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Published in final edited form as:
Nature. 2011 April 28; 472(7344): 443–447. doi:10.1038/nature10009.

Local sleep in awake rats
Vladyslav V. Vyazovskiy1, Umberto Olcese1,2, Erin C. Hanlon1, Yuval Nir1, Chiara Cirelli1,
and Giulio Tononi1
1 Department of Psychiatry; University of Wisconsin-Madison, Madison, Wisconsin 53719
2 PERCRO Laboratory, Scuola Superiore Sant’Anna, Pisa, Italy

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Abstract

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When the brain is awake, neurons in the cerebral cortex fire irregularly and theelectroencephalogram (EEG) displays low amplitude, high frequency fluctuations. After falling
asleep, neurons start oscillating between ON periods, when they fire as during wake, and OFF
periods, when they stop firing altogether, and the EEG displays high amplitude slow waves. But
what happens to neuronal firing after a long period of wake? We show here in freely behaving rats
that, after prolonged wake, cortical neurons can go briefly “OFF line” as they do in sleep,
accompanied by slower waves in the local EEG. Strikingly, neurons often go OFF line in one
cortical area and not in another. During these periods of “local sleep”, whose incidence increases
with wake duration, rats appear awake, active, and display a wake EEG. However, they areprogressively impaired in a sugar pellet reaching task. Thus, though both the EEG and behavior
indicate wakefulness, local populations of neurons in the cortex may be falling asleep, withnegative consequences on performance.

Keywords
slow wave sleep; slow oscillations; EEG; cerebral cortex; multi-unit recording; reaching task;
sleep deprivation

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Everybody knows the difference between sleep and wake. During wake, the eyes are usuallyopen, animals move around, and they respond to their surroundings. During sleep eyes close,behavior stops, and animals fail to respond to stimuli. Studies of brain activity also showmajor changes between wake and non-rapid eye movement (NREM) sleep, which makes up~80% of sleep. During wake neurons in the cerebral cortex fire irregularly, their membranepotential is tonically depolarized, and the EEG shows low voltage high frequency activity.
During NREM sleep, due to a decrease in the level of neuromodulators, neurons become

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Correspondence: Giulio Tononi, Department of Psychiatry, University of Wisconsin-Madison, 6001 Research Park Blvd, Madison,
WI 53719, USA, gtononi@wisc.edu.
COI statement: All authors indicated no financial conflicts of interest.
Author contribution
G.T. and C.C. conceived and directed the study, G.T., C.C. and V.V.V. designed the experiments and wrote the manuscript, V.V.V.
and E.C.H. performed the experiments, V.V.V. and U.O performed data analysis, Y.N. contributed to experiments and writing.

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bistable: their membrane potential oscillates between a depolarized up state similar to wake,and a hyperpolarized down state during which they cease firing altogether 1. These slow
oscillations occur in the range between 0.1 and 6 Hz and they are visible both in multiunit(MUA) activity (ON and OFF periods) and in the EEG (slow waves) 2.

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However, we also know that by staying awake too long one becomes tired, and many studies
have demonstrated attention lapses, frequent mistakes in various cognitive tasks, and poor
judgment, even when we may not feel particularly sleepy 2,3. Moreover, the EEG showssome trace of the sleep/wake history: the longer one has been awake, the higher the spectral
power in the slow wave range (SWA, 0.5–4 Hz) of the EEG in subsequent sleep 4,
corresponding to larger and more frequent slow waves, and to more intense and synchronous
neuronal activity 2. Also, local variations in cortical activity during wake are associated withlocal sleep changes and sleep-dependent increase in task performance5–7. These changes arereversed progressively in the course of sleep 4. The wake EEG also shows changes thatreflect the duration of previous wake, with power increasing in the theta range (5–7
Hz) 8–10. Likewise, neuroimaging studies show blood flow and metabolic changes after
sleep deprivation, with some brain regions undergoing decreases and other increases in
activation 11. However, it is poorly understood how the underlying neuronal activity may bechanging.

