Sleep is a ubiquitous state in animals (Anafi et al., 2019) that is controlled by an ensemble of nuclei and their brain-wide interactions (Pace-Schott and Hobson, 2002; Sakai, 2020; Saper et al., 2010). In mammals (Cirelli, 2009), sleep is broadly comprised of two stages: non-rapid eye movement (NREM or slow-wave sleep) and rapid eye movement (REM or paradoxical sleep) (Weber and Dan, 2016). Each of these states is associated with distinct patterns of electrical activity in the brain. During NREM sleep, there are broad-band increases in the local field potential (LFP) power in the cortex that are modulated at <1 Hz (Amzica and Steriade, 1998; Datta and Maclean, 2007; Saper and Fuller, 2017). During REM sleep, gamma band power (nominally 30–100 Hz) is elevated in the cortex (Cantero et al., 2004; Le Van Quyen et al., 2010). In the hippocampus, REM sleep is also associated with a marked increase in power in the theta band (nominally 4–10 Hz) in rodents (Montgomery et al., 2008; Sullivan et al., 2014).

While the dynamics of neural activity in the cortex and other brain structures during sleep are well characterized, the cerebrovascular manifestations of sleep are less clear. Pioneering studies using positron-emission tomography (PET) or ¹³³Xenon in humans have suggested that cerebral blood flow (CBF) and metabolism is reduced during NREM sleep as compared with those during awake levels, and increased above said levels during REM sleep (Braun et al., 1997; Townsend et al., 1973), though the temporal and spatial resolutions of these techniques are poor. The degree to which CBF changes during the different sleep states appears dependent upon brain region (Madsen et al., 1991; Maquet and Phillips, 1998), complicating the interpretation of functional connectivity studies looking at correlations between brain regions where subjects may be transitioning among arousal states (Gu et al., 2019). Several fMRI studies have shown significant alterations in hemodynamic signals and functional connectivity during NREM sleep (Boly et al., 2012; Dang-Vu et al., 2008; Fukunaga et al., 2006; Horovitz et al., 2008; Larson-Prior et al., 2009; Mitra et al., 2015), suggesting that the blood oxygen level dependent (BOLD) signal changes during sleep. Because BOLD signals are generated by a complicated interplay of cerebral metabolism and changes in blood flow and volume (Kim and Ogawa, 2012), the vascular basis of these changes and their relation to neural activity are not well understood. Although functional ultrasound measures indicate cerebral blood volume rises during sleep (likely due to arterial dilation Mateo et al., 2017) and is correlated with hippocampal theta and gamma band power (Bergel et al., 2018), the relationship of cortical vascular changes to local cortical neural activity is unknown.

Understanding the vascular basis of hemodynamic signals during sleep is relevant to many aspects of brain health and function. First, BOLD signal changes during sleep are associated with movement of cerebrospinal fluid (CSF) (Fultz et al., 2019), and the movement of CSF is thought to play an important role in maintaining brain health (Simon and Iliff, 2016; Tarasoff-Conway et al., 2015; Xie et al., 2013). Elucidating the vascular changes associated with these fluid movements would help resolve the actual drivers of fluid movement. Secondly, there is accumulating evidence that arousal state transitions drive large hemodynamic changes, both in animals performing tasks (Cardoso et al., 2019), and in humans and animals undergoing ‘resting-state’ studies (Chang et al., 2016; Liu, 2016; Liu et al., 2018; Tagliazucchi and Laufs, 2014). If the hemodynamic signals during the periods of altered arousal are large or correlated enough, the activity during the sleep states could dominate the functional connectivity signal. Complicating mechanistic studies in mice is the fact that head-fixed mice do not close their eyes during NREM and REM sleep (Yüzgeç et al., 2018), meaning that without careful monitoring or a task it is possible that many studies examining ‘resting-state’ correlations in head-fixed mice were compounded by sleep.

Here we measured behavior, neural activity, blood volume and arteriole dilations from head-fixed mice during the awake state and NREM and REM sleep. We found that arteriole dilations and blood volume changes during NREM and REM sleep could be two to five times larger than those occurring in the awake animal. The correlations between neural activity and hemodynamic signals was greatly increased during NREM sleep, and the functional connectivity between interhemispheric regions of somatosensory cortex also increased.

We used intrinsic optical signal (IOS) (Huo et al., 2014; Winder et al., 2017) (14 mice, nine males) and 2-photon microscopy (Drew et al., 2011; Echagarruga et al., 2020; Shih et al., 2012a) (six mice, two males) in concert with electrophysiology to measure neural activity (Buzsáki et al., 2012; Harris et al., 2016) from the whisker representation of somatosensory cortex and the CA1 region of the hippocampus in un-anesthetized, head-fixed C57BL6/J mice (Figure 1A) during the light cycle. After mice were habituated to head-fixation, data was acquired from each mouse for 5–7 days. We obtained 357.2 total hours of data from these mice (mean: 23 ± 5.1 hr per mouse from IOS mice Supplementary file 1), 5.8 ± 2.0 hr per mouse from 2-photon imaged mice (Supplementary file 1). All experiments were performed during the animal’s light cycle. We tracked whisker position (Kleinfeld and Deschênes, 2011; O'Connor et al., 2010), body movement, and nuchal muscle EMG (Datta and Maclean, 2007; Veasey et al., 2000), as spontaneous ‘fidgeting’ behaviors drive a substantial portion of neural activity and hemodynamic signals in the awake mouse (Drew et al., 2019; Musall et al., 2019; Stringer et al., 2019; Winder et al., 2017), and these measures can be used to determine the arousal state of the animal. We recorded neural activity differentially across stereotrodes from the whisker representation of the somatosensory cortex and the hippocampus to reject non-local electrical signals (Buzsáki et al., 2012; Kajikawa and Schroeder, 2011; Nicholson and Freeman, 1975). For IOS data, we used a bootstrap aggregation random forest to determine the arousal state from these behavioral measures and hippocampal and cortical LFPs (see Materials and methods), categorizing every non-overlapping five second interval into one of three categories: rfc-Awake, rfc-NREM, or rfc-REM, where ‘rfc’ denotes the arousal state assigned via an automated random forest classifier. rfc-Awake periods were further characterized into awake rest (defined as periods lasting longer than 10 s that lack whisker and body movement), awake whisking (defined as bouts of whisking lasting between 2 and 5 s in duration), and awake stimulation (directed air puffs to contralateral whiskers). Each of these three awake behaviors were manually identified in rfc-Awake data to exclude transitional arousal states. Periods of contiguous NREM sleep and contiguous REM sleep were defined as events lasting at least 30 s and 60 s, respectively (Supplementary file 1). The 2-photon microscopy data was manually scored in a fashion similar to rfc-IOS data (Manual(m)-Awake, m-NREM, m-REM) and then further subdivided into awake rest, awake whisking, contiguous NREM, and contiguous REM (no whisker was stimulation presented during 2-photon experiments) (Supplementary file 1). As awake behaviors can be much shorter in duration than NREM and REM events, we used different durations when categorizing each arousal state. We chose the specific durations for each arousal state based on the typical minimum duration of each event, allowing us to catch the neural and vascular dynamics of each behavior. All reported values are mean ± standard deviation, unless otherwise indicated.

