower orders of animals, single celled organisms, and even in many plants? In fact, a general criticism of restorative theories is that they apply only to a limited set of tissues or species. Dreams may be an essential function of sleep in humans and other primates or even all mammals [ 19 ], but extending that function to earthworms and daylilies is difficult to imagine.

Circadian rhythms are ubiquitous across nature as nearly every organism studied, including bacteria, algae, fungi, plants and animals, all exhibit physiological cycles that are near to 24 hours in length, even when uncoupled from the daily light/dark cycle [ 24 ]. Furthermore all cells of all organisms studied so far have such rhythms [ 24 ]. Yet the more that is known about these rhythms at the cellular and molecular level, the less such explanations as detoxification, immune enhancement, memory consolidation and tissue repair make sense as underlying causes for the presence of sleep.

Intuitively, an organism that could stay awake for 24 hours would survive better, with the ability to be productive around the clock, competing for precious resources. A sleeping animal is not engaging in productive activities and is vulnerable to predation. Adaptive theories state that sleep is a way of keeping us away from dangerous conditions such as predators. I believe adaptive theories may be partially correct, but they do not go far enough. Discovering the true evolutionary function of sleep, i.e. the reason sleep adds to the fitness of an organism, would explain ‘Why we sleep.’

I propose a subtle yet profound explanation to the question of why we and every animal species on earth sleep. This theory in no way invalidates or takes the place of any of the current explanations of the functions and purposes of sleep, the proximate causes. Rather, it relegates these functions into physiological tactics that serve a larger evolutionary strategy – the true ultimate purpose of sleep.

From an evolutionary standpoint, all structure and function (i.e. physiology) exists because it confers some survival advantage. Since physiology includes behavior, sleep must confer an evolutionary advantage as a behavior. The central question concerning sleep is thus: what evolutionary advantage could sleep possibly provide?

All organisms occupy a niche, and the better adapted to that niche, the more ‘fit’ and the more likely that organism will reproduce, passing on the characteristics that fit that particular niche. While we may simplistically think of each organism occupying a single niche, realistically nearly all occupy at least two. Daytime and nighttime are different and distinct niches, creating an evolutionary push and pull that would make a perfect ‘fit’ impossible. Evolutionarily, being forced to evolve into two separate niches at the same time forces an organism to develop structures and functions that fit neither fully.

We and nearly every other species on earth must navigate an environment of approximately half light and half dark. Since other organisms change in response to that cycle, there are also different biological environments that exist between night and day, further enforcing the differences between the two niches.

The physiology of every organism is driven by evolutionary pressure from its environment. Millions of years before there were animals, plants, or even DNA on the Earth, there were two niches: day and night. The instant life began these two very different environments were waiting for it, at nearly every spot on Earth. Early life and all subsequent life was forced to confront a dilemma of how to adapt to these two rapidly alternating niches. Being awake day and night forces an organism to adapt to both light and dark, as well as to the different organisms populating each, and to do so would compromise its ability to perform well in either. Since the day/night schedule has been around since before there was life on Earth it would be ingrained in all but the most sequestered organisms. The daily cycle is so constant, and at such a high frequency, repeating every single day, that it is impossible to keep up (i.e. circumnavigating the earth every day) so the only alternative is to change physiology to match. The day/night problem therefore created a powerful, pervasive and inescapable selection pressure. Adaptation to these two niches must therefore lie deep within our DNA, as in fact, we find a circadian clock functioning in all cells of all organisms.

Evolution quickly devised a way for organisms, particularly animals to change physiology each day – they go to sleep. It is as if evolution were saying ‘Pick one, either day or night, and optimize your structure, function and behavior for that environment, then get away from the one you did not choose, to avoid attempting to optimize to both.’

Thought of in this way, sleep is not only controlled by, but acts a powerful guardian of, circadian rhythms.

To be sure, we do of course exist in both the day and night niches, but we and nearly every other animal species have consequently developed a way to change our physiology and behavior sufficiently that we become, not literally, but in a sense two distinct organisms, one adapted to each niche, rather than a single physiology adapted to both, and therefore poorly adapted to either. The “asleep” organism is not living its own life but exists in order to deliver at wake time the shared organism in the best possible shape for the wake journey of foraging, hunting, or working.

The adaptive theory states that animals sleep to avoid danger [ 12 , 25 ]. For example, animals that are active during the day are vulnerable to predators at night, and therefore sleep to avoid being hunted and eaten. This theory explains why such an animal would need to hide during the night, but not why they sleep, although it makes sense that an animal might want to conserve energy while hiding away and not actively foraging for food. The adaptive theory goes part way to the truth, but instead of simply hiding from danger and conserving energy while doing so, the physiological need for sleep not only hides these animals from danger during the night, but ensures they will stay hidden on evolutionary time scales.

Sleep is important and we can’t live without it – on the surface because of some need to repair or replenish our bodies. More importantly, deep down, the need to repair or replenish is a trick our DNA plays on us to keep us from being up at night. Because we need sleep every day we never have the opportunity to adapt to the darkness, which would compromise our ability to survive during the day. In the adaptive theory, we hide from the danger of darkness; in this new theory we hide from the danger of adapting to the darkness.

Of course there is no absolute division between diurnal and nocturnal species. Across species, the quantity and timing of sleep patterns varies greatly [ 20 ]. While some animals are clearly one or the other, there are those that are active for short periods both day and night, or active at twilight, and others for which some members are diurnal and others nocturnal, and in some cases even a single individual can change from one to the other based on environmental conditions [ 26 ]. Like other traits sleep can be modified experimentally [ 27 , 28 , 29 ]. Yet the fundamental need for sleep itself is non-negotiable from an evolutionary standpoint. All of these organisms, even those that are intermittently active day and night still have functioning circadian clocks and all of them still require sleep [ 2 ]. The variety of sleep patterns illustrates that whatever the ultimate cause for sleep is, it does not require a single prolonged period of rest, as in humans, and is obviously not dependent on any particular sleep architecture. Even animals that are in a niche that allows day and night activity have not given up the need for sleep, showing how fundamentally important it is. The need for sleep is in all organisms’ DNA, but does not govern how that need is satisfied; that physiology evolves with each species as the environment still has a lot to say. All animals, for example need to retain the ability for arousal during sleep, but to different amounts and in different ways depending on their circumstances. Unihemispheric sleep is another example of how sleep has been adapted to specific needs [ 19 , 30 , 31 , 32 ]. Evolution has allowed for a variety of ways to spend the day and night, within which the ultimate need for sleep persists, yet has no direct control over.

Learning is an essential process of the human species, and sleep is essential to the consolidation of memories. Yet, it is not sleep, but rather sleep architecture that has co-evolved with learning and other recuperative processes to its present state, to optimize these processes for each species.

One advantage this theory has over others is that it can be broadly applied to all species, from unicellular organisms all the way to Homo sapiens .

This theory can be extended to other biological rhythms: There are of course two examples of periodic and predictable changes in environment, the diurnal and the seasonal. Marcus Hall introduces the term “chronophilia” to explain how life has adapted to the periodic environmental changes occurring on Earth due to its rotation and axial tilt [ 33 ]. While his article is mostly concerned with human activity, nearly all life on Earth is subjected to the same temporal constraints.

In temperate and many tropical climates there are profound differences between the conditions of summer and winter, chiefly hot/cold and wet/dry, and few organisms can adapt well to both. Triggered by changes in light, temperature and moisture, animals and plants, by well understood biochemical processes, change their biology to survive in the two climates [ 34 , 35 , 36 ]. Deciduous trees lose their leaves and go into a hibernation mode to survive the winter, while evergreen trees change the chemistry of their internal fluids to keep from freezing [ 37 ]. Many animals go through similar changes, such as hibernating or changing color to blend in to different surroundings. These examples illustrate that when an organism is faced with periodic and predictable environmental changes, in order to survive in both environments, it must periodically change either 1) its biology; or 2) its surroundings; or 3) both.

Migrating birds, mammals and insects effectively change their environment seasonally. Why do some birds fly south for the winter? Not simply to stay warm. They fly south to prevent genetic adaptation to the winter, which would compromise their physiology and fitness in the summer. Similarly some animals hibernate not just to avoid the cold winter, but to keep from adapting to it. Honeybee workers in winter live months rather than weeks to avoid having to raise brood during the cold winter [ 35 ].

Why do some trees lose their leaves in the fall? The mechanisms and evolutionary advantages of this process, known as abscission, are well understood [ 38 , 39 ]. However, I hypothesize that the evolutionary reasons behind abscission go beyond a simple ‘cost benefit’ analysis. Leaves are not useful in the dark and cold of winter; so many plants lose them to prevent the creation of an “all season” leaf, which would be an arboreal bad idea. It takes a lot of energy and material to make a leaf; trees would not give them up without a very good reason. Evergreen trees of course do not shed, but fundamentally change the physiology of their needles in the winter [ 37 ].

Humans today have the technology to control their environment to a large extent. If we so choose we could live in a world of constant light or constant darkness. The reason we don’t is that we sleep better in darkness, the night niche. This behaviour keeps us in sync with the sunlight. Our circadian clocks, are, by the process of entrainment, constantly resetting to changes that occur with the seasons, our schedules, or with daylight savings time. When we travel across time zones our circadian clocks become significantly out of sync, but still manage to use the local zeitgebers to reset so that we keep our daylight physiology and night physiology in tune with the local day/night schedule.

Adaptive theories of sleep have never really taken hold, while research on recuperative, physiological theories has exploded, possibly due to the fact that recuperative theories are so much more testable than adaptive. This new theory may open a window for experimentation. While difficult, my theory is testable, both by experimental and investigative methods.

If we had evolved on a planet with no day night cycles would we still sleep? Unfortunately we are a long way from being able to perform that experiment now, if ever. For seasonal variation the experiment has been done: Do organisms that live in constant climates need to change physiology twice each year? Of course not – why would they need to?

The bottom line and the answer to the question: “Why do we sleep?”

Sleep is a tactic evolution uses to serve a greater strategy: We sleep to allow adaptation into two competing niches. The need for sleep forces us to change our physiology into a different state for the second niche. All the other answers (why we NEED sleep) are but mechanisms to achieve this goal.

Competing Interests

The author has no competing interests to declare.

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Sleep memory processing: the sequential hypothesis

Antonio giuditta.

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Edited by: Federico Bermudez-Rattoni, Universidad Nacional Autónoma de México, Mexico

Reviewed by: Jorge Medina, Universidad de Buenos Aires, Argentina; Sara J. Aton, University of Michigan, USA; Federico Bermudez-Rattoni, Universidad Nacional Autónoma de México, Mexico

*Correspondence: Antonio Giuditta, Department of Biology, Federico II University, Via Mezzocannone 8, Naples 80134, Italy e-mail: [email protected]

This article was submitted to the journal Frontiers in Systems Neuroscience.

Received 2014 Jul 30; Accepted 2014 Oct 19; Collection date 2014.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

According to the sequential hypothesis (SH) memories acquired during wakefulness are processed during sleep in two serial steps respectively occurring during slow wave sleep (SWS) and rapid eye movement (REM) sleep. During SWS memories to be retained are distinguished from irrelevant or competing traces that undergo downgrading or elimination. Processed memories are stored again during REM sleep which integrates them with preexisting memories. The hypothesis received support from a wealth of EEG, behavioral, and biochemical analyses of trained rats. Further evidence was provided by independent studies of human subjects. SH basic premises, data, and interpretations have been compared with corresponding viewpoints of the synaptic homeostatic hypothesis (SHY). Their similarities and differences are presented and discussed within the framework of sleep processing operations. SHY’s emphasis on synaptic renormalization during SWS is acknowledged to underline a key sleep effect, but this cannot marginalize sleep’s main role in selecting memories to be retained from downgrading traces, and in their integration with preexisting memories. In addition, SHY’s synaptic renormalization raises an unsolved dilemma that clashes with the accepted memory storage mechanism exclusively based on modifications of synaptic strength. This difficulty may be bypassed by the assumption that SWS-processed memories are stored again by REM sleep in brain subnuclear quantum particles. Storing of memories in quantum particles may also occur in other vigilance states. Hints are provided on ways to subject the quantum hypothesis to experimental tests.

Keywords: synaptic homeostasis, slow wave sleep, REM sleep, memory processing, memory storage, quantum hypothesis

Introduction

The recent publication of an extended review article on sleep memory processing (the synaptic homeostatic hypothesis or SHY; Tononi and Cirelli, 2014 ) has suggested a comparison of its premises, data, and overall perspective with the corresponding viewpoints of the sequential hypothesis (SH) that was proposed more than 30 years ago (Giuditta, 1977 , 1985 ) but is now largely marginalized. SH was the first to examine and demonstrate the involvement of slow wave sleep (SWS) in memory processing, and to point out its primary role in downscaling irrelevant or competing memory traces. The two-step mechanism of memory processing proposed by SH was concluded by the renewed storage of SWS-processed memories during REM sleep (Giuditta et al., 1995 , 2003 ; Ambrosini and Giuditta, 2001 ). These views were originally demonstrated in rats, and later confirmed by independent investigations of human subjects (Ficca et al., 2000 ; Gais et al., 2000 ; Stickgold et al., 2000 ).

Comparing SHY with SH has not been attempted so far despite their similar premises and their different interpretations of the role of SWS and REM sleep. Similarities obviously include the involvement of SWS in memory processing, and in its weakening or elimination of memory traces. The latter operations were later raised by SHY to the rank of a primary homeostatic function assumed to fulfill the main goal of sleep. Most differences appear to have roots in different views of brain activity that is largely random for SHY but well ordered for SH.

The current predominance of SHY in interpreting the role of sleep has not prevented a good deal of criticism mainly stressing SWS capacity to potentiate synaptic strength and consolidate memories (Diekelmann and Born, 2010 ; Timofeev, 2011 ; Frank, 2012 , 2013 , 2014 ; Ribeiro, 2012 ; Abel et al., 2013 ).

A brief summary of SHY

According to SHY, environmental regularities captured by brain during waking strengthen synaptic connections, increase plastic supplies and energy needs, and saturate learning capacity. Conversely, in the ensuing sleep most changes are reversed, and acquired memories are integrated within the vast repertoire of previous memories. Support for a net synaptic potentiation during waking and a net synaptic depression during sleep was provided by a wealth of biochemical, morphological and neurophysiological data (Tononi and Cirelli, 2003 , 2006 , 2014 ). Sleep effects were attributed to delta EEG waves in view of their elevated intensity at sleep onset, and of their progressive increase after prolonged waking. Further support came from their decrease with time asleep and after naps, and from their specific increase in defined brain regions involved in selected learned tasks. According to SHY the process of synaptic renormalization during sleep fulfills the homeostatic need to reset experience-modified brain synapses to their native state. This process renews brain capacity to acquire information in the following waking period. In short, “sleep is the price the brain pays for plasticity”.

The genesis of SH

At the time the SH was envisaged (Giuditta, 1977 , 1985 ) sleep literature was redundant with hypotheses regarding the role of REM sleep (synonymous with paradoxical sleep or PS) and SWS (synonymous with NREM or non rapid eye movement sleep). They were considered functional states serving widely different roles. Most attention was paid to REM sleep in view of its involvement in learning (Fishbein and Gutwein, 1977 ; Pearlman, 1979 ) but also because of its elevated brain metabolism and unique relation with dreams. Completely different roles were assigned to SWS. They included restoration, anabolism, energy saving, etc. (Allison and Cicchetti, 1976 ; Adams and Oswald, 1977 ; Horne, 1977 ; Walker and Berger, 1980 ; Shapiro et al., 1981 ).

Despite their presumed divergent roles, REM sleep and SWS were not independent sleep stages. SWS started the sleep period of the adult mammal long before REM sleep, and markedly prevailed in duration; it also displayed sharply different features from REM sleep and from waking. In addition, waking experience modulated either type of sleep: REM sleep by memory acquisition, SWS by the load of visual percepts (Horne and Walmsley, 1976 ), the intensity of physical exercise (Horne, 1981 ), or the novelty and stress of experience (Reich et al., 1972 ). The latter observation was of relevance for the genesis of the SH since it demonstrated that brain energy needs during SWS depended on the nature of the previous waking period. Indeed, the concentrations of brain lactate and pyruvate remained essentially unchanged during quiet or active waking but markedly differed in the following SWS. They slightly decreased after quiet waking but markedly increased after active waking. The data suggested that active waking induced a special brain condition that could only be renormalized at energy expenses during the following SWS.

Additional considerations regarded the ontogenetic development of sleep. In mammals sleep appears in late fetal life as a primitive form of REM sleep (active sleep) that persists in the newborn while progressively assuming adult features. On the other hand, waking and SWS make their appearance in the newborn and start differentiating into mature states. Notably SWS periods that initially occur at random, start positioning themselves between waking and REM sleep. These modifications suggested that in early ontogenetic stages active sleep promoted the laying down of basic brain circuits that were elicited by innate instructions (Roffwarg et al., 1966 ) while in later developmental stages brain growth started to be molded by waking experience. This drastic switch in the source of information implied that, at variance with innate instructions, waking experience also contained irrelevant or inappropriate information that was to be cleared to prevent interference with the further growth of brain circuits. In view of its strategic position between waking and REM sleep, SWS appeared to be well fit to perform this cleaning operation before the final memory storage promoted by REM sleep (Giuditta et al., 1995 ). This scenario was in agreement with the logic of food acquisition also requiring a differential processing of what is to be retained from what is to be eliminated. Brain appeared to adopt the same formal logic with regard to newly acquired information.

These considerations led to the proposal that memory processing did not only involve REM sleep, as generally believed, but also included SWS to preliminarily sort memories to be retained and weaken or eliminate remaining traces. Accordingly, waking memories were assumed to be initially trimmed of irrelevant, non adaptive, or competing traces during SWS, and to be eventually stored again in better format during REM sleep. The latter step was also promoting the integration of retained memories with preexisting memories (Giuditta, 1977 , 1985 ; for reviews, see Giuditta et al., 1995 , 2003 ; Ambrosini and Giuditta, 2001 ). In this new perspective, the operations performed by SWS were assumed to take memories into a state comparable to that of innate instructions. Overall, the flow of information from waking to REM sleep was summarized as I W ->I SWS ->I REM sleep .

The validity of SH was tested in a variety of training conditions that mostly concerned adult rats exposed to a two way active avoidance task (Giuditta et al., 1985 ; Ambrosini et al., 1988 , 1992 , 1995 ; Langella et al., 1992 ). In the initial experiments (Giuditta et al., 1985 ) EEG analyses demonstrated that post-trial SWS episodes were markedly longer in either learning and non learning (NL) rats with respect to baseline sleep and to control rats. However, lengthening of SWS episodes markedly differed in the two groups depending on their following episode that could be waking (SWS->W) or REM sleep (SWS->REM sleep). In learning rats SWS episodes initially lengthened during SWS->W sequences and much later in SWS->REM sleep sequences. Conversely, in NL rats the same effect mostly concerned SWS->W sequences. Furthermore, the number of REM sleep episodes only increased in learning rats. As predicted by SH, the participation of SWS in memory processing started much earlier than that of REM sleep. The data demonstrated that REM sleep was not the only sleep involved in memory processing. Rather, SWS episodes increased their duration while REM sleep episodes increased their number. Differences between learning and NL rats appeared to reflect the processing of avoidance memories that prevailed in the former group, and the processing of innate memories that prevailed in the latter group. Interestingly, comparable differences between learning and NL rats were also detected in baseline sleep (Ambrosini et al., 1993 ). This suggested that to a certain extent rats were innately conditioned to learn the avoidance task.

In these early studies the experimental protocol included a training day and a previous baseline day during which sleep was EEG recorded at the same time (about 1.00 pm) and for the same duration (3 h) of post-trial sleep. The training session lasted 4 h and included 4 training periods of 30 min separated by resting periods of the same duration. Rats were assigned to a fast learning (FL) or to a NL group depending on their attaining the learning criterion. In later experiments (Ambrosini et al., 1988 , 1992 , 1995 ; Langella et al., 1992 ) a test session was added on the third experimental day to assess the long-term retention of the task. The addition revealed that the NL group also included rats that attained criterion the third day (slow learning or SL rats) together with rats that persisted in their failure to learn (NL* rats). SL rats belonged to the category of reminiscent animals which attain the learning criterion after a long delay but without additional training (Anderson, 1940 ; Huppert and Deutsch, 1969 ; Jaffard et al., 1974 ). In our hands, lengthening of SWS episodes in SL rats only occurred in the third hour of post-trial sleep and the number of REM sleep episodes only increased in the sixth hour (Ambrosini et al., 1995 ).