Neurons can go OFF-line during prolonged wake

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To investigate this question, we implanted a group of adult rats (n=11) with 16-channel
microwire arrays in deep layers of the frontal motor cortex and recorded both local fieldpotentials (LFP) and local MUA 2 across spontaneous sleep and wake (Supplementary
Information). As expected, the wake LFP was characterized by low amplitude fast and theta
waves, accompanied by irregular, tonic MUA, and was readily distinguishable from the LFPof NREM sleep, when high amplitude slow waves occurred concomitantly with synchronous
ON and OFF periods at the level of MUA (Fig. 1a,a′).

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We then kept rats awake with novel objects for 4 hours starting at light onset 2. As expected,
by the end of sleep deprivation the LFP showed an approximately 30% increase in spectral
power in the slow/theta range between 2 and 6 Hz (Fig. 1b, Supplementary Fig. 1a).
However, close inspection of the recordings revealed an occasional change in firing patterns(Fig. 1c): unlike at the beginning of sleep deprivation (SD1), towards the end (SD4)neuronal activity sporadically showed brief periods of silence, involving all or most of therecorded neurons. These short-lasting population OFF periods were often associated withslow/theta waves in the LFP. An opposite dynamic was observed during 6 hours of recovery
sleep, when LFP showed a progressive decline of SWA (Fig. 1b′; Supplementary Fig. 1b).
At the beginning of sleep (S1), large LFP slow waves were associated with synchronous
ON-OFF oscillations in MUA (Fig. 1c′). At the end of recovery sleep (S6), large slow waves
became infrequent and MUA became sparse and irregular. Thus, at the level of neuronalfiring, wake under high sleep pressure occasionally resembles late NREM sleep, while lowpressure sleep may occasionally resemble wake.

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Crucially, we found that the number of OFF periods in wake increased significantly by
57.7±16.5% from SD1 to SD4 (Fig. 1d, left) suggesting that the tendency of neurons to entera “sleep-like” mode increases with sleep pressure. The number of high amplitude LFP 2–6
Hz waves also increased significantly by 23.3±5.2% (Fig. 1d, right). The initial number of
OFF periods or LFP 2–6 Hz waves during SD1 correlated negatively with their increase
from SD1 to SD4 (OFF periods, R=−0.53, p<0.1, LFP waves, R=−0.87, p<0.0001),
consistent with a saturating increase of sleep pressure4. Again, an opposite dynamic wasapparent during recovery sleep: sleep OFF periods and high amplitude LFP slow waves
decreased significantly by 36.9±11.2 and 59.5±9.0%, respectively, from S1 to S6 (Fig. 1d′).
Of note, OFF periods, as defined here, and 2–6 Hz LFP waves were also observed duringbaseline spontaneous wake in all rats, but their frequency was lower than that observed
during the first hour of sleep deprivation (OFF periods: 7.1±4.1/1 min, p=0.023; 2–6 Hz
waves: 25.05±8.1, p=0.06). Thus, high sleep pressure is associated with increased tendencyof neurons to go “OFF line” in both wake and sleep, and MUA OFF periods underlie themacroscopic changes in LFP low frequency power.

Wake OFF periods occur asynchronously in distant cortical regions

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Sleep is usually thought of as a global behavior and a global cortical/EEG state 12, raising
the question of whether OFF periods can be detected simultaneously in distant cortical areas.
In several animals (n=9), we implanted an additional microwire array in the deep layers ofparietal cortex. We found that OFF periods in awake rats were also present in parietal cortex(average duration: 79.02±7.7 ms, incidence: 37.51±6.16/1min) and, similar to the frontal
OFF periods, their occurrence increased from SD1 to SD4 (56.6±19.5%, n=9, F(1,17)=6.23,
p=0.041). Moreover, during sleep deprivation we found instances in which all recorded
neurons in frontal and parietal areas underwent OFF periods near simultaneously, consistent
with a global phenomenon (Fig. 2a, left). However, often neurons recorded in one corticalarea showed an OFF period, whereas in the other area they stayed ON, as they normally do
during wake (Fig. 2a, right). Further analysis revealed that most OFF periods were local, e.g.
observed only in one cortical region at a time (frontal: 76.9±2.9%, parietal: 82.8±3.1%;
frontal vs parietal: F(1,27)=4.6, p=0.0981). Importantly, both global and local OFF periods
increased from SD1 to SD4 (Fig. 2b, left and middle), but the former more than the latter(Fig. 2b, right). Consistent with the MUA findings, most wake 2–6 Hz waves occurredexclusively in the LFP from one of the two areas, while the remaining waves were seen
near-simultaneously in both areas. Both local 2–6 Hz waves, and those occurring
simultaneously in frontal and parietal areas, became more frequent from SD1 to SD4, but
the relative proportion of global waves increased during SD4 compared to SD1 (Fig. 2c),suggesting tha,t as sleep pressure builds up, neuronal activity in wake becomes more
synchronized, as it does in sleep.
Since in behaviorally awake animals during sleep deprivation distant brain areas can enter
OFF periods independently, we asked next if nearby (~2 mm) neurons can also do so. We
found that, even among units recorded with the same microelectrode array, a substantial
fraction could stop firing together for up to hundreds of milliseconds, while the remaining
neurons maintained their spiking activity at virtually unaltered or even elevated rates(Supplementary Fig. 2a). On average, while a subset of neurons ceased firing abruptly, theNature. Author manuscript; available in PMC 2011 October 28.