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Sleep drives larger fluctuations than awake behaviors

We first examined how arousal state affected hemodynamic signals using intrinsic optical signal imaging (Huo et al., 2014; Sirotin and Das, 2009; Vazquez et al., 2014), which detects changes in total hemoglobin [HbT] from changes in reflectance. Periods of awake rest without whisking or stimulation were set as the zero baseline (see Methods). Increases in blood volume (vasodilation) cause decreases in reflectance, which are converted into hemoglobin changes (∆[HbT]) using the Beer-Lambert law (Ma et al., 2016). These hemodynamic measurements were done bilaterally through a polished and reinforced thinned-skull window (Figure 1A; Drew et al., 2010a; Shih et al., 2012b) encompassing the whisker-related section of the somatosensory cortex (Petersen, 2007). The LFP was recorded from the vibrissa cortex within the window (Figure 1B,C) to provide a direct measurement of neural activity from whisker-related portions of somatosensory cortex from both hemispheres (Winder et al., 2017). To assist in arousal state classification, we also recorded the CA1 hippocampal LFP (Figure 1B,C), nuchal muscle electromyography (EMG), motion of the whiskers, body motion, and heart rate. Hemodynamic measurements were taken from a region of interest within a 1 mm diameter circle centered on the pixels that showed the highest cross-correlation between reflectance and gamma band power (1–2 s lag) (Winder et al., 2017) during the first 15–60 min of data (see Figure 1—figure supplement 1A–C). This period contains not just rest, but also whisking and some whisker stimulation. This differs from Winder et al., 2017, where only resting periods, lack whisking and stimulation, were used to define the ROI. These pixels corresponded to a region putatively within the vibrissa cortex and were consistent across imaging days. This is consistent with the hemodynamic point-spread function having full-width at half-max of several hundred microns (Vazquez et al., 2014) and the hyperemic response being conducted over several hundred microns (Rungta et al., 2018). The spatial scale of the measured hemodynamic signal is very close to that measured by the electrodes.

At the beginning of each day’s imaging session, we stimulated the vibrissae on either side with a brief puff of air, which drove canonical neural and vascular responses (Figure 1D). Consistent with previous work (Winder et al., 2017), gamma band power in the somatosensory cortex (30–100 Hz) increased by 78.1 ± 66.7%, followed by a 16.8 ± 5.1 µM increase in [HbT] (corresponding to a −2.4 ± 0.7% decrease in reflectance, ∆R/R, see Figure 1—figure supplement 2). Volitional, awake whisking led to an increase in gamma band power and [HbT] that increased with the duration of the whisking event. Brief (0.5–2 s), moderate (2–5 s), and extended (>5 s) whisking events lead to an increase of 5 ± 4.8%, 9.7 ± 7%, and 16.5 ± 10.8% in gamma band power in the somatosensory cortex. These changes led to an increase in [HbT] of 2.5 ± 1.4 µM, 7.1 ± 3.3 µM, 12.1 ± 5.6 µM respectively (see Figure 1—figure supplement 3).

In comparison to the sensory-evoked responses, we found much larger changes in [HbT] associated with sleep. NREM sleep is characterized by low EMG activity, lack of whisker and body movement, and pronounced power in the low-frequency bands of the cortical LFP (Figure 1E–J, Videos 1–3; Scammell et al., 2017; Vyazovskiy and Harris, 2013). REM sleep is characterized by neck muscle atonia, sporadic whisker movement, and increased theta band power in the hippocampus (Figure 1E–J; Hobson and Pace-Schott, 2002; Walker and Stickgold, 2004). During contiguous NREM sleep, we observed large oscillations in total hemoglobin, up to 87.3 ± 9.9 µM [HbT] in peak-to-peak amplitude, compared to 32.3 ± 4.4 µM during awake rest (generalized linear mixed-effects (GLME), p<9 × 10⁻³²). During contiguous REM bouts, there was a prolonged (>30 s) increase of [HbT] of up to 107.7 ± 13.3 µM, greatly surpassing the maximum of 17 ± 3.2 µM during awake rest (GLME, p<3.5 × 10⁻⁵⁵, see Figure 1—figure supplement 4A,B). Examples of sleep-related changes in blood volume are shown in Figure 1E–J and Figure 1—figure supplement 5, Figure 1—figure supplement 6, Figure 1—figure supplement 7, and Figure 1—figure supplement 8. Note that as previously observed (Yüzgeç et al., 2018), the eyes of the mouse are open during both REM and NREM sleep states (Figure 1G). These results show that sleep in head-fixed mice is associated with large increase in blood volume, particularly during REM sleep.