In these later studies EEG records were analyzed with a higher time resolution (1 s) that allowed the identification of brief episodes of transition sleep (TS) that were previously missed. In a TS episode the delta waves of a previous SWS episodes suddenly mix with theta and alpha waves. Their identification led to the definition of two additional sleep sequences (SWS->TS->W and SWS->TS->REM sleep) and to the more accurate identification of SWS->W and SWS->REM sleep sequences previously lumped together with related TS-containing sequences. These improved analyses revealed that in the post-trial sleep of learning rats longer SWS episodes initially occurred in SWS->TS->W sequences and eventually in SWS->TS->REM sleep sequences. Conversely, in NL rats longer SWS episodes largely occurred in SWS->W and SWS->REM sleep sequences. Hence, avoidance memories appeared to be processed during TS-containing sequences, while innate memories were processed during sleep sequences lacking TS. Comparable differences were present in baseline sleep of FL, SL and NL* rats (Vescia et al., 1996 ; Mandile et al., 2000 ). This further indicated that rapidly learning, slowly learning, and NL rats were partly conditioned by their respective innate tendencies.

The identification of different sleep sequences that started with an apparently similar SWS episode but were selectively involved in processing adaptive or innate memories suggested that the SWS episodes were differing from each other, and that their differences conditioned the appearance of the following sleep episode (TS, W, or REM sleep). Comparable differences were presumed to regard TS episodes. In addition, SWS differences appeared to be generated by the behavioral responses prevailing in FL, SL, and NL* rats, in full agreement with SH premises and with the constraints of an ordered brain activity. In view of the basic differences in nucleic acid metabolism exhibited by waking and sleeping brains (see below), SWS differences seemed related to the nature of newly synthesized nucleic acids.

Differences in sleep episodes and sleep sequences were eventually extended to the large clusters of baseline sleep sequences (trains) that were bordered by relatively long (>60 s) W episodes (Piscopo et al., 2001 ). Analyses of such baseline structures allowed the identification of mixed and homogeneous trains. The former trains contained all the four sleep sequences and was most abundant; they were labeled +TSW trains in view of the SWS->TS->W sequence they contained that was lacking in the less abundant −TSW trains. Homogeneous trains were least abundant. Baseline trains were not artificial entities lacking meaningful roles since they were non randomly distributed in FL, SL and NL* rats. Variables of +TSW trains and of their sleep sequences and episodes were prevalent in FL rats; they also exhibited significant correlations with the avoidances scored the following day. Conversely, variables of −TSW trains and of their sleep sequences and episodes were prevalent in NL* rats; they correlated with freezings rather than with avoidances (Piscopo et al., 2001 ). Interestingly, the role of sleep sequences depended on the train that included them. This was best shown by variables of SWS->TS->REM sleep sequences that correlated with avoidances if present in +TSW trains, but correlated with freezings (and inversely correlated with avoidances) if present in −TSW trains. Variables of homogeneous trains also exhibited specific correlations with behavioral responses. Those of homogeneous SWS->TS->W trains directly correlated with avoidances while those of homogeneous SWS->W trains inversely correlated with avoidances.

An additional observation that is worth reporting concerned the increment in delta waves that was selectively present in FL rats while they were performing in the initial training period of the two way active avoidance task. This unexpected effect significantly contributed to their selectively attaining the learning criterion in that period (Mandile et al., 2003 ). It was also observed that the SWS->TS->W sequence that consistently initiates processing of avoidance memories by displaying longer SWS episodes, was absent in the post-trial sleep of FL rats. Surprisingly the SWS->TS->REM sleep sequence that normally concludes avoidance processing (Mandile et al., 2000 ) was instead present. It appeared that the lack of the former sequence in post-trial sleep was due to the selective increment in delta waves present in performing FL rats that presumably fulfilled the same role.

Brain nucleic acid metabolism in waking and sleep

In adult rabbits monitored by EEG recording newly synthesized brain cortex RNA contained a larger proportion of ribosomal RNA if rabbits were prevalently awake, but a larger proportion of heterogeneous nuclear RNA if they were mostly sleeping (Vitale-Neugebauer et al., 1970 ). Comparable results were obtained with purified large nuclei from neurons and astrocytes that also exhibited a marked increment in the content of newly synthesized RNA when rabbits were mostly asleep. These effects were absent in purified small nuclei presumably derived from oligodendrocytes and small neurons. Increments in newly synthesized RNA also occurred in nuclear fractions from purified neuronal perikarya and partially purified glial cells (Giuditta et al., 1980 ).

Direct evidence that brain nucleic acid metabolism is modulated by post-trial sleep was provided by comparing rat brain DNA synthesized during a training session before and after post-trial sleep. At the end of the training session the content of newly synthesized brain DNA was about the same in learning rats scoring a high number of avoidances and in NL rats receiving a high number of foot-shocks triggered by their innate responses (Giuditta et al., 1995 ). On the other hand, major differences were present after post-trial sleep since half of the newly synthesized DNA was selectively lacking in NL rats displaying an increment in REM sleep episodes with regard to baseline sleep (Giuditta et al., 1985 ; Langella et al., 1992 ). In the same rats newly synthesized brain DNA inversely correlated with variables of post-trial SWS->REM sleep sequences. These effects were absent in learning rats. Data were interpreted to indicate that brain DNA synthesized during the training session was a molecular correlate of acquired memories. As such, its recovery after post-trial sleep depended on the fate of those memories: the DNA associated with the avoidance memories prevailing in learning rats was retained while that associated with the innate memories prevailing in NL rats was largely eliminated. These processes occurred during the post-trial SWS->REM sleep sequences of NL rats as shown by their inverse correlation with newly synthesized brain DNA.

These experiments were suggested by data demonstrating the high turnover of brain DNA in adult rats (Perrone Capano et al., 1982 ) and its modulation by a variety of activities (Pelc and Viola-Magni, 1969 ; Pelc, 1972 ) and learning protocols (Reinis and Lamble, 1972 ; Ashapkin et al., 1983 ; Giuditta et al., 1986 ). Brain metabolic DNA is distributed in brain cellular and subcellular fractions and is highly dispersed throughout the genome in a learning-dependent pattern (Giuditta et al., 1986 ). These features exclude its involvement in neurogenesis, gliogenesis, or DNA repair. Rather, they strongly suggest its participation to the acquisition and processing of novel information.

Comparing SHY with SH

As briefly mentioned in the Introduction, SHY and SH share some basic similarities but markedly differ in experimental methods and results, and in their interpretation and significance. Differences between the two hypotheses are numerous and basic as they touch the essence of brain activity and the role of sleep in memory processing. Accordingly, they are being presented and discussed one at a time in the following list of topics.

Brain energy needs

A first comment regards SHY’s assumption that brain energy needs are high in waking due to synaptic potentiation, and low in SWS due to synaptic depression. The former process may well require some extra energy but it does not follow that synaptic depression may need less. Rather, the concentration of brain energy metabolites indicates that an opposite situation may prevail (Reich et al., 1972 ). Indeed, the content of brain lactate and pyruvate in active waking did not differ from quiet waking, but markedly increased in the ensuing SWS. No such increase occurred in SWS after a period of quiet waking. Since synaptic potentiation is likely to prevail in active waking, these observations are at variance with SHY’s expectation. It should also be noted that the above data are in agreement with the values of total EEG spectral power (µV 2 ) determined in adult rats. They are almost the same in active and quiet waking but attain markedly higher values during SWS (Ambrosini et al., 1994 ).

The role of sleep in memory processing

SH was concerned with the differential processing of memories during sleep, and highlighted the adaptive role of retained memories. On the other hand SHY focused interest on the homeostatic significance of sleep synaptic renormalization. Attention was largely devoted to the necessary turnover of synaptic strength and to its significance in re-establishing the conditions for memory acquisition in the following waking period. This goal is of obvious relevance for the wellbeing and survival of organisms, but the grand objective of sleep remains the retention of selected memories and their integration with preexisting memories. The homeostatic role of synaptic renormalization should not be minimized but at the same time its emphasized significance should not marginalize the differential processing of memories occurring during SWS and their diverging paths of retention or elimination. These considerations make SHY fall short of being the ultimate hypothesis on sleep memory processing.

An implicit more basic difference between SHY and SH regards brain activity. Its nature is largely random by SHY but deeply ordered for SH. Accordingly, the net modifications of synaptic strength reported by SHY in waking and sleep are the result of a random approach that leaves in the background the significance of retained memories. Synaptic renormalization is also touching a key problem that seems to have escaped notice. If modified synapses are renormalized during sleep, what happens to the memories they store? This question will be discussed in more detail further on. For the time being let it be emphasized that sleep memory processing is likely to include the following operations: (i) tagging memories to be retained; they are often adaptive memories but may also be innate memories; (ii) retaining the tagged memories and concurrently weakening or eliminating the remaining memory traces; (iii) integrating the retained memories within the much larger population of preexisting memories. These operations are likely to elicit a quicker and more specific retrieval of memories during waking, and possibly from a wider repertoire of percepts. Sleep memory processing might also play a role in transferring compacted memories to germ cells as epigenetic modifications (Rudenko and Tsai, 2014 ) or according to a Lamarckian mechanism of brain evolution (Barry, 2013 ).

SHY’s experimental approaches

Memories acquired during waking are unlikely to be exclusively stored in clusters of potentiated synapses. Neural circuits are frequently modified by waking experiences that down modulate synaptic strength. It follows that synaptic renormalization during sleep cannot only downscale potentiated synapses but is likely to also include reinforcing weak synapses. This consideration brings to light a major limit of the random methods used to assess the net strength of heterogeneous synaptic populations (Tononi and Cirelli, 2014 ). They provide statistical information unable to reveal the origin of the assessed changes, whether they derive from potentiated or normal synapses, or from adaptive, innate, or irrelevant memories. It may also be worth noting that after a waking experience brain activity keeps on acquiring new percepts in addition to processing waking percepts. Notably during sleep brain cortical regions are not exclusively involved in processing environmental information; they are also busy receiving and processing stimuli largely derived from the gastrointestinal tract (Pigarev, 1994 ; Pigarev et al., 2013 ; Pigarev and Pigarev, 2014 ). It follows that heterogeneous synaptic populations analyzed by random methods may include synaptic elements that are not related to waking memories.

SHY’s preference in attributing net changes in synaptic strength to waking and sleep rather than to memories acquired during waking or processed during sleep appears indulging in the view that brain activity is largely random. This may justify the random methods used and the claim that “sleep is the price paid to plasticity”, but cannot account for the selective changes in synaptic strength supporting memory acquisition and the selective modifications they undergo during sleep. There are reasons to believe that the role of sleep in memory processing may be worth a more generous evaluation.

At variance with SHY, the modifications undergone by adaptive or innate memories during sleep may be followed with the procedures adopted by SH. In addition to high resolution EEG analyses they include behavioral methods and the monitoring of brain macromolecules synthesized during training before and after post-trial sleep (Giuditta et al., 1985 , 1995 ; Langella et al., 1992 ). This protocol allows the correlative analysis of memories with variables of post-trial sleep, and with brain macromolecules synthesized during the training session. More incisive information may be provided by investigating the selective pattern of genomic expression prevailing in sleep (Mackiewicz et al., 2007 ), or by monitoring the newly synthesized proteins selectively triggered by learning in the local synaptic system of gene expression (Eyman et al., 2013 ) or the activated synaptic RNAs (Ferrara et al., 2009 ). The local system of synaptic gene expression is supported and modulated by the intercellular transfer of perisynaptic glial transcripts to nerve terminals (Giuditta et al., 2008 ). These data might shed some light on the macromolecules that are presumably conditioning post-trial SWS episodes in their participation to sleep sequences selectively involved in processing novel or innate memories.

Integration of SWS-processed memories

An additional key difference between SH and SHY regards the mechanism of integration of retained memories in the larger population of preexisting memories. According to SH, memories processed during SWS are stored again as core memories during REM sleep that concurrently integrates them in the wider realm of preexisting memories (Giuditta et al., 1995 , 2003 ; Ambrosini and Giuditta, 2001 ). This additional storage step was attributed to REM sleep in view of the similar but not identical features it shares with waking which is the brain state in which memories are initially acquired and are most frequently recalled. Their common features might also facilitate memory retrieval during waking. These considerations are largely ignored by SHY that attributes the integration of new memories with preexisting memories to the comprehensive sampling of statistical regularities in brain activity occurring during SWS. What role is left for REM sleep? In their recent review (Tononi and Cirelli, 2014 ) REM sleep is even doubted to have a role in memory processing, presumably in view of the pervading capacities attributed to SWS. This peculiar stand is turning upside down in a somewhat ironical way the initial considerations that made REM sleep the first and only sleep involved in memory processing (Fishbein and Gutwein, 1977 ; Pearlman, 1979 ).

Memory storage and the role of REM sleep

According to SHY, brain synapses potentiated during waking undergo renormalization in the ensuing SWS to prevent the experience-dependent impoverishment of native synapses that is needed to implement learning capacity. Since SHY’s random methods cannot identify which memories are supported by renormalized synapses, it may be conveniently assumed that they do not belong to the category of retained memories. If they did, those memories would be lost. Since this ill-omened consequence was rarely reported, the consequent worrying scenario may be discounted. Nonetheless, a comparable scenario might be in sight even if renormalized synapses consistently supported memories not worth retaining. Indeed, given the widely held assumption that long term storage of memories may only be implemented by modifications of synaptic strength, modified synapses supporting retained memories would accumulate in brain with each waking/sleep cycle. Consequently the number of native synapses available for learning would be reduced.

Synaptic potentiation and depression cannot be doubted, but their persistence in time remains largely undefined. Even assuming their lifelong existence, would the number of native synapses be sufficient for storing all available memories? Answers to this question are likely to be based on shaky premises and may only lead to uncertain outcomes. What may not be debatable is the expectation that given the present SHY formulation and the accepted mechanism of memory storage, modified synapses would start accumulating in brain since an early age, thereby interfering with learning capacity. The timing of such outcome may be questionable, but the end result could not. The problem may also be examined from a different point of view. When memories of past events are retrieved after long time intervals (months, years, or decades), does this imply that the same (or an equivalent) configuration of modified synapses persisted? How strong is the evidence that modified synapses might survive such long times? Are we sure that other storing mechanisms do not exist?

If properly considered, the latter question might prompt a search for an additional mechanism possibly endowed with a much larger capacity for information storage. Before attempting to deal with such a problem it may be of value to compare SHY with other homeostatic systems governing organisms. Their general goals are similar. They have to counteract changes in biological set values as soon as triggering conditions are sensed. This mechanism is serving a positive role in a multitude of biological processes but might not be blindly adopted when homeostatic variables regard basic features of learning capacity as brain modifiable synapses. In the latter case re-establishing homeostatic set values under the currently accepted framework of memory storage is likely to interfere with the same learning capacity needing protection. SHY did not ignore this constraint but failed to take it to its extreme consequences. The two horns of the dilemma were considered but adopted responses did not lead to a suitable solution. Memories were partly saved, synapses were partly renormalized but this accommodating attitude failed to consider that retained memories would progressively accumulate with each sleep/waking cycle, and thereby deprive the reservoir of native synapses. Memories are known to persist for the entire life of an organism (Mariucci et al., 1998 ), and in some human subjects they may attain an incredibly large load (LePort et al., 2012 ). Would modified synapses persist for such a long time and to such a large extent without hindering learning capacity?

Not so long ago the philogenetic increase in brain size was attributed to the accumulation of memories (Crick and Mitchison, 1983 , 1995 ), and their storage mechanism was discussed in the perspective of a generalized molecular turnover (Crick, 1984 ). Brain size was assumed to be kept under control by the elimination of “parasitic” memories during REM sleep in a process of “reverse learning”. These speculations call attention to the striking philogenetic increase in brain size. Was it really due to an increment in the load of memories? Could this suggest that memories might be transferred to the progeny? Epigenetic memories are known to cross the trans-generational barrier (Rudenko and Tsai, 2014 ) but no specific reference was made to brain size. What could be a satisfying solution?

The quantum hypothesis

In principle an additional site of memory storage should fulfill the twin requirement of being located near brain circuits while occupying a much smaller fraction of their allotted space. An attractive option is offered by the vanishingly small size and peculiar quantum properties of subnuclear brain particles. Quantum computers feature an unbelievably huge storage capacity and an incredibly fast computation rate. Quantum physicist Richard Feynman is known to say: “there’s plenty of room at the bottom”. This intriguing option is however running against the generalized opinion that biological features derive from macromolecular components that are often considered the ultimate source of biological capacities. This view is clearly lacking logical support. No compelling reason was ever proposed that prevented submolecular components from contributing to biological capacities (Giuditta, 2012 ). Indeed, quantum features are increasingly considered as potential intermediates in a number of biological processes that include the photosynthetic reaction and even consciousness (Dawlaty et al., 2012 ; for a review, see Hamenoff and Penrose, 2014 ). Why should subnuclear particles be unable to store brain memories? Information might quickly reach them from modified synapses, and likewise be quickly retrieved in a kind of seesaw between different levels of the same entities. Notably, subnuclear particles are the ultimate components of brain synapses as they are of everything else in the universe (Giuditta, 2012 ).

The quantum hypothesis is clearly lacking a mechanism, but mechanisms start to be investigated only after hypotheses reach experimental consistency. SHY’s unsolved dilemma requires that the present mechanism of memory storage be supplemented by an additional high capacity mechanism allowing full synaptic renormalization without memory loss. The quantum hypothesis offers a possible alternative that is worth considering. One may then ask at what stage of sleep information might be transferred to subnuclear particles. Most likely such step would not occur before SWS processing is completed. Hence, memory transfer in mammals and birds might take place during REM sleep. The proposal is in agreement with SH view of REM sleep concluding the two-step memory processing by promoting the renewed storage of SWS-processed memories and integrating them with preexisting memories (Giuditta et al., 2003 ). In the framework of the quantum hypothesis, REM sleep would still promote the storage of SWS-processed memories but in brain subnuclear particles rather than in modified neural circuits. Interestingly, this novel location would assume additional relevance by the plausible suggestion that preexisting memories may also been stored in comparable locations, in line with the hypothesis of a phylogenetic origin of human mind (Giuditta, 2012 ).

The role of REM sleep in the adult organism should also be compared to that of its precursor active sleep, and to its proposed involvement in the laying down of basic brain circuits (Roffwarg et al., 1966 ; Giuditta et al., 1995 ). The latter operations are assumed to be guided by innate instructions despite the substantial lack of knowledge regarding their genomic or epigenomic nature, and the transferring modalities to the growing brain. In view of the intimate relation between active sleep and REM sleep, one may wonder whether innate instructions are somehow related with the new knowledge memorized by parents when learning to grow new brain circuits. Some kind of connection might only be envisaged in the framework of a Lamarckian mechanism of brain evolution (Barry, 2013 ). While such questions are likely to remain without suitable answers for some time, it may still be worth noting that deprivation of active sleep in fetal rats induces a marked increment in brain DNA synthesis (Grassi Zucconi et al., 1986 ).

Testing the quantum hypothesis

The hypothesis of a REM sleep-mediated transfer of memories from neural circuits to brain subnuclear particles might be tested in relatively simple but less direct analyses, and in more complex but direct experiments. In the former approach, rats or mice trained for a complex task should be selectively deprived of REM sleep in their post-trial period. In comparison with trained animals allowed normal sleep they may be expected to exhibit a higher net degree of synaptic potentiation presumably due to an impaired memory transfer to subnuclear particles. Alternative interpretations should be examined by training animals for different tasks, including those involving the participation of local brain regions. These experiments should also be made as a follow up response to the observation that “many of the findings suggestive of renormalization were obtained in relation to total sleep, not just NREM sleep” (Tononi and Cirelli, 2014 ). The possibility that REM sleep might contribute to the renormalization process finds support in the marked loss of newly synthesized brain DNA during the post-trial sleep of NL rats exhibiting an increment in REM sleep episodes (Giuditta et al., 1985 ). Notably, the content of newly synthesized brain DNA inversely correlated with variables of SWS->REM sleep sequences, and was attributed to a related loss of memories (Langella et al., 1992 ). Investigations on the participation of REM sleep in synaptic renormalization are still lacking. They might reveal its specific involvement in the homeostatic process but would not demonstrate memory transfer to subnuclear brain particles.

In more direct tests trained animals should be exposed to the intense magnetic field of fNMR machines during post-trial sleep or, more selectively, during REM sleep. Since these fields interfere with the spin of subnuclear brain particles, this specific alteration might prevent memory transfer. Trained animals should also be exposed to the magnetic field during SWS or waking, and to the fNMR machine when magnetic field is not in operation. Interference with the spin of brain subnuclear particles during REM sleep might induce effects comparable to those elicited by available procedures of REM sleep deprivation. They might include repeated attempts to re-enter REM sleep, memory loss, and a higher net degree of synaptic potentiation. Comparable analyses of rats or mice raised in sensory and socially enriched environments could also be of interest.