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remaining neurons increased firing transiently with a ~20 ms delay and then slowed downslightly (~15%, Supplementary Fig. 2b). Controls by shuffling units between subsets
indicated that these “hyper-local” OFF periods were unlikely to be an artifact of different
firing rates of cortical neurons (Supplementary Information). Hyper-local OFF periods
increased by almost 40% from SD1 to SD4, suggesting that they too are related to sleep
pressure (Supplementary Fig. 2c).

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Having found evidence for local OFF periods during wake, we then asked if sleep OFF
periods could also be local. Previous evidence has shown that sleep can be regulated
locally13, as demonstrated by a local increase in SWA after manipulations that affectplasticity during wake 12,13. Moreover, high-density EEG studies in humans combined withsource localization 14, as well as modeling studies 15, have suggested that sleep slow waves
with multiple peaks may result from the summation or interference of separate slow waves
originating at different locations. Finally, recent depth recordings in humans have provided
evidence that sleep slow waves and OFF periods can be local16. As shown in Fig. 2a′ (left),
we found that, in rats, OFF periods during NREM sleep occurred not only synchronously at
frontal and parietal areas (Fig. 2a′, left), but also locally, in which case they were associatedwith local slow waves in the LFP (Fig. 2a′, right). The incidence of both global and local
OFF periods in NREM sleep decreased significantly from S1 to S6 (Fig. 2b′, left andmiddle), accompanied by a relative reduction of global slow waves (Fig. 2b′, right). Thus,
just like wake 2–6 Hz waves became more global from SD1 to SD4 (Fig. 2b, right), sleep
slow waves became more local from S1 to S6 (Fig. 2b′, right), suggesting that populations ofneurons are more easily recruited into synchronous slow oscillations when sleep pressure ishigh than when it has dissipated 17.

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Local OFF periods in wake lead to behavioral deficits
It is common experience that tiredness following prolonged sleep loss can be manifested in“microsleeps”, brief episodes (3–15 seconds) during which a person appears suddenly asleep(eyes closed or closing), may not respond to stimuli, and the EEG shows sleep-like
activity 18. Clearly, such microsleeps can be dangerous during tasks requiring alertness, andthe detection of sleep-like behavior or EEG changes is being pursued to reduce risks 19.
However, careful observation of our rats, which were exposed to a relatively short period ofsleep deprivation, did not reveal any indication of sleep: their eyes were open, they
responded to stimuli, and their EEG was unambiguously a wake EEG (Fig. 1a,
Supplementary Information, Supplementary Figs. 3,4). Moreover, a retrospective analysis ofvideo recordings showed no behavioral signs of sleep specifically during MUA OFF
periods.