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Mice regularly enter into NREM and REM sleep during head-fixation

We then asked how often head-fixed mice sleep, and how the probability of sleep changes over time from the start of head-fixation. A hypnogram for a single mouse over 6 days with 5 s resolution is shown in Figure 2A. The white lines denote brief breaks in recording while data is saved. From this example, it is clear that the mouse has many periods of NREM and REM sleep interspersed with awake. Periods of REM sleep canonically follow NREM sleep (Saper et al., 2010). REM periods are typically followed by awakening, though REM sleep can be followed by NREM periods. The mean percentage of each classified state is shown in Figure 2B, and the breakdown for individual animals in Figure 2C. We noted no clear difference in (rfc-)Awake:NREM:REM sleep ratios between males and females, so they were pooled for other analyses. Plotting the probability of finding the mouse in each of the states as a function of time throughout the imaging session (Figure 2D) shows that as time goes by the mouse is more likely to be asleep. As awake mice typically whisk every ~10 s, we quantified the probability that the mouse had fallen asleep after a given period lacking whisking and body movement (‘Rest’) across all animals. We found that during only ~50% of ‘resting’ events lasting 10–15 s were the mice awake for the whole event, and with longer ‘resting’ events showing even lower probability of wakefulness (Figure 2E). This result is reminiscent of studies in humans showing the probability of being awake falls rapidly with time during a resting-state fMRI scan (Tagliazucchi and Laufs, 2014), though human sleep/wake behavior is much less fragmented than in the mouse, and the transition times are correspondingly longer. EMG activity (Figure 2F) was much lower during sleep than the awake state (see Figure 2—figure supplement 1A: rfc-NREM: p<3.2 × 10⁻¹², rfc-REM: p<1.7 × 10⁻²¹, GLME), though the amount of whisking (quantified as the variance in the whisker angle) was much smaller during rfc-REM and rfc-NREM sleep than during the awake state (Figure 2G, see Figure 2—figure supplement 1B: rfc-NREM: p<1.3 × 10⁻¹², rfc-REM: p<0.003, GLME). The heart rate was quantified by finding the peak in the intrinsic signal power spectrum between 5–15 Hz (Huo et al., 2015a; Huo et al., 2015b; Winder et al., 2017). Heart rate was lowest during rfc-NREM sleep (Figure 2H, see Figure 2—figure supplement 1C: rfc-NREM: p<1.8 × 10⁻¹³, rfc-REM: p<0.004, GLME), though heart rate during contiguous REM was comparable to heart rate in the awake, resting mouse, and the heart rate was elevated during awake whisking (Figure 2I, Awake Whisk: p<1.1 × 10⁻¹⁰, contiguous NREM: p<3.8 × 10⁻¹⁴, contiguous REM: p<0.0009, GLME). These observations demonstrate the prevalence of sleep in ‘resting-state’ data in head-fixed mice and show how unimodal measures, such as whisker movement or heart rate alone, are insufficient to detect these sleep states.

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Sleep drives arteriole dilatations much larger than those seen in the awake brain

The intrinsic optical signal contains contributions from arteries, veins, and capillaries (Huo et al., 2015a; Huo et al., 2015b; Zhang et al., 2019), so to better understand how arterioles changes contribute to the signal, we used two-photon microscopy (Shih et al., 2012a) to image pial and penetrating arterioles (25 and 4 arterioles respectively, from six mice) (Figure 3A). We recorded hippocampal and cortical LFP contralateral to the imaging window (Figure 3B,C). Two-photon imaging in awake mice has shown that sensory stimulation and locomotion drive dilations of arteries of approximately 20% above the baseline diameter (Drew et al., 2011; Echagarruga et al., 2020; Gao and Drew, 2016; Huo et al., 2015a; Huo et al., 2015b) and whisking drives dilations of 5–10% (Drew et al., 2020). We observed a similar dilation with spontaneous whisking as a function of whisking duration. Brief (0.5–2 s), moderate (2–5 s), and extended (>5 s) whisking events led to an increase in vessel diameter of 0.8 ± 0.9%, 8.3 ± 4.9%, and 10.9 ± 4.3% respectively (Figure 3D, see Figure 3—figure supplement 1). Examples of sleep-related changes in arteriole diameter is shown in Figure 3E–I and Figure 3—figure supplement 2, Figure 3—figure supplement 3, Figure 3—figure supplement 4, and Figure 3—figure supplement 5. In comparison to the awake state, arteriole diameter during NREM sleep follows a low-frequency dilation/constriction with peak dilations that can exceed those seen during moderate whisking (Figure 3D). During REM sleep, the arterioles slowly dilate over tens of seconds, and can reach peak dilations in excess of 50% of the baseline diameter during periods of awake rest. The dilation amplitudes during NREM and REM sleep dwarf those seen in the awake animals. Penetrating arterioles had similar sleep-wake dynamics as the pial arterioles (see Figure 3—figure supplement 2 vs Figure 3—figure supplement 3) and were combined into a single group for all analysis. These results show that both NREM and REM sleep cause pronounced cortical arteriole dilations. Contiguous NREM was associated with peak-to-peak diameter changes of 38 ± 15.8%, compared to 16.6 ± 4% seen during awake rest (GLME, p<3.6 × 10⁻¹⁰). Contiguous REM drove a prolonged ramp-up to peak dilation of 49.9 ± 9.1% compared to a peak dilation of 7.4 ± 2.6% during the awake resting baseline (GLME, p<2 × 10⁻²⁴, see Figure 1—figure supplement 4C,D). These large dilations were followed by a pronounced and rapid constriction upon waking.