A detailed comparison of the current hypotheses on sleep memory processing proposed by SH and SHY has outlined their similarities and differences with regard to postulates, experimental approaches, data, and interpretations. According to SH the role of sleep cannot be reduced to a renormalization of experience-modified synapses, notwithstanding the value of the homeostatic process emphasized by SHY. Indeed, the primary function of sleep consists in sorting and retaining select memories, weakening or eliminating other memory traces, and integrating retained memories with preexisting memories. SHY may also be criticized for a number of other issues, including its failure to solve the implicit dilemma that emerges from framing synaptic renormalization in the current memory storage mechanism exclusively rooted in synaptic modifications. Indeed, if synapses are partly renormalized, modified synapses would accumulate in brain and hinder learning capacity. Conversely, if all synapses are renormalized, acquired memories would be deleted. The dilemma might be solved by assuming that memories are transferred to quantum subnuclear brain particles during REM. The hypothesis may be subjected to experimental verification.

Conflict of interest statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

I warmly thank Dr. Marcos Frank and Dr. Gigliola Grassi-Zucconi for reading earlier versions of the manuscript and suggesting adequate comments and suggestions.

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The energy hypothesis of sleep revisited

Matthew t scharf, nirinjini naidoo, john e zimmerman, allan i pack.

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Corresponding author: Matthew Scharf, Center for Sleep and Respiratory Neurobiology, University of Pennsylvania School of Medicine, Translational Research Building, Suite 2100, 125 S. 31st St. Philadelphia, PA 19104-3403, Phone: (215) 746-4816, Fax: (215) 746-4814, [email protected]

Issue date 2008 Nov.

One of the proposed functions of sleep is to replenish energy stores in the brain that have been depleted during wakefulness. Benington and Heller formulated a version of the energy hypothesis of sleep in terms of the metabolites adenosine and glycogen. They postulated that during wakefulness, adenosine increases and astrocytic glycogen decreases reflecting the increased energetic demand of wakefulness. We review recent studies on adenosine and glycogen stimulated by this hypothesis. We also discuss other evidence that wakefulness is an energetic challenge to the brain including the unfolded protein response, the electron transport chain, NPAS2, AMP-activated protein kinase, the astrocyte-neuron lactate shuttle, production of reactive oxygen species and uncoupling proteins. We believe the available evidence supports the notion that wakefulness is an energetic challenge to the brain, and that sleep restores energy balance in the brain, although the mechanisms by which this is accomplished are considerably more complex than envisaged by Benington and Heller.

Introduction

Sleep has been investigated for over a century. Yet, the fundamental question of why we need to sleep remains unanswered. In particular, what physiological functions are fulfilled by sleep? One hypothesis is that sleep is necessary to replenish energy stores in the brain that are depleted during wakefulness. This theory posits that during waking, a relatively active metabolic period in the brain, energy stores become progressively diminished, thereby promoting sleep. During sleep, there is recovery of energy stores and thus restoration of energy balance.

Based on this concept, in 1995, Benington and Heller proposed that the energy-related substrates glycogen and adenosine are key sleep regulators ( Benington and Heller, 1995 ). They suggested that alterations in astrocytic glycogen and extracellular adenosine in the brain both reflect metabolic alterations that occur during wakefulness and sleep and can influence the amount and quality of subsequent sleep. In particular, this model proposes that glycogen depletion during wakefulness leads to an increase in extracellular adenosine, which facilitates sleepiness and influences delta power during sleep, a measure of sleep homeostasis ( Borbely and Achermann, 1999 ).

This hypothesis led to extensive studies and, over the ensuing years, numerous studies have examined, in relation to sleep and wakefulness, the role of glycogen and adenosine in addition to other aspects of regulation of brain energetics. We review the recent studies on adenosine and glycogen and place them in the context of earlier studies and Benington and Heller’s formulation of the energy hypothesis of sleep. We also discuss changes in other aspects of energy regulation in the brain with wakefulness and sleep. In particular, we discuss new information on the electron transport chain, AMP-activated protein kinase, NPAS2 and clock, uncoupling proteins, reactive oxygen species, and the unfolded protein response. We believe that available evidence does support the notion that wakefulness provides an energetic challenge to the brain, and one of the functions of sleep is to allow for recovery from this energy-challenged state, thereby allowing needed synthetic processes in the brain to occur during sleep. The Benington-Heller hypothesis has been fruitful in terms of stimulating research. The situation is, however, more complex than they proposed.

A. Adenosine

The production of adenosine has been linked to energy depletion. As the major cellular energy molecule ATP is depleted, the byproduct, AMP, is produced. AMP can be further metabolized to adenosine. Therefore, an increase in adenosine may reflect increased degradation of ATP, and a net decrease in the availability of cellular energy stores ( Figure 1 ).

Adenosine metabolic pathways. In the cell, ATP is metabolized to AMP and then to adenosine by cytosolic 5’-nucleotidase. Adenosine can be converted back to AMP by adenosine kinase or metabolized to inosine by adenosine deaminase. Extracellularly, ATP is metabolized to AMP and then to adenosine by ecto 5’-nucleotidase. Adenosine can be converted to inosine by exo adenosine deaminase.

One of the proposed functions of sleep is to replenish energy stores in the brain that are depleted during wake. This theory would, therefore, predict that as energy stores diminish, adenosine would increase progressively during wake, thereby increasing sleepiness and then subsequently decrease during resultant sleep ( Benington and Heller, 1995 ). Indeed, alterations in adenosine levels during normal sleep/wake have been demonstrated. Extracellular adenosine in the cat, as measured by microdialysis, is lower during NREM and REM sleep compared to wakefulness in the basal forebrain, cortex, thalamus and preoptic area of the hypothalamus ( Porkka-Heiskanen et al., 2000 ). However, with 6 hours of sleep deprivation, extracellular adenosine increases only in the basal forebrain and cortex but in the other areas measured (thalamus, preoptic area of the hypothalamus, dorsal raphe nucleus, pedunculopontine tegmental area) it either remains the same or decreases ( Figure 2 ). The reason why adenosine increases with sleep deprivation in certain brain regions but not others is unknown. It likely reflects that unlike cortex, the metabolism of these areas shows little difference between sleep and wake. Nonetheless, these findings indicate that adenosine may act, at least in the basal forebrain or the cortex, to alter aspects of sleep ( Porkka-Heiskanen et al., 2000 ).

Alterations in extracellular adenosine measured by microdialysis following sleep deprivation and in subsequent recovery sleep in cats. Cats were sleep deprived for 6 hours and then allowed to sleep for 3 hours. Extracellular adenosine was measured at the beginning of the experiment by microdialysis and then in 1 hour intervals for the duration of the experiment. Adenosine is presented as a percentage of the baseline value. Extracellular adenosine increases during sleep deprivation only in the basal forebrain and cortex. In the other areas studied, adenosine levels progressively decline. Adenosine stays elevated during recovery sleep only in the basal forebrain. BF, basal forebrain; POA, preoptic area of the hypothalamus; DRN, dorsal raphe nucleus; PPT, pedunculopontine tegmental area. ( Porkka-Heiskanen et al. 2000 ; reprinted with permission).

Early studies showed that intracerebroventricular infusion of adenosine leads to a decrease in wake and an increase in sleep including slow wave sleep ( Virus et al., 1983 ). Also, caffeine, a non-specific antagonist of adenosine receptors, increases wake, and decreases both NREM and REM sleep including slow wave sleep ( Yanik et al., 1987 ). These and other early studies led Radulovacki to initially propose that adenosine is a sleep-promoting molecule ( Radulovacki, 1985 ). Several early studies using different pharmacologic agents demonstrated that adenosine in the brain increases the homeostatic drive to sleep reflected in both the amount of sleep as well as in the quality of sleep as indicated by the increase in slow wave sleep during NREM sleep (reviewed in Radulovacki, 1985 ). Furthermore, systemic or intracerebroventricular administration of the A 1 receptor agonist CPA increases EEG delta power during NREM sleep ( Benington et al., 1995 ). The demonstration that activation of the A 1 receptor directly leads to increases in delta power in sleep suggested a putative mechanism for how changes in cellular metabolism could alter sleep homeostasis since increases in EEG delta power are thought to be a measure of the homeostatic response to sleep deprivation ( Borbely and Achermann, 1999 ). This evidence connecting adenosine to EEG delta power led Benington and Heller to propose that adenosine increases during wakefulness in cortex increase sleepiness and that when sleep occurs there is an increase in delta power ( Benington and Heller, 1995 ). They did not envisage that adenosine played a role in the switch from wakefulness to sleep although others have proposed this (see further below).

A1. Adenosine Receptors

The effects of adenosine are mediated by G-protein coupled adenosine receptors of which there are 4 subtypes (reviewed in Dunwiddie and Masino, 2001 ). The A 1 , A 2A and A 3 receptors have a widespread distribution in the brain while the A 2B has a more limited distribution with high expression primarily in the striatum, olfactory tubercle and nucleus accumbens (reviewed in Dunwiddie and Masino, 2001 ). The A 1 and A 3 receptors couple negatively to adenylyl cyclase and positively to phospholipase C. The latter may be an indirect effect. The A 2A and A 2B receptors couple positively to adenylyl cyclase (reviewed in Dunwiddie and Masino, 2001 ). Adenosine receptors can act on a number of ion channels including potassium and calcium channels (reviewed in Dunwiddie and Masino, 2001 ). The A 1 receptor is primarily inhibitory while the A 2A receptor is primarily excitatory (reviewed in Dunwiddie and Masino, 2001 ).

Both the A 1 and A 2A receptors have been implicated in the regulation of sleep. Intraperitoneal administration of the A 1 receptor agonist CHA leads to an increase in slow wave and REM sleep ( Radulovacki et al., 1984 ). Similarly, stimulation of the A 1 receptor by either intraperitoneal or intracerebroventricular administration of the A 1 receptor agonist CPA increases slow wave activity during NREM sleep ( Benington et al., 1995 ). Consistent with these results implicating the A 1 receptor, antagonizing the A 1 receptor by intraperitoneal administration of CPT causes the opposite effect, namely, an increase in wake and a decrease in slow wave and REM sleep ( Virus et al., 1990 ). The A 2A receptor is also involved in the regulation of sleep since infusion into the subarachnoid space or the ventricular system of the A 2A selective agonist CGS21680 increases NREM and REM sleep ( Gerashchenko et al., 2000 ; Satoh et al., 1998 ; Satoh et al., 1999 ). These effects of the A 1 receptor and A 2A are likely mediated by actions of adenosine in different brain regions (see further below).

However, mice with genetic deletion of the A 1 receptor have the same amounts of NREM and REM sleep as wild-type mice ( Stenberg et al., 2003 ). Furthermore, both the wild-type and knockout mice show the same rebound in NREM and delta power following 6 hours of sleep deprivation indicating that sleep homeostasis is normal in the A 1 receptor knockout mice ( Stenberg et al., 2003 ). Thus, these results would argue that A 1 receptor is not essential for sleep homeostasis. A 2A receptor knockout mice have not been as well characterized but are reported to have impairments in NREM rebound following sleep deprivation ( Urade et al., 2003 ). A 2A receptor knockout mice do not respond to intraperitoneal administration of caffeine but A 1 receptor knockout mice display a normal response to caffeine. This suggests that the A 2A receptor is likely the more important mediator of the effects of caffeine on sleep ( Huang et al., 2005 ). Although these results from knockout mice suggest that the A 2A receptor is the key receptor for sleep/wake control and the effects of caffeine, constitutive lack of a gene can lead to compensatory changes, which may confound interpretation of the data. Genetic deletion of the A 1 and A 2A receptors in a spatially and temporally controlled manner would help elucidate the particular functions of A 1 and A 2A receptors in sleep homeostasis. Local administration of antisense oligonucleotides to selectively reduce expression of adenosine receptors ( Thakkar et al., 2003b ) has led to additional insights as we discuss more fully below, where we consider the action of adenosine in different neuronal groups.

A2. Adenosine Metabolism

Studies have also assessed whether adenosine metabolism is altered during sleep. Adenosine can be metabolized either intracellularly or extracellularly. A schematic of the adenosine metabolic pathways is presented in Figure 1 . Within the cell, adenosine can be formed from AMP in a reaction catalyzed by cytosolic 5’-nucleotidase ( Meghji, 1991 ). Also within a cell, adenosine can be converted back to AMP via adenylate kinase or converted to inosine via adenosine deaminase ( Meghji, 1991 ). In the extracellular space, ATP, ADP and AMP can be converted to adenosine by ecto-5’-nucleotidases ( Cunha et al., 1998 ; Dunwiddie et al., 1997 ).

In a study from our laboratory, we assessed activities of both the cytosolic and extracellular forms of 5’-nucleotidase using a new method to assess their activities ( Mackiewicz et al., 2000 ), as well as activity of adenosine deaminase and adenosine kinase. We found that while activity of these enzymes did exhibit diurnal variation in a number of different brain regions, sleep deprivation did not alter the activity of any of these enzymes ( Mackiewicz et al., 2003 ). Similarly, others have reported that activity of 5’-nucleotidase is altered following only 4 days of REM sleep deprivation but not following 2 days of REM sleep deprivation, suggesting that 5’-nucleotidase activity is not likely altered during spontaneous wake ( Thakkar and Mallick, 1996 ). These studies suggest that modulation of the activities of adenosine enzymes is not a likely mechanism to explain the increase in adenosine that occurs during wakefulness. The activity of extracellular 5’-nucleotidase is, however, increased with age in cortex ( Mackiewicz et al., 2006 ). This likely explains the increase in extracellular adenosine with age in basal forebrain that has been found using microdialysis in rat ( Murillo-Rodriguez et al., 2004 ).

Adenosine has been extensively studied because increases in extracellular adenosine may reflect depletion of ATP in the cell concomitant with energy depletion. However, the connection between cellular metabolism and extracellular adenosine levels is not straightforward since there are numerous sources for extracellular adenosine. Adenosine can be produced in the cell and transported across the cell membrane by a number of transporters (reviewed in Cass et al., 1998 ). But adenosine can also be produced in the extracellular space by conversion of ATP, ADP and AMP ( Cunha et al., 1998 ; Dunwiddie et al., 1997 ). ATP is co-localized with neurotransmitters such as acetylcholine, norepinephrine, serotonin and dopamine and can be released upon stimulation (reviewed in Dunwiddie and Masino, 2001 ). Impairment of synaptic transmission selectively in astrocytes by spatially and temporally restricted expression of a dominant negative SNARE domain leads to decreased ATP release, which decreases extracellular adenosine implicating astrocytes as a source of extracellular adenosine in the brain ( Pascual et al., 2005 ). This is particularly interesting since mutant mice with the dominant negative SNARE have impaired sleep homeostasis, i.e., an attenuated increase in NREM sleep and delta power following sleep deprivation ( Halassa et al., 2007 ). In particular, in these mice the increase in delta power following sleep deprivation is short and not sustained ( Halassa et al., 2007 ). Furthermore, in basal forebrain, impairing adenosine transport with NBTI increases extracellular adenosine ( Porkka-Heiskanen et al., 1997 ) suggesting that, at least in basal conditions in this region, the net flow of adenosine is from the extracellular space into the cell. Therefore, adenosine in the extracellular space may be produced within the cell or outside it, and the observed increase in extracellular adenosine induced by sleep deprivation does not necessarily reflect cellular energy depletion occurring with prolonged wakefulness. However, lesions of the cholinergic cells in the basal forebrain (see section A3a) completely negate the increase in adenosine with sleep deprivation in this region suggesting that during sleep deprivation adenosine is coming into the extracellular space from these neurons, i.e., it has an intracellular source. Thus, the source of adenosine in baseline conditions and during sleep deprivation may be different. It is also possible that the lesioned animals could have abnormalities in astrocytes, which may be responsible for the decrease in adenosine. Damage to the central nervous system is accompanied by changes to astrocytes, a process known as reactive gliosis (reviewed in Pekny et al., 2007 ). It is possible, therefore, that lesioning of the cholinergic neurons of the basal forebrain leads to alterations in astrocytes, which results in alteration in extracellular adenosine. Indeed, the enzyme adenosine kinase, which metabolizes adenosine to AMP, is increased in astrocytes during the process of reactive gliosis ( Boison, 2006 ), which could contribute to the decreased extracellular adenosine observed in the lesioned animals. Nonetheless, the increase in adenosine following sleep deprivation is correlated with the restorative processes occurring during NREM sleep and may be caused by the synthesis of glycogen.

A3. Action of Adenosine in Different Brain Regions

Manipulation of the adenosine system in a number of different brain regions alters sleep/wake states. The basal forebrain, laterodorsal tegmental area, pontine reticular formation and ventrolateral preoptic area have all been implicated as important regions mediating the effects of adenosine on sleep. Following is an examination of the role of adenosine in each of these systems that are involved in sleep regulation and the adenosine receptor subtypes involved.

A3a. Basal Forebrain

The basal forebrain is involved in sleep/wake regulation. The term “basal forebrain” has become synonymous with the magnocellular cholinergic system in the medial septum, vertical and horizontal limbs of the diagonal band of Broca, magnocellular preoptic area, the substantia innominata and the nucleus basalis of Meynert ( Szymusiak, 1995 ). Other cell types, however, are contained in the basal forebrain including GABA-ergic and various peptidergic neurons and these may be of functional significance ( Szymusiak, 1995 ). In this review, we will refer to the basal forebrain as the anatomical region containing both the cholinergic neurons and noncholinergic neurons. The cholinergic neurons of the basal forebrain project diffusely and play a role in the cortical activation associated with wake and REM sleep ( Jones, 2005 ; Szymusiak, 1995 ). Given the role of the cholinergic system of the basal forebrain in sleep/wake regulation, a number of studies have examined whether the basal forebrain is the “adenosine sensor” of the brain that is responsible for the adenosinergic modulation of sleep/wake.

If the adenosine sensor is in the basal forebrain, adenosine should increase with wakefulness in the basal forebrain. Indeed, extracellular adenosine increases during wake in a number of brain regions including the basal forebrain ( Porkka-Heiskanen et al., 2000 ; Porkka-Heiskanen et al., 1997 ). Of these areas, extracellular adenosine increases only in the basal forebrain and cortex during sleep deprivation. During 3 hours of sleep recovery, adenosine declines in the cortex below pre-sleep deprivation levels. In basal forebrain it also decreases during recovery sleep but is still elevated above pre-sleep deprivation levels even after 3 hours of recovery sleep ( Porkka-Heiskanen et al., 2000 ) ( Figure 2 ). Therefore, McCarley and colleagues have suggested that the basal forebrain may be uniquely situated to detect changes in adenosine occurring with sleep and wake and be able to modulate sleep accordingly ( Basheer et al., 2004 ; Porkka-Heiskanen et al., 2000 ). It should be noted, however, that a single location of action of adenosine is unlikely to explain the many effects that are observed with sleep deprivation and the recovery from sleep deprivation. In particular, it cannot explain the local increase in slow wave activity during sleep in brain areas specifically activated during wakefulness ( Vyazovskiy and Tobler, 2008 ).

As further proof of the role of the basal forebrain in mediating the effects of adenosine on sleep/wake, modulation of the adenosinergic system in the basal forebrain can alter sleep. Administration of the adenosine transport inhibitor NBTI increases extracellular adenosine and concomitantly decreases wake and increases slow wave and REM sleep as well as delta power during NREM sleep ( Methippara et al., 2005 ; Porkka-Heiskanen et al., 1997 ). Similarly, perfusion of adenosine into the basal forebrain by microdialysis decreases wake and increases NREM, REM and delta power ( Basheer et al., 1999 ; Portas et al., 1997 ). These studies demonstrate that increasing adenosine levels in the basal forebrain can increase both the amount of sleep and the quality of sleep as reflected in the delta power, which is a sensitive indicator of the homeostatic component of sleep ( Borbely and Achermann, 1999 ).

A 1 and A 2A receptors mediate the effects of adenosine on sleep/wake (see section A1 ). In the basal forebrain, A 1 receptor mRNA increases following sleep deprivation but A 2A receptor expression is undetectable ( Basheer et al., 2001a ). Reduction of A 1 receptor in the basal forebrain by antisense oligonucleotides increases wake and decreases NREM and REM sleep ( Thakkar et al., 2003b ), although one needs to be aware of the potential for non-specific effects of antisense oligonucleotides (reviewed in Lebedeva and Stein, 2000 ). Since this intervention has the opposite effect to activating the A 1 receptor, the results are compatible with the concept that adenosine promotes sleep in this region through the effects of its action on the A 1 receptor. Furthermore, animals with their A 1 receptors reduced in basal forebrain by antisense oligonucleotides show decreases in NREM and delta power following sleep deprivation, suggesting that it is the A 1 receptor in the basal forebrain that mediates sleep homeostasis ( Thakkar et al., 2003b ). This result is incongruent with that from studies of mice with knockout of specific adenosine receptors that we described above.

