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28 Sleep 389
28.2 Measuring Human Sleep
How do we measure sleep objectively? A method that detects sleep by integrating
multiple physiological measurements is known as polysomnography (PSG)
(Carskadon & Dement, 2011). PSG typically consists of three measures: EEG, the
electroocul
activity using electrodes placed on the scalp, and EOG measures eye movements
using electrodes placed around the eyes. EMG measures muscle tone using electrodes attached to the chin. Thus, by combining these measures, one can determine
the patterns of EEG, whether the eyes are moving, and if so, whether they move
slowly or rapidly, and whether muscle tone is strong enough to support posture or is
relaxed.
These indices allow researchers and clinicians to assess the depth and type of
sleep
recording, while EMG activity is low and EEG patterns are desynchronized, the
participant is likely to be in REM sleep rather than wakefulness. If the eyes move
rapidly, while alpha waves are continuously present on the EEG and EMG activity is
high, the participant may be awake. In sleep clinics, PSG is used for the diagnosis of
sleep disorders.
In addition to EEG, EOG, and EMG, respiration, heart rate, and leg movements
may
restless leg syndrome, or periodic limb movement disorder.
ogram (EOG), and the electromyogram (EMG). EEG measures brain
. For example, if rapid eye movements (REMs) are observed on the EOG
be monitored to evaluate sleep disorders, for example, sleep apnea syndrome,
28.2.1 The Various Forms of Sleep
As mentioned at the beginning of this chapter, the brain does not go to sleep or stay
awake as a whole. By contrast, sleep or wakefulness can occur regionally, like in
gradation. Below I’ll begin by introducing an interesting form of sleep in animals,
the unihemispheric sleep (Rattenborg et al.,
ndings in human sleep, showing asymmetric patterns in the brain in association
fi
with the first-night effect, and brain usage.
2000; Lyamin et al., 2008 ), then some
28.2.2 Unihemispheric Sleep
The unihemispheric sleep, or unihemispheric slow-wave sleep, is a form of sleep
where only one hemisphere of the brain sleeps while the other remains awake
(Rattenborg et al., 2000; Lyamin et al., 2008). Certain marine mammals such as
dolphi
ns, whales, seals, and mallard ducks, have been found to be capable of
unihemispheric sleep. Unihemispheric sleep is measured by the amount of slow
waves (delta power) or the state of eyes (closed or open) for each brain hemisphere.
390 M. Tamaki
Bihemispheric sleep indicates delta power being higher than during wakefulness in
both sides of the brain, while unihemispheric sleep denotes a state where one of the
brain hemispheres showing greater delta power while the other showing somewhat
comparable to wakefulness. Typically, the eye contralateral to the brain hemisphere
showing greater delta power is closed, and the other remains open, when an animal is
in unihemispheric sleep.
Several hypotheses have been proposed as to why certain animals have
unihemispheric sleep. One possibility is that unihemispheric sleep serves a protective function essential for survival, such as allowing migratory birds to keep flying,
dolphins to continue swimming, or enabling the detection of predators.
For example, mallards rest in groups, and those positioned at the edge of the
are at higher risk of predation (Rattenborg et al.,
group
ispheric sleep in mallards be associated with a survival strategy? If so,
unihem
ducks on the outside of the group may be more likely to exhibit unihemispheric
sleep. A study has found that this is exactly what has been observed (Rattenborg
et al.,
1999b). Mallards tend to keep the eye facing outward open, while the
alateral (corresponding) hemisphere of the brain remains awake. At the same
contr
time, they close the inward-facing eye, allowing the corresponding hemisphere to
sleep deeply. In this way, mallards can rest their brain while simultaneously monitoring for potential predators. Sleep deprivation studies, in which animals are
experimentally deprived of sleep, have shown that unihemispheric sleep involves
sleep homeostasis. For example, when bottlenose dolphins are sleep-deprived for
each hemisphere, the sleep-deprived hemisphere shows increased slow-wave activity the following ‘recovery’ day, indicating homeostatic rebound (Oleksenko et al.,
1992).
1999a, b). Could
28.2.3 The First-Night Effect, Asymmetric Sleep, and Local
Sleep
What about humans? Many people have trouble sleeping in a new hotel room.