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Hemodynamic changes associated with transitions between arousal states

To quantify the dynamics of sleep-related changes in blood volume and arteriole diameter, we looked at the dynamics of these signals, as well as LFP and EMG signals, aligned to the arousal state transition (Figure 4A). We used transitions between two arousal states, where time in each arousal state was at least 30 s in duration. Mammals will typically progress through the Awake-NREM-REM-Awake pattern of the sleep cycle (Saper et al., 2010), even if the awake periods between the end of a REM event and the initiation of the subsequent NREM period are very brief. The transition from the rfc-Awake state into rfc-NREM (Figure 4B) shows an increase in total hemoglobin from the baseline of very low [HbT] in the awake state, up to 30 µM over the course of 30 s. During this time, the EMG power decreases with similar temporal dynamics. The LFP power in the vibrissa cortex shows increased power in the delta band [1–4 Hz] of around 300%. The transition from rfc-NREM into the rfc-Awake state (Figure 4C) was largely a temporally reversed version of the awake to NREM transition, where [HbT], LFP, and EMG signals all quickly return to baseline values as the animal wakes up. During the transition from rfc-NREM to rfc-REM (Figure 4D) there was a slow increase in total hemoglobin from 45 µM up to 75 µM. During the NREM-REM transition, the muscles become atonic and the EMG power decreased even more. The theta band [4–10 Hz] power in the hippocampal LFP increased by approximately 300%. The transition from rfc-REM into the rfc-Awake state (Figure 4E) was the largest in terms of the magnitude of the total hemoglobin change, as the large blood volume increase seen during REM rapidly reverses (within seconds) as the animal wakes up. Transitions from awake to REM, as well as REM to NREM are possible, but are much less common, and did not occur often enough to quantify reliably. The amplitude and temporal dynamics of ∆[HbT] transitions between each arousal state were consistent across imaging days (see Figure 4—figure supplement 1).

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When looking at single arterioles during the transition from NREM to REM, arterioles will on average go from an approximate 20% dilation, up to approximately 40% dilation after a minute or so in REM (Figure 4F). Upon waking from REM, arterioles constrict back to the baseline diameter within a few seconds (Figure 4G). When the arterial diameter and ∆[HbT] are plotted together, they show very similar temporal dynamics during the NREM to REM transition and during the REM to awake transition (Figure 4F,G). The close match in dynamics suggests that arteriole dilations are a significant driver of the blood volume changes during sleep, as is seen in the awake brain (Huo et al., 2015a; Rungta et al., 2018).

Cortical hemodynamic signals increase during NREM and REM sleep

We quantitatively compared hemodynamic signals during different arousal states. For this quantification, we used awake resting events ≥ 10 s in duration, awake whisking events 2–5 s in duration, brief stimulations of the whiskers, contiguous NREM sleep events ≥ 30 s in duration, and contiguous REM sleep events ≥ 60 s in duration. The average ∆[HbT] during each day’s awake resting condition was set as zero (n = 14 mice, 28 hemispheres). Awake whisking events between 2 and 5 s in duration caused a slight increase in [HbT] of 3.1 ± 2.5 µM (GLME, p<0.11). The increase caused by whisking and fidgeting, which are the primary drivers of resting-state neural and hemodynamic signals in the awake mouse (Drew et al., 2019; Drew et al., 2020; Musall et al., 2019; Stringer et al., 2019; Winder et al., 2017), were dwarfed by those that occurred during contiguous NREM sleep (32.2 ± 11.2 µM, GLME, p<1 × 10⁻³⁴) and during contiguous REM sleep (77.1 ± 11.9 µM, GLME, p<1 × 10⁻⁷⁶). These sleep-driven changes were much larger than sensory-evoked changes generated by contralateral whisker stimulation (12.9 ± 5.6 µM, GLME, p<4.9 × 10⁻¹⁰). The probability distribution of ∆[HbT] during each arousal state is shown in Figure 5D. There was a clear and pronounced separation in the hemodynamic signals between the sleep and awake states, as well as between contiguous NREM and REM sleep. However, the increases in [HbT] during sleep were smaller than those caused by the vasodilator isoflurane (Flynn et al., 1992; Gao et al., 2015; Figure 5—figure supplement 1), showing the vessels are not maximally dilated during sleep.

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The average dilation of arterioles during each arousal state followed the same trend as the ∆[HbT] data (Figure 5B). Awake whisking events spurred an average diameter increase of 4.2 ± 3.2% (GLME, p<0.0006, n = 6 mice, 29 arterioles), with contiguous NREM sleep dilating 9.4 ± 9.3% (GLME, p<8.7 × 10⁻¹⁹, n = 6 mice, 21 arterioles) and contiguous REM sleep reaching dilations of 32.7 ± 8.6% (GLME, p<5 × 10⁻³⁴, n = 5 mice, 10 arterioles). The probability distribution of arteriole ∆D/D during each arousal state with data taken from all animal’s events is shown in Figure 5E Recordings of changes in blood flow measured using laser Doppler flowmetry (∆Q/Q) followed the same trend (Figure 5C) (n = 8 mice, one mouse was excluded due to a weak signal), with the largest flow increases during sleep. Awake volitional whisking events caused negligible increases of 0.4 ± 2.6% (GLME,  p<0.91). Flow changes were more prominent during contiguous NREM sleep, 14.4 ± 9.1% (GLME, p<0.0004), and even more-so during contiguous REM sleep, 28.3 ± 12.8% (GLME, p<1.5 × 10⁻⁸). The probability distribution of ∆Q/Q during each arousal state with data taken from all animal’s events is shown in Figure 5F. These measurements of blood volume, arteriole diameter, and flow all show a consistent and pronounced trend of vasodilation in the somatosensory cortex during sleep which far surpassed those seen during volitional behaviors and sensory-evoked stimulation in the awake animal.