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Given that the OFF periods we detected have no overt manifestations, are short-lasting, andare often local, can they have any impact on performance? To investigate potential
consequences of neuronal “tiredness”, the rats were trained on a sugar pellet reaching task 13
for 2 hours between SD1 and SD4 (Supplementary Information). Learning the reaching task
engages a circumscribed cortical area within the motor cortex, leads to local plasticity inwake 20, and to increased SWA during subsequent sleep 13,20. To investigate directly
whether increased incidence of neuronal OFF periods leads to impaired performance, in a

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subset of animals (n=8) we performed simultaneous video and MUA recordings during thereaching attempts with high temporal resolution. Although the number of OFF periods
decreased steadily towards each reaching attempt in both frontal and parietal cortex,
possibly reflecting increased global arousal necessary for performing the reach, we foundthat the occurrence of an OFF period within several hundreds of ms before the reachingattempt was often associated with failure to successfully grasp a sugar pellet (Fig. 3a,b).
Specifically, OFF periods occurred more frequently ~300–800 ms before an unsuccessful
reaching attempt as compared to successful trials (Fig. 3c, left), and the probability of a
successful reach decreased by 37.5% if there was at least one OFF period before the reach(OFF+: 26.1±6.3, OFF−: 41.8±4.1; F(1,11)= 15.6, p=0.01). Importantly, this effect wasobserved in frontal but not parietal cortex (Fig. 3c, right). We also found that the overall
number of misses increased significantly across the training periods (p<0.05), and the
behavior became progressively unstable. Thus, while at the beginning of the task hits andmisses alternated regularly, as time progressed longer “clusters” of misses became more
frequent and had increasingly variable duration (duration: F(2,32)= 4.69, p=0.021; variance:F(2,32)= 4.31, p=0.028, rANOVA). These results suggest that neuronal OFF periods andcorresponding increases in low frequency LFP power may be associated with decreased
behavioral performance as is typical of sleep deprived individuals.

Discussion

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These findings reveal that, in animals kept awake beyond their normal sleep time,
populations of neurons in different cortical areas can suddenly go ‘OFF line’ in a way that
resembles the OFF periods of NREM sleep. The main differences are that during sleep
virtually all cortical neurons show ON-OFF oscillations in the SWA frequency range, theEEG displays typical sleep slow waves and spindles, and the animal is behaviorally
immobile, with eyes closed, and unresponsive. During prolonged wake, instead, only subsetsof neurons enter OFF periods, usually for shorter durations, the EEG is typical of
wakefulness, and the animal appears behaviorally awake, with eyes open, and responsive tostimuli. Also, the number of OFF periods increases with the duration of wakefulness,suggesting that the likelihood of subsets of neurons going OFF line in the context of anotherwise awake cortex increases with sleep pressure. As shown here, the progressivechanges observed during sleep deprivation are the mirror-image of changes during recovery
sleep: neuronal firing rates during ON periods, number and duration of OFF periods, numberof neurons participating synchronously in the OFF periods, and low frequency content of theEEG, all increase during wake just as they decrease during sleep, in agreement with theconcept of a homeostatic regulation of sleep need 4.

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Perhaps the most striking result of this study is that, in the sleep deprived brain, subsets ofneurons may enter an OFF period in one cortical area but not in another, and even within thesame cortical area some neurons may be OFF while others are ON, seemingly in apiecemeal fashion. Based on this evidence, the wake behavior of a sleep deprived subject
might be better characterized as a covert form of “dormiveglia” 21. Moreover, as shown here
using a sugar pellet reaching task, the increasing occurrence of local OFF periods during
prolonged wake was associated with worsening performance in the task. While paradigms
should be developed to associate more precisely the occurrence of OFF periods in specificNature. Author manuscript; available in PMC 2011 October 28.

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subsets of neurons with specific performance failures, these initial findings raise theintriguing possibility that “local sleep” in an awake brain may be responsible for cognitive
impairments due to sleep deprivation or restriction3,4,9. It is especially relevant thatcognitive impairments, including defective judgment and irritability 22, may occur despite
an outward impression of full-fledged wakefulness, the lack of subjective insight 4, and awake EEG. The sporadic occurrence of local neuronal OFF periods in sleep deprived
subjects may be analogous to the sporadic occurrence of local hypersynchronous discharges
in partial epilepsy. Such local events can be detected with careful EEG recordings asinterictal spikes and may cause momentary lapses (“absence”) without overt behavioral
signs 23.