A number of studies have examined the electrophysiological mechanisms by which adenosine can modulate the neuronal activity responsible for sleep/wake alteration. In the basal forebrain, microdialysis perfusion of adenosine, the adenosine transport inhibitor NBTI (which leads to an increase in extracellular adenosine) (see above), or the A 1 receptor agonist CHA, decreases the neuronal activity of wake-active neurons ( Alam et al., 1999 ; Thakkar et al., 2003a ). Consistent with this result, the A 1 receptor antagonists CPDX or CPT, also delivered by microdialysis perfusion into the basal forebrain, increase the discharge of wake-active neurons ( Alam et al., 1999 ; Thakkar et al., 2003a ). Administration of the A 2A receptor agonist CGS had no effect on these wake-active neurons ( Thakkar et al., 2003a ). Therefore, adenosine decreases the activity of wake-active neurons in the basal forebrain by acting on the A 1 receptor. One study reported a decrease in the firing rate of sleep-active neurons in this region with adenosine activation by NBTI ( Alam et al., 1999 ), and one study with adenosine activation by the A 1 receptor agonist CHA did not observe a change in the firing rate of these sleep-active neurons ( Thakkar et al., 2003a ).

The A 1 receptor decreases neuronal activity by acting on inwardly rectifying potassium channels or the hyperpolarization activated cation current ( Rainnie et al., 1994 ). In in vitro brain slices, adenosine inhibits the cholinergic neurons of the basal forebrain by activating an inwardly rectifying potassium channel ( Arrigoni et al., 2006 ). This effect is blocked by the A 1 receptor antagonist CPT ( Arrigoni et al., 2006 ). Some of the non-cholinergic neurons of the basal forebrain are also inhibited by adenosine ( Arrigoni et al., 2006 ). In contrast to the cholinergic neurons, however, the non-cholinergic neurons decrease activity in response to adenosine by inhibiting the hyperpolarization-activated cation current ( Arrigoni et al., 2006 ). Therefore, although both cholinergic and non-cholinergic neurons are inhibited by adenosine, this inhibition is mediated by different ion currents; for the former it is an inwardly rectifying potassium current and the latter it is the hyperpolarization-activated cation current.

The A 1 receptor mediates intracellular events by acting on cell signaling pathways, in particular by increasing the activity of phospholipase C (reviewed in Dunwiddie and Masino, 2001 ). Phospholipase C catalyzes the production of inositol triphosphate, which leads to activation of calcium channels and increases in intracellular calcium. Consistent with phospholipase C activation, adenosine causes an increase in intracellular calcium in cholinergic neurons of the basal forebrain by acting primarily through the A 1 receptor and to a lesser extent, the A 3 receptor ( Basheer et al., 2002 ). This calcium is released from internal stores via inositol triphosphate receptors ( Basheer et al., 2002 ).

Phospholipase C activation leads to activation of protein kinase C ( Alberts et al., 2002 ). Protein kinase C alters transcription by acting on a number of downstream targets (reviewed in Ventura and Maioli, 2001 ) including NF-κB (reviewed in Weil and Israel, 2006 ). NF-κB translocates to the nucleus when one of its binding partners, I-κB, is phosphorylated and detaches from it ( Alberts et al., 2002 ). A number of different kinases can phosphorylate I-κB including protein kinase C (reviewed in Siebenlist et al., 1994 ). In basal forebrain brain slices, adenosine increases NF-κB DNA binding activity in nuclear extracts compatible with it translocating to the nucleus and pretreatment with the A 1 receptor selective antagonist CPT decreases NF-κB DNA binding activity ( Basheer et al., 2001b ). NF-κB translocation to the nucleus following sleep deprivation as well as following microinjection into the basal forebrain of either adenosine or the A 1 receptor agonist CHA occurs almost exclusively in the cholinergic cells of the basal forebrain ( Ramesh et al., 2007 ). Furthermore, blocking the nuclear translocation of NF-κB with an inhibitor peptide, SN50, reduces delta power following sleep deprivation ( Ramesh et al., 2007 ). These observations are consistent with an A 1 receptor-mediated activation of the phospholipase C signaling cascade in the basal forebrain and suggest that NF-κB may be an important mediator of both the molecular and behavioral effects of adenosine in this region.

The behavioral, electrophysiological and molecular studies of McCarley and colleagues lead to the hypothesis that the basal forebrain is a critical site for the effects of adenosine on sleep homeostasis (reviewed in Basheer et al., 2004 ). Benington and Heller posited that adenosine produced sleepiness and subsequently increased delta power during sleep by its action in cortex ( Benington and Heller, 1995 ). McCarley and colleagues redefined the Benington-Heller hypothesis by positing that adenosine mediates it effect on sleep by acting focally in the basal forebrain. They specifically proposed that the wake-promoting cholinergic neurons of the basal forebrain are mediators of the action of adenosine on wakefulness and sleep ( Porkka-Heiskanen et al., 1997 ). Although McCarley and coworkers provided much evidence for their hypothesis, they did not test it directly. In particular, is the increase in adenosine in the basal forebrain required for the homoeostatic response to sleep deprivation? Furthermore, are the cholinergic neurons of the basal forebrain required for the increases in sleep and EEG delta power following sleep deprivation?

A recent study ( Blanco-Centurion et al., 2006 ) addressed these critical questions by destroying the cholinergic neurons of the basal forebrain by intracerebroventricular injection of the neurotoxin saporin conjugated to an antibody that is taken up by the p75 nerve growth factor receptor, which is selectively expressed in cholinergic neurons of the basal forebrain ( Heckers et al., 1994 ). Lesioning of the cholinergic neurons of the basal forebrain eliminates the increase in extracellular adenosine induced by sleep deprivation in this region. However, surprisingly, loss of the cholinergic neurons of the basal forebrain does not alter baseline sleep/wake or recovery sleep following 6 or 12 hours of sleep deprivation. The increase in EEG delta power following sleep deprivation was also normal indicating an intact sleep homeostatic response ( Blanco-Centurion et al., 2006 ). The lack of an increase in adenosine following sleep deprivation while the homeostatic response stays intact indicates that the increase in adenosine in the basal forebrain that occurs during sleep deprivation is not required for sleep homeostasis. Furthermore, this provocative study indicates that the cholinergic neurons of the basal forebrain are not necessary for sleep/wake control or for sleep homeostasis.

There is, however, some controversy surrounding this study and the role of the cholinergic neurons of the basal forebrain in mediating sleep homeostasis (for commentaries, see Heller, 2006 ; Kalinchuk et al., 2006 ; Noor Alam et al., 2006 ; Radulovacki, 2006 ). It is possible that since there is redundancy in the processes regulating sleep homeostasis, the loss of one component, i.e., the cholinergic cells of the basal forebrain, may not alter overall sleep homeostasis ( Noor Alam et al., 2006 ). However, lesioning of the histamine neurons in the tuberomammillary nucleus and the noradrenergic locus ceruleus neurons in addition to the cholinergic neurons of the basal forebrain decreases wake and increases NREM sleep during only the first 4 hours of the dark period but over a full 24 hour period produces no significant change in wake or NREM ( Blanco-Centurion et al., 2007 ). This result challenges the notion that the lack of an effect on total sleep in the animals with a lesion of the cholinergic basal forebrain is due to compensatory changes in other wake-promoting systems since lesioning of other wake-promoting neurons also fails to alter overall sleep. Since the triple lesioned animals had increased bout lengths for both wake and NREM, the role of these systems may be to alter the architecture of sleep rather than the total amount of NREM and wake ( BlancoCenturion et al., 2007 ).

It is also possible that when the conjugated saporin is administered intracerebroventricularly, as was done in the study of Blanco-Centurion et al. ( Blanco-Centurion et al., 2006 ), there are different effects than would be found if the saporin was delivered locally to the basal forebrain ( Kalinchuk et al., 2006 ). This could be the result of the toxin acting in regions other than basal forebrain. Indeed, injection of saporin conjugated to an antibody that is taken up by the p75 nerve growth factor receptor as was used in the study of Blanco-Centurion et al. directly into the basal forebrain causes a decrease in recovery NREM sleep and delta power following sleep deprivation, indicating impaired sleep homeostasis ( Kaur et al., 2008 ). Adenosine was not measured, however, in this study ( Kaur et al., 2008 ) and the cause for the different results from local microinjection and ICV injection is currently not known. While much remains to be learned, the notion of the exclusivity of the cholinergic neurons of the basal forebrain as the adenosine sensor and mediator of the effects of adenosine on sleep homeostasis is seriously challenged, and can no longer be considered as a tenable hypothesis.

A3b. Laterodorsal Tegmental Nucleus

The laterodorsal tegmental nucleus is a sleep-promoting region with a particular role in REM sleep generation ( Monti and Monti, 2000 ). The laterodorsal tegmental nucleus includes cholinergic and glutamatergic neurons ( Lee et al., 2003 ; Monti and Monti, 2000 ). Microdialysis perfusion of adenosine into the laterodorsal tegmental nucleus in cats decreases wake and increases slow wave sleep and REM sleep ( Portas et al., 1997 ). In vitro in brain slices, adenosine and the A 1 receptor agonist CHA inhibit the firing of both cholinergic and non-cholinergic neurons in the laterodorsal tegmental nucleus by activating an inwardly rectifying potassium current and inhibiting the hyperpolarization-activated cation current ( Rainnie et al., 1994 ). This study did not identify whether these two currents were active in both the cholinergic and noncholinergic cells or whether the effect of adenosine on cholinergic and non-cholinergic neurons was specifically mediated by one of the channels as was observed in the basal forebrain (see above). Adenosine also inhibits glutamatergic transmission in the laterodorsal tegmental nucleus by activating presynaptic A 1 receptors ( Arrigoni et al., 2001 ). Therefore, adenosine may modulate sleep/wake by acting on the neurons of the laterodorsal tegmental nucleus. If so, the precise role of adenosine in this region in sleep/wake control in vivo is currently unknown.

A3c. Pontine Reticular Formation

Another region to consider is the pontine reticular formation. The pontine reticular formation has a well-established role in the modulation of REM sleep ( Monti and Monti, 2000 ). Microinjection of the A 1 receptor agonist CHA into the pontine reticular formation of the rat decreases wake and increases REM ( Marks and Birabil, 1998 ; Marks et al., 2003 ) and to a lesser extent NREM ( Marks and Birabil, 1998 ). Microinjection of the A 2A receptor agonist CGS into the pontine reticular formation also increases REM sleep ( Coleman et al., 2006 ; Marks et al., 2003 ) and NREM sleep ( Coleman et al., 2006 ), and decreases wake ( Marks et al., 2003 ). The pontine reticular formation receives cholinergic projections from the laterodorsal and pedunculopontine tegmental nuclei, which are important for REM sleep generation ( Lydic and Baghdoyan, 2005 ). Interestingly, CGS injected into the pontine reticular formation increases acetylcholine release in the pontine reticular formation ( Coleman et al., 2006 ). These data suggest that adenosine may also act in the pontine reticular formation to increase acetylcholine release and thereby increase REM sleep.

A3d. Preoptic Area of the Hypothalamus

The preoptic area of the hypothalamus is an important sleep-promoting region. In particular, a population of neurons in the ventrolateral preoptic area shows increased fos levels ( Sherin et al., 1996 ), and increased neuronal firing activity during sleep consistent with a sleep-promoting function ( McGinty and Szymusiak, 2001 ; reviewed in Saper et al., 2005 ). However, despite this key difference that neurons in the ventrolateral preoptic area are sleep-active, microinjection of adenosine into the preoptic area increases total sleep, slow wave sleep and REM sleep as it does in the basal forebrain, pontine reticular formation and laterodorsal tegmental nucleus ( Ticho and Radulovacki, 1991 ). This suggests that adenosine is excitatory to sleep-active neurons in the ventrolateral preoptic area in contrast to the inhibitory action on the wake-active cells in the basal forebrain. Local administration of the adenosine transport inhibitor NBTI into the lateral preoptic area, which in the basal forebrain has been shown to increase extracellular adenosine levels and sleep ( Porkka-Heiskanen et al., 1997 ), decreases NREM and increases wake ( Methippara et al., 2005 ). Thus, administration of adenosine ( Ticho and Radulovacki, 1991 ) or NBTI ( Methippara et al., 2005 ) produces opposite effects. But we do not know the source of adenosine in the preoptic area, i.e., extracellular or intracellular (see above). If in this region the net flux of adenosine through the transporter is from intracellular to extracellular space, NBTI could lead to a decline in extracellular adenosine. In comparison to studies described above for basal forebrain, the effect of local administration of NBTI on extracellular adenosine levels in the preoptic area has not been assessed. There may, moreover, be issues about the precise location of injection sites in this small region that could affect results. This seems likely since studies report opposite results for the administration of the A 1 receptor agonist CPA into the preoptic area of the hypothalamus, which in one study decreases NREM and increases wake ( Methippara et al., 2005 ) and in the other study increases sleep ( Ticho and Radulovacki, 1991 ). The discrepancy in these results may reflect that although the ventrolateral preoptic area has a population of sleep-active neurons, injection of drugs cannot specifically target these neurons and may act on other populations of cells.

Different studies have also led to different conclusions about the role of the A 2A receptor in the lateral preoptic area. Administration of the less selective A 2 receptor agonist CV-1808 does not alter sleep or wake ( Ticho and Radulovacki, 1991 ), but the relatively selective A 2A receptor agonist CGS increases NREM and decreases wake ( Methippara et al., 2005 ). The specificities of these drugs for the adenosine receptor subtypes are different ( Hutchison et al., 1989 ), and therefore the results of these studies need to be interpreted with caution. Nonetheless, the latter study suggests that A 2A receptor activation, which is excitatory in the lateral preoptic area, increases sleep. Neuronal excitation of sleep-active neurons mediated by the A 2A receptor is a plausible explanation for the sleep-promoting role of adenosine in the ventrolateral preoptic area.

Adenosine reduces spontaneous GABA release onto neurons of the ventrolateral preoptic area suggesting a presynaptic mechanism for ventrolateral preoptic area activation, whereby adenosine disinhibits sleep-promoting neurons of the ventrolateral preoptic area ( Chamberlin et al., 2003 ; Morairty et al., 2004 ). In a separate study, two groups of GABA-ergic sleep-promoting neurons in the ventrolateral preoptic area have been identified that are both inhibited by adenosine and the A 1 receptor agonist CPA via a post-synaptic mechanism ( Gallopin et al., 2005 ). The discrepancy between the former and latter studies may have to do with method of recording (patch clamp in whole cell recording versus loose cell-attached configuration). However, the latter study went further by also assessing the role of the A 2A receptor in the two types of neurons that are inhibited by adenosine. The A 2A receptor agonist CGS excites only one type ( Gallopin et al., 2005 ) demonstrating that different neurons in the ventrolateral preoptic area have the capacity to respond differently to adenosine. There may be a subgroup of neurons in this region that are responsible for the sleep-promoting role of adenosine.

The A 2A receptor may influence ventrolateral preoptic area neurons indirectly as well. Infusion of the A 2A receptor agonist CGS into the subarachnoid space increases fos expression in the ventrolateral preoptic area suggesting that activation of A 2A receptors in the leptomeninges or nucleus accumbens may also increase NREM sleep by increasing the activity of sleep-active ventrolateral preoptic area neurons ( Scammell et al., 2001 ). Therefore, adenosine likely acts on the A 2A receptor both directly in the preoptic area and indirectly to excite the ventrolateral preoptic area sleep-promoting neurons thereby increasing sleep.

A4. Synthesis of Studies of Adenosine

While the idea that adenosine might be a sleep-promoting molecule did not originate with Benington-Heller ( Benington and Heller, 1995 ), but rather as a result of important earlier studies ( Feldberg and Sherwood, 1954 ; Haulica et al., 1973 ; Marley and Nistico, 1972 ; Radulovacki, 1985 ; Virus et al., 1983 ), their manuscript did provide a cogent synthesis that stimulated research in this area. Benington et al. observed that activation of the A 1 receptor leads to increases in EEG delta power during NREM sleep ( Benington et al., 1995 ). This finding was a stimulus for Benington and Heller (1995) to propose that changes in adenosine in the cortex could promote sleepiness and subsequently, when sleep occurred, alter delta power, the main electrophysiologic correlate of sleep homeostasis ( Borbely and Achermann, 1999 ). They did not envisage that adenosine played a role in the wakefulness to sleep switch. However, other investigators have examined whether changes in adenosine in other brain regions could alter not only sleep homeostasis as indicated by delta power, but also the total amounts of sleep and wake, i.e., could adenosine also play a role in the initiation of sleep or wake? There is now evidence that at least in some brain areas, e.g., the basal forebrain, extracellular adenosine increases in relationship to the duration of prior wakefulness ( Basheer et al., 1999 ; Porkka-Heiskanen et al., 2000 ; Porkka-Heiskanen et al., 1997 ). However, this does not occur uniformly throughout the brain and it appears that in some wake-active brain regions such as the dorsal raphe nucleus, adenosine levels actually decline as wakefulness is prolonged ( Porkka-Heiskanen et al., 2000 ). The basis for this result is unknown but likely reflects local differences in the metabolic changes between sleep and wakefulness. Moreover, it may be somewhat artifactual, since determining extracellular adenosine by microdialysis in these small neuronal populations is challenging.

There is extensive evidence that adenosine can alter sleep/wake states. To date, the basal forebrain, the laterodorsal tegmental nucleus, the pontine reticular formation and the preoptic area have been implicated as sites of adenosine action on sleep/wake control. The role of adenosine in sleep/wake control may result from inhibition of basal forebrain wake-active neurons ( Thakkar et al., 2003a ) or activation of preoptic area sleep-active neurons, the latter effect being mediated by A 2A receptors ( Gallopin et al., 2005 ). The role of the basal forebrain adenosinergic system in altering sleep has been the most studied. McCarley and co-workers have suggested that cholinergic neurons in the basal forebrain sense alterations of adenosine that occur during wake or sleep and alter the quantity and quality of sleep appropriately (reviewed in Basheer et al., 2004 ).

This modification of the Benington-Heller hypothesis, i.e., that for its role in control of sleep/wake, adenosine is specifically “sensed” by cholinergic neurons in the basal forebrain, while intriguing, now seems disproven ( Blanco-Centurion et al., 2006 ). A challenge to this hypothesis from the beginning is that it has never been shown in what way cholinergic neurons of the basal forebrain had any special or unique properties with respect to the effects of adenosine on their function, i.e., what was special about the basal forebrain neurons that allowed them to play this role? There is no evidence, for example, that the regulation of adenosine enzymes controlling adenosine metabolism across the day or with sleep deprivation is different in the basal forebrain compared to other brain regions ( Mackiewicz et al., 2003 ).

Other cell types in the basal forebrain or in other areas of the brain are likely also to be important for the mechanisms of action of adenosine on sleep/wake control. As discussed earlier, the laterodorsal tegmental nucleus, the pontine reticular formation and the preoptic area have all been shown to alter sleep upon modulation of their intrinsic adenosinergic system. These areas may work independently or, more likely, in concert with other regions of the brain to alter sleep. One intriguing possibility is that adenosine acts in the cortex to increase slow wave activity ( Benington and Heller, 1995 ) while adenosine acts in other regions such as the basal forebrain to initiate sleep.

A recent exciting discovery is the role of astrocytes in response to sleep deprivation. These cells are involved in modulating effects of synapses- the tripartite synapse (reviewed in Fellin et al., 2006 ). Since a single astrocyte can contain greater than 100,000 synapses ( Bushong et al., 2002 ), they provide the ability to coordinate responses over a large numbers of synapses. Astrocytes release neurochemicals that can affect synaptic transmission (reviewed in Fellin et al., 2006 ). One particular neurochemical that is released is ATP that is metabolized extracellularly to adenosine ( Pascual et al., 2005 ). Thus, astrocytes can modulate adenosine “tone” of synapses and are likely to play a key role in the coupling of adenosine to the regulation of sleep. This discovery as to the role of astrocytes is likely to lead to more intensive investigation of the biology of astrocytes in relation to sleep/wake control and the response to sleep deprivation.

Pharmacologic studies implicate adenosine as a modulator of sleep/wake with the effects mediated by the A 1 and A 2A receptors. Antisense oligonucleotide reduction of the A 1 receptor produces alterations in sleep homeostasis consistent with the pharmacologic data ( Thakkar et al., 2003b ). However, genetic deletion of the A 1 receptor produces no alterations in sleep or in the response to sleep deprivation ( Stenberg et al., 2003 ), while genetic deletion of the A 2A receptor has been reported to produce sleep alterations in mice such as unresponsiveness to the wake-promoting effects of caffeine ( Huang et al., 2005 ; Urade et al., 2003 ). Pharmacologic and genetic tools each have, however, their limitations. Pharmacologic agents are confounded by issues of specificity, delivery and dosage. However, given the number of drugs tested and the number of different experimental models used, it is extremely unlikely that non-specific effects can explain the sleep/wake alterations observed in these studies. These pharmacologic experiments implicate the adenosine system as being a modulator of both the quantity and quality of sleep.