Taking longer to fall asleep, waking up during the night, and not feeling fully rested
the next morning are common experiences when traveling or staying over at a
friend’s place. This phenomenon is well-known and called the first-night effect
(FNE) in sleep research (Agnew et al.,
term “first-night effect” is used because it is most pronounced on the first night
The
(day) of an experiment. In sleep studies, participants are asked to sleep in a
laboratory, but because the environments of the lab are so different from their
usual sleep environment, it is often difficult to sleep well, affecting the quality of
sleep. For this reason, an adaptation session, a practice session to help participants
get used to sleeping in a new environment, is usually conducted before the main
experimental session.
1966; Tamaki et al., 2005, 2014, 2024).
28 Sleep 391
How the brain activity during sleep is altered in new environments remained a
mystery for a long time. However, a previous study showed that a safety-monitoring
system is involved (Tamaki et al.,
animals, Tamaki et al. (2016) hypothesized that humans also have an intrinsic
in
system
for environmental monitoring which could be asymmetric in association with
2016). Based on findings of unihemispheric sleep
the first-night effect.
They examined sleep depth (the strength of delta activity) across various brain
ns and analyzed the “networks” of the brain, which are the functional groupings
regio
of regions, such as the attention network, the sensorimotor network, the default
mode network (DMN), and the visual network (Raichle,
particula
rly interesting because it shows increased activity during mind-wandering,
2015). The DMN is
a state in which one is not focused on a task but instead lets thoughts drift
automatically (Raichle,
Mason
perfor
et al.,
2007). For instance, DMN activity decreases during focused task
mance but rises when the mind is not engaged in specific external demands.
2015; Buckner et al., 2008; Andrews-Hanna et al., 2010;
In the first experiment, magnetoencephalography was used concurrently with
EEG
to measure slow-wave activity during slow-wave sleep, then using individual
structural brain information from MRI, the strength of sleep was measured in several
brain networks, including the DMN. They found that in the DMN, the depth of sleep
differed between the brain hemispheres on the first night of the sleep experiment
(Fig.
28.2a). Specifically, on Day 1 when the first-night effect was present, the left
hemispher
e of the DMN showed lighter sleep. Once the FNE subsided on Day 2, this
interhemispheric difference was mitigated. The amount of the FNE measured by
sleep-onset latency was significantly related to the amount of asymmetry (asymmetry index) on Day 1 (Fig. 28.2b, c).
They next tested whether the FNE is related to the hemispheric asymmetry in
vigilance. To examine this, the amplitude of the N3 component, an event-related
potential that correlates with the strength of vigilance during sleep, was measured
from EEG measured during slow-wave sleep. This may be surprising: the brain is not
A
2.6
*
*
2.2
1.8
Strength (nAm)
0
Day 1 Day 2
Fig. 28.2 Asymmetric sleep related to the first-night effect in humans. (a) Interhemispheric
differences in slow-wave activity in the default mode network. Red, left hemisphere; blue, right
hemisphere. (b) Relationship between the sleep onset latency and the asymmetry index on Day
1. (c) Relationship between the sleep onset latency and the asymmetry index on Day 2. (Cited from
Tamaki et al.,
2016; Fig. 1)
B
0.08
0.04
0.00
-0.04
-0.08
Asymmetry index
-0.12
Day 1
r = -0.68* r = 0.03
10
20
30
0
Sleep latency (min)
40
C
0.08
0.04
0.00
-0.04
-0.08
Asymmetry index
-0.12
50
Day 2
0 20304010
Sleep latency (min)
5
0
392 M. Tamaki
completely shut off from the environment during sleep. Instead, it continues to
monitor the environment, even during deep sleep. The researchers wondered
whether the vigilance specific to sleep was somehow altered by FNE. They found
that on Day 1, during slow-wave sleep, deviant (rare, unexpected) sounds elicited
larger N3 responses in the left hemisphere, showing asymmetry in vigilance. This
asymmetry in vigilance was attenuated on Day 2 when the FNE subsided.