Neurovascular coupling is strongest during NREM sleep

It has previously been shown that neurovascular coupling is similar across awake arousal states (Winder et al., 2017) and that both spontaneous and sensory-evoked hemodynamics are most strongly correlated with gamma band power and multi-unit average (MUA – a measure of local spiking activity) in the absence of overt stimulation (Mateo et al., 2017; Schölvinck et al., 2010; Shmuel and Leopold, 2008; Winder et al., 2017). To see if these relationships held true for neurovascular coupling in other arousal states, we looked at the relationship between the power in different frequency bands of the LFP or multi-unit activity and ∆[HbT] during periods of contiguous NREM and REM sleep. Correlations between the MUA and ∆[HbT] as well as the LFP and ∆[HbT] during awake rest showed a peak correlation of 0.24 ± 0.06 for the MUA and 0.16 ± 0.04 for the gamma band, consistent with previous work (Winder et al., 2017; Mateo et al., 2017). The hemodynamic response lagged the MUA by 1.16 ± 0.12 s and the gamma band power by 1.15 ± 0.13 s, respectively (Figure 6A, see Figure 6—figure supplement 1). The peak cross-correlation during contiguous NREM sleep was nearly double that of awake rest: MUA: 0.44 ± 0.06 (GLME, p<1.2 × 10⁻²³ compared with awake rest); gamma band: 0.3 ± 0.05 (GLME, p<8 × 10⁻²³ compared with awake rest). During NREM, there were similar dynamics in the lag of the hemodynamic response to the MUA as during awake rest (1.28 ± 0.11 s, GLME, p<0.08) and for the gamma band power (1.26 ± 0.12 s, GLME, p<0.15) (Figure 6B). The peak cross-correlations during contiguous REM sleep were significantly higher than those during awake rest for the MUA (0.32 ± 0.07, GLME, p<4.8 × 10⁻⁷), but not for gamma band power (0.15 ± 0.05; GLME, p<0.22). During REM there was a slight, but significant increase in the lag of the hemodynamic response to the MUA (1.49 ± 0.49 s, GLME, p<1.2 × 10⁻⁵) and for the gamma band power (1.4 ± 0.47 s; GLME, p<0.0016) (Figure 6C). However, the distribution of correlations across frequencies differed across states. In addition to a highly correlated gamma band, the correlations during contiguous NREM extended down into the beta band [13–30 Hz]. These results show that the correlation between neural activity and cerebral blood volume change markedly across states, though the hemodynamic lag differences are of order a few hundred milliseconds. We found weak to negative correlations for frequencies below 30 Hz in the awake state and during contiguous REM. In contrast, there were strong positive correlations between lower frequencies of LFP power and the [HbT] in contiguous NREM sleep. This suggests that the positive correlations between lower frequencies of the LFP and vasodilation seen in some studies may be due to the inclusion of NREM sleep in the ‘resting-state’.

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Neural and hemodynamic correlations across hemispheres increase during sleep

We then explored how the correlations and coherency of left and right hemisphere [HbT] and gamma band power were affected by arousal state. When quantifying signals in the frequency domain, the lowest resolvable frequency will be the inverse of the event duration. Because there were very few periods of awake rest greater than 10 s in duration (see Figure 2E), the lowest frequency for resting periods we could characterize was 0.1 Hz (Mitra and Pesaran, 1999). We thus examined all 15 min periods from our data without whisker stimulation that had at least 12 min (>80%) of rfc-Awake arousal state classifications as alert, as an extension of the awake resting and awake whisking arousal state into the lower frequencies. We also examined all 15 min periods without whisker stimulation that had at least 12 min (>80%) of rfc-NREM or rfc-REM arousal state classifications as asleep to extrapolate the contiguous NREM/REM arousal states into the lower frequencies. Note that the asleep condition contains some awake state and transitions, which may explain the lower power in the gamma band envelope above 0.1 Hz than the pure REM and NREM conditions. Lastly, we took every 15 min period without whisker stimulation, regardless of random forest classified arousal state, as all data, which has an arousal state distribution similar to Figure 2B and is more representative of the type of data obtained if behavioral monitoring and arousal state classification were not used.

The power spectrum of ∆[HbT], normalized to the peak power in the resting condition, is shown in Figure 7D (see Table 1, see Figure 7—figure supplement 1E,F). For [HbT], there was substantially more power in the lower frequencies for nearly all arousal states. The lower power of the ∆[HbT] power spectra at higher frequencies were consistent with the lowpass nature of the hemodynamic response (Drew, 2019; Silva et al., 2007; Winder et al., 2017; de Zwart et al., 2005). We examined the power in two frequency bands, 0.1 Hz (the ‘vasomotion’ frequency Mateo et al., 2017), and the ultra-low 0.01 Hz band. The power at 0.1 Hz was highest during contiguous NREM (awake rest: 0.9 ± 0.1 (A.U.); contiguous NREM: 6.7 ± 3.4, GLME vs. awake rest, p<7.6 × 10⁻¹⁹), and was also higher during contiguous REM than during awake rest (contiguous REM: 4.1 ± 2.6, GLME vs. awake rest, p<1.3 × 10⁻⁷). Extending into the ultra-low frequencies, power in the ∆[HbT] signal at 0.01 Hz was significantly higher in the asleep state than the alert state (176.2 ± 98.9 vs. 36.5 ± 28.6, GLME, p<5.3 × 10⁻¹⁰). Similar low-frequency dominated power spectra were seen in the diameters of arterioles, with the power at 0.1 Hz during contiguous NREM (4.5 ± 3.9, GLME, p<2.3 × 10⁻⁸) and contiguous REM (5.1 ± 5.7, GLME, p<2.7 × 10⁻⁷) also exceeding those seen during awake rest (0.9 ± 0.1). There was not enough single arteriole data that met the inclusion criteria for the asleep condition for 0.01 Hz (Figure 7G, Table 1, see Figure 7—figure supplement 1I,J).