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One can only speculate about the mechanisms underlying the local wake OFF periods. A
spontaneous slow oscillation of membrane potentials can occur in mouse barrel cortex
during quiet wake 24 and affect the amplitude of evoked responses 13, although it is not clear
whether such down states occur during active behavior, are local, affect performance and,critically, reflect increasing sleep pressure. While we do not know if the wake OFF periods
we observed in freely moving rats are associated with neuronal hyperpolarization, theiroverall similarity to sleep OFF periods, and the finding that they become more frequent withincreasing sleep pressure, suggests that they may be an expression of increasing neuronalbistability 25. Thus, in addition to the global state instability that is a hallmark of sleep
deprivation 26, there can also be a local instability, at least in the cerebral cortex. A
bistability between ON and OFF periods could be triggered by decreasing levels of arousalpromoting neuromodulators 27, especially since cho-linergic and noradrenergic neurons, forinstance, do not always discharge in tight synchrony 30,31, and presynaptic release can be
modulated locally 28,29.

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Is local sleep in awake rats an adaptive or maladaptive response? In some cetaceans andbirds, one hemisphere can remain awake while the other is in slow wave sleep, an adaptive
response that permits them to continue swimming, flying, or monitoring the environment 30.
The ability to actively control behavior with some neural circuits while others may be
idling 31 could also be evolutionary advantageous. However, dissociated behavioral states,
such as sleepwalking or REM sleep behavior disorder and other parasomnias, are clearly
maladaptive 35,36. Since local wake OFF periods are associated with locally increased
excitability after intensive training and with failures in performance, it is more likely thatthey represent a form of neuronal tiredness due to use-dependent factors, such as synaptic
overload 32. A question for the future is whether local OFF periods during wake may also
serve a functional role, from energy saving 37,38 to the initiation of a local restorative
process.

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METHODS SUMMARY
In male WKY rats, local field potentials (LFPs) and multiunit activity (MUA) were recorded
from deep layers of frontal (n=11 rats) and/or parietal (n=9) cortex with 16-ch (2×8)
polyimide-insulated tungsten microwire arrays. Rats were housed individually in transparent
Plexiglas cages (light:dark 12:12, light on at 10am, 23±1°C; food and water ad libitum andreplaced daily at 10am, except for the sugar pellet reaching task; see Supplementary

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Information). Animal protocols followed the National Institutes of Health Guide for the Careand Use of Laboratory Animals and were in accordance with institutional guidelines.

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Data acquisition and online spike sorting were performed with the Multichannel
Neurophysiology Recording and Stimulation System (Tucker-Davis Technologies Inc.,
TDT). MUA was collected continuously (25 kHz, 300 Hz – 5 kHz), concomitantly with the
LFPs from the same electrodes and epidural EEGs (both 256 Hz, 0.1–100 Hz). Amplitude
thresholds for online spike detection were set manually and allowed only crossings of spikes
below −25uV. LFP power spectra were computed by a Fast Fourier Transform (FFT) routine
for 4-sec epochs (Hanning window, 0.25 Hz resolution). Sleep stages were scored off-line
by visual inspection of 4-sec epochs, where the EEG, LFP, EMG and spike activity weredisplayed. Spike sorting was performed by PCA followed by SMEM clustering algorithm.
Population OFF periods in wake and NREM sleep were defined as periods with suppressedor absent neuronal activity. Recordings were performed continuously for 2–3 weeks. In eachanimal 2–4 experiments (at least 5 days apart) with 4h of sleep deprivation were performed,
one of which combined with the sugar pellet reaching task. For details about the analysis of
firing rates and neuronal population OFF periods, see Supplementary Information.

Supplementary Material
Refer to Web version on PubMed Central for supplementary material.

Acknowledgments
Supported by NIMH P20 MH077967 (CC), NIH Director’s Pioneer award (GT) and AFOSR FA9550-08-1-0244(GT). We thank Aaron Nelson, Mike Dash and Dr Ugo Faraguna for help with the experiments, Dr Lisa KrugnerHigby for advice about surgical procedures and Dr. Paolo Frumento for advice on statistical procedures.