Global deletion of a gene may produce developmental defects (for example see Lee et al., 2007 ) or lead to compensatory changes that may mask the physiological functions of the deleted gene when compared to temporally and/or spatially restricted deletion (for example see Balschun et al., 2003 ). Genetic tools are now available that allow for spatial and temporal control of gene expression (for reviews, see Mallo, 2006 ; Miyoshi and Fishell, 2006 ). These elegant genetic tools avoid many of the problems associated with pharmacologic agents and deletion of a target gene prior to development. Given the recent controversy of the role of the basal forebrain in mediating the effects of adenosine ( Blanco-Centurion et al., 2006 ; Heller, 2006 ; Kalinchuk et al., 2006 ; Noor Alam et al., 2006 ; Radulovacki, 2006 ), future studies utilizing these genetic tools will allow for more precisely controlled experiments and should prove illuminating.

There is, however, no doubt that adenosine does play some role in sleep/wake control, and likely more prominently in the behavioral response to sleep deprivation. Adenosine is, however, not likely to be the major player but rather one of many mechanisms used to promote sleep when wakefulness is prolonged. Microarray studies conducted by us in both Drosophila ( Zimmerman et al., 2006 ) and mice ( Mackiewicz et al., 2007 ) show that the most common changes in gene expression with sleep and wakefulness are downregulation of expression of many genes during sleep deprivation, supporting the notion that multiple mechanisms are used to limit wakefulness. The adenosine studies to date are consistent with a role of adenosine in regulation of sleep but are insufficient on their own to explain how changes in brain energy are linked to control of sleep.

B. Glycogen

The other component of the Benington-Heller hypothesis is alteration in glycogen. In addition to being stored and produced in the liver, there is a relatively small amount of glycogen that is synthesized and stored in astrocytes of the brain. Benington and Heller proposed that glycogen stores in brain are depleted during wakefulness and restored during sleep ( Benington and Heller, 1995 ). We first describe how glycogen is regulated and then review the evidence for changes in glycogen in brain with sleep/wake and sleep deprivation.

B1. Metabolic Regulation of Glycogen

There are many recent in-depth reviews of glycogen metabolism and the regulation of glycogen synthesis and degradation ( Brown, 2004 ; Brown et al., 2003 ; Brushia and Walsh, 1999 ; Greenberg et al., 2006 ; Gruetter, 2003 ; Newgard et al., 2000 ; Oikonomakos, 2002 ). Briefly follows a summary of glycogen regulation in the brain. Glycogen is present in astrocytes in the brain. The synthesis of glycogen chains from glucose molecules is catalyzed by glycogen synthase and the release of glucose-1-phosphate from glycogen is catalyzed by glycogen phosphorylase. The activities of both of these enzymes are dependent on their state of phosphorylation, but in opposite directions. Glycogen phosphorylase is activated when phosphorylated and glycogen synthase is activated when dephosphorylated. Therefore, dephosphorylation of these 2 enzymes favors the synthesis of glycogen removing glucose from the cell energy pool and phosphorylation favors the release of glucose and availability of energy for the cell.

Both glycogen phosphorylase and glycogen synthase are dephosphorylated by the same enzyme, protein phosphatase 1. In the brain, complexes are formed that place all three of these enzymes in close proximity to glycogen by protein targeting to glycogen (PTG), a scaffolding molecule. PTG is a targeting subunit of protein phosphatase 1, which directs it to its substrates ( Brady et al., 1997 ; Ou et al., 2005 ; Printen et al., 1997 ). Unlike other targeting subunits of protein phosphatase 1, the binding of PTG to protein phosphatase 1 is not regulated by phosphorylation ( Brady et al., 1997 ). The concentration of PTG alters the magnitude of the increase in glycogen synthesis ( Green et al., 2004 ).

Glycogen phosphorylase is phosphorylated by phosphorylase kinase ( Brushia and Walsh, 1999 ), while glycogen synthase is a substrate of multiple enzymes that lead to its phosphorylation ( Larner et al., 1979 ; Lawrence et al., 1997 ). The change in phosphorylation state is rapid and can lead to degradation of glycogen within minutes ( Cruz and Dienel, 2002 ; Hutchins and Rogers, 1970 ; Karnovsky et al., 1983 ). This makes accurately measuring the glycogen content of any tissue problematic, and especially difficult in the brain as glycogen levels are lower than in other tissues, although estimates of glycogen in brain may be artifactually low as a result of tissue isolation techniques during which time glycogen is degraded ( Cruz and Dienel, 2002 ). Regardless of the basal amount of glycogen in brain tissue, measuring glycogen levels in the brain requires methods that inactivate kinase and phosphatase pathways instantly.

B2. Changes in Brain Glycogen with Sleep/Wake and Sleep Deprivation

Two general strategies have been used to preserve the glycogen content in brain tissue, thereby permitting assessment of its amount. One strategy is extreme heat, generated by exposure to high-energy focused microwave irradiation in mammalian studies ( Franken et al., 2003 , 2006b ; Gip et al., 2002 ; Gip et al., 2004 ; Kong et al., 2002 ; Swanson et al., 1992 ) or immersion in boiling water in a Drosophila study ( Zimmerman et al., 2004 ). The other strategy is extreme cold achieved by immersion in liquid nitrogen ( Cruz and Dienel, 2002 ; Hutchins and Rogers, 1970 ; Karnovsky et al., 1983 ). These strategies have been used to assess changes in brain glycogen occurring with short durations of wakefulness or sleep, diurnal variations and changes following sleep deprivation.

Glycogen stores can be depleted and repleted rapidly during wake or sleep, respectively. In one study, sleep/wake state was assessed in rats and then whole animals were dropped into liquid nitrogen after a specified duration of sleep or wake. A significant decrease in whole brain glycogen is seen after 2 to 5 minutes of spontaneous wake compared to sleep levels. In contrast, 5 to 11 minutes of spontaneous sleep is sufficient to restore glycogen levels ( Karnovsky et al., 1983 ). These studies, which have not been repeated, imply that rapid changes in glycogen take place in the brain in wake or sleep, and likely result from changes in the phosphorylation state of glycogen synthase and glycogen phosphorylase. However, since freezing in liquid nitrogen is not sufficient to prevent post-sacrifice changes in glycogen ( Kong et al., 2002 ), these results need to be interpreted with caution.

Other studies have indicated longer-term alterations in brain glycogen. There is a diurnal variation in total brain glycogen content, which correlates with the activity of animals, both in mice ( Hutchins and Rogers, 1970 ) and flies ( Zimmerman et al., 2004 ). In the fly, brain glycogen content increases during the consolidated rest period to the highest level at ZT 18 and then significantly declines between ZT 18 to ZT 22; this is the beginning of the early active period for the fly ( Zimmerman et al., 2004 ). In flies, the glycogen content of the entire head (including brain) and the rest of the body stores do not demonstrate any significant diurnal variation; only the brain exhibits diurnal changes ( Zimmerman et al., 2004 ). In male albino mouse brains, glycogen content also rises during a period of low locomotor activity, ZT 18 (6 hours after lights off) to ZT 22, and the greatest drop in glycogen levels is from ZT 10 to ZT 14 (2 hours after the beginning of their locomotor active period) ( Hutchins and Rogers, 1970 ). That diurnal variation in brain glycogen correlates with overall activity, both in mouse and fly, is in agreement with studies that show stimulation of facial vibrissae in the rat leads to glycogen depletion in the lateral forebrain structures, which are known to be activated by such stimulation ( Swanson, 1992 ; Swanson et al., 1992 ). Thus, it is hypothesized that increased neuronal activity results in reduced glycogen levels ( Swanson, 1992 ; Swanson et al., 1992 ).

However, the studies of the effects of sleep deprivation on brain glycogen have produced seemingly contradictory results both in rats and mice. One study examined the effects of prolonged sleep deprivation on 1-month-old Sprague-Dawley rats sacrificed using high-energy focused microwave irradiation. Whole brains minus cerebellum and brainstem from rats sleep deprived for 6, 12 and 24 hours were examined for glycogen content and compared to that in rats left undisturbed, allowed to sleep normally, and sacrificed at the same diurnal time ( Kong et al., 2002 ). No differences in brain glycogen were seen between groups after 6 hours of sleep deprivation but significant decreases were observed after 12 and 24 hours of sleep deprivation ( Kong et al., 2002 ). A separate study using 24-day-old Long-Evans rats sacrificed with focused microwave irradiation also found no changes in brain glycogen after 6 hours of sleep deprivation in the cerebral cortex, but did find a significant decrease in the cerebellum ( Gip et al., 2002 ). (The cerebellum was not examined in the study of Kong et al. (2002) .) However, after 12 hours of sleep deprivation in 59-day-old rats, glycogen had actually increased in the cortex but was unchanged in the cerebellum ( Gip et al., 2002 ). Thus, in rats, there are regional differences in the brain with respect to changes in glycogen following sleep deprivation. Both decreases and increases have been reported and the changes observed depend on rat strain and age. Changes in glycogen in one brain region, i.e., cerebral cortex, are summarized in Table 1 .

Effect of sleep deprivation on glycogen in cerebral cortex. Various studies assessing glycogen in the cortex of mouse and rat. Glycogen increases, decreases or stays the same depending on species, strain and length of sleep deprivation.

Franken et al. 2006

Franken et al. 2003

Kong et al. 2002

Gip et al. 2002

Note: Kong et al. 2002 assessed tissue consisting mostly of cortex but other brain regions were present as well.

Several mouse strains have also been examined for the effects of sleep deprivation on glycogen content. In C57BL/6J mice sacrificed by focused microwave irradiation, there is no change in glycogen content in cerebral cortex after 1 hour of sleep deprivation ( Franken et al., 2006b ), a significant increase after 3 hours of sleep deprivation, and a further increase after 6 hours of sleep deprivation ( Franken et al., 2003 , 2006b ). No significant changes were observed in the glycogen content of the cerebellum or brainstem after 6 hours of sleep deprivation ( Franken et al., 2003 ). Two other strains, AKR/J and DBA/2J, demonstrated brain glycogen changes very similar to those observed in Long-Evans rats, i.e., no change in glycogen in the cortex and a decrease in the cerebellum and brainstem following 6 hours of sleep deprivation ( Franken et al., 2003 ). Therefore, the changes in glycogen are dependent on the strain of mouse and the region of the brain ( Franken et al., 2003 ).

In Drosophila, the effects of sleep deprivation on glycogen content are also not straightforward. Brain glycogen levels are decreased slightly but significantly after 3 hours of sleep deprivation, but, after 6 hours of sleep deprivation, glycogen levels are unaltered compared to sleeping controls sacrificed at the same diurnal time point ( Zimmerman et al., 2004 ). Regional differences in glycogen content in the brain were not assessed in Drosophila so the extent to which regional differences contribute to these effects remains unknown.

The majority of studies to date examining the effects of sleep deprivation on glycogen in the brain have in common a study involving 6 hours of sleep deprivation (see Table 1 ). Following 6 hours of sleep deprivation, the changes in glycogen levels in vivo are different in rats and mice, different strains of rats and mice and different regions of the brain (see Table 1 ).

In vitro studies of glycogen utilization in primary cultures of astrocytes may hold the key to understanding these seemingly contradictory in vivo results. Exposure of primary cell cultures of mouse cerebral cortical astrocytes to wake-active neurotransmitters such as vasoactive intestinal peptide and noradrenaline depletes their glycogen content in minutes ( Sorg and Magistretti, 1991 ), similar to the in vivo studies ( Karnovsky et al., 1983 ) (see Figure 3 ). Within 30 minutes, the glycogen content drops to 25% of starting levels. However, thereafter there is synthesis of glycogen such that after 2 hours from application of the wake-active neurotransmitter, glycogen has returned to baseline levels (i.e., no change) (see Figure 3 ). At 8 hours following administration of the neurotransmitter, there is synthesis of glycogen and the glycogen content of the cells actually exceeds baseline levels by a substantial amount. This synthesis of glycogen is dependent upon cAMP signaling and protein synthesis ( Allaman et al., 2000 ; Sorg and Magistretti, 1992 ). Finally, at 48 hours, glycogen levels return to baseline ( Sorg and Magistretti, 1992 ) ( Figure 3 ). Therefore, under constant exposure to wake-active neurotransmitters simulating the effects of wakefulness, there is a three-phase dynamic response: an initial glycogen degradation followed by synthesis to levels eventually above the starting levels followed by depletion once again. A similar temporal pattern of glycogen levels is found in vitro after brief exposures to wake-active neurotransmitters, although the changes are not as large ( Sorg and Magistretti, 1992 ).

Changes in glycogen in astrocytic cell culture following application of vasoactive intestinal peptide. Primary cultures of cerebral cortical astrocytes were treated with vasoactive intestinal peptide. Glycogen (in nmol/mg protein) was measured at 0.5, 1, 2, 4, 8, 24 and 48 hours after treatment. There is an initial decrease in glycogen (see inset) followed by an increase, which peaks at 8 hours. Levels of glycogen then decline again at 24 hours and 48 hours post-treatment. ( Sorg and Magistretti, 1992 ; reprinted with permission, copyright 1992 by the Society for Neuroscience.)

The synthesis of glycogen some time after exposure to wake-active neurotransmitters (see above) is likely mediated by the scaffolding protein PTG. PTG is abundant in astrocytes and transcription of this gene is induced in primary cell cultures of astrocytes after exposure to wake-active neurotransmitters ( Allaman et al., 2000 ). Interestingly, in vivo , PTG mRNA increases in the cerebral cortex of mice following 6 hours of sleep deprivation ( Petit et al., 2002 ). Therefore, there is evidence in vivo that extended wakefulness, similar to the in vitro exposure of wake-active neurotransmitters, leads to an increase in PTG, which would promote glycogen synthesis.

An extension of the in vitro observations to the in vivo situation would predict that the change in glycogen in vivo , i.e., increase, decrease, or no change, depends on the temporal dynamics of two processes: glycogen depletion and synthesis ( Gip et al., 2002 ). These two processes are predicted to occur at different rates in different species, strains of the same species or brain regions within an animal. Therefore, measurement of glycogen at any given time is a measurement of the current balance of the two opposite processes of glycogen depletion and synthesis. Differing rates of glycogen depletion and synthesis could explain the species, strain and regional variations in glycogen content observed after sleep deprivation.

B3. Conclusions about Brain Glycogen and Sleep/Wake Control

The hypothesis proposed by Benington-Heller ( Benington and Heller, 1995 ) had the advantage that it was testable. It led to a series of studies primarily by Heller and collaborators ( Franken et al., 2003 , 2006b ; Gip et al., 2002 ; Gip et al., 2004 ), and by our laboratory and collaborators ( Kong et al., 2002 ; Zimmerman et al., 2004 ), to address whether glycogen was depleted with prolonged wakefulness as the hypothesis suggested.

While at first sight it may seem that disparate data arose from these studies, the results of the various studies can be rationalized when one considers the complex time course that has been demonstrated in vitro in response to administration of wake-active neurotransmitters such as noradrenaline ( Sorg and Magistretti, 1991 ) that we have just described. In addition, the different levels of glycogen depletion and synthesis in vivo between strains of the same rodent at a single duration of sleep deprivation may be due to differential glucocorticoid response to the stress of sleep deprivation. Indeed, 34-day-old adrenalectomized Long-Evans rats have increased glycogen levels in the cerebral cortex after 6 hours of sleep deprivation compared to no significant change in intact animals ( Gip et al., 2004 ). This effect of adrenalectomy may be explained by in vitro data that shows that the addition of a synthetic glucocorticoid to primary astrocyte cultures suppresses the synthesis of glycogen normally induced by noradrenaline ( Allaman et al., 2004 ).

Studies of the time course of glycogen change in vivo are not as complete as the in vitro data, but the results are compatible with the major aspects: early depletion of glycogen, later return to baseline, still later repletion of glycogen with increased activity of glycogen synthase, and much later depletion again ( Figure 3 ). That the in vivo data are not as complete is not surprising since, as discussed above, measurement of glycogen in vivo is challenging due to the rapid decline in its level post-sacrifice, making detailed time course measurements difficult.

The early depletion of glycogen on awakening was demonstrated in a seminal study ( Karnovsky et al., 1983 ). This is compatible with sudden activation through phosphorylation of the degradative enzyme glycogen phosphorylase, mediated by increased cyclic AMP and activation of PKA. This suggests that early depletion of glycogen is likely part of the arousal mechanism. On sudden awakening, there is a rapid increase in neuronal firing, which will require immediately available ATP. Glycogen depletion is likely part of this as increased neuronal activity leads to increased glycogen degradation ( Swanson, 1992 ; Swanson et al., 1992 ).

After this early depletion, it appears that glycogen returns to control levels as it does in vitro . Studies in rodents ( Franken et al., 2003 ; Gip et al., 2002 ; Gip et al., 2004 ; Kong et al., 2002 ) and Drosophila ( Zimmerman et al., 2004 ) show no alterations in glycogen from baseline in the cortex of most strains of rodents or whole brain of the fly after 6 hours of sleep deprivation ( Table 1 ). Other brain areas were examined in some but not all of these studies. However, in two studies a significant increase in glycogen was observed in cortex after 3 and 6 hours of deprivation in C57BL/6J mice ( Franken et al., 2003 ) and 12 hours in the cortex of 59-day-old Long-Evans rats ( Gip et al., 2002 ).

The concept that glycogen is repleted even as wakefulness continues is supported by the C57BL/6J data ( Franken et al., 2006b ). This glycogen repletion is likely mediated by the increase in PTG ( Allaman et al., 2000 ; Petit et al., 2002 ) that enhances the function of glycogen synthase ( Green et al., 2004 ). It is surprising that given the increased energy demand of wakefulness that there would be increased synthesis of glycogen when there is continued need for increased ATP. Therefore, could this repletion of glycogen play a role in reducing ATP levels and increasing adenosine? One possible mechanism would be that synthesizing glycogen would utilize ATP and lead to increased adenosine that would promote sleepiness as predicted by Benington and Heller. If so, this would argue that timed repletion of glycogen is one of the mechanisms serving to promote sleepiness , rather than depletion of glycogen as envisaged by Benington-Heller ( Benington and Heller, 1995 ). But this is likely to be only one of many mechanisms since multiple mechanisms seem to be used to limit wakefulness when it is prolonged (see Mackiewicz et al., 2007 ; Zimmerman et al., 2006 ; and above).

Finally, it seems that with even longer-term sleep deprivation glycogen is again depleted, i.e., after 24 hours of sleep deprivation in rat. This duration of sleep deprivation has, however, been evaluated in only one study and this needs to be replicated ( Kong et al., 2002 ). Moreover, these data need to be interpreted with caution since longer term sleep deprivation may lead to a greater stress response, mediated by the hypothalamic-pituitary-adrenal axis, since keeping rodents awake for this long period of time is challenging due to the increasing pressure for sleep.

Thus, some data about changes in brain glycogen are compatible with the Benington-Heller hypothesis but control of glycogen in relationship to sleep/wake and sleep deprivation is more complex than they envisaged. Our analyses lead to two additional concepts about changes in brain glycogen. First, sudden depletion of glycogen is part of the arousal mechanism. Second, repletion of glycogen, which occurs as wakefulness is prolonged, is, we propose, part of the mechanism used to contribute to sleepiness.

C. Other Aspects of Energy Regulation in the Brain

While the Benington-Heller hypothesis specifically focused on changes in adenosine and glycogen, regulation of cellular energetics in the brain is more complex and likely involves many other mediators. In this section, we discuss, albeit briefly, other energy-related mechanisms that may play a role in sleep/wake control.

C1. Unfolded Protein Response

Folding of proteins in the endoplasmic reticulum is an energy-dependent process ( Braakman et al., 1992 ; Dorner et al., 1990 ). When cells are energetically-challenged, there may be stress in the endoplasmic reticulum with protein misfolding ensuing ( Kaufman, 2002 ; Lee, 2001 ; Schroder and Kaufman, 2005b ). One of the defense mechanisms cells have to respond to misfolded proteins is to activate the unfolded protein response (reviewed in Harding et al., 2002 ; Ma and Hendershot, 2002 ; Schroder and Kaufman, 2005a ; 2005b , 2006 ; Zhang and Kaufman, 2004 ). The unfolded protein response, acting through a master regulator, BiP (also known as Glucose Regulated Protein 78), and three transducers, PERK, IRE1, and ATF6, serves to reduce energy-costly protein translation, increase degradation of misfolded proteins, and to bolster the protein-folding capacity by increasing the expression of specific chaperones. BiP is an endoplasmic reticulum specific chaperone, which in addition to chaperoning misfolded proteins, plays an essential role in activating IRE1, PERK, and ATF6 in response to endoplasmic reticulum stress ( Shen et al., 2002 ). Under conditions of endoplasmic reticulum stress, PERK phosphorylates the translation initiation factor eIF2α. When eIF2α is phosphorylated, the formation of the ternary translation initiation complex eIF2-GTP-tRNA Met i is prevented, leading to inhibition of protein translation.