Finally, they tested whether the FNE is related to a protective function during
sleep. If so, the presentation of deviant sounds during sleep should result in a rapid
awakening. Indeed, on Day 1, when the left hemisphere detects deviant sounds,
participants were able to wake up and produce quicker behavioral responses. These
findings suggest that when humans sleep in a new environment, parts of the left
hemisphere remain in a lighter sleep stat e to monitor the surroundings.
These are just a few examples of how EEG can be applied in human sleep
ch.
resear
Other form
regional sleep where a part of the brain shows stronger slow wave activity in
response to brain usage or stimulation that occurred prior to sleep (Kattler et al.,
1994; Seitz & Roland, 1993).
s of regional sleep are also known. These include use-dependent
28.3 Offline Learning Process During Sleep
Have you ever experienced being unable to master a task no matter how many times
you practiced it during the day, only to find yourself able to perform it effortlessly
the next morning? Research has shown that brain plasticity, the ability of the brain to
change in response to stimuli and the environment, undergoes major fluctuations not
only during practice, but also offline during subsequent sleep (Tamaki et al.,
2020a, b; Tamaki & Sasaki, 2022). These changes strongly influence the degree to
skills improve and become stabilized after sleep. In other words, sleep is not
which
merely a state of rest; it is also a period in which the brain actively changes.
28.3.1 NREM Sleep and Learning
Memory is broadly classified into declarative memory (information that can be
explicitly described, such as facts or episodes) and procedural memory (knowledge
of “how” to do things, such as riding a bicycle, which is difficult to verbalize)
(Squire,
stabilizing
&
associated with the replay and reactivation of neural activity that occurred during
wakeful training. Experiments have shown that presenting auditory stimulation
2004).
Brain activities during NREM sleep play important roles in strengthening and
learning and memory (for reviews see Diekelmann & Born, 2010; Tononi
Cirelli, 2014; Yamada
et al., 2023). For example, sleep spindles are thought to be
28 Sleep 393
during NREM sleep can enhance spindle activity and improve later memory recall,
whereas disrupting spindle activity through auditory stimulation can impair performance. It is thought that during NREM sleep, reactivation of task-related neural
activity linked to spindles enhances neural plasticity and supports memory
consolidation.
Another important brain activity during NREM sleep is slow waves (Steriade,
2006; Bernardi et al., 2018; Staresina, 2024). Slow waves are classified into slow
oscillations (<1 Hz) and delta waves (0.5–4 Hz). Slow oscillations alternate between
depolarized “up states,” when neuronal activity is high, and hyperpo larized “down
states,” when neuronal activity is largely silent (Steriade, 2005). Spindles and
ampal sharp-wave ripples tend to cluster during up states, and these events
hippoc
are thought to be critical for memory processes. Animal studies have suggested that
delta waves may weaken memory traces, whereas slow oscillations may strengthen
them (Kim et al.,
ins an important question for future research.
rema
These are further examples of how EEG allows us to investigate spontaneously
occurr
ing neural activity during sleep in a noninvasive manner.
2019). Whether the same functional distinction exists in humans
28.3.2 REM Sleep and Learning
Compared with NREM sleep, the role of REM sleep in learning and memory is less
well understood. Theta rhythms are characteristic of REM sleep, and have been
reported to be involved in memory processing (Popa et al.,
but
compared with spindles and slow waves, relatively little is known about their
functional significance.
In humans, Tamaki et al. (2020a) report
stabilization of visual learning (a form of perceptual learning), making newly
acquired skills more resistant to disruption. Visual learning refers to improvements
in perceptual skills (e.g., detecting motion direction or line orientation) that persist
over time following visual experience. The degree of stabilization by sleep can be
assessed by testing whether learning effects observed before sleep are maintained
after sleep without being disrupted. The study investigated the role of REM sleep in
stabilizing visual learning by using retrograde interference. For example, if participants train on Task A and then immediately train on a similar but different Task B,
performance on Task A typically does not improve, because training on Task B
disrupts consolidation of Task A. This is known as retrograde interference, the
negative effect of later learning on earlier learning. Conversely, when earlier learning disrupts later learning, it is referred to as anterograde interference. The study
found that when both NREM and REM sleep occurred between training on Tasks A
and B, training on Task B no longer interfered with learning on Task A. However, if
only NREM sleep occurred and REM sleep was absent during the interval, Task B
training disrupted Task A learning. These findings suggest that REM sleep plays a
crucial role in protecting learning from interference and thereby supports memory
stabilization.