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Table 1

Spectral Power at 0.1 Hz	Awake Rest	Cont. NREM	Cont. REM	Alert	Asleep	All Data
Gamma band Power (a.u.)	0.9 ± 0.1	13.3 ± 37.4 (p<0.01)	12.8 ± 23.4 (p<0.02)	1.3 ± 0.8 (p<0.93)	6.2 ± 8 (p<0.29)	3.6 ± 2.5 (p<0.58)
∆[HbT] Power (a.u.)	0.9 ± 0.1	6.7 ± 3.4 (p<7.6 × 10−19)	4.1 ± 2.6 (p<1.3 × 10−7)	2.2 ± 1.3 (p<0.03)	4.7 ± 2.3 (p<1.3 × 10−9)	3.8 ± 1.6 (p<1.3 × 10−6)
∆D/D Power (a.u.)	0.9 ± 0.1	4.5 ± 3.9 (p<2.7 × 10−10)	3.8 ± 3.7 (p<2.1 × 10−5)	3.4 ± 2.1 (p<9.3 × 10−7)		3.4 ± 1.9 (p<5.8 × 10−7)

1. Mean ± 1 standard deviation. p-values as a comparison to ‘Rest’. Shaded region indicates insufficient data for that arousal state.

Table 2

Spectral Power at 0.01 Hz	Alert	Asleep	All Data d
Gamma band Power (a.u.)	5 ± 3.2	34 ± 51 (p<0.001)	21.7 ± 20.6 (p<0.06)
∆[HbT] Power (a.u.)	36.5 ± 28.6	176.2 ± 98.9 (p<5.3 × 10−10)	128.9 ± 63.8 (p<7.9 × 10−6)
∆D/D Power (a.u.)	34.9 ± 25		45.6 ± 33.6 (p<0.06)

1. Mean ± 1 standard deviation. p-values as a comparison to ‘Alert’. Shaded region indicates insufficient data for that arousal state.

Previous functional imaging studies have observed marked increases in hemodynamic activity and functional connectivity between brain regions during periods of sleep (Boly et al., 2012; Dang-Vu et al., 2008; Fukunaga et al., 2006; Horovitz et al., 2008; Larson-Prior et al., 2009; Mitra et al., 2015), but how these hemodynamic changes relate to neural activity was unknown.

We then looked at gamma band power, which of all neural signals is most closely related to hemodynamic changes in the awake brain (Echagarruga et al., 2020; Mateo et al., 2017; Schölvinck et al., 2010; Winder et al., 2017). The fluctuations in the gamma band power had substantial power at all frequencies (Figure 7A, Table 1, see Figure 7—figure supplement 1A,B), consistent with previous recordings in primates (Leopold et al., 2003). Fluctuations in the gamma band power envelope at frequencies near 0.1 Hz have been proposed to drive vasomotion and underly functional connectivity (Mateo et al., 2017), and lower frequency fluctuations in the envelope (in the ~0.01 Hz range) could underly changes in connectivity at these frequencies (Chang and Glover, 2010). The power in the gamma band envelope at 0.1 Hz was substantially higher during sleep (awake rest: 0.9 ± 0.1; contiguous NREM sleep: 13.3 ± 37.4, GLME, p<0.01; contiguous REM Sleep: 12.8 ± 23.4, GLME, p<0.02). All power normalized by the peak in the power spectrum during rest. There was a similar increase in the gamma band power fluctuations at 0.01 Hz during sleep relative to the awake condition (alert: 5 ± 3.2; asleep: 34 ± 51, GLME, p<0.001). During sleep, the modulations of gamma band power at 0.1 and 0.01 Hz were much larger than during the awake state.

To quantify the relationship of neural activity or [HbT] between the left and right hemispheres, we looked at the magnitude of the coherence squared (MoC2) (Figure 7B, Tables 3 and 4, see Figure 7—figure supplement 1C,D). We used MoC2 because it tells us the amount of variance explained in one signal by the other for any given frequency, making MoC2 equivalent to the R² between two signals at any given frequency (Drew et al., 2020). The MoC2 between left and right hemisphere gamma band power at 0.1 Hz was relatively low. In the 0.01 Hz frequency band, left-right somatosensory cortex gamma band power MoC2 was higher. Interestingly, for all conditions, the MoC2 for the gamma band power was substantially lower than the MoC2 of [HbT] in the same frequency band (Tables 3 and 4).

Table 3

Coherence2 at 0.1 Hz	Awake Rest	Cont. NREM	Cont. REM	Alert	Asleep	All Data
Gamma band (Coherence2)	0.08 ± 0.06	0.26 ± 0.14 (p<4.8 × 10−8)	0.16 ± 0.1 (p<0.006)	0.13 ± 0.08 (p<0.04)	0.2 ± 0.11 (p<0.0002)	0.19 ± 0.09 (p<0.0004)
∆[HbT] (Coherence2)	0.61 ± 0.11	0.86 ± 0.05 (p<1.2 × 10−16)	0.77 ± 0.08 (p<5.7 × 10−10)	0.69 ± 0.14 (p<0.002)	0.82 ± 0.07 (p<2.9 × 10−13)	0.78 ± 0.06 (p<2.6 × 10−10)

1. Mean ± 1 standard deviation. p-values as a comparison to ‘Rest’.

Table 4

Coherence2 at 0.01 Hz	Alert	Asleep	All Data
Gamma band (Coherence2)	0.38 ± 0.27	0.69 ± 0.18 (p<7.2 × 10−6)	0.61 ± 0.21 (p<0.0002)
∆[HbT] Power (Coherence2)	0.82 ± 0.15	0.97 ± 0.02 (p<1.5 × 10−5)	0.95 ± 0.03 (p<7.5 × 10−5)