Upregulation of BiP mRNA with acute short-term sleep deprivation has been described in rat cortex ( Cirelli et al., 2004a ; Terao et al., 2003 ), mouse cortex and hypothalamus ( Mackiewicz et al., 2007 ), fly head ( Shaw et al., 2000 ) and bird brain ( Jones et al., 2008 ). BiP protein levels have also been shown to increase with short term sleep deprivation in mouse cortex and in fly brain ( Naidoo et al., 2007 ; Naidoo et al., 2005 ). Following 6 hours of sleep deprivation, the increased BiP protein levels in fly brain decline slowly over the next 24 hours if the flies are left to sleep undisturbed ( Naidoo et al., 2007 ). Furthermore, with 6 hours of sleep deprivation in mouse, there is dissociation of PERK from BiP, PERK activation through autophosphorylation and phosphorylation of eIF2α in the cerebral cortex ( Naidoo et al., 2005 ) ( Figure 4 ). These observations demonstrate activation of the unfolded protein response with sleep deprivation.

Unfolded protein response during sleep deprivation. Conditions of endoplasmic reticulum stress such as sleep deprivation lead to increases in unfolded proteins. When proteins are misfolded, the chaperone BiP binds to them. BiP dissociates from ATF6, PERK, and IRE1. PERK autophosphorylates and then phosphorylates eIF2α leading to decreases in protein translation. ATF6 initiates transcription of BiP, which is translated independent of eIF2α.

While activation of the unfolded protein response is one mechanism to defend against the endoplasmic reticulum stress that occurs with extended wakefulness, the ultimate defense is to sleep so that the endoplasmic reticulum is no longer under stress. Sleep deprivation leads to increased sleep amounts in rodents ( Franken et al., 1999 ) and in flies ( Hendricks et al., 2000 ), i.e., there is recovery sleep. The unfolded protein response itself plays a role in determining the amount of recovery sleep. Flies overexpressing BiP show no alteration in baseline sleep/wake amounts but do show increased recovery sleep following 6 hours of sleep deprivation compared to wild-type control flies ( Naidoo et al., 2007 ). Flies expressing a dominant negative of BiP, with reduced BiP activity, have reduced amounts of recovery sleep following sleep deprivation ( Naidoo et al., 2007 ). These results may imply that increased BiP itself can promote sleep, although more likely is the effect of altered BiP on the unfolded protein response. Overexpression of BiP leads to a delay in the unfolded protein response since it delays the kinase PERK becoming free ( Dorner et al., 1992 ). Thus, if the unfolded protein response is delayed, recovery sleep increases. The converse occurs with loss of function of BiP, i.e., earlier activation of the unfolded protein response and less subsequent recovery sleep.

These results indicate that the unfolded protein response is induced with sleep deprivation. The mechanism by which the unfolded protein response is activated during sleep deprivation is not known but an attractive explanation is that the unfolded protein response is activated by depletion of cellular energy occurring with extended wakefulness. We believe that the unfolded protein response is a mechanism by which the brain responds to the energetic challenge of extended wakefulness.

C2. Electron Transport Chain

Oxidative phosphorylation occurring in mitochondria is the main process by which eukaryotic cells produce ATP. In mammals, the oxidative phosphorylation system comprises five tightly regulated multi-subunit enzyme complexes, complexes I to V, transcripts of which originate from both nuclear and mitochondrial genomes and are expressed in a tissue-specific fashion ( Lenka et al., 1998 ). Addition of electrons to complex I or II allows for the establishment of a proton gradient, which ultimately leads to ATP synthesis.

There is evidence of alterations in mitochondrial ATP production with sleep/wake. Increased transcription of components of the oxidative phosphorylation machinery from the mitochondrial genome (subunit I of cytochrome C oxidase, subunit 2 of NADH dehydrogenase, and 12S rRNA) has been shown in cortex after 3 hours of spontaneous wakefulness or sleep deprivation ( Cirelli and Tononi, 1998 ). An early up-regulation of the mRNA of the mitochondrial encoded subunit 1 of cytochrome C oxidase with 3 hours of sleep deprivation has also been seen in fly heads ( Shaw et al., 2000 ). In addition to transcriptional upregulation, increased activity of cytochrome C oxidase enzyme, a key enzyme in respiratory control, has been described after 3 hours of sleep deprivation in multiple brain regions in rat, including cortex ( Nikonova et al., 2005b ). Further studies extend that finding by demonstrating that cytochrome C oxidase enzyme activity in mouse cerebral cortex remains elevated even after 12 hours of sleep deprivation ( Nikonova et al., 2005a ). Both transcript and protein levels of mitochondrial-encoded cytochrome C oxidase subunit I and nuclear-encoded cytochrome C oxidase subunit IV are increased after 12 hours of sleep deprivation ( Nikonova et al., 2005a ). Somewhat surprisingly, an extensive microarray study in mouse found that many nuclear encoded components for the enzyme subunits of the various complexes in the electron transport chain are upregulated during sleep, not wakefulness ( Mackiewicz et al., 2007 ). It is possible that mitochondrial and nuclear proteins are synthesized in temporally distinct profiles, which may simply reflect the fact that mitochondrial proteins can be made in areas of the cell that are very metabolically active such as the nerve terminal ( Wong-Riley et al., 1997 ), whereas nuclear proteins need to be transported longer distances to their sites of action. Nonetheless, the increase in cytochrome C oxidase enzyme activity and the upregulation of subunits I and IV suggests that mitochondria increase energy production to meet the increased energy demand occurring during wakefulness.

C3. Transcriptional Factors of the Molecular Clock

The CLOCK protein is a key component of the circadian clock in the suprachiasmatic nucleus, the master circadian pacemaker. It is a transcription factor that is part of the negative feedback loop producing the circadian oscillation in gene expression (reviewed in Lowrey and Takahashi, 2004 ). Mice with a mutation of clock have increased activity during the normally quiescent light period and decreased activity during the active dark period ( Turek et al., 2005 ). Furthermore, clock mutant mice have increased food intake and metabolic rate during the light period and decreased food intake and metabolic rate during the dark period ( Turek et al., 2005 ). Overall, the clock mutant mice are hyperphagic and obese ( Turek et al., 2005 ). These data suggest a link between circadian and energy-regulating processes. However, it should be noted, that the mechanism by which clock alters energy regulation is unknown, it may be a central or a peripheral process, e.g., in kidney or liver, since clock is expressed in many tissues ( Chilov et al., 2001 ).

Another transcription factor involved in the clock mechanism is NPAS2. NPAS2 has been proposed as a specific energy sensor for control of rest/activity (sleep/wake) ( Rutter et al., 2002 ). NPAS2 is a transcription factor that is involved in regulation of expression of circadian clock genes. Transcription of these genes is controlled by a transcription complex of CLOCK/BMAL or NPAS2/BMAL, i.e., NPAS2 acts in a similar fashion to CLOCK ( Reick et al., 2001 ). The main transcription factor complex in the master clock, the suprachiasmatic nucleus, is CLOCK/BMAL, while NPAS2/BMAL functions outside the suprachiasmatic nucleus, including in other brain regions ( Rutter et al., 2002 ).

The concept that NPAS2 may act as an energy sensor came from in vitro observations that the dimerization and the DNA binding of NPAS2:BMAL heterodimers are markedly affected by the energy status as assessed by the redox state of NAD cofactors ( Rutter et al., 2001 ). NPAS2 alters sleep/wake amounts and entrainment of sleep/wake cycles to stimuli other than light as revealed by studies of the NPAS2 knockout mice ( Dudley et al., 2003 ; Franken et al., 2006a ). The NPAS2 knockout mouse has less sleep, specifically NREM sleep, and more wakefulness than wild-type controls during the night, which is the active period of the mouse ( Franken et al., 2006a ). During the night, wild-type mice have "siesta sleep" that is not found in NPAS2 knockout mice ( Dudley et al., 2003 ; Franken et al., 2006a ).

NPAS2 knockout mice also have markedly impaired entrainment to altered feeding patterns, i.e., restricted feeding ( Dudley et al., 2003 ). In restricted feeding, mice are fed only during the day when they are normally inactive. Over a short period of days their rest/activity patterns shift such that mice become active during the day in anticipation of food. This phenomenon is described in both mice ( Marchant and Mistlberger, 1997 ) and rats ( Mistlberger et al., 1990 ). It occurs in suprachiasmatic nucleus lesioned animals and hence does not involve the suprachiasmatic nucleus ( Marchant and Mistlberger, 1997 ). After entrainment to this new pattern of feeding, the diurnal rhythm of clock gene expression is altered in, for example, liver and heart, but not in the suprachiasmatic nucleus ( Damiola et al., 2000 ; Stokkan et al., 2001 ; Watanabe et al., 2006 ). NPAS2 is very relevant to this process. Mice lacking NPAS2 have very delayed entrainment to restricted feeding; such knockout mice lose weight due to lack of food and some die before being entrained ( Dudley et al., 2003 ). In NPAS2 knockout mice, entrainment to light (mediated by the suprachiasmatic nucleus) is normal, or indeed, enhanced ( Dudley et al., 2003 ). Thus, there is a clock mechanism that involves NPAS2 outside the suprachiasmatic nucleus, thought to be in forebrain ( Dudley et al., 2003 ) that can alter both the timing of rest/activity (sleep/wake) cycles as well as the amount of sleep. This mechanism may be affected by energy status and hence provides a direct mechanism to link the energy state of brain to sleep/wake control.

C4. AMP-activated protein kinase

Another mechanism by which a cell can respond to an energetic challenge is by the AMP-activated protein kinase (AMPK), which is a cellular adaptor to alterations in energy state. AMPK is activated by phosphorylation when energy consumed exceeds energy produced and the ratio of AMP to ATP increases. Activated AMPK preserves ATP by decreasing energy consuming processes and increasing energy producing processes (reviewed in Hardie et al., 2006 ).

Assessing the phosphorylation status of AMPK in vivo is challenging ( Scharf et al., 2008 ). Preliminary results from our laboratory have shown that AMPK phosphorylation is greater following 6 hours of sleep deprivation than sleep in whole brain, cortex and basal forebrain consistent with the brain being energetically challenged in wakefulness ( Scharf et al., 2006 ), and we are currently assessing changes in AMPK phosphorylation with sleep deprivation utilizing a methodology we developed to preserve in vivo AMPK phosphorylation ( Scharf et al., 2008 ). Thus, AMPK may serve as a molecular sensor to shift the brain from energy-consuming synthetic processes that occur during sleep to catabolic energy-producing processes that occur during extended wakefulness.

C5. Astrocyte-to-Neuron Lactate Shuttle

Another postulated metabolic role for astrocytes is the astrocyte-to-neuron lactate shuttle although this idea has been controversial since it was first proposed ( Pellerin and Magistretti, 1994 ). Recent reviews have either supported ( Bergersen, 2007 ; Pellerin et al., 2007 ) or contested ( Fillenz, 2005 ; Simpson et al., 2007 ) the hypothesis. The central tenet of the hypothesis is that during neuronal activation, astrocytes take up glucose from blood in adjacent capillaries and metabolize the glucose into lactate. The astrocytes release lactate, which is transported into neurons, where it serves as an energy source for neurons to meet increased energy demand occurring with neuronal activation ( Pellerin and Magistretti, 1994 ).

Components that could mediate the lactate shuttle have been found to be upregulated during sleep deprivation. Lactate dehydrogenase 2 B protein, which is associated with astrocytes ( Pellerin et al., 1998 ) and catalyzes the conversion of pyruvate to lactate ( Bishop et al., 1972 ), is significantly increased in mouse cerebral cortex after 6 hours of sleep deprivation ( Pawlyk et al., 2007 ). Furthermore, glucose transporter 1, associated with epithelial cells of the blood-brain barrier and astrocytes (reviewed in Duelli and Kuschinsky, 2001 ), shows increased gene expression in rat cerebral cortex following sleep deprivation, and is also increased in spontaneous wakefulness compared to sleep ( Cirelli et al., 2004b ; Cirelli and Tononi, 2000 ). Presumably, increased glucose transporter 1 would facilitate glucose transport into astrocytes. The observations of increased lactate dehydrogenase 2 B protein and glucose transporter 1 mRNA are consistent with increased activity of the astrocyte-neuron lactate shuttle during wakefulness, and suggest that this may be another mechanism by which the brain adapts to the increased metabolic demand of wakefulness.

C6. Reactive Oxygen Species

One of the consequences of increased metabolic activity and, hence, an increase in oxygen consumption, is an increase in production of reactive oxygen species. Ikeda et al. proposed that reactive oxygen species are produced during wakefulness and that increased antioxidant enzyme activity during sleep reduces their levels in preparation for subsequent wakefulness ( Ikeda et al., 2005 ). Reactive oxygen species are difficult to measure in vivo since they are so labile. Hence, evidence that they change in the brain with sleep/wake is indirect.

The evidence for alteration in reactive oxygen species production with sleep/wake is limited. Activity of a key antioxidant enzyme, superoxide dismutase, decreases in hippocampus and brainstem following sleep deprivation ( Ramanathan et al., 2002 ). Moreover, mRNA for a number of antioxidant enzymes, including superoxide dismutase, glutathione S-transferase, glutathione peroxidase, glutathione reductase, catalase, methionine sulfoxide reductase, thioredoxin reductase and the transcription factor Nfe2, which is involved in induction of genes encoding antioxidant proteins and certain detoxifying enzymes, are all upregulated during sleep in cerebral cortex as revealed by a microarray study in mouse ( Mackiewicz et al., 2007 ).

There is also evidence that reactive oxygen species may also directly alter sleep. Intracerebroventricular infusion of oxidized glutathione increases slow wave and REM sleep in rats ( Ikeda et al., 2005 ) although the mechanism by which it does so is unknown. Intracerebroventricular administration of an inhibitor of glutathione peroxidase, tert-butyl hydroperoxide (TBHP), which would attenuate metabolism of reactive oxygen species thereby increasing their level, also increases the amount of NREM and REM sleep ( Ikeda et al., 2005 ). Although the data are limited, these studies are consistent with the notion that there are alterations in reactive oxygen species with sleep/wake.

C6. Uncoupling Proteins

The first described uncoupling protein, UCP1, was shown to increase the proton conductance of the inner mitochondrial membrane and, therefore, uncouple oxidative phosphorylation. Consistent with this uncoupling mechanism, UCP1 in brown adipose tissue was shown to catalyze adaptive thermogenesis by increasing heat production ( Cannon and Nedergaard, 2004 ). Additional UCPs found in other tissues without a clear thermogenic function include UCP2 ( Fleury et al., 1997 ), UCP3 ( Boss et al., 1997 ), UCP4 ( Mao et al., 1999 ) and UCP5 (BMCP1) ( Sanchis et al., 1998 ). Interestingly, UCP2, UCP4 and UCP5 are expressed in brain (see Andrews et al., 2005 ). The role of these new UCPs, in particular in the brain, is a subject of debate, i.e., do they act to uncouple oxidative phosphorylation (see Kim-Han and Dugan, 2005 )?

There are no differences in UCP2 or UCP5 mRNA expression in the cerebral cortex between spontaneous wakefulness and sleep in rats ( Cirelli and Tononi, 2004 ) (mRNA for UCP4 could not be detected). However, short-term sleep deprivation of 8 hours in rats leads to a 40–50% increase in mRNA for UCP2 and UCP5 in cerebral cortex ( Cirelli and Tononi, 2004 ). Thus, increased expression of UCPs in brain may be, like the unfolded protein response, part of the mechanism to protect against the effects of extending wakefulness. Uncoupling of oxidative phosphorylation will likely result in decreased production of reactive oxygen species.

The Benington-Heller hypothesis stimulated a lot of useful research. While it has turned out to be an oversimplification, this does not detract from its value in providing a focus to investigate the role of energy utilization in determining sleep/wake states. As proposed, there is increasing evidence that adenosine does act to promote sleep. There are, however, questions about whether changes in adenosine with wakefulness reflect altered cellular energy states. The elaboration of the Benington-Heller hypothesis that adenosine changes are specifically “sensed” by cholinergic neurons of the basal forebrain to effect sleep/wake control now seems disproven. Adenosine is likely to act in different brain regions to alter both the amount of sleep as well as delta power. How adenosine regulates the quantity and quality of sleep and how adenosine exerts its effects in specific brain regions are questions that are being actively investigated. The emerging role of astrocytes in sleep homeostasis is likely to be important since astrocytes contribute to adenosinergic tone.

The predictions of Benington and Heller about glycogen changes with sleep/wake and sleep deprivation have been supported, at least in part. However, the dynamic changes are more complex than they proposed. A reassessment of data obtained in experiments stimulated by their hypothesis leads to new hypotheses: a) depletion of glycogen is part of the arousal response providing ATP for the sudden increase in energy required to support enhanced neuronal firing on sudden awakening; and b) it is repletion, not depletion, of glycogen that is likely the signal to promote sleepiness.

Control of energy utilization in the brain is, however, more complex than is simply reflected by changes in adenosine and glycogen. There are growing data that many aspects of the regulation of cellular energetics in the brain are altered with sleep/wake and sleep deprivation: the unfolded protein response; components of the electron transport chain; the astrocyte-to-neuron lactate shuttle; antioxidant enzymes; uncoupling proteins; clock transcription factors and the degree of activation of the cellular energy regulator AMPK. Available data suggest that there may be regulation of many components to provide the necessary amounts of ATP that are required during wakefulness and to provide responses to the energetic challenge of wakefulness. In turn, these pathways likely mediate the quantity and quality of sleep.

Altogether, the evidence reviewed here supports the fundamental tenet that extended wakefulness poses an energetic challenge to the brain. It would therefore appear likely that sleep is a stage of synthesis necessary to respond to the “insult” of wakefulness as we have proposed ( Mackiewicz et al., 2007 ). We believe that energy-related pathways in addition to glycogen and adenosine are involved in transitioning the brain from the metabolically-deplete catabolic state of wakefulness to the metabolically-replete anabolic state of sleep. This elaboration of the energy hypothesis of sleep is illustrated in Figure 5 . Hopefully, this new view of the energy hypothesis of sleep-wake control will stimulate as much helpful research as did the provocative review of Benington and Heller ( Benington and Heller, 1995 ).

Proposed model of alterations in energy homeostasis during extended wakefulness. In astrocytes, glycogen breakdown and synthesis occur concomitantly. Lactate is transported out of astrocytes and taken up by neurons where it enters the mitochondria and the krebs cycle to produce ATP. Also within the mitochondria, there is an increase in the activity of the electron transport chain to increase ATP production. Reactive oxygen species are produced as a byproduct of this increased activity of the electron transport chain. UCP-2 increases to attenuate production of reactive oxygen species. In the endoplasmic reticulum, the unfolded protein response occurs. This ultimately leads to transcriptional changes as well as decreases in energy-consuming synthetic processes. AMPK is activated, which also attenuates energy-consuming synthetic processes. NPAS2 most likely also acts to preserve energy. Multiple pathways lead to changes in gene expression. Adenosine increases in the extracellular space either by transport out of the cell and/or by conversion of ATP released from glia or neurons into adenosine.

Acknowledgements

We would like to thank Daniel Barrett and Jennifer Montoya for help in preparation of this manuscript. This research was supported by the National Institute of Aging Grant AG-17628 and National Heart, Lung, and Blood Institute Grants HL-60287 and HL-07953.

Abbreviations Used

adenosine 1

adenosine 2A

adenosine 2B

adenosine 3

adenosine diphosphate

adenosine monophosphate

AMP-activated protein kinase

adenosine triphosphate

2-[p-(2-carboxyethyl)phenethylamino]-5'-N-ethylcarboxamido adenosine

N 6 -cyclo-hexyladenosine

cyclopentyladenosine

8-cyclopentyl-1, 3-dimethylxanthine

cyclopentyl-1-3-dimethylxanthine

2-phenylaminoadenosine

electroencephalogram

gamma-amino butyric acid

inhibitory κ B

nitrobenzylthioinosine

nuclear factor κ B

neuronal Per-Arnt-Sim-type signal-sensor protein (PAS) domain protein 2

non-rapid eye movement sleep

protein targeting to glycogen

rapid eye movement sleep

tert-butyl hydroperoxide

uncoupling protein

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What Are Examples of a Hypothesis?