ed that REM sleep contributes to the
2010; Boyce et al., 2016),
394 M. Tamaki
28.4 Combining EEG with Neuroimaging Methods
for Investigating Sleep
Over the past few decades , non-invasive brain imaging techniques for measuring
human brain activity have improved dramatically. When MRI and EEG are combined, they complement each other’s limitations, allowing the acquisition of data
with high spatiotemporal resolution (Uji & Tamaki,
metho
d allows us to investigate the brain network changes in high spatial resolution,
specific oscillations, or evoked potentials, measured by EEG. Adding EOG and
EMG, comprising PSG, with MRI, the method has evolved into unique ‘simultaneous recording techniques’ specifically for human sleep research.
2023; Warbrick, 2022). This
28.4.1 Active Brain Networks During Sleep
Using fMRI with EEG, studies have found that different brain networks are activated
depending on the depth of sleep or existence of brain oscillations. For example,
increased activation is found in the thalamus to tones during NREM sleep when
sleep spindles are absent (Schabus et al.,
incre
ases in the sensorimotor network, frontal, precuneus, and hippocampal areas
(Uji et al., 2025; Dang-Vu et al., 2008) in addition to the thalamus. The brain regions
recruited
during deep NREM sleep involve synaptic plasticity and homeostatic regulation (Uji
et al.,
fMRI
studies
and visual cortices. Recently, using a simultaneous fMRI and PSG method, one
study has succes sfully characterized brain regio ns associ ated with sawtooth waves
during REM sleep (Uji et al.,
wave
was specific to sawtooth waves. The inferior frontal cortex may serve as a potential
source of sawtooth wave generation (Frauscher et al.,
related
ation, may be another interesting question to pursue.
during light NREM sleep involve an arousal-related circuit, while those
2025). There are only a small numbe r of studies investigated REM sleep using
(Uji et al., 2025; Wehrle et al., 2005, 2007; Miyauchi et al., 2009). These
have some key brain regions activating during REMs, including the thalamus
2025). While the brain regions activated for sawtooth
s were largely the same as those activated for REMs, the inferior frontal cortex
to saw tooth waves is involved in learning and memory, or in dream gener-
2024). Slow waves are associated with
2020). How brain activation
28.4.2 Measuring the Balance of Excitation and Inhibition in the Human Brain
At present, the only neuroimaging technique that allows non-invasive estimation of
neurotransmitter concentrations correlated with brain plasticity is magnetic resonance spectroscopy (MRS). Using MRS, it is possible to estimate the concentrations
28 Sleep 395
of excitatory neurotransmitter glutamate and inhibitory neurotransmitter gammaaminobutyric acid (GABA) in specific brain regions (Edden & Barker,
Mesch
er et al., 1998; Muthukumaraswamy et al., 2009). From the ratio of these
concent
rations (glutamate/GABA), one can derive the excitation-inhibition (E/I)
2007;
balance. Previous studies have reported that the E/I balance in the early visual cortex
correlates with plasticity in visual learning (Shibata et al.,
2017).
How, then, can we measure the E/I balance during sleep? A group has developed
a technique for simultaneous acquisition of MRS and polysomnography within the
MRI environment (Tamaki et al., 2020a, 2021, 2024) (Fig. 28.3). Using this
combi
ned method, they examined the E/I balance in the early visual cortex during
NREM and REM sleep. Results showed that the E/I balance increased beyond wake
levels during NREM sleep (i.e., became more excitatory), but decreased below wake
levels during REM sleep (i.e., becam e more inhibitory; Fig.
28.3a) (Tamaki et al.,
2020a).