1. Mean ± 1 standard deviation. p-values as a comparison to ‘Alert’.

The MoC2 between left and right cortical somatosensory cortex ∆[HbT] was uniformly high (Figure 7E, Tables 3 and 4, see Figure 7—figure supplement 1G,H), though it was higher during contiguous NREM sleep (0.86 ± 0.05, GLME, p<1.2 × 10⁻¹⁶) and contiguous REM sleep (0.77 ± 0.08, GLME, p<5.7 × 10⁻¹⁰) as compared to awake rest (0.61 ± 0.11). low-frequency ∆[HbT] MoC2 during the asleep state reached near-unity at 0.01 Hz (0.97 ± 0.02, GLME, p<1.5 × 10⁻⁵), continuously surpassing those seen during the alert state (0.82 ± 0.15). To compare these results to functional connectivity measures, we evaluated the Pearson’s correlation coefficient between bilateral ∆[HbT] during each arousal state (Figure 7F, Table 5). The correlation between the left and right hemisphere ∆[HbT] was 0.74 ± 0.06 during the awake resting condition. They were elevated during awake whisking 2–5 s in duration (0.82 ± 0.09, GLME, p<4.8 × 10⁻⁸), during periods of contiguous NREM (0.9 ± 0.03, GLME, p<9.8 × 10⁻²⁰) and contiguous REM (0.91 ± 0.03, GLME, p<1.4 × 10⁻²¹). These correlations, however, were not fully explained by the correlations in underlying neural activity, which were much lower. This may be explained by a lower signal-to-noise ratio for neural signals. The correlation coefficients for the envelope of bilateral gamma band power [30–100 Hz] (Figure 7C, Table 5) were relatively small during rest (0.16 ± 0.07), and were not significantly elevated during awake whisking (0.15 ± 0.05, GLME, p<0.47) or during contiguous REM sleep (0.21 ± 0.13, GLME, p<0.11). However, correlations in gamma band power doubled during contiguous NREM sleep (0.3 ± 0.12, GLME, p<6.6 × 10⁻⁷). Analysis of the cross-hemisphere delta band [1–4 Hz], theta band [4–10 Hz], alpha band [10–13 Hz], and beta band [13–30 Hz] power showed similar trends as the gamma band, with elevated power, MoC2, and Pearson’s correlations during contiguous NREM/REM sleep compared to awake rest (see Figure 7—figure supplement 2, Figure 7—figure supplement 3, Supplementary file 1). The exception was the delta band, where the sleeping MoC2 and Pearson’s correlations were not statistically different than those during rest. These results show large changes in gamma band power during sleep, but relatively low cross-hemisphere correlations in neural activity. In contrast, the correlation coefficients for the left and right ∆[HbT] were uniformly high (Table 3), and significantly elevated by awake whisking. Contiguous NREM and REM sleep substantially elevated the ∆[HbT] correlations.

Table 5

Pearson’s Correlation Coef.	Awake Rest	Whisking	Cont. NREM	Cont. REM	Alert	Asleep	All Data
Gamma band (R)	0.16 ± 0.07	0.15 ± 0.05 (p<0.47)	0.3 ± 0.12 (p<6.1 × 10−7)	0.21 ± 0.13 (p<0.11)	0.26 ± 0.1 (p<0.0004)	0.35 ± 0.14 (p<4.7 × 10−10)	0.33 ± 0.11 (p<8.5 × 10−9)
∆[HbT] (R)	0.74 ± 0.06	0.82 ± 0.09 (p<3 × 10−8)	0.9 ± 0.03 (p<3.6 × 10−20)	0.91 ± 0.03 (p<4.9 × 10−22)	0.87 ± 0.07 (p<7.9 × 10−15)	0.96 ± 0.02 (p<8.1 × 10−28)	0.93 ± 0.02 (p<1.1 × 10−24)

1. Mean ± 1 standard deviation. p-values as a comparison to ‘Rest’.

All and all, these result show that sleep enhances the already strong cross-hemisphere coherency and correlation in ∆[HbT]. The increases in ∆[HbT] during sleep are larger than those during whisking/fidgeting behavior, which are the primary drivers of spontaneous hemodynamic signals in the awake mouse (Drew et al., 2019; Drew et al., 2020; Winder et al., 2017). Sleep increases the cross-hemisphere correlations and coherency of the LFP envelope as well, but the neural correlations are substantially lower than the hemodynamic correlations.

Impact of arousal state on total blood volume and neural-vascular coherence

To understand how well we can determine the arousal state (Awake/NREM/REM) from the changes in hemodynamic measurements (∆[HbT] or ∆D/D), we plotted the probability that the animals were in a given arousal state for a given amount of ∆[HbT] (Figure 8A) or ∆D/D (Figure 8B). Using only [HbT], the strength of the coupling between spontaneous neural activity and changes in blood volume is relevant to resting-state fMRI. Many studies have looked into this coupling (Ma et al., 2016; Mateo et al., 2017; Schölvinck et al., 2010; Shmuel and Leopold, 2008; Winder et al., 2017) but the measured strength of this coupling varies from study to study (Drew et al., 2020). One possible contribution to the varying strength of the coupling observed between neural and hemodynamic signals could be changes in the arousal state. When solely looking at changes in arteriole diameter (∆D/D), the variability in arteriole diameter during NREM sleep make it difficult to deduce arousal state. Fluctuations in arteriole diameter during NREM may appear similar to volitional whisking events, which occur every 10–15 s (Drew et al., 2020; Winder et al., 2017).

Figure 8 with 1 supplement see all

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The coherence between [HbT] and the gamma band power in various arousal states is shown in Figure 8C (other LFP bands are shown in Figure 8—figure supplement 1). Coherence at lower frequencies was highest in contiguous NREM sleep and for all data, and lowest during awake rest and during contiguous REM sleep. It may seem puzzling that the coherence is higher when taking all the data together than for any individual subset, but this can be explained by a restriction of range effect when grouping by arousal state. In Figure 8D, we show the ∆[HbT] versus the power in the gamma band for all of our ‘rfc’ data including stimulation. When all the data is considered together, it is clear that there was a robust relationship between neural activity and blood volume. However, as the level of neural activity and [HbT] were different across the arousal states, within any given state this relationship may not be so strong, again due to a restriction of range effect. All in all, we would like to emphasize the observation that correlations between ongoing neural activity and hemodynamic signals appear to be lowest during the awake state and highest during sleep (Figure 8e) and is most prominent in the lower frequencies.