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A hypothesis is an explanation for a set of observations. Hypothesis examples can help you understand how this scientific method works.

Although you could state a scientific hypothesis in various ways, most hypotheses are either "If, then" statements or forms of the null hypothesis. The null hypothesis is sometimes called the "no difference" hypothesis. The null hypothesis is good for experimentation because it's simple to disprove. If you disprove a null hypothesis, that is evidence for a relationship between the variables you are examining.

Hypotheses Examples: Null

• All daisies have the same number of petals.
• Hyperactivity is unrelated to eating sugar.
• The number of pets in a household is unrelated to the number of people living in it.
• A person's preference for a shirt is unrelated to its color.

Hypotheses Examples: If, Then

• If you get at least 6 hours of sleep, you will do better on tests than if you get less sleep.
• If you drop a ball, it will fall toward the ground.
• If you drink coffee before going to bed, then it will take longer to fall asleep.
• If you cover a wound with a bandage, then it will heal with less scarring.

Improving a Hypothesis to Make It Testable

You may wish to revise your first hypothesis to make it easier to design an experiment to test. For example, let's say you have a bad breakout the morning after eating a lot of greasy food. You may wonder if there is a correlation between eating greasy food and getting pimples. You propose the hypothesis example:

Eating greasy food causes pimples.

Next, you need to design an experiment to test this hypothesis. Let's say you decide to eat greasy food every day for a week and record the effect on your face. Then, as a control, you'll avoid greasy food for the next week and see what happens. Now, this is not a good experiment because it does not take into account other factors such as hormone levels, stress, sun exposure, exercise, or any number of other variables that might conceivably affect your skin.

The problem is that you cannot assign cause to your effect . If you eat french fries for a week and suffer a breakout, can you definitely say it was the grease in the food that caused it? Maybe it was the salt. Maybe it was the potato. Maybe it was unrelated to diet. You can't prove your hypothesis. It's much easier to disprove a hypothesis.

So, let's restate the hypothesis to make it easier to evaluate the data:

Getting pimples is unaffected by eating greasy food.

So, if you eat fatty food every day for a week and suffer breakouts and then don't break out the week that you avoid greasy food, you can be pretty sure something is up. Can you disprove the hypothesis? Probably not, since it is so hard to assign cause and effect. However, you can make a strong case that there is some relationship between diet and acne.

If your skin stays clear for the entire test, you may decide to accept your hypothesis . Again, you didn't prove or disprove anything, which is fine

• Null Hypothesis Examples
• The Role of a Controlled Variable in an Experiment
• Random Error vs. Systematic Error
• What Is a Testable Hypothesis?
• What Are the Elements of a Good Hypothesis?
• Scientific Hypothesis Examples
• What Is a Hypothesis? (Science)
• Scientific Method Vocabulary Terms
• Scientific Method Flow Chart
• Understanding Simple vs Controlled Experiments
• Six Steps of the Scientific Method
• What Is an Experimental Constant?
• What Is the Difference Between a Control Variable and Control Group?
• Scientific Variable
• What Is a Controlled Experiment?
• DRY MIX Experiment Variables Acronym

15 Hypothesis Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

Learn about our Editorial Process

A hypothesis is defined as a testable prediction , and is used primarily in scientific experiments as a potential or predicted outcome that scientists attempt to prove or disprove (Atkinson et al., 2021; Tan, 2022).

In my types of hypothesis article, I outlined 13 different hypotheses, including the directional hypothesis (which makes a prediction about an effect of a treatment will be positive or negative) and the associative hypothesis (which makes a prediction about the association between two variables).

This article will dive into some interesting examples of hypotheses and examine potential ways you might test each one.

Hypothesis Examples

1. “inadequate sleep decreases memory retention”.

Field: Psychology

Type: Causal Hypothesis A causal hypothesis explores the effect of one variable on another. This example posits that a lack of adequate sleep causes decreased memory retention. In other words, if you are not getting enough sleep, your ability to remember and recall information may suffer.

How to Test:

To test this hypothesis, you might devise an experiment whereby your participants are divided into two groups: one receives an average of 8 hours of sleep per night for a week, while the other gets less than the recommended sleep amount.

During this time, all participants would daily study and recall new, specific information. You’d then measure memory retention of this information for both groups using standard memory tests and compare the results.

Should the group with less sleep have statistically significant poorer memory scores, the hypothesis would be supported.

Ensuring the integrity of the experiment requires taking into account factors such as individual health differences, stress levels, and daily nutrition.

Relevant Study: Sleep loss, learning capacity and academic performance (Curcio, Ferrara & De Gennaro, 2006)

2. “Increase in Temperature Leads to Increase in Kinetic Energy”

Field: Physics

Type: Deductive Hypothesis The deductive hypothesis applies the logic of deductive reasoning – it moves from a general premise to a more specific conclusion. This specific hypothesis assumes that as temperature increases, the kinetic energy of particles also increases – that is, when you heat something up, its particles move around more rapidly.

This hypothesis could be examined by heating a gas in a controlled environment and capturing the movement of its particles as a function of temperature.

You’d gradually increase the temperature and measure the kinetic energy of the gas particles with each increment. If the kinetic energy consistently rises with the temperature, your hypothesis gets supporting evidence.

Variables such as pressure and volume of the gas would need to be held constant to ensure validity of results.

3. “Children Raised in Bilingual Homes Develop Better Cognitive Skills”

Field: Psychology/Linguistics

Type: Comparative Hypothesis The comparative hypothesis posits a difference between two or more groups based on certain variables. In this context, you might propose that children raised in bilingual homes have superior cognitive skills compared to those raised in monolingual homes.

Testing this hypothesis could involve identifying two groups of children: those raised in bilingual homes, and those raised in monolingual homes.

Cognitive skills in both groups would be evaluated using a standard cognitive ability test at different stages of development. The examination would be repeated over a significant time period for consistency.

If the group raised in bilingual homes persistently scores higher than the other, the hypothesis would thereby be supported.

The challenge for the researcher would be controlling for other variables that could impact cognitive development, such as socio-economic status, education level of parents, and parenting styles.

Relevant Study: The cognitive benefits of being bilingual (Marian & Shook, 2012)

4. “High-Fiber Diet Leads to Lower Incidences of Cardiovascular Diseases”

Field: Medicine/Nutrition

Type: Alternative Hypothesis The alternative hypothesis suggests an alternative to a null hypothesis. In this context, the implied null hypothesis could be that diet has no effect on cardiovascular health, which the alternative hypothesis contradicts by suggesting that a high-fiber diet leads to fewer instances of cardiovascular diseases.

To test this hypothesis, a longitudinal study could be conducted on two groups of participants; one adheres to a high-fiber diet, while the other follows a diet low in fiber.

After a fixed period, the cardiovascular health of participants in both groups could be analyzed and compared. If the group following a high-fiber diet has a lower number of recorded cases of cardiovascular diseases, it would provide evidence supporting the hypothesis.

Control measures should be implemented to exclude the influence of other lifestyle and genetic factors that contribute to cardiovascular health.

Relevant Study: Dietary fiber, inflammation, and cardiovascular disease (King, 2005)

5. “Gravity Influences the Directional Growth of Plants”

Field: Agronomy / Botany

Type: Explanatory Hypothesis An explanatory hypothesis attempts to explain a phenomenon. In this case, the hypothesis proposes that gravity affects how plants direct their growth – both above-ground (toward sunlight) and below-ground (towards water and other resources).

The testing could be conducted by growing plants in a rotating cylinder to create artificial gravity.

Observations on the direction of growth, over a specified period, can provide insights into the influencing factors. If plants consistently direct their growth in a manner that indicates the influence of gravitational pull, the hypothesis is substantiated.

It is crucial to ensure that other growth-influencing factors, such as light and water, are uniformly distributed so that only gravity influences the directional growth.

6. “The Implementation of Gamified Learning Improves Students’ Motivation”

Field: Education

Type: Relational Hypothesis The relational hypothesis describes the relation between two variables. Here, the hypothesis is that the implementation of gamified learning has a positive effect on the motivation of students.

To validate this proposition, two sets of classes could be compared: one that implements a learning approach with game-based elements, and another that follows a traditional learning approach.

The students’ motivation levels could be gauged by monitoring their engagement, performance, and feedback over a considerable timeframe.

If the students engaged in the gamified learning context present higher levels of motivation and achievement, the hypothesis would be supported.

Control measures ought to be put into place to account for individual differences, including prior knowledge and attitudes towards learning.

Relevant Study: Does educational gamification improve students’ motivation? (Chapman & Rich, 2018)

7. “Mathematics Anxiety Negatively Affects Performance”

Field: Educational Psychology

Type: Research Hypothesis The research hypothesis involves making a prediction that will be tested. In this case, the hypothesis proposes that a student’s anxiety about math can negatively influence their performance in math-related tasks.

To assess this hypothesis, researchers must first measure the mathematics anxiety levels of a sample of students using a validated instrument, such as the Mathematics Anxiety Rating Scale.

Then, the students’ performance in mathematics would be evaluated through standard testing. If there’s a negative correlation between the levels of math anxiety and math performance (meaning as anxiety increases, performance decreases), the hypothesis would be supported.

It would be crucial to control for relevant factors such as overall academic performance and previous mathematical achievement.

8. “Disruption of Natural Sleep Cycle Impairs Worker Productivity”

Field: Organizational Psychology

Type: Operational Hypothesis The operational hypothesis involves defining the variables in measurable terms. In this example, the hypothesis posits that disrupting the natural sleep cycle, for instance through shift work or irregular working hours, can lessen productivity among workers.

To test this hypothesis, you could collect data from workers who maintain regular working hours and those with irregular schedules.

Measuring productivity could involve examining the worker’s ability to complete tasks, the quality of their work, and their efficiency.

If workers with interrupted sleep cycles demonstrate lower productivity compared to those with regular sleep patterns, it would lend support to the hypothesis.

Consideration should be given to potential confounding variables such as job type, worker age, and overall health.

9. “Regular Physical Activity Reduces the Risk of Depression”

Field: Health Psychology

Type: Predictive Hypothesis A predictive hypothesis involves making a prediction about the outcome of a study based on the observed relationship between variables. In this case, it is hypothesized that individuals who engage in regular physical activity are less likely to suffer from depression.

Longitudinal studies would suit to test this hypothesis, tracking participants’ levels of physical activity and their mental health status over time.

The level of physical activity could be self-reported or monitored, while mental health status could be assessed using standard diagnostic tools or surveys.

If data analysis shows that participants maintaining regular physical activity have a lower incidence of depression, this would endorse the hypothesis.

However, care should be taken to control other lifestyle and behavioral factors that could intervene with the results.

Relevant Study: Regular physical exercise and its association with depression (Kim, 2022)

10. “Regular Meditation Enhances Emotional Stability”

Type: Empirical Hypothesis In the empirical hypothesis, predictions are based on amassed empirical evidence . This particular hypothesis theorizes that frequent meditation leads to improved emotional stability, resonating with numerous studies linking meditation to a variety of psychological benefits.

Earlier studies reported some correlations, but to test this hypothesis directly, you’d organize an experiment where one group meditates regularly over a set period while a control group doesn’t.

Both groups’ emotional stability levels would be measured at the start and end of the experiment using a validated emotional stability assessment.

If regular meditators display noticeable improvements in emotional stability compared to the control group, the hypothesis gains credit.

You’d have to ensure a similar emotional baseline for all participants at the start to avoid skewed results.

11. “Children Exposed to Reading at an Early Age Show Superior Academic Progress”

Type: Directional Hypothesis The directional hypothesis predicts the direction of an expected relationship between variables. Here, the hypothesis anticipates that early exposure to reading positively affects a child’s academic advancement.

A longitudinal study tracking children’s reading habits from an early age and their consequent academic performance could validate this hypothesis.

Parents could report their children’s exposure to reading at home, while standardized school exam results would provide a measure of academic achievement.

If the children exposed to early reading consistently perform better acadically, it gives weight to the hypothesis.

However, it would be important to control for variables that might impact academic performance, such as socioeconomic background, parental education level, and school quality.

12. “Adopting Energy-efficient Technologies Reduces Carbon Footprint of Industries”

Field: Environmental Science

Type: Descriptive Hypothesis A descriptive hypothesis predicts the existence of an association or pattern related to variables. In this scenario, the hypothesis suggests that industries adopting energy-efficient technologies will resultantly show a reduced carbon footprint.

Global industries making use of energy-efficient technologies could track their carbon emissions over time. At the same time, others not implementing such technologies continue their regular tracking.

After a defined time, the carbon emission data of both groups could be compared. If industries that adopted energy-efficient technologies demonstrate a notable reduction in their carbon footprints, the hypothesis would hold strong.

In the experiment, you would exclude variations brought by factors such as industry type, size, and location.

13. “Reduced Screen Time Improves Sleep Quality”

Type: Simple Hypothesis The simple hypothesis is a prediction about the relationship between two variables, excluding any other variables from consideration. This example posits that by reducing time spent on devices like smartphones and computers, an individual should experience improved sleep quality.

A sample group would need to reduce their daily screen time for a pre-determined period. Sleep quality before and after the reduction could be measured using self-report sleep diaries and objective measures like actigraphy, monitoring movement and wakefulness during sleep.

If the data shows that sleep quality improved post the screen time reduction, the hypothesis would be validated.

Other aspects affecting sleep quality, like caffeine intake, should be controlled during the experiment.

Relevant Study: Screen time use impacts low‐income preschool children’s sleep quality, tiredness, and ability to fall asleep (Waller et al., 2021)

14. Engaging in Brain-Training Games Improves Cognitive Functioning in Elderly

Field: Gerontology

Type: Inductive Hypothesis Inductive hypotheses are based on observations leading to broader generalizations and theories. In this context, the hypothesis deduces from observed instances that engaging in brain-training games can help improve cognitive functioning in the elderly.

A longitudinal study could be conducted where an experimental group of elderly people partakes in regular brain-training games.

Their cognitive functioning could be assessed at the start of the study and at regular intervals using standard neuropsychological tests.

If the group engaging in brain-training games shows better cognitive functioning scores over time compared to a control group not playing these games, the hypothesis would be supported.

15. Farming Practices Influence Soil Erosion Rates

Type: Null Hypothesis A null hypothesis is a negative statement assuming no relationship or difference between variables. The hypothesis in this context asserts there’s no effect of different farming practices on the rates of soil erosion.

Comparing soil erosion rates in areas with different farming practices over a considerable timeframe could help test this hypothesis.

If, statistically, the farming practices do not lead to differences in soil erosion rates, the null hypothesis is accepted.

However, if marked variation appears, the null hypothesis is rejected, meaning farming practices do influence soil erosion rates. It would be crucial to control for external factors like weather, soil type, and natural vegetation.

The variety of hypotheses mentioned above underscores the diversity of research constructs inherent in different fields, each with its unique purpose and way of testing.

While researchers may develop hypotheses primarily as tools to define and narrow the focus of the study, these hypotheses also serve as valuable guiding forces for the data collection and analysis procedures, making the research process more efficient and direction-focused.

Hypotheses serve as a compass for any form of academic research. The diverse examples provided, from Psychology to Educational Studies, Environmental Science to Gerontology, clearly demonstrate how certain hypotheses suit specific fields more aptly than others.

It is important to underline that although these varied hypotheses differ in their structure and methods of testing, each endorses the fundamental value of empiricism in research. Evidence-based decision making remains at the heart of scholarly inquiry, regardless of the research field, thus aligning all hypotheses to the core purpose of scientific investigation.

Testing hypotheses is an essential part of the scientific method . By doing so, researchers can either confirm their predictions, giving further validity to an existing theory, or they might uncover new insights that could potentially shift the field’s understanding of a particular phenomenon. In either case, hypotheses serve as the stepping stones for scientific exploration and discovery.

Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J. W., & Williams, R. A. (2021).  SAGE research methods foundations . SAGE Publications Ltd.

Curcio, G., Ferrara, M., & De Gennaro, L. (2006). Sleep loss, learning capacity and academic performance.  Sleep medicine reviews ,  10 (5), 323-337.

Kim, J. H. (2022). Regular physical exercise and its association with depression: A population-based study short title: Exercise and depression.  Psychiatry Research ,  309 , 114406.

King, D. E. (2005). Dietary fiber, inflammation, and cardiovascular disease.  Molecular nutrition & food research ,  49 (6), 594-600.

Marian, V., & Shook, A. (2012, September). The cognitive benefits of being bilingual. In Cerebrum: the Dana forum on brain science (Vol. 2012). Dana Foundation.

Tan, W. C. K. (2022). Research Methods: A Practical Guide For Students And Researchers (Second Edition) . World Scientific Publishing Company.

Waller, N. A., Zhang, N., Cocci, A. H., D’Agostino, C., Wesolek‐Greenson, S., Wheelock, K., … & Resnicow, K. (2021). Screen time use impacts low‐income preschool children’s sleep quality, tiredness, and ability to fall asleep. Child: care, health and development, 47 (5), 618-626.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

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Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Psychology Hypothesis

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Delving into the realm of human behavior and cognition, Psychology Hypothesis Statement Examples illuminate the intricate workings of the mind. These thesis statement examples span various psychological phenomena, offering insights into crafting hypotheses that drive impactful research. From personality traits to cognitive processes, explore the guide to formulate precise and insightful psychology hypothesis statements that shed light on the complexities of human psychology.

What is the Psychology Hypothesis?

In psychology, a good hypothesis is a tentative statement or educated guess that proposes a potential relationship between variables. It serves as a foundation for research, guiding the investigation into specific psychological phenomena or behaviors. A well-constructed psychology hypothesis outlines the expected outcome of the study and provides a framework for data collection and analysis.

Example of a Psychology Hypothesis Statement :

Research Question: Does exposure to nature improve individuals’ mood and well-being?

Hypothesis Statement: “Individuals who spend more time in natural environments will report higher levels of positive mood and overall well-being compared to those who spend less time outdoors.”

In this example, the psychology hypothesis predicts a positive relationship between exposure to nature and improved mood and well-being. The statement sets the direction for the study and provides a clear basis for data collection and analysis.

100 Psychology Hypothesis Statement Examples

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Psychology Hypothesis Statement Examples encompass a diverse range of human behaviors and mental processes. Dive into the complexities of the human mind with Simple hypothesis that explore relationships, patterns, and influences on behavior. From memory recall to social interactions, these examples offer insights into crafting precise and impactful psychology hypotheses that drive meaningful research.

• Effect of Color on Mood : Exposure to blue hues elevates mood in individuals.
• Social Media and Self-Esteem : Higher social media usage correlates with lower self-esteem levels.
• Sleep Quality and Cognitive Performance : Improved sleep quality enhances cognitive performance.
• Personality Traits and Leadership : Extroverted individuals are more likely to assume leadership roles.
• Parent-Child Attachment and Behavior : Strong parent-child attachment fosters positive behavior in children.
• Cognitive Load and Decision Making : Increased cognitive load leads to poorer decision-making abilities.
• Mindfulness Meditation and Stress Reduction : Regular mindfulness practice reduces stress levels.
• Empathy and Altruistic Behavior : Higher empathy levels predict increased altruistic actions.
• Positive Reinforcement and Learning : Positive reinforcement enhances learning outcomes in children.
• Attachment Style and Romantic Relationships : Securely attached individuals experience more satisfying romantic relationships.
• Body Image and Media Exposure : Greater exposure to idealized body images leads to negative body image perceptions.
• Anxiety Levels and Academic Performance : Higher anxiety levels negatively impact academic achievement.
• Parenting Style and Aggression : Authoritarian parenting style correlates with higher aggression in children.
• Cognitive Aging and Memory Recall : Older adults experience reduced memory recall compared to younger individuals.
• Peer Pressure and Risky Behavior : Peer pressure increases engagement in risky behaviors among adolescents.
• Emotional Intelligence and Relationship Satisfaction : High emotional intelligence leads to greater relationship satisfaction.
• Attachment Style and Coping Mechanisms : Insecure attachment is linked to maladaptive coping strategies.
• Perceived Control and Stress Resilience : Higher perceived control buffers against the negative effects of stress.
• Social Comparison and Self-Esteem : Frequent social comparison diminishes self-esteem levels.
• Gender Stereotypes and Career Aspirations : Gender stereotypes influence career aspirations of young adults.
• Technology Usage and Social Isolation : Increased technology usage contributes to feelings of social isolation.
• Empathy and Conflict Resolution : Higher empathy levels facilitate effective conflict resolution.
• Parental Influence and Academic Motivation : Parental involvement positively impacts student academic motivation.
• Attention Deficit Hyperactivity Disorder (ADHD) and Video Games : Children with ADHD show increased hyperactivity after playing video games.
• Positive Psychology Interventions and Well-being : Engaging in positive psychology interventions enhances overall well-being.
• Social Support and Mental Health : Adequate social support leads to better mental health outcomes.
• Parent-Child Communication and Risky Behavior : Open parent-child communication reduces engagement in risky behaviors.
• Social Media and Body Dissatisfaction : Extensive social media use is linked to increased body dissatisfaction.
• Personality Traits and Coping Strategies : Different personality traits influence varied coping mechanisms.
• Peer Influence and Substance Abuse : Peer influence contributes to higher rates of substance abuse among adolescents.
• Attentional Bias and Anxiety : Individuals with attentional bias are more prone to experiencing anxiety.
• Attachment Style and Romantic Jealousy : Insecure attachment predicts higher levels of romantic jealousy.
• Emotion Regulation and Well-being : Effective emotion regulation leads to greater overall well-being.
• Parenting Styles and Academic Resilience : Supportive parenting styles enhance academic resilience in children.
• Cultural Identity and Self-Esteem : Strong cultural identity is linked to higher self-esteem among minority individuals.
• Working Memory and Problem-Solving : Better working memory capacity improves problem-solving abilities.
• Fear Conditioning and Phobias : Fear conditioning contributes to the development of specific phobias.
• Empathy and Prosocial Behavior : Higher empathy levels result in increased prosocial behaviors.
• Social Anxiety and Online Communication : Individuals with social anxiety prefer online communication over face-to-face interactions.
• Cognitive Biases and Decision-Making Errors : Cognitive biases lead to errors in judgment and decision-making.
• Attachment Style and Romantic Attachment Patterns : Attachment style influences the development of romantic attachment patterns.
• Self-Efficacy and Goal Achievement : Higher self-efficacy predicts greater success in achieving personal goals.
• Stress Levels and Immune System Functioning : Elevated stress levels impair immune system functioning.
• Social Media Use and Loneliness : Excessive social media use is associated with increased feelings of loneliness.
• Emotion Recognition and Social Interaction : Improved emotion recognition skills enhance positive social interactions.
• Perceived Control and Psychological Resilience : Strong perceived control fosters psychological resilience in adverse situations.
• Narcissism and Online Self-Presentation : Narcissistic individuals engage in heightened self-promotion on social media.
• Fear of Failure and Performance Anxiety : Fear of failure contributes to performance anxiety in high-pressure situations.
• Gratitude Practice and Well-being : Regular gratitude practice leads to improved overall well-being.
• Cultural Norms and Communication Styles : Cultural norms shape distinct communication styles among different groups.
• Gender Identity and Mental Health : The alignment between gender identity and assigned sex at birth affects mental health outcomes.
• Social Influence and Conformity : Social influence leads to increased conformity in group settings.
• Parenting Styles and Attachment Security : Parenting styles influence the development of secure or insecure attachment in children.
• Perceived Discrimination and Psychological Distress : Perceived discrimination is associated with higher levels of psychological distress.
• Emotional Regulation Strategies and Impulse Control : Effective emotional regulation strategies enhance impulse control.
• Cognitive Dissonance and Attitude Change : Cognitive dissonance prompts individuals to change attitudes to reduce discomfort.
• Prejudice and Stereotype Formation : Exposure to prejudiced attitudes contributes to the formation of stereotypes.
• Motivation and Goal Setting : High intrinsic motivation leads to more effective goal setting and achievement.
• Coping Mechanisms and Trauma Recovery : Adaptive coping mechanisms facilitate better trauma recovery outcomes.
• Personality Traits and Perceived Stress : Certain personality traits influence how individuals perceive and respond to stress.
• Cognitive Biases and Decision-Making Strategies : Cognitive biases impact the strategies individuals use in decision-making.
• Emotional Intelligence and Interpersonal Relationships : High emotional intelligence fosters healthier and more fulfilling interpersonal relationships.
• Sensory Perception and Memory Formation : The accuracy of sensory perception influences the formation of memories.
• Parental Influences and Peer Relationships : Parental attitudes shape the quality of adolescents’ peer relationships.
• Social Comparison and Body Image : Frequent social comparison contributes to negative body image perceptions.
• Attention Deficit Hyperactivity Disorder (ADHD) and Academic Achievement : Children with ADHD face challenges in achieving academic success.
• Cultural Identity and Mental Health Stigma : Strong cultural identity buffers against the negative effects of mental health stigma.
• Self-Esteem and Risk-Taking Behavior : Individuals with high self-esteem are more likely to engage in risk-taking behaviors.
• Resilience and Adversity Coping : High resilience levels enhance individuals’ ability to cope with adversity.
• Motivation and Learning Styles : Different types of motivation influence preferred learning styles.
• Body Language and Nonverbal Communication : Body language cues play a significant role in nonverbal communication effectiveness.
• Social Identity and Intergroup Bias : Strong identification with a social group contributes to intergroup bias.
• Mindfulness Practice and Anxiety Reduction : Regular mindfulness practice leads to decreased levels of anxiety.
• Attachment Style and Romantic Satisfaction : Attachment style influences satisfaction levels in romantic relationships.
• Intrinsic vs. Extrinsic Motivation : Intrinsic motivation yields more sustainable outcomes than extrinsic motivation.
• Attention Allocation and Multitasking Performance : Efficient attention allocation enhances multitasking performance.
• Neuroplasticity and Skill Acquisition : Neuroplasticity supports the acquisition and refinement of new skills.
• Prejudice Reduction Interventions and Attitude Change : Prejudice reduction interventions lead to positive attitude changes.
• Parental Support and Adolescent Resilience : Strong parental support enhances resilience in adolescents facing challenges.
• Social Media Use and FOMO (Fear of Missing Out) : Extensive social media use contributes to higher levels of FOMO.
• Mood and Decision-Making Biases : Different mood states influence cognitive biases in decision-making.
• Parental Attachment and Peer Influence : Strong parental attachment moderates the impact of peer influence on adolescents.
• Personality Traits and Job Satisfaction : Certain personality traits predict higher job satisfaction levels.
• Social Support and Post-Traumatic Growth : Adequate social support fosters post-traumatic growth after adversity.
• Cognitive Load and Creativity : High cognitive load impedes creative thinking and problem-solving.
• Self-Efficacy and Goal Persistence : Higher self-efficacy leads to increased persistence in achieving goals.
• Stress and Physical Health : Chronic stress negatively affects physical health outcomes.
• Perceived Control and Psychological Well-being : Strong perceived control is linked to greater psychological well-being.
• Parenting Styles and Emotional Regulation in Children : Authoritative parenting styles promote effective emotional regulation.
• Cultural Exposure and Empathy Levels : Exposure to diverse cultures enhances empathetic understanding.
• Emotional Intelligence and Conflict Resolution : High emotional intelligence leads to more effective conflict resolution strategies.
• Personality Traits and Leadership Styles : Different personality traits align with distinct leadership approaches.
• Attachment Style and Romantic Relationship Quality : Secure attachment predicts higher quality romantic relationships.
• Social Comparison and Self-Perception : Frequent social comparison impacts individuals’ self-perception and self-esteem.
• Mindfulness Meditation and Stress Resilience : Regular mindfulness practice enhances resilience in the face of stress.
• Cognitive Biases and Prejudice Formation : Cognitive biases contribute to the formation and reinforcement of prejudices.
• Parenting Styles and Social Skills Development : Authoritative parenting styles foster positive social skills in children.
• Emotion Regulation Strategies and Mental Health : Effective emotion regulation strategies contribute to better mental health outcomes.
• Self-Esteem and Academic Achievement : Higher self-esteem correlates with improved academic performance.
• Cultural Identity and Intergroup Bias : Strong cultural identity buffers against the effects of intergroup bias.

Psychology Hypothesis Statement Examples for Social Experiments & Studies : Dive into social dynamics with hypotheses that explore human behavior in various contexts. These examples delve into the intricate interplay of psychological factors in social experiments and studies, shedding light on how individuals interact, perceive, and respond within social environments. You may also be interested in our two tailed hypothesis .

• Influence of Group Size on Conformity : Larger group sizes lead to higher levels of conformity in social experiments.
• Effects of Positive Reinforcement on Prosocial Behavior : Positive reinforcement increases the likelihood of engaging in prosocial actions.
• Role of Normative Social Influence in Decision Making : Normative social influence influences decision-making processes in group settings.
• Impact of Obedience to Authority on Ethical Decision Making : Obedience to authority influences ethical decision-making tendencies.
• Attribution Bias in Social Interactions : Attribution bias leads individuals to attribute their successes to internal factors and failures to external factors.
• Social Comparison and Body Dissatisfaction : Frequent social comparison contributes to negative body image perceptions.
• Perceived Control and Social Stress Resilience : Strong perceived control mitigates the negative effects of social stress.
• Impression Management in Online Social Networks : Individuals engage in impression management to create a favorable online image.
• Social Identity and Group Behavior : Strong social identity fosters a sense of belonging and influences group behavior.
• Altruistic Behavior and Empathy Levels : Higher empathy levels correlate with increased engagement in altruistic actions.

Social Psychology Hypothesis Statement Examples : Explore the intricacies of human behavior within social contexts through these social psychology hypotheses. These examples delve into the dynamics of social interactions, group dynamics, and the psychological factors that influence how individuals perceive and respond to the social world.

• Social Norms and Conformity : Individuals conform to social norms to gain social acceptance and avoid rejection.
• Bystander Effect and Helping Behavior : The bystander effect decreases the likelihood of individuals offering help in emergency situations.
• In-Group Bias and Intergroup Relations : In-group bias leads to favoritism toward members of one’s own social group.
• Social Influence and Decision Making : Social influence impacts decision-making processes in group settings.
• Deindividuation and Uninhibited Behavior : Deindividuation leads to reduced self-awareness and increased uninhibited behavior.
• Perceived Social Support and Coping Mechanisms : Adequate social support enhances effective coping strategies in challenging situations.
• Group Polarization and Risky Decision Making : Group discussions intensify individuals’ pre-existing inclinations, leading to riskier decisions.
• Self-Esteem and Social Comparison : Individuals with lower self-esteem are more prone to engaging in negative social comparison.
• Cultural Norms and Nonverbal Communication : Cultural norms influence nonverbal communication cues and interpretations.

Alternative Psychology Hypothesis Statement Examples : Explore alternative hypothesis perspectives on psychological phenomena with these hypotheses. These examples challenge conventional wisdom and encourage critical thinking, providing a fresh outlook on various aspects of human behavior, cognition, and emotions.

• Nonverbal Communication and Introversion : Nonverbal cues may play a more significant role in communication for introverted individuals.
• Perceived Control and External Locus of Control : High perceived control may lead to an external locus of control in certain situations.
• Cognitive Dissonance and Reinforcement Theory : Cognitive dissonance can be explained through the lens of reinforcement theory.
• Bystander Effect and Social Responsibility : The bystander effect may stem from individuals’ heightened sense of social responsibility.
• Emotion Regulation and Emotional Suppression : Emotion regulation strategies like emotional suppression might lead to long-term emotional well-being.
• Perceived Social Support and Emotional Independence : Adequate social support may contribute to emotional independence rather than dependence.
• Cultural Identity and Interpersonal Conflict : Strong cultural identity might lead to increased interpersonal conflict due to differing values.
• Parenting Styles and Personality Development : Parenting styles might have a limited impact on the formation of certain personality traits.
• Social Media Use and Positive Self-Presentation : Extensive social media use may lead to a more authentic self-presentation.
• Attentional Bias and Cognitive Flexibility : Attentional bias might enhance cognitive flexibility in specific cognitive tasks.

Psychology Hypothesis Statement Examples in Research : Explore the realms of psychological research hypothesis that guide scientific inquiry. These examples span various subfields of psychology, offering insights into human behavior, cognition, and emotions through the lens of empirical investigation.

• Effects of Meditation on Mindfulness : Regular meditation practice enhances individuals’ mindfulness levels.
• Impact of Parenting Styles on Self-Esteem : Parenting styles significantly influence children’s self-esteem development.
• Emotion Regulation Strategies and Anxiety Levels : Effective emotion regulation strategies lead to decreased anxiety levels.
• Cultural Identity and Academic Achievement : Strong cultural identity positively impacts academic achievement in multicultural settings.
• Influence of Peer Pressure on Risky Behavior : Peer pressure increases engagement in risky behaviors among adolescents.
• Effects of Social Support on Depression : Adequate social support leads to decreased depression symptoms in individuals.
• Mindfulness Meditation and Attention Span : Regular mindfulness practice improves individuals’ attention span and focus.
• Attachment Style and Romantic Satisfaction : Attachment style predicts satisfaction levels in romantic relationships.
• Effects of Positive Feedback on Motivation : Positive feedback enhances intrinsic motivation for challenging tasks.
• Impact of Sleep Quality on Memory Consolidation : Better sleep quality leads to improved memory consolidation during sleep.

Experimental Research in Psychology Hypothesis Examples : Embark on experimental journeys with hypotheses that guide controlled investigations into psychological phenomena. These examples facilitate the design and execution of experiments, allowing researchers to manipulate variables, observe outcomes, and draw evidence-based conclusions.

• Effects of Color on Mood : Exposure to warm colors enhances positive mood, while cool colors evoke calmness.
• Impact of Visual Distractions on Concentration : Visual distractions negatively affect individuals’ ability to concentrate on tasks.
• Influence of Music Tempo on Heart Rate : Upbeat music tempo leads to increased heart rate and arousal.
• Effects of Humor on Stress Reduction : Humor interventions reduce stress levels and increase feelings of relaxation.
• Impact of Exercise on Cognitive Function : Regular aerobic exercise improves cognitive function and memory retention.
• Influence of Social Norms on Helping Behavior : Observing prosocial behavior in others increases individuals’ likelihood of offering help.
• Effects of Sleep Duration on Reaction Time : Longer sleep duration leads to faster reaction times in cognitive tasks.
• Impact of Positive Affirmations on Self-Esteem : Repeating positive affirmations boosts self-esteem and self-confidence.
• Influence of Noise Levels on Task Performance : High noise levels impair individuals’ performance on cognitive tasks.
• Effects of Temperature on Aggressive Behavior : Elevated temperatures lead to an increase in aggressive behavior.

Psychology Hypothesis Tentative Statement Examples : Embark on the journey of exploration and inquiry with these tentative hypotheses. These examples reflect the initial assumptions and predictions that researchers formulate before conducting in-depth investigations, paving the way for further study and empirical examination.

• Possible Effects of Mindfulness on Stress Reduction : Mindfulness practices might contribute to reduced stress levels in individuals.
• Potential Impact of Social Media Use on Loneliness : Extensive social media use could be linked to increased feelings of loneliness.
• Tentative Connection Between Personality Traits and Leadership Styles : Certain personality traits may align with specific leadership approaches.
• Potential Relationship Between Parenting Styles and Academic Motivation : Different parenting styles might influence students’ motivation for academics.
• Hypothesized Impact of Cognitive Training on Memory Enhancement : Cognitive training interventions may lead to improved memory function.
• Preliminary Association Between Emotional Intelligence and Conflict Resolution : Higher emotional intelligence might be related to more effective conflict resolution.
• Possible Effects of Music Exposure on Emotional Regulation : Listening to music might impact individuals’ ability to regulate emotions.
• Tentative Link Between Self-Esteem and Resilience : Higher self-esteem may contribute to increased resilience in the face of challenges.
• Potential Connection Between Cultural Exposure and Empathy Levels : Exposure to diverse cultures might influence individuals’ empathetic understanding.
• Tentative Association Between Sleep Quality and Cognitive Performance : Better sleep quality could be linked to improved cognitive function.

Psychology Hypothesis Development Statement Examples : Formulate hypotheses that lay the groundwork for deeper exploration and understanding. These examples illustrate the process of hypothesis development, where researchers craft well-structured statements that guide empirical investigations and contribute to the advancement of psychological knowledge.

• Development of a Hypothesis on Emotional Intelligence and Workplace Performance : Emotional intelligence positively influences workplace performance through enhanced interpersonal interactions and adaptive coping mechanisms.
• Constructing a Hypothesis on Social Media Use and Well-being : Extensive social media use negatively impacts psychological well-being by fostering social comparison, reducing real-life social interactions, and increasing feelings of inadequacy.
• Formulating a Hypothesis on Attachment Styles and Relationship Satisfaction : Secure attachment styles correlate positively with higher relationship satisfaction due to increased trust, effective communication, and emotional support.
• Creating a Hypothesis on Parenting Styles and Child Aggression : Authoritative parenting styles lead to reduced child aggression through the cultivation of emotional regulation skills, consistent discipline, and nurturance.
• Developing a Hypothesis on Cognitive Biases and Decision Making : Cognitive biases influence decision-making processes by shaping information processing, leading to deviations from rational decision-making models.
• Constructing a Hypothesis on Cultural Identity and Psychological Well-being : Strong cultural identity positively impacts psychological well-being by fostering a sense of belonging, social support, and cultural pride.
• Formulating a Hypothesis on Attachment Style and Coping Mechanisms : Attachment style influences coping mechanisms in response to stress, with secure attachments leading to adaptive strategies and insecure attachments resulting in maladaptive ones.
• Creating a Hypothesis on Self-Efficacy and Academic Performance : High self-efficacy predicts better academic performance due to increased motivation, perseverance, and effective learning strategies.
• Developing a Hypothesis on Gender Stereotypes and Career Aspirations : Gender stereotypes negatively impact women’s career aspirations by reinforcing traditional gender roles and limiting their perceived competence in certain fields.
• Constructing a Hypothesis on Cultural Exposure and Empathy Levels : Exposure to diverse cultures enhances empathy levels by fostering cross-cultural understanding, reducing ethnocentrism, and promoting perspective-taking.

These psychology hypothesis development statement examples showcase the critical process of crafting hypotheses that guide research investigations and contribute to the depth and breadth of psychological knowledge.  In addition, you should review our  biology hypothesis .

How Do You Write a Psychology Hypothesis Statement? – Step by Step Guide

Crafting a psychology hypothesis statement is a crucial step in formulating research questions and hypothesis designing empirical investigations. A well-structured hypothesis guides your research, helping you explore, analyze, and understand psychological phenomena. Follow this step-by-step guide to create effective psychology hypothesis statements:

• Identify Your Research Question : Start by identifying the specific psychological phenomenon or relationship you want to explore. Your hypothesis should address a clear research question.
• Choose the Appropriate Type of Hypothesis : Decide whether your hypothesis will be directional (predicting a specific relationship) or non-directional (predicting a relationship without specifying its direction).
• State Your Variables : Clearly identify the independent variable (the factor you’re manipulating or examining) and the dependent variable (the outcome you’re measuring).
• Write a Null Hypothesis (If Applicable) : If your research involves comparing groups or conditions, formulate a null hypothesis that states there’s no significant difference or relationship.
• Formulate the Hypothesis : Craft a clear and concise statement that predicts the expected relationship between your variables. Use specific language and avoid vague terms.
• Use Clear Language : Write your hypothesis in a simple, straightforward manner that is easily understandable by both researchers and readers.
• Ensure Testability : Your hypothesis should be testable through empirical research. It should allow you to collect data, analyze results, and draw conclusions.
• Consider the Population : Specify the population you’re studying (e.g., adults, adolescents, specific groups) to make your hypothesis more precise.
• Be Falsifiable : A good hypothesis can be proven false through empirical evidence. Avoid making statements that cannot be tested or verified.
• Revise and Refine : Review your hypothesis for clarity, coherence, and accuracy. Make revisions as needed to ensure it accurately reflects your research question.

Tips for Writing a Psychology Hypothesis

Writing an effective psychology hypothesis statement requires careful consideration and attention to detail. Follow these tips to craft compelling hypotheses:

• Be Specific : Clearly define your variables and the expected relationship between them. Avoid vague or ambiguous language.
• Avoid Bias : Ensure your hypothesis is objective and unbiased. Avoid making assumptions or including personal opinions.
• Use Measurable Terms : Use terms that can be quantified and measured in your research. This makes data collection and analysis more manageable.
• Consult Existing Literature : Review relevant literature to ensure your hypothesis aligns with existing research and theories in the field.
• Consider Alternative Explanations : Acknowledge other potential explanations for your findings and consider how they might influence your hypothesis.
• Stay Consistent : Keep your hypothesis consistent with the overall research question and objectives of your study.
• Keep It Concise : Write your hypothesis in a concise manner, avoiding unnecessary complexity or jargon.
• Test Your Hypothesis : Consider how you would test your hypothesis using empirical methods. Ensure it’s feasible and practical to gather data to support or refute it.
• Seek Feedback : Share your hypothesis with peers, mentors, or advisors to receive constructive feedback and suggestions for improvement.
• Refine as Needed : As you gather data and analyze results, be open to revising your hypothesis based on the evidence you uncover.

Crafting a psychology hypothesis statement is a dynamic process that involves careful thought, research, and refinement. A well-constructed hypothesis sets the stage for rigorous and meaningful scientific inquiry in the field of psychology.

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