Is sleep-related E/I balance linked to learning? Correlations between visual
ing task performance and sleep E/I balance revealed that the E/I balance during
learn
NREM sleep was associated with the rate of post-sleep performance improvement
(the so-called “offline gain,” referring to skill enhancement without additional
training; Fig. 28.3b). By contrast, the E/I balance during REM sleep correlated
the stabilization of learning (Fig. 28.3c). These findings indicate that brain
with
ty fluctuates dynamically during sleep, and suggest that NREM and REM
plastici
sleep contribute to different aspects of learning through opposing neurochemical
processes.
Fig. 28.3 E/I balance during sleep. (a) The E/I balance changes during NREM (orange) and REM
(blue) sleep. During NREM sleep, the E/I balance is significantly higher than during the wake
baseline, whereas during REM sleep, the E/I balance is significantly lower than the wake baseline.
(b) The correlation between E/I balance changes during NREM sleep and offline performance
gains. Red, NREM + REM group. Gray, NREM-only group. (c) The correlation between E/I
balance changes during REM sleep and stabilization of learning. (Cited from Tamaki et al.,
2020a; Fig. 1c, d, and f, respectively)
396 M. Tamaki
28.4.3 The Cerebrospinal Fluid Dynamics in Human Sleep
How does sleep maintain healthy brain functions and are alterations in sleep
associated with diseases? Cerebrospinal fluid (CSF) during sleep has recently been
proposed as a crucial mechanism for brain function. Accumulating evidence indicates that the brain metabolite waste is reduced specifically during deep sleep when
CSF flows are increased. Animal studies have shown that the amount of CSF tracer
influx increases during sleep compared to during wakefulness (Xie et al.,
rmore, sleep was associated with βA clearance (Xie et al., 2013; Cankar et al.,
Furthe
2024). How CSF dynamics are driven in the healthy human brain during deep sleep
had
remained unclear, while several studies have reported findings during light sleep
(Fultz et al., 2019).
One of the reasons why deep sleep in humans had long been unreported is that
fMRI
produces substantial acoustic no ise. If you’ve ever been inside an MRI
scanner, you know how loud it can be! As a result, recording brain activity during
deep sleep using fMRI is extremely difficult.
A group of scientists set out to tackle this challenge. As described above, a study
previously succeeded in measuring brain activity during deep sleep using MRS,
had
an MRI technique distinct from conventional fMRI (Tamaki et al., 2020a, 2021,
2024). In this study, they took advantage of the fact that MRS produces intermittent
rathe
r than continuous scanner noise, and accordingly adopted a sparse fMRI
approach that acquires brain signals intermittently and slowly, instead of the conventional continuous fMRI method that generates persistent high-pitched noise.
Furthermore, by simultaneously recording physiological signals such as EEG, electrooculography (EOG), and electromyography (EMG), they succeeded in obtaining
brain activity data during slow-wave sleep and REM sleep, the states that have been
notoriously difficult to measure with conventional MRI techniques (Uji et al.,
As a result, the study has revealed that CSF dynamics are facilitated, especially
g deep sleep in healthy young human participants (Uji et al., 2025). The CSF
durin
signa
ls are tightly linked to spontaneous brain oscillations. Slow waves and sleep
spindles during slow-wave sleep, the deepest NREM sleep, are followed by shortcycle, frequent, yet moderate changes in the CSF signals, the fMRI signals measured
from the lateral ventricles (Fig.
arousals (brief awakening from sleep) are also followed by CSF signal changes,
and
but the changes are slow, infrequent, and steep (Fig.
well,
CSF signals are time-locked to neural events. Rapid eye movements and
sawtooth waves are linked to CSF signal changes.
They also found that these brain oscillations and neural events recruit different
brain netw
and homeostatic circuits, while lighter sleep involves sensory and motor networks
(Fig. 28.5). Thus, human deep sleep may have a specific way of facilitating CSF
dynam
during deep sleep are involved in metabolic clearance in healthy humans remains
unclear. Investigating how CSF alterations are involved in various clinical
orks depending on the depth of sleep, with deep sleep involving memory
ics in tight link to learning and memory. However, how the CSF dynamics
28.4b). Interestingly, slow waves during light sleep
28.4a). During REM sleep as
2013).
2025).