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Figure 1. OFF periods in sleep and wake

Author Manuscripta, a ′, LFP records from frontal cortex and raster plots of corresponding multiunit activity(MUA; 6 putative neurons, each vertical line is a spike). b, time course of wake LFP slow/
theta power (2–6 Hz) for consecutive 5-min bins during 4h of sleep deprivation in one rat. b′, time course of LFP slow wave activity (0.5–4 Hz) plotted for consecutive 5-min bins
during 6h of recovery after sleep deprivation in the same rat. Note different Y-axis scale in b
and b′. Bottom: corresponding hypnograms. c, c′, LFP records in wake at the beginning(SD1) and end (SD4) of sleep deprivation, and in NREM sleep at the beginning (S1) and end(S6) of recovery. Bottom: corresponding MUA raster plots. d, d′, left, changes in OFF
periods and 2–6 Hz waves in wake, and of OFF periods and 0.5–4 Hz waves in NREM
sleep. Black lines: mean (SEM, n=11 rats); grey lines: individual rats. Triangles depict
significant differences (wake, OFF periods: F (1,21)=7.03, p=0.024; 2–6 Hz LFP waves: F

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Author Manuscript(1,21)=18.61, p=0.0015; NREM sleep, OFF periods: F (1,21)=10.40, p=0.009; 0.5–4 Hz
LFP waves: F (1,21)=34.83, p=1.5069e-004, fixed-effects model ANOVA).

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Figure 2. Local wake OFF periods

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a, top: wake LFP records in frontal (F) and parietal (P) cortex, depicting global or local
frontal 2–6 Hz waves (boxed); bottom, raster plots of corresponding MUA. a′, top: LFP
records in NREM sleep depicting global or local frontal slow waves (boxed); bottom: rasterplots of corresponding MUA. b, left and middle: change in global and local OFF periods
during sleep deprivation (mean, n=7 rats, 1–3 experiments/rat). Triangles here and in thenext panels depict differences at a significant (filled) or tendency (open) level (global OFF
periods: F(1,21)=94.95, p=0.0104; local OFF periods: F(1,21)=20.08, p=0.0464). Right:
number of global OFF periods (as % of local, F+P) during SD1 and SD4 (F(1,21)=67.05,
p=0.0146, fixed-effects model ANOVA). b′, left and middle: change in global and local
OFF periods during NREM sleep (mean, n=7 rats, 1–3 experiments/rat; global OFF periods:
F(1,21)=60.72, p=0.0161; local OFF periods: F(1,21)=11.56, p=0.0767). Right: number ofglobal OFF periods (as % of local, F+P) during S1 and S6 (F(1,21)=99.17, p=0.0099, fixedeffects model ANOVA). Note different Y-axis scale in b and b′. c, left and middle: changes
in global and local waves from SD1 to SD4 (mean, n=7 rats, 1–3 experiments per rat, SEM;
global waves: F(1,21)=34.08, p=0.0281; local waves: F(1,21)=28.54, p=0.0333). Right:
number of global waves (as % of local, F+P) during SD1 and SD4 (F(1,21)=52.53,
p=0.0185, fixed-effects model ANOVA). c′, left and middle: changes in global and local
waves during NREM sleep (mean, n=7 rats, 1–3 experiments/rat, SEM; global waves:F(1,21)=254.42, p=0.0039; local waves: F(1,21)=529.31, p=0.0019). Right: number ofglobal waves (as % of local, F+P) during S1 and S6 (F(1,21)= 37.38, p=0.0253, fixed-effects
model ANOVA). Note different Y-axis scale in c and c′.

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Figure 3. Wake OFF periods affect performancea, average neuronal activity in frontal cortex triggered by the OFF periods (mean, SEM, n=6
rats; shown as % of mean firing rate in the last 200 ms before the OFF period). b, top:
individual examples of frontal LFP records immediately preceding a successful or
unsuccessful reaching attempts (Hit or Miss, arrows); middle, raster plots of corresponding
MUA; bottom: instantaneous firing rates of the entire population (20 putative neurons). Notea generalized suppression of firing before Miss. c, number of OFF periods before Hits orMisses (frontal, n=6 rats; parietal, n=5 rats). Average values are plotted for consecutive
overlapping 300ms windows with 50ms shifts against the midpoint of the corresponding
window (e.g. the value at 500 ms depicts the number of OFF periods occurring between 350
and 650 ms). Squares show significant differences between Hits and Misses (grey: p<0.1,
black: p<0.05, rANOVA).

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Nature. Author manuscript; available in PMC 2011 October 28.