Using optical imaging and electrophysiology in head-fixed mice, we found that sleep was associated with large increases in blood volume and arterial dilation in the somatosensory cortex. These vasodilations and increases in cerebral blood volume were very large, up to five times the size of those evoked by sensory stimulation or fidgeting behaviors like whisking. We found that the correlation and coherence between neural activity and blood volume were substantially stronger during NREM sleep than during any other arousal state, as were bilateral correlations and coherency. Because, at least under our imaging conditions, mice frequently and rapidly enter into sleep, the hemodynamic signals due to sleep will dominate over any awake signals due to the very large changes in neural activity and blood volume in the sleeping cortex. The net effect is that, without careful monitoring of arousal state, sleep-related hemodynamics and neural activity will dominate any ‘resting-state’ study in the un-anesthetized mouse.

We note that care should be taken in interpreting these results. The sleep patterns and depth may be altered by head-fixation. We only imaged cortical blood volume changes, and other areas of the brain may have different patterns of vasodilation (Braun et al., 1997; Townsend et al., 1973). We used electrophysiology to assay neural activity as it is used in humans (Buzsáki et al., 2012), and calcium indicators buffer intracellular calcium, can cause epilepsy, and do not report the majority of spikes even under optimal imaging conditions (McMahon and Jackson, 2018; Steinmetz et al., 2017; Theis et al., 2016). The activity of interneurons that express neuronal nitric oxide synthase (nNOS) can drive vasodilation without causing detectable changes in the LFP (Echagarruga et al., 2020; Krawchuk et al., 2020; Lee et al., 2020), so the overall neural activity captured in the LFP may not reflect the activity of neurons that drive hemodynamic responses. We note there may be a difference in sleep-state identification with local LFP recordings when compared with conventional EEG, which sum electrical signals over a larger area. Previous studies in rats have shown discrete regions of cortex going into periods of ‘localized sleep’ (Vyazovskiy et al., 2011), and in some cases, slow-wave oscillations can be seen during REM sleep in the cortex (Funk et al., 2016), either of which may reduce the accuracy of our arousal state classifications. We noted no arousal state discrepancies between left and right somatosensory cortices during manual sleep scoring, however, this does not exclude localized sleep in other cortical regions. The spatial resolution of IOS and our electrodes are of order 100 µm, and any vascular or neural changes on smaller spatial scales would be ‘blurred’, potentially resulting in lower estimates of neurovascular coupling strength. Additionally, as hemoglobin is the major absorber of both visible and infrared light in the brain, and changes in the level of hemoglobin can cause decreases in florescence signals (Haiss et al., 2009; Shen et al., 2012), sleep-related vasodilations attenuate signals from calcium indicators in neurons when visualized with either 1- or 2-photon methods. As there are multiple pathways by which neurons (and astrocytes) communicate with the vasculature (Attwell et al., 2010; Drew, 2019; Iadecola, 2017), it is likely that multiple mechanisms underly the vasodilation we observed during sleep (Özbay et al., 2018).

There are several implications for our work. First, it shows that it is critical to monitor arousal state, particularly in head-fixed mice, as they frequently fall asleep. Arousal state cannot be detected by simple monitoring of the face/eyes, as mice can sleep with their eyes open (Yüzgeç et al., 2018), and thus requires additional methods such as a combination of EMG/LFP/whisker tracking. If an animal falls asleep, this will result in larger neurovascular and bilateral correlations than in the awake state. There has been some disagreement as to how well hemodynamic signals track neural activity in the absence of any sensory stimulation (the ‘resting-state’) (Drew et al., 2020). With a few exceptions (e.g. Winder et al., 2017), these studies did not carefully monitor arousal state, and it could be that studies finding higher neurovascular correlations have episodes of sleep in them. Similar issues have been noted for human resting-state fMRI studies (Tagliazucchi and Laufs, 2014) and with non-human primates (Cardoso et al., 2019; Chang et al., 2016), so this is likely a ubiquitous problem with any experiment where the subject is unmotivated to stay awake. On a more fundamental level, these results show that the vasodilation seen in the awake brain is smaller than the hemodynamic changes seen during sleep. This vasodilation during sleep may serve to circulate cerebrospinal fluid (Aldea et al., 2019; Fultz et al., 2019; Kedarasetti et al., 2020; van Veluw et al., 2020) or some other physiological role.

Source data and code for generation of all figures can be found here: Code repository location:https://github.com/DrewLab/Turner_Gheres_Proctor_Drew_eLife2020 (copy archived at https://archive.softwareheritage.org/swh:1:rev:a8318fc5cb29b88504fe72f5d3d80867bd9791f2/) Data repository location: https://doi.org/10.5061/dryad.6hdr7sqz5.

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Author details

1. Kevin L Turner

   1. Department of Biomedical Engineering, The Pennsylvania State University, University Park, United States
   2. Center for Neural Engineering, The Pennsylvania State University, University Park, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3044-7079

2. Kyle W Gheres

   1. Center for Neural Engineering, The Pennsylvania State University, University Park, United States
   2. Graduate Program in Molecular, Cellular, and Integrative Biosciences, The Pennsylvania State University, University Park, United States

   Contribution

   Resources, Software, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7568-9023

3. Elizabeth A Proctor

   1. Department of Biomedical Engineering, The Pennsylvania State University, University Park, United States
   2. Center for Neural Engineering, The Pennsylvania State University, University Park, United States
   3. Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, United States
   4. Department of Neurosurgery, Penn State College of Medicine, Hershey, United States
   5. Department of Pharmacology, Penn State College of Medicine, Hershey, United States

   Contribution

   Formal analysis, Supervision, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7627-2198

4. Patrick J Drew

   1. Department of Biomedical Engineering, The Pennsylvania State University, University Park, United States
   2. Center for Neural Engineering, The Pennsylvania State University, University Park, United States
   3. Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, United States
   4. Department of Neurosurgery, Penn State College of Medicine, Hershey, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Visualization, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   PJD17@PSU.EDU

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7483-7378

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