28 Sleep 397
Fig. 28.4 CSF dynamics during sleep. (a) CSF signal changes to slow waves and sleep spindles
during light NREM sleep. During light NREM sleep, sleep spindles were correlated with significant
CSF signal changes at 6 s after onset, whereas slow waves preceded a large CSF signal peak at 8 s
after onset. (b) CSF signals changes to slow waves and sleep spindles during deep NREM sleep or
slow-wave sleep. During slow-wave sleep, slow waves triggered CSF signal changes peaking at
5.5 s , in which latency was significantly shorter and smaller in amplitude. Sleep spindles preceded
CSF signal changes at 4 s after their onset, which lasted for approximately 10 s. (Cited from Uji
et al.,
2025; Fig. 1d, e)
conditions in humans is also needed (Elabasy et al., 2025). To this end, advanced
noninvasive along with invasive methods will be useful (Elabasy et al., 2025;
Hirschl
er et al., 2025).
28.5 Conclusions
In this chapter, I’ve introduced how EEG has been utilized in human sleep research.
These studies demonstrate that EEG can be used not only to detect macroscopic
sleep structure but also to assess the depth of sleep within brain networks, to probe
the plasticity processes underlying it, and potentially to capture the brain’s cleaning
processes.
Strikingly, EEG becom es even more powerful when combined with other modalities,
including structural MRI, functional MRI, MRS, and MEG, providing rich
information about various offline brain processes.
The studies presented
established that sleep problems become more prevalent with aging, and individuals
with neuropsychiatric disorders almost invariably experience sleep disturbances.
Moving forward, it will be important to investigate how sleep is altered in clinical
populations and older adults using EEG and multimodal approaches.
here focus on healthy young adults; however, it is well
398 M. Tamaki
Lateral Medial Subcortical
Parietal and visual areas
Cerebellum
Light NREM
sleep
Frontal areas
Hippocampus,
striatum, amygdala
Slow-wave
sleep
Visual and thalamic areas
Hippocampus,
striatum, amygdala
REM sleep
Arousals
Fig. 28.5 Brain regions recruited during various stages of sleep and for arousals. The
red-highlighted parts indicate brain areas activated to slow waves during light NREM (top row)
and slow-wave sleep (the second row), rapid eye movements during REM sleep (the third row), and
arousals (the bottom row). Light NREM sleep activates sensory and motor regions including the
parietal and visual cortices and cerebellum. Slow-wave sleep engages regions involved in plasticity
and homeostatic processing including the prefrontal and hippocampal regions, striatum, and
amygdala. REM sleep recruits visual and plasticity circuits including the visual areas, the thalamus,
the hippocampus, the striatum, and the amygdala. Arousals accompanied widespread brain activation not localized to specific brain regions. (Citated from Uji et al.,
found in SI Appendix, Tables S4-10 in Uji et al.,
2025)
2025; Fig. 5. More details can be
References
Agnew, H. W., Webb, W. B., & Williams, R. L. (1966). The first night effect: An EEG study of
sleep. Psychophysiology, 2(3), 263–266.
Albouy, G., Sterpenich, V., Vandewalle, G., Darsaud, A., Gais, S., Rauchs, G., et al. (2013).
Interaction
motor sequence memory. Robertson E, editor. PLoS ONE, 8(3), e59490.
Andrews-Hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R., & Buckner, R. L. (2010). Functional-
anatomic
Aserinsky, E., & Kleitman, N. (1953).
phenomena, during sleep. Science, 118, 273–274.
Bastian, L., Samanta, A., Ribeiro De Paula, D., Weber, F. D., Schoenfeld, R., Dresler, M., et al.
(2022).
ity changes in a hippocampal network after sleep. Human Brain Mapping, 43(13), 3923–3943.
Bernardi, G., Siclari, F.,
slow waves in stable NREM sleep: Evidence for distinct regulation mechanisms. Frontiers in
Human Neuroscience, 12, 248.
between hippocampal and striatal systems predicts subsequent consolidation of
fractionation of the brain’s default network. Neuron, 65(4), 550–562.
Regularly occurring periods of eye motility, and concomitant
Spindle–slow oscillation coupling correlates with memory performance and connectiv-
Handjaras, G., Riedner, B. A., & Tononi, G. (2018). Local and widespread