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Stability of neocortical synapses across sleep and wake states during the critical period in rats

  1. Brian A Cary
  2. Gina G Turrigiano  Is a corresponding author
  1. Department of Biology, Brandeis University, United States
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Cite this article as: eLife 2021;10:e66304 doi: 10.7554/eLife.66304

Abstract

Sleep is important for brain plasticity, but its exact function remains mysterious. An influential but controversial idea is that a crucial function of sleep is to drive widespread downscaling of excitatory synaptic strengths. Here, we used real-time sleep classification, ex vivo measurements of postsynaptic strength, and in vivo optogenetic monitoring of thalamocortical synaptic efficacy to ask whether sleep and wake states can constitutively drive changes in synaptic strength within the neocortex of juvenile rats. We found that miniature excitatory postsynaptic current amplitudes onto L4 and L2/3 pyramidal neurons were stable across sleep- and wake-dense epochs in both primary visual (V1) and prefrontal cortex (PFC). Further, chronic monitoring of thalamocortical synaptic efficacy in V1 of freely behaving animals revealed stable responses across even prolonged periods of natural sleep and wake. Together, these data demonstrate that sleep does not drive widespread downscaling of synaptic strengths during the highly plastic critical period in juvenile animals. Whether this remarkable stability across sleep and wake generalizes to the fully mature nervous system remains to be seen.

Introduction

Sleep is a widely expressed behavior, present in animals as evolutionarily distant as Cassiopea jellyfish and the Australian dragon lizard (Nath et al., 2017; Shein-Idelson et al., 2016). Despite its ubiquity and long history of scientific study, sleep – and more broadly the function of brain states – remains deeply mysterious. Sleep disruption can perturb learning and memory consolidation (Walker and Stickgold, 2004; Krause et al., 2017), presumably through the modulation of synaptic plasticity (Frank and Cantera, 2014). However, there is little agreement on the nature of this regulation. Researchers have variously proposed that sleep stabilizes, strengthens, weakens, or even prunes synapses (Kavanau, 1996; Datta et al., 2008; Chauvette et al., 2012; de Vivo et al., 2017; Li et al., 2017). Further, there is disagreement on whether sleep primarily enables correlation-based plasticity mechanisms such as long-term potentiation (LTP), or homeostatic forms of plasticity that serve the function of regulating overall synaptic strength to stabilize neuron and circuit function (Frank and Cantera, 2014; Aton et al., 2014; Hengen et al., 2016). Finally, it is unclear whether sleep is merely permissive for some forms of synaptic plasticity (Torrado Pacheco et al., 2021), or whether being asleep is by itself sufficient to induce synaptic changes without a preceding salient learning event (Tononi and Cirelli, 2014). Here, we use a combination of real-time sleep classification, ex vivo measurements of postsynaptic strength in two neocortical areas and two different cell types, and in vivo optogenetic monitoring of evoked thalamocortical transmission in primary visual cortex (V1), to ask whether sleep and wake states constitutively drive widespread synaptic plasticity within neocortical circuits.

One influential hypothesis, the synaptic homeostasis hypothesis (SHY), has motivated much work on the role of sleep in brain plasticity (Tononi and Cirelli, 2014). SHY proposes that memories are formed during wake when animals actively sample their environment, primarily through the induction of Hebbian LTP-like mechanisms that causes a net potentiation of synapses. This process would saturate synapses if left unopposed (Miller and MacKay, 1994; Abbott and Nelson, 2000; Turrigiano and Nelson, 2004), and sleep is proposed to be an offline state that allows neurons to renormalize synaptic weights by downscaling synaptic strengths (Tononi and Cirelli, 2014). This renormalization is postulated to be a global process that affects all or most synapses in many brain regions (Tononi and Cirelli, 2014). Critically, SHY predicts that excitatory synapses should on average be stronger after a period of wake, and weaker after a period of sleep. A feature of non-rapid eye movement (NREM) sleep is slow wave activity (SWA), in which the electroencephalogram (EEG) expresses large, low-frequency waves (<4.5 Hz, Dijk, 2009). It is well established that SWA is high during NREM immediately after prolonged wake and progressively decreases as sleep pressure diminishes (Dijk, 2009). SHY additionally proposes that the size of slow waves is correlated with cortical synaptic strengths, and that the oscillation in SWA with sleep and wake is driven by sleep-dependent synaptic downscaling that then diminishes SWA (Tononi and Cirelli, 2014; Tononi, 2009).

Studies supporting SHY have found that the expression of proteins associated with synaptic potentiation are higher, axon-spine interfaces (a corollary of synaptic strength) are larger, and evoked transcallosal cortical responses increase in slope after a period of wake (Vyazovskiy et al., 2008; de Vivo et al., 2017; de Vivo et al., 2019; Diering et al., 2017). These studies were conducted on animals ranging from early postnatal (de Vivo et al., 2019) to juvenile (Liu et al., 2010; Spano et al., 2019) to adult (Vyazovskiy et al., 2008). Importantly, these changes were observed without introducing any salient learning experiences for the animal, suggesting that simply being asleep or awake reduces or increases net synaptic weights, respectively. In contrast, other recent studies have found that synaptic transmission is potentiated by sleep (Chauvette et al., 2012) or is unaffected by sleep deprivation (Matsumoto et al., 2020). If excitatory synapses indeed oscillate in strength across sleep and wake states, then the firing rate of individual neurons would be expected to oscillate as well; while some studies have found such oscillations (but with variable effects between brain regions and neuronal populations; Vyazovskiy et al., 2009; Miyawaki and Diba, 2016; Miyawaki et al., 2019; Watson et al., 2016), others have observed stable firing across periods of sleep and wake (Hengen et al., 2016; Torrado Pacheco et al., 2021).

Together, these studies paint a complex picture of the possible roles of sleep and wake states in modulating synaptic plasticity. Some discrepancies between studies are likely due to methodological differences; for instance, not all studies clearly differentiate between effects of sleep and circadian cycle, carefully classify sleep states and prior sleep history, or directly measured synaptic strengths. Here, we set out to carefully test the hypothesis that neocortical synaptic weights weaken during sleep and strengthen during wake, using both ex vivo slice physiology and in vivo monitoring of synaptic strengths and evoked spiking. We used in vivo behavioral state classification in real time to track the accumulation of sleep and wake, which allowed us to detect natural sleep- or wake-dense epochs that occurred within a defined 5 hr circadian window (zeitgeber time [ZT] 3–8). We then immediately cut slices and measured miniature excitatory postsynaptic currents (mEPSCs) from pyramidal neurons in V1 or prefrontal cortex (PFC; specifically, prelimbic and infralimbic cortex). We found that mEPSC amplitudes were stable across sleep- and wake-dense epochs in both brain regions. To extend this finding to evoked transmission, we used in vivo optogenetics to monitor thalamocortical synaptic drive to visual cortex across natural sleep-wake epochs. Again, we found that thalamocortical synaptic drive and evoked spiking were stable across even prolonged periods of sleep and wake. Together, our data show that neocortical synaptic strengths are remarkably stable across naturally occurring periods of sleep and wake, indicating that sleep does not drive widespread constitutive weakening of excitatory neocortical synaptic strengths.

Results

Sleep/wake behavior of juvenile Long-Evans rats

We wished to compare several functional measures of neocortical synaptic strength after natural periods of prolonged waking or sleeping. Previous studies in support of sleep-dependent regulation of synaptic strength in rodents have spanned a wide age range, from early postnatal to juvenile to adult (de Vivo et al., 2019; Spano et al., 2019; de Vivo et al., 2017; Liu et al., 2010; Vyazovskiy et al., 2008). Here and in subsequent experiments, we used juvenile rats (postnatal days 25–31) to allow for high-quality endpoint slice recordings, and to ensure that our experiments were performed within a highly plastic developmental window when Hebbian and homeostatic plasticity are known to be pronounced (Lambo and Turrigiano, 2013; Hengen et al., 2016; Smith et al., 2009; Espinosa and Stryker, 2012).

To this end we first analyzed behavioral data from animals in this age range, to understand their natural rhythms of sleep and wake across zeitgeber time (ZT), and determine when they are likely to experience prolonged periods of sleep or wake (Figure 1A–D; see Figure 1—figure supplement 1 for breakdown of states). We monitored electromyograms (EMGs), EEG, and video, and used standard approaches to classify sleep states into wake and rapid eye movement (REM) or NREM slow wave sleep, as described previously (Hengen et al., 2016; Torrado Pacheco et al., 2019; Torrado Pacheco et al., 2021; see Materials and methods). In some experiments, we also differentiated active and quiet wake, as noted below. As expected, animals on average slept more during the light phase (ZT 0–12, ~60% asleep) than during the dark phase (ZT 12–24, ~40% asleep, Figure 1B,D; Frank and Heller, 1997; Frank et al., 2017); however, there was considerable variability from day to day and animal to animal in when they experienced periods enriched in sleep or wake (defined as a 4 hr period with >65% of time in that state, Figure 1A, compare animals 1 and 2). Another prominent feature of sleep is the modulation of slow wave amplitude by prior sleep history (Dijk, 2009; Tononi and Cirelli, 2014). As expected, the average power in the delta band (0.5–4 Hz), a proxy for slow wave amplitude (Dijk, 2009; Vyazovskiy et al., 2008), rose as net time spent awake accumulated in the dark (Figure 1C), and dropped during the day when animals typically spent more time sleeping (Figure 1B,C). These changes in delta power over ZT are similar to those seen in adult rodents (Leemburg et al., 2010). Thus, these animals exhibit homeostasis in slow wave amplitude across sleep and wake cycles, which SHY suggests is causally linked to synaptic downscaling (Tononi and Cirelli, 2014; Tononi, 2009).

Figure 1 with 2 supplements see all
Characterization of sleep/wake behavior in juvenile Long-Evans rats.

(A) Two example sleep/wake histories from two different animals. The y axis is a moving mean showing percent of time spent awake in the previous 4 hr; x axis is zeitgeber time (ZT) in hours. Periods where the animal is in a sleep-dense epoch (>65% time asleep in previous 4 hr) are colored blue, while >65% wake in previous 4 hr are colored green. Red arrows indicate points showing differing sleep/wake-dense experiences between animals at the same ZT. (B) Average percentage time spent awake in the previous 4 hr as a function of ZT. Light green shading indicates the standard deviation between animals; n = 30 animals. (C) Normalized delta power from the electroencephalogram (EEG) of animals in (B) as a percent of baseline (hours 7–11 in the light period). Shading represents SEM between animals. (D) Average time spent in each behavioral state shown as a proportion for each animal broken down by light period (left) and dark period (right); each point represents one animal. Mean and SEM shown, n = 30 animals.

Real-time sleep classification

To study the impact of sleep and wake on synaptic efficacy, it is necessary to disentangle sleep from circadian effects. Since prolonged natural periods of wake and sleep tend to occur during distinct circadian periods, many studies have accomplished this by sacrificing animals at the same ZT (typically several hours after lights on) and comparing animals that have slept naturally to those that were continuously kept awake via sleep deprivation (Liu et al., 2010; Diering et al., 2017; de Vivo et al., 2017; de Vivo et al., 2019). However, the animal-to-animal and cycle-to-cycle variability in the timing of enriched sleep means that even when animals are sacrificed at the exact same ZT, there are likely to be considerable differences in their recent history of sleep and wake (e.g. compare animals 1 and 2 several hours after lights on, red arrows in Figure 1A). This prompted us to develop an approach to classify sleep and wake in real time, so we could reliably detect concentrated periods of sleep or wake within a defined circadian window for each animal, and then sacrifice them to probe synaptic strengths ex vivo.

To classify behavioral state in real time, we collected EEG, EMG from the nuchal (neck) muscles, and video as they freely behaved in an enriched environment. The recording chamber included a littermate separated by a thin, transparent plexiglass wall with nose-poke holes to allow for social interactions, toys for stimulation and play, and food and water available ad libitum. The data were acquired, analyzed, and plotted all within a custom program, which uses canonical markers to classify behavioral state into NREM, REM, or wake (Figure 2A). Briefly, the program computes a delta/beta and a theta/delta ratio from the corresponding frequency bands in the EEG (delta: 0.5–4 Hz; theta: 5–8 Hz; beta: ~20–35 Hz). Large deflections in the EMG are z-scored and animal pixel movement is extracted from the video recording. The classifier reads in these variables and applies a simple semi-automated decision tree, which uses three manually adjustable thresholds (delta, theta, and movement thresholds). Given what is known about the probability of sleep transitions, the classifier then adjusts these thresholds based on recent state history (additional details in Materials and methods). With this technique, we could determine the sleep/wake state of the animal with an accuracy of 93.3% when benchmarked against manual coding (Figure 2D). All classifications made in real time were manually verified post hoc. We could specify the length and required sleep or wake density for an epoch to pass threshold; for this set of experiments we chose threshold values that were within the range used by others to test SHY (Liu et al., 2010; Diering et al., 2017; Vyazovskiy et al., 2009; de Vivo et al., 2019): 4 hr of >65% within a state, with the last hour >70% within the state. The 4 hr requirement was chosen because it is the longest span of time the rats naturally spent in a sleep-dense epoch in a typical day, and the last hour threshold was to ensure that synaptic differences were not rapidly reversed by recent behavioral state changes. The average density of sleep epochs that exceeded threshold and were included in the mEPSC analysis was 69%; the average wake density during the light period was 76% and during the dark period was 71%. NREM bouts were also more consolidated (longer) in the sleep-dense epoch as compared to wake (sleep 161.53 ± 10.4 s; light period, wake 120.97 ± 7.8 s; dark period, wake 122.16 ± 9.6 s; mean ± SEM).

Figure 2 with 1 supplement see all
Real-time behavioral classification.

(A) One hour of behavioral data illustrating variables used for classification of sleep/wake state, described in descending order. Top shows expanded raw electroencephalogram (EEG) traces from the indicated periods; scale bar next to non-rapid eye movement (NREM) example, 1 V, 0.5 s. Below expanded EEG is the sleep/wake classification expressed as a hypnogram, where colored bars indicate periods of wake (green), NREM (blue), or rapid eye movement (REM) (magenta). Beneath the hypnogram is the full raw EEG trace; scale is 2 V. Spectral frequency (Spect. Freq.) plots the full spectrogram of the EEG from 0 to 20 Hz, colored by power (normalized to maximum). Extracted features of the Spect. Freq. are plotted below: delta(0.5–4 Hz)/beta(20–35 Hz) power ratio (shown on log scale) is high during periods of NREM, while the theta(5–8 Hz)/delta(0.5–4 Hz) power ratio is high during periods of REM. Absolute electromyogram (EMG) values were normalized and expressed as standard deviation. Finally, animal movement in pixels is plotted. The bottom four plots of vigilance state features each have an adjustable threshold for state classification, shown as dashed line; points above the threshold are shown as colored dots. (B) Schematic of real-time classifier rig. (C) 3D plot of vigilance state features: delta ratio, theta ratio, and movement measures (zero movement assigned lowest observed value for log axis) colored by brain state. Data from example animal. (D) Accuracy of real-time classifier compared to manual scoring by state: overall (% time matching between all three states)=93.3%; wake = 94.2%; NREM = 93.2%; REM = 90.5%; wake dense (matching wake dense on and off times)=96.0%; sleep dense = 96.0%.

We wished to compare three conditions: sleep-dense and wake-dense epochs that ended during a similar circadian period (ZT 3–8, Figure 3A,B), and wake-dense epochs that ended during the opposite circadian period (dark period, ZT 13–16, Figure 3C); this three-way comparison allowed us to look for differences driven by sleep/wake (sleep and wake at the same ZT) and differences driven by circadian time (wake at opposite ZTs). Spontaneous wake-dense epochs were infrequent in the light period, so when we detected a long natural wake epoch during the early light phase we added new toys to the chamber (e.g. red arrow in Figure 3B, top panel) to encourage additional waking. This was sufficient to extend natural waking epochs to reach threshold for wake dense within the specified circadian window (Figure 3B, Figure 3—figure supplement 1).

Figure 3 with 2 supplements see all
Stability of miniature excitatory postsynaptic current (mEPSC) amplitude after prolonged periods of sleep or wake.

The left column represents example sleep histories for the VC L2/3 experiments while right column shows mEPSC characteristics for the indicated cell types and brain regions. (A) Top figure, hypnograms for three animals used for sleep-dense mEPSC recordings. Green bars indicate instances of wake, magenta indicates rapid eye movement (REM), and blue non-rapid eye movement (NREM). Bottom, this same behavioral state data represented as a moving average of % time spent awake in the preceding 4 hr; the dashed lines represent the thresholds for achieving the required density in a given state, and the dots represent endpoints for slice recordings. (B) and (C) are same as for (A), but for wake-dense recordings performed during the light and dark periods, respectively. Red arrow indicates wake encouragement (introduction of toy). Black circle around endpoint dot indicates which trace is represented by example hypnogram. (D) Traces on the left, example mEPSC recordings from L2/3 of primary visual cortex (V1); scale bar, 10 pA, 1 s. On the right, cell average mEPSC events; scale bar, 5 pA, 5 ms. (E) On left, cell average mEPSC amplitudes from L2/3 V1; error bars = 95% CI of mean (p=0.17; Kruskal-Wallis). (E) On right, cumulative histogram of sampled mEPSC amplitudes (SD – L. WD p=0.075, L. WD – D. WD p>0.9, SD – D. WD p=0.77; Kolmogorov-Smirnov (KS) test with Bonferroni correction). Inset for (E)–(G) is a peak scaled average mEPSC waveform; scale bar, 50% peak, 5 ms. Sleep dense n = 17 cells, three animals; L. wake dense n = 25 cells, four animals; D. wake dense n = 18 cells, five animals. (F) On left, cell average mEPSC from L4 V1 (p=0.52; Kruskal-Wallis). (F) On right, cumulative mEPSC amp. from L4 V1 (p=0.99; KS test). Sleep dense n = 47 cells, six animals; L. wake dense n = 32 cells, four animals. (G) On left, cell average mEPSC amplitudes from L2/3 prefrontal cortex (PFC) (p=0.13; Kruskal-Wallis). (G) On right, cumulative histogram of sampled mEPSC amplitudes (SD – L. WD p>0.9, L. WD – D. WD p=0.096, SD – D. WD p=0.015; KS test with Bonferroni correction). Sleep dense n = 31 cells, six animals; L. wake dense n = 28 cells, five animals; D. wake dense n = 18 cells, six animals.

Postsynaptic strengths are stable across extended periods of sleep or wake

In order to assess whether sleep induces a global downscaling of postsynaptic strengths, we tracked sleep/wake history in real time, cut acute slices once an animal finished a sleep- or wake-dense epoch (Figure 3), and performed whole-cell patch electrophysiology to record mEPSCs onto pyramidal neurons in two brain regions (V1 and PFC) and two layers (L4 and L2/3, see below for rationale for these targets). Ex vivo slice physiology is an established method to probe experience-dependent changes in synaptic strengths (Khurana and Li, 2013; Lambo and Turrigiano, 2013; Nataraj et al., 2010Heynen et al., 2003; Miska et al., 2018), and mEPSC amplitudes are a standard readout of synaptic up- or downscaling and are a direct correlate of postsynaptic strength (Turrigiano, 2008). The sleep history (expressed as hypnograms that show the time in each sleep/wake state) of each animal included in our mEPSC analysis is shown in Figure 3A–C, top traces, and in Figure 3—figure supplement 1. Another way of visualizing an individual animal’s sleep/wake history is to generate a moving average of the percent of time spent awake in the last 4 hr (Figure 3A–C, bottom traces; Figure 3—figure supplement 1); this illustrates when in ZT each animal passed the threshold for our endpoint mEPSC analysis. Using this approach we were able to measure mEPSC amplitudes at similar circadian time points from animals that had spent the majority of the proceeding 4 hr either awake or asleep, with minimal perturbation (Figure 3A,B); and from animals with similar wake experience at the opposite circadian time (Figure 3C).

We first recorded from L2/3 pyramidal neurons in V1 (Figure 3D). We chose visual cortex as it is highly plastic during this developmental window, which is within the classic visual system critical period (Espinosa and Stryker, 2012). Further, homeostatic mechanisms are present and are able to up- and downscale mEPSC amplitudes onto L2/3 pyramidal neurons during this developmental window (Kaneko et al., 2008; Lambo and Turrigiano, 2013; Hengen et al., 2013; Torrado Pacheco et al., 2021). When we computed the mean mEPSC amplitude by cell and compared this across conditions, we found no differences in amplitude after sleep-dense or wake-dense epochs in the light phase (sleep and L. wake), or wake-dense epochs in the dark period (D. wake; Figure 3D,E). Here and below, cellular properties such as input resistance and resting potential were not different between conditions; mEPSC kinetics (rise times, decay tau, and scaled waveforms) were also not different between conditions. To examine more closely the mEPSC amplitude distribution in the different conditions, we randomly selected the same number of events from each neuron and plotted individual mEPSC amplitudes as a cumulative distribution (Figure 3E, right). There were no significant differences in the amplitude distribution between conditions. Thus neither prior sleep history nor circadian time significantly modulated mEPSC amplitude onto visual cortical L2/3 pyramidal neurons.

We next examined mEPSCs onto pyramidal neurons in L4 of V1. L4 pyramidal neurons receive extensive monosynaptic input from thalamic relay neurons, which vary their firing patterns dramatically during sleep (Steriade, 2001; Steriade and Timofeev, 2003), and these thalamocortical synapses are also highly plastic at this time (Cooke and Bear, 2010; Miska et al., 2018). Similar to our observations in L2/3, mEPSC amplitudes were indistinguishable after wake-dense or sleep-dense epochs, and there was no difference in the cumulative amplitude distribution between conditions (Figure 3F).

To determine whether this stability in postsynaptic strength was evident outside of primary sensory cortex, we next examined mEPSCs onto L2/3 pyramidal neurons in PFC. The PFC shows a strong modulation of SWA (Leemburg et al., 2010), has been suggested to be especially impacted by extended wake (Muzur et al., 2002; Jones and Harrison, 2001), and a previous study reported weaker synapses within PFC after sleep (Liu et al., 2010). However, consistent with our findings in V1, mean mEPSC amplitude was also stable across sleep- and wake-dense epochs in PFC and was not significantly different after extended wake at opposite circadian periods (Figure 3G, left). The cumulative amplitude distributions following extended sleep or wake in the light phase were indistinguishable (Figure 3G, right), but interestingly there was a small but significant rightward shift at the high end of this distribution in the D. wake condition (SD − D. wake, p=0.015). This suggests that there is a small circadian modulation of mEPSC amplitude onto L2/3 pyramidal neurons in PFC.

We next examined the frequency of mEPSC events. mEPSC frequency can be influenced by many factors (Han and Stevens, 2009; Wierenga et al., 2006) and is generally more variable between neurons than amplitude (Lambo and Turrigiano, 2013; Hengen et al., 2013; Liu et al., 2010; Figure 4). We found no significant differences in mean mEPSC frequency across any of the conditions or brain regions examined here (Figure 4). Inter-event intervals were not different between conditions in visual cortex L2/3 or L4. In PFC inter-event intervals were shorter in L. wake compared to both D. wake and L. sleep; thus there may be some interaction between time of day and sleep/wake state on mEPSC frequency in PFC, which does not generalize to visual cortex.

Impact of prolonged sleep/wake on miniature excitatory postsynaptic currents (mEPSC) frequency.

mEPSC frequency is not consistently modulated by sleep/wake history across brain regions. (A) Data from visual cortex L2/3. Left plot, cell average mEPSC frequencies for the indicated conditions; error bars = 95% CI of mean (p=0.75, Kruskal-Wallis). Right plot, cumulative plot of inter-event interval (SD − L. WD p=0.35, SD − D. WD p=0.15, L. WD − D. WD p>0.9; Kolmogorov-Smirnov (KS) test with Bonferroni correction). Sleep dense n = 17 cells, three animals; L. wake dense n = 25 cells, four animals; D. wake dense n = 18 cells, five animals. (B) Same as in (A) but data from visual cortex L4; cell average on left (p>0.9, Kruskal-Wallis); cumulative plot on right (p=0.45, KS test). Sleep dense n = 47 cells, six animals; L. wake dense n = 32 cells, four animals. (C) Same as in (A) but data from prefrontal cortex L2/3. Left, cell average values for the indicated conditions (p=0.056, Kruskal-Wallis). Cumulative plot of inter-event interval (SD − L. WD p<1e-6, SD − D. WD p=0.74, L. WD − D. WD p=0.007; KS test with Bonferroni correction). Sleep dense n = 31 cells, six animals; L. wake dense n = 28 cells, five animals; D. wake dense n = 18 cells, six animals.

Because we have the full behavioral history of each animal prior to preparing brain slices, we could examine potential influences on mEPSCs beyond the duration of sleep or wake. There was a reasonable range of wake densities (from 65% to 90%) within the wake-dense condition, which allowed us to evaluate possible correlations between wake density and amplitude or frequency of mEPSCs; neither of these were significantly correlated (Figure 3—figure supplement 2). Further, we could test an important proposed relationship between changes in slow wave amplitude and excitatory synapses: according to SHY, a larger reduction in slow wave amplitude (i.e. decrease in delta power) should correlate with weaker synapses (Tononi and Cirelli, 2014; Tononi, 2009). However, when we plotted the decrease in delta power during prior sleep against the endpoint mEPSC amplitude or frequency, we again found no relationship (Figure 3—figure supplement 2A,B). Together, our data show that postsynaptic strength in a range of excitatory neocortical cell types is remarkably stable across prolonged periods of sleep or wake.

Thalamocortical evoked field EPSPs are stable over the course of sleep and wake

mEPSC amplitude is a useful measure of postsynaptic strength, but does not take into account potential changes in presynaptic function, and cannot be used to follow synaptic strengths prospectively across sleep and wake epochs. To complement the mEPSC measurements, we therefore designed a paradigm to measure functional synaptic strength continuously in vivo across sleep and wake epochs in freely behaving animals. In order to evoke synaptic responses in visual cortex from a defined population of inputs, we took advantage of our ability to express ChR2 in thalamocortical terminals within V1 (Miska et al., 2018) by virally expressing ChR2 (AAV-ChR2-mCherry) in dorsal lateral geniculate nucleus (dLGN) of the thalamus, which projects extensively to L4 of V1. After viral injection into dLGN, animals were given 1.5–2 weeks to allow for expression and transport of ChR2 to thalamocortical axon terminals in V1 (Figure 5A), and we then implanted linear 16-channel silicon probes into V1 with adhered optic fibers that rested on the cortical surface. With this configuration, we could reliably evoke thalamocortical responses with 1 ms pulses of blue light in freely behaving animals (Figure 5B–E). The linear design of the probe with exact electrode spacing, combined with current-source density analysis (Figure 5B) and post hoc histology, allowed us to estimate the placement of electrical sites with high confidence. These evoked responses were similar to those evoked by electrical stimulation of LGN, as well as visually evoked potentials (Cooke and Bear, 2010; Niell and Stryker, 2008). The amplitude of the first negative trough of this evoked response has the shortest latency in L4 (Figure 5B–D), where it arises primarily from monosynaptic thalamic excitatory synapses onto L4 neurons (Khibnik et al., 2010; Cooke and Bear, 2010), and thus represents an evoked field excitatory postsynaptic potential (fEPSP). We monitored and analyzed the amplitude and slope of this short latency fEPSP (Figure 5B–G, Figure 6—figure supplement 1) as a readout of functional thalamocortical synaptic efficacy in L4.

Thalamocortical (TC) evoked synaptic responses in freely behaving animals.

(A) AAV-Chr2-mCherry viral expression targeted to dorsal lateral geniculate nucleus (dLGN) projects to V1. Left, coronal slice showing viral expression in magenta. Right, visual cortex showing neuronal nuclei marked with NeuN and ChR2-mCherry in magenta. (B) Evoked field excitatory postsynaptic potentials (fEPSPs) recorded with a linear silicon array show clear layer-specific waveforms consistently with TC afferent location. (C) Current source density heat plot identifies layers; cool colors indicate current sink. (D) fEPSP latency to first negative peak identifies layers. Red circles represent latency of individual events; black line represents mean for each channel. (E) fEPSPs recorded over 3 days in a single freely behaving animal. Amplitude of the response plotted and colored by brain state. Dark periods (lights off) are represented by gray rectangles. (F) fEPSP waveforms by brain state, where each waveform is from a different animal; normalized to QW peak (indicated by dashed line) for each animal. Scale bar, 50% of peak amplitude, 10 ms. (G) fEPSP amplitudes across circadian time, normalized to quiet wake in the light period. n = 4 animals.

To chronically monitor the amplitude of thalamocortical fEPSPs, we used light intensities below the peak saturation of responses (~50% of the peak response), and very low stimulation frequencies (1/20–1/40 Hz) to avoid phototoxicity or the induction of plasticity from the stimulation itself. With these parameters, we were able to measure stable fEPSPs continuously for several days (Figure 5E), while simultaneously monitoring the sleep/wake state of the animal. An interesting feature of these recordings is that fEPSP amplitude was rapidly modulated by behavioral states (Figure 5E,F), as previously reported (Vyazovskiy et al., 2008; Matsumoto et al., 2020). To more fully capture behavioral state modulation of the fEPSP, we further classified wake into quiet wake and active wake using local field potential (LFP) and video data (Figure 2—figure supplement 1). We found that evoked fEPSPs were largest on average during NREM, of similar size during quiet wake and REM, and smallest during active wake (Figure 5F). These changes in fEPSP amplitude manifested rapidly upon transitions between states (Figure 5E), consistent with a rapid neuromodulatory effect (Lee and Dan, 2012; Brown et al., 2012; Matsumoto et al., 2020). A second feature of these fEPSPs is that they were of consistently higher amplitude in all behavioral states in the light than in the dark; there were no obvious circadian oscillations beyond a rapid change in amplitude at the transitions between light and dark periods (Figure 5G).

SHY predicts that there should be a gradual decrease in the size of the fEPSPs during sleep, while wake should produce a gradual increase (Vyazovskiy et al., 2008). To test this, we evoked fEPSPs continuously at low frequency, identified sleep- or wake-dense epochs (using either a >65% or >75% time-in-state threshold, Figure 6A), and separately measured the fEPSP amplitude during each of the four behavioral states (REM, NREM, active wake, and quiet wake; Figure 6). We then plotted fEPSP amplitude across sleep-dense epochs measured either in NREM or REM (Figure 6B,C) and across wake-dense epochs measured in active or quiet wake (Figure 6D,E). Using a 65% threshold (Figure 6B–E, left column), we were able to isolate natural sleep-dense epochs of up to 4.75 hr and wake-dense epochs of up to 12.5 hr in duration; with a more stringent >75% threshold (Figure 6B–E, right column), these durations were somewhat shorter; up to 2.75 hr for sleep dense and 9 hr for wake dense.

Figure 6 with 1 supplement see all
Stability of thalamocortical field excitatory postsynaptic potential (fEPSP) amplitude during prolonged periods of sleep or wake.

Time series data of thalamocortical evoked fEPSPs over the course of either sleep- or wake-dense epochs. (A) Example hypnograms showing wake- (left) and sleep- (right) dense epochs. Gray rectangles indicate times in which stimulations were analyzed for this example. (B) fEPSP amplitudes during sleep-dense epochs, measured in non-rapid eye movement (NREM) (as illustrated by box around hypnogram on left). Epochs passing the >65% threshold are shown on left, and those passing the >75% threshold are shown on right. fEPSP amplitude was normalized to the beginning of each epoch. Each dot is data from one epoch, averaged over 10 min bins; n = 4 animals. Blue line = mean, shading = SEM, black dotted line = 1 (i.e. no change). (C) Same as (B) but for rapid eye movement (REM) events. (D) Same as (B) but normalized fEPSP amplitude during wake, measured during active wake. (E) Same as (D) but for quiet wake events.

Strikingly, we saw no progressive changes in fEPSP amplitude during prolonged sleep (Figure 6B,C) or wake (Figure 6D,E) using either density threshold, even for the longest duration epochs. This stability of the responses was evident regardless of which state we measured fEPSPs in Figure 6. As fEPSP amplitude estimates can be contaminated by polysynaptic or spiking activity occurring after the evoked monosynaptic thalamocortical synaptic potentials, we repeated this analysis using a second standard measure of fEPSP magnitude, the slope of the initial downward deflection. Consistent with the amplitude measurements, the slopes did not change across sleep and wake states (Figure 6—figure supplement 1).

As a second means of probing for sleep- or wake-induced changes in the fEPSP, we designed a ‘sandwich’ method for detecting changes across extended periods of sleep, by measuring the amplitude in flanking periods of wake separated by an extended period of sleep, and vice versa for extended periods of wake (Figure 7A); all measurements were made in NREM for flanking periods of sleep, and quiet wake for flanking periods of wake, and the results were identical when we instead used REM and active wake, respectively. For this analysis, we defined dense epochs as >30 min periods when the animal was >75% asleep/awake and identified all such epochs for each animal. Using this sandwich approach, we again found that fEPSP amplitudes were largely stable across periods of sleep or wake (Figure 7B,C; <2% change), indicating that the intervening periods of sleep and wake produce no persistent change in synaptic strength. When we plotted the change in fEPSP amplitude against the duration of the intervening sleep or wake epoch, we found no significant correlation between epoch duration and change in fEPSP amplitude (Figure 7D,E). Taken together with our mEPSC data, these experiments show that synaptic efficacy onto L4 excitatory neurons is stable across extended periods of sleep and wake.

Thalamocortical field excitatory postsynaptic potential (fEPSP) amplitude measured before and after prolonged periods of sleep or wake.

(A) Hypnograms showing an example of a wake- (left) and sleep- (right) dense epoch, with flanking periods of the opposite state. Gray rectangles indicate when measurements were taken for comparison. (B) Example fEPSP waveforms before and after a wake- (left) or sleep- (right) dense epoch. (C) Normalized change in fEPSP amplitude after wake or sleep (wake change, p>0.9; sleep change, p>0.9; Wilcoxon signed-rank test). (D) Correlative plot of duration of wake-dense epoch against normalized change in fEPSP amplitude. Black line = linear fit; R2 value and p-value included on plot. Inset in top right is an expansion of plot between 0 and 3.5 hr. (E) Same as (D) but for sleep dense. n = 39 wake-dense epochs, 55 sleep-dense epochs, four animals.

The ability of thalamocortical inputs to evoke spikes is stable over the course of sleep and wake

Although thalamocortical fEPSP amplitudes were not modulated by time spent awake or asleep, it is possible that the ability of thalamocortical synapses to evoke spiking in postsynaptic V1 neurons – a readout of functional connection strength – could be modified by prolonged periods of sleep or wake. In addition to recording fEPSPs, our optrodes allowed us to sample spikes in V1 evoked by stimulation of thalamocortical inputs (Figure 8A). Using spike sorting as described (Hengen et al., 2016; Torrado Pacheco et al., 2021), we were able to follow spiking in individual regular-spiking (putative pyramidal) neurons over the course of sleep and wake epochs. Although the probability of evoking a spike for any given stimulus was low, when averaged across many stimuli we found that most well-isolated units had a detectable response to the thalamocortical stimulation (Figure 8A,B). Consistent with the rapid brain-state modulation of fEPSP amplitude, there were more evoked spikes on average during NREM, an intermediate number during REM, and fewer during wake (Figure 8B). To determine whether evoked firing changed as a result of time spent asleep or awake, we used the same ‘sandwich’ approach we used for the fEPSP data to compare the magnitude of evoked firing in flanking periods of wake separated by an extended period of sleep, and vice versa for extended periods of wake (Figure 8C–E). We averaged the response for each isolated unit across all sleep- or wake-dense epochs (defined as >30 min periods when the animal was >75% asleep/awake), and found no significant differences in evoked spiking (Figure 8E).

Characterization of thalamocortical evoked spiking in visual cortex between behavioral states and its stability across sleep and wake.

(A) Heat plot response of the firing rate of each cell isolated during stimulation. Data are grouped by layer and sorted by the magnitude of response within each layer. n = 45 cells, four animals. (B) Evoked spikes per stimulation for each of the four vigilance states. The y axis represents the average number of evoked spikes for a single stimulation, calculated as spikes in a 15 ms post-stimulus window, minus the expected number of baseline spikes in that window; each dot represents a cell. Evoked spikes varied significantly by vigilance state (AW-QW, p=3.4e-4; AW-NREM, p=6.56e-4; AW-REM, p=4.70e-4; QW-NREM, p=0.0022; QW-REM, p=0.0122; NREM-REM, p=0.0380). (C) To determine whether evoked spike rates were affected by time spent awake or asleep, we compared evoked spiking before and after a sleep- or wake-dense epoch, using a >75% time in state threshold, as for field excitatory postsynaptic potentials (fEPSPs) in Figure 7. Hypnograms show examples of wake- or sleep-dense epochs with flanking periods of the opposite state. Gray rectangles indicate the stimulation times chosen for the analysis. (D) Example raster plots and post-stimulus time histograms of firing rate for an example cell pre-post sleep. (E) The number of evoked spikes before and after wake- (left) or sleep- (right) dense epochs; each dot is one cell, averaged across all of the wake- or sleep-dense epochs (wake dense n = 32 cells, four animals; sleep dense n = 24 cells, four animals). (F) Correlative plot of duration of wake-dense epoch against change in evoked spikes; each dot represents the response of a single cell to a single epoch (post – pre). Black line = linear fit; R2 value and p-value included on plot. Inset is an expansion of plot between 0.25 and 3.5 hr. (G) Same as (F) but for sleep-dense epochs. n = 35 wake-dense epochs, 50 sleep-dense epochs, four animals.

Next, we took advantage of the variation in epoch durations to look for a correlation between the length of an epoch and the change in number of evoked spikes. We plotted the change in evoked spikes for each unit for each epoch, and found no significant correlation between duration of a sleep/wake epoch and ability of the thalamocortical stimulation to elicit spikes. Taken in concert with the fEPSP results, this shows that time spent asleep or awake does not cause progressive changes in the efficacy of thalamocortical synaptic transmission in vivo.

Discussion

It has been postulated that patterns of brain activity during sleep induce a widespread downscaling of excitatory synaptic strengths throughout CNS circuits, but the evidence for this has been contradictory. Here, we used three complementary measures of functional synaptic strengths, coupled with real-time sleep classification in freely behaving animals, to ask whether simply being asleep is able to constitutively drive widespread synaptic weakening. We found that mEPSCs onto L4 or L2/3 pyramidal neurons were stable across sleep- and wake-dense epochs in both V1 and PFC. Further, chronic monitoring of thalamocortical synaptic efficacy in V1 of freely behaving animals revealed remarkable stability of thalamocortical transmission across prolonged natural sleep and wake epochs. Together, these data provide strong evidence that sleep does not drive widespread constitutive weakening of neocortical excitatory synaptic strengths.

Several previous studies have reported that molecular or morphological correlates of synaptic strength are lower after a period of sleep than a period of wake (Vyazovskiy et al., 2008; de Vivo et al., 2017; Diering et al., 2017). There are several methodological differences between our approach and previous studies that might explain some of these differences. First, we carefully classified the sleep history of every animal used for our ex vivo recordings, which allowed us to control for the considerable variability across individual animals in sleep habits. In the young animals studied here (4–5 weeks of age), the average chance of having experienced a sleep-dense epoch at ZT 4 or 6 (time points used in many studies) is <50%; our approach allowed us to wait until a sleep- or wake-dense epoch was detected within a specified circadian window for each animal to ensure consistency in sleep history. Second, we carefully controlled for circadian time, which has often been used as a proxy for sleep and wake, despite the potential impact of circadian time itself on synaptic strengths (discussed in Frank and Cantera, 2014; Bridi et al., 2020). Third, we used direct electrophysiological measurements of synaptic strength across largely unperturbed natural sleep and wake cycles, whereas many previous studies used indirect measures of synaptic function as proxies for synaptic strength (Diering et al., 2017; de Vivo et al., 2017), and/or relied on extended sleep deprivation paradigms to manipulate the amount of sleep (Diering et al., 2017; de Vivo et al., 2017; Vyazovskiy et al., 2008; Liu et al., 2010; Khlghatyan et al., 2020). Finally, there is wide variation in brain areas and cell types examined across studies. Correlates of synaptic weakening after sleep have been reported in motor cortex, somatosensory cortex, and PFC (Liu et al., 2010; Vyazovskiy et al., 2008; de Vivo et al., 2017; Diering et al., 2017), while here we examined synaptic strengths in visual cortex and PFC. It is possible that the impact of sleep on synaptic function is distinct in different brain areas and cell types (discussed in Frank and Cantera, 2014). Regardless, our data show that synaptic downscaling is not a universal function of natural sleep across cortical cell types and brain regions.

Using a direct measure of postsynaptic strength, we found that mEPSC amplitude was stable across sleep and wake epochs in three cell types from two very different cortical areas, V1 and PFC. These findings are inconsistent with a previous study that reported that mEPSCs in frontal cortex (including the PFC region we recorded from in this study) were larger and more frequent after a period of sleep deprivation Liu et al., 2010; a third recent study in PFC found increased mEPSC amplitude but no change in frequency following sleep deprivation (Khlghatyan et al., 2020). While there are a number of possible explanations for these discrepancies between studies, one major difference is that here we more carefully controlled for sleep history in individual animals, and our study design allowed us to examine mEPSCs after relatively unmanipulated periods of natural sleep and wake that ended in the same circadian window. A second major difference is that we recorded from defined cell types and layers within well-defined brain regions, whereas previous studies combined recordings from several distinct regions and did not report cell type (Liu et al., 2010), or combined data from pyramidal neurons in several layers (Khlghatyan et al., 2020). As mEPSC frequency and amplitude can vary considerably between cell types, these measures could be strongly affected by sampling differences between conditions. By virtue of the EEG recordings performed in our experiments (generally absent in these previous studies for mEPSC datasets), we were also able to look for correlations in delta power drops and mEPSC amplitude, and were unable to see a significant relationship. An important question is whether mEPSC measurements are sensitive enough to detect changes in synaptic strength induced by 4 hr of consolidated sleep or wake. We have used a similar approach to successfully quantify changes in mEPSC amplitude induced by visual deprivation and eye reopening, and can reliably detect both increases and decreases of <10% with a similar sample size to the present study (Lambo and Turrigiano, 2013; Hengen et al., 2016; Torrado Pacheco et al., 2021). Previously reported changes in synaptic parameters following sleep or sleep deprivation are larger than that (de Vivo et al., 2017; Liu et al., 2010; Vyazovskiy et al., 2008), suggesting that if they were occurring at these synapses we would have been able to detect them. Taken together, our data show that natural periods of sleep and wake do not by themselves globally modulate postsynaptic strengths.

Whereas mEPSC amplitude strongly correlates with synaptic glutamate receptor accumulation and postsynaptic strength (e.g. Ibata et al., 2008; Wierenga et al., 2005), mEPSC frequency is influenced by many parameters and does not have a straightforward correlate with presynaptic function. The frequency of mEPSCs can be impacted by changes in the number of functional synaptic sites, but while some studies have found learning-dependent changes in spine turnover during sleep (Li et al., 2017), sleep does not constitutively drive changes in neocortical spine density (de Vivo et al., 2017). mEPSC frequency can also be modulated by factors that affect presynaptic excitability, including presence of neuromodulators (Choy et al., 2018; Sharma and Vijayaraghavan, 2003), and by vesicle refilling and pool size (Zhou et al., 2000; Liu and Tsien, 1995). Previous studies from PFC found inconsistent effects of sleep on mEPSC frequency; one study found increased mEPSC frequency after sleep deprivation (Liu et al., 2010) while another found no effect (Khlghatyan et al., 2020). While we found that mean mEPSC frequency was unaffected by sleep or wake in any condition, there were small shifts in inter-event interval distributions in some conditions. It is not clear if these effects are tied to circadian or sleep/wake differences, and they were not consistent across brain regions.

To complement our ex vivo mEPSC recordings, we also devised an approach that let us follow synaptic strength at a defined set of synaptic inputs in real time during naturally occurring periods of extended sleep or wake. We evoked thalamocortical fEPSPs at low frequency in L4 of V1 while monitoring vigilance state, and found that – like mEPSC amplitude – these evoked synaptic events were remarkably stable even across very long periods of sleep and wake. As for changes in mEPSCs, previous studies have reported a wide range of effects of sleep on evoked synaptic transmission. Sleep and/or SWA has been variously found to potentiate evoked responses (Chauvette et al., 2012) or bias evoked responses toward depression (González-Rueda et al., 2018; Vyazovskiy et al., 2008), while a recent study using optogenetically evoked responses in mouse motor cortex found no impact of sleep deprivation on evoked transmission (Matsumoto et al., 2020). Our approach (similar to Matsumoto et al., 2020) has the advantage of sampling thalamocortical synaptic strengths continuously over many iterations of nature sleep and wake cycles, and revealed no impact of time spent asleep or awake on evoked synaptic strengths in V1. One concern with this technique is that repeated optogenetic measurements from the same synapses might itself affect the synaptic responses, as high levels of blue light can affect neuronal health and RNA expression (Tyssowski and Gray, 2019). However, we used low light intensities and very low stimulation frequencies (1/20–1/40 Hz), so that total exposure to blue light was ~8000-fold less per hour than levels shown to impact function (Tyssowski and Gray, 2019); further, we were able to evoke consistent and stable responses over several days, suggesting that our stimulation paradigm was not inducing significant phototoxicity or otherwise compromising evoked transmission.

While time spent awake or asleep had no impact on evoked thalamocortical fEPSP responses, these responses were rapidly modulated by transitions between brain states, as has been noted previously (Matsumoto et al., 2020; Vyazovskiy et al., 2008; Reinhold et al., 2015; Hall and Borbely, 1970). This is likely due to rapid changes in neuromodulatory tone (Lee and Dan, 2012), and/or changes in the network activity in the thalamus or V1 (Steriade, 2001; Steriade and Timofeev, 2003) between sleep and wake states. The changes we observed in fEPSP amplitude correlated with changes in the ability of thalamocortical activation to evoke spikes in V1 neurons. Interestingly, thalamocortical transmission was most effective at evoking spikes during NREM sleep, with implications for information transfer during sleep states. We also found rapid modulation of thalamocortical efficacy at transitions between the light and dark phase of the 12/12 L-D cycle; as there were no other obvious circadian oscillations in efficacy, this is likely a rapid effect of changes in visual input. These data make clear that we can readily detect changes in thalamocortical synaptic efficacy driven by neuromodulatory or sensory input, indicating that the stability we observe during prolonged periods of sleep and wake is not due to a lack of sensitivity of our measurements.

Another method researchers have used to probe for sleep/wake-driven changes in excitability is to measure spontaneous firing of neurons in vivo across sleep and wake states. While some chronic long-term recordings have revealed an oscillation in frontal cortex firing rates consistent with changes in net excitatory drive (Vyazovskiy et al., 2009), other studies have either found variable effects between regions and across neuronal populations (Miyawaki and Diba, 2016; Miyawaki et al., 2019; Watson et al., 2016) or have observed stable firing rates absent a plasticity induction paradigm (Hengen et al., 2016; Torrado Pacheco et al., 2021), suggesting that the impact of sleep and wake on spontaneous firing is complex and brain area dependent. Spontaneous firing arises from many sources, so here we instead measured the ability of thalamocortical inputs to V1 to evoke spikes. Consistent with our findings that mEPSC amplitudes and thalamocortical fEPSPs are stable across extended periods of sleep and wake, we found that the functional connection strength between thalamus and V1 was also stable (Figure 8). Thus, in this study we used three complementary approaches to measure synaptic efficacy, and all three measures paint the same picture: that synaptic efficacy in V1 (as well as mEPSC amplitude in PFC) is not constitutively modulated by time spent awake or asleep. We note that this does not rule out a role for sleep in increasing or decreasing specific connections within specific brain circuits during specific developmental windows (e.g. Chauvette et al., 2012; Vyazovskiy et al., 2008), but taken together our data show that synaptic downscaling is not a universal constitutive function of sleep.

While sleep is not sufficient to induce widespread changes in cortical synaptic strengths, there is a large body of work showing that sleep and wake states can profoundly affect the induction of various forms of plasticity when they are initiated by robust learning or sensory deprivation paradigms. For example, sleep facilitates and is required for several forms of visual system plasticity, and roles for NREM and REM sleep have been found in promoting growth, maintenance, and loss of specific synaptic connections during learning (Frank et al., 2001; Aton et al., 2014; Yang et al., 2014; Li et al., 2017; Durkin et al., 2017). Further, when firing in V1 is perturbed using visual deprivation/eye reopening paradigms to induce homeostatic compensation, upward firing rate homeostasis is confined to periods of active wake (Hengen et al., 2016), while downward firing rate homeostasis is confined to periods of sleep (Torrado Pacheco et al., 2021). Upward and downward firing rate homeostasis are driven in part through homeostatic changes in synaptic strengths (Hengen et al., 2013; Torrado Pacheco et al., 2021), indicating that while natural wake and sleep episodes do not drive constitutive changes in V1 excitatory synaptic strengths, they are able to gate the induction of upward and downward homeostatic synaptic plasticity, respectively. Combined with this larger literature, our findings are most consistent with the view that sleep does not have a single unified effect on synaptic strengths, but rather that sleep and wake states are able to gate various forms of synaptic plasticity when they are induced by salient experiences.

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional information
Strain, strainbackground
(Rattus norvegicus)
Long-Evans RatCharles River
Labs
Charles River 006; RRID:RGD_2308852
Recombinant DNA reagentAAV-ChR2(H134R)-mCherryUPenn Vector CorePenn ID: AV-9–20938M; Addgene: 100054-AAV9

Overview

All experimental measurements were performed on Long-Evans rats of both sexes between postnatal day P25 and P31. Litters were housed on a 12/12 light cycle with the dam and with free access to food and water. All animals were housed, cared for, surgerized, and sacrificed in accordance with Brandeis IBC and IACAUC protocols. Rat pups were weaned at ~P21. All animals received 2 days of post-operative care including daily injection of meloxicam and penicillin. During the few days before recording, animals were handled twice daily by the experimenter. The day before recording started, animals were transferred to a clear plexiglass cage of dimensions 12”×12”×16’ (length, depth, height) and separated into two arenas by a clear plastic divider with 1’ holes, which safely allows for tactile and olfactory interaction with a littermate that was present for all recordings. The recording chamber contained bedding, toys, and had walls with black/white edged patterns for enrichment. Animals were kept on a 12/12 L-D cycle in a temperature controlled room (21°C, 25–55% humidity). Animals in the dark period wake-dense condition (D. wake) were on an inverted L-D cycle, in order to keep all other experimental variables (time of slicing and recording) the same for the experimenter. To do this, the whole littler with dam were transferred to an L-D chamber with inverted light schedule (lights off 7:30 am to 7:30 pm) at least 2 weeks prior to surgery, and maintained on an inverted schedule while recording EEG/EMG.

Virus injections into dLGN

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Viral injections of AAV-CAG-ChR2-mCherry were performed between P12 and P16 using stereotaxic surgery under isoflurane anesthesia at 1.5–2.5%. dLGN was targeted bilaterally using stereotaxic coordinates after adjusting for the lambda-bregma distance for age. A glass micropipette pulled to a fine point delivered ~400 nL of virus-containing solution at the targeted depth. Targeting and expression was verified via post hoc histology.

Electrode implantation surgeries

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Animals underwent electrode implantation at ages P22–P26. For animals to undergo acute slice physiology, we implanted three EEG screws (PlasticsOne, Roanoke, VA): frontal lobe (B: +1–2, L: 1.5–2.5 mm), parietal lobe (B: −3–4, L: 1.5–2.5 mm), and cerebellum (for reference). In addition, two stainless steel spring electrodes were placed in the nuchal (neck) muscles of the rats for EMG recording. Screw electrodes were fed into a pedestal (PlasticsOne) and everything was secured with dental cement. For in vivo recording and stimulation experiments, a 16-channel silicon probe (Neuronexus, Ann Arbor, MI) with adhered optic fiber (200–400 μm diameter; Doric Lenses, Quebec City, Quebec; ThorLabs, Newton, NJ) were stereotaxically inserted into V1 in dLGN virus-injected animals such that the fiber tip rested on the surface of the brain. In the majority of these surgeries, light stimulation and recording were performed during the operation to verify correct placement of the optrode. The exposed craniotomy was surrounded with a silicone elastomer (Kwik-Cast, World Precision Instruments, Sarasota, FL). The array was secured using dental cement, then grounded to two screws (cerebellum and frontal lobe) using steel wire and soldering paste. Screws were not used for EEG data collection in the optrode animals as LFP was sufficient for behavioral classification. Animals were given 2–3 days of recovery before habituation to the recording chamber and data collection, at which point sleep/wake behavior had stabilized and was comparable to unsurgerized animals (see Figure 1—figure supplement 2).

Real-time behavioral recording and classification

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Animals with EEG/EMG head caps were connected to a flexible cable and low resistance commutator (PlasticsOne). EEG signal was recorded as the difference between the parietal and frontal screw, grounded to the cerebellum screw. The signal was amplified and filtered with EEG and EMG amplifiers at 5000× and 500×, respectively (BioPac, Goleta, CA). This analog signal was then digitized with a NIDAQ board (National Instruments, Austin, TX). The data were acquired, analyzed, and plotted all within a custom MATLAB program, which uses canonical markers to classify behavioral state as either NREM, REM, or wake. Briefly, the program computes a delta/beta and a theta/delta ratio from the corresponding frequency bands in the EEG (delta: 0.5–4 Hz; theta: 5–8 Hz; beta: ~20–35 Hz). Ratios were chosen as features because they normalize the power band measurements between animals. Because delta is high during NREM, and beta typically low (Uchida et al., 1992), taking the delta/beta ratio was logical and proved to be a stable and informative feature for classifying NREM. Likewise, theta is high during REM, and delta low, and therefore was indicative of REM sleep (ratios used in Rempe et al., 2015). EMG data is normalized to standard deviation and large deflections that cross a threshold (3–4 standard deviations) were extracted and considered as significant muscle activity. Animal pixel movement is extracted from the video recording (infrared light allowed video monitoring in darkness) using open-source software written in C++ (Collins and Hashemi, 2019; Video Blob Tracking, Open Source Instruments, Watertown, MA; Github: https://github.com/OSI-INC/VBT) and modified in-house to suit our needs (adding video cropping, tracking in both light and dark conditions). The program uses three manually adjustable thresholds (delta, theta, and movement thresholds) for classification, which are additionally adjusted given recent behavioral states. This moving threshold was chosen because the transition probabilities between different states are far from uniform (e.g. 10 s of NREM is more likely to follow prior NREM than wake; Perez-Atencio et al., 2018). Using known transition probabilities makes a scalar threshold more accurate. The classifier uses the EEG ratios and movement measures and applies a decision tree which predicts states in 10 s epochs given the thresholds. The recent behavioral data is plotted and added to a graph that contains the full behavioral history of the animal. This cycle repeats every 4 min. Animals were randomly assigned to either the sleep- or wake-dense conditions.

To extend wakefulness in light period wake-dense recordings, we generally waited until animals had experienced ~50% wake in the previous 4 hr, and then encouraged further wakefulness by moving, removing, or adding new toys or stirring the bedding; this procedure was generally initiated 1.25–2.25 hr before slicing and maintained until animals had reached criterion for wake density. It is important to note that all animals experience the removal and addition of new toys and changes to bedding regularly, so although the frequency of these manipulations is higher during this wake extension they are familiar procedures to the animals.

Post hoc semi-automated behavioral state scoring

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In order to perform the final classification of behavior after experiments, an in-house MATLAB/Python GUI was used. The GUI initially uses the threshold-based algorithm used in the real-time classifier for the first several hours, which is manually reviewed for accuracy. These data from each animal are then used as training input for the machine learning algorithm, a 200–300 tree random forest machine learning algorithm that uses the features described above and additional features found to be useful for the decision tree. These include the proportional contribution of delta, theta, and gamma powers; min/max EEG; and a past value of many of these features up to three 10 s bins ago. The random forest classifier was then applied and manually reviewed for the rest of the recording. While the automated classification step occurred in 10 s blocks, the subsequent manual review had a resolution of 1 s, ensuring our ability to capture particularly short microarousals. Random forest accuracy was roughly 97% benchmarked against manual coding.

In order to classify active vs. quiet wake, the same procedure was used except the training data included a manual separation of these states (Figure 2—figure supplement 1). Manual differentiation of active vs. quiet wake relied primarily on the video data (extracted movement measures and manual review of video segments to observe animal movement by eye), but also included use of gamma power. Animals were considered to be in active wake when LFP signals were especially desynchronized (high gamma power, low delta) and were physically moving around the cage/exploring. In contrast, quiet wake periods contained much less full-body displacement and were mostly composed of sitting or grooming/ingesting.

NREM delta analysis

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Delta power (0.5 Hz < Freq. < 4 Hz) was extracted from the EEG data of animals used for slice experiments, using FFT with 10 s windows. To calculate the change in delta power across ZT, we corrected for time-of-day differences in the amount of NREM (according to the approach of Franken et al., 2001). As delta power varies greatly between animals/recordings, we normalized it to the value at the end of the light period where it tended to be lowest (ZT 7–11). To calculate the change in delta power for each animal (Figure 3—figure supplement 2), we found the percentage drop from sleep epochs that occurred in the window ZT 23.5–1.5 to the last hour of sleep right before sacrificing animals.

Ex vivo acute brain slice preparation

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For brain slice preparation, animals between P25 and P31 were briefly anesthetized with isoflurane (usually under 60 s), and coronal brain slices (300 μm) containing V1 or PFC were obtained from both hemispheres of each animal. After slicing in carbogenated (95% O2, 5% CO2) standard ACSF (in mM: 126 NaCl, 25 NaHCO3, 3 KCl, 2 CaCl2, 2 MgSO4, 1 NaH2PO4, 0.5 Na-ascorbate, osmolarity adjusted to 315 mOsm with dextrose, pH 7.35), slices were immediately transferred to a warm (34°C) chamber filled with a continuously carbogenated ‘protective recovery’ (Ting et al., 2014) choline-based solution (in mM: 110 choline-Cl, 25 NaHCO3, 11.6 Na-ascorbate, 7 MgCl2, 3.1 Na-pyruvate, 2.5 KCl, 1.25 NaH2PO4, and 0.5 CaCl2, osmolarity 315 mOsm, pH 7.35) for 10 min, then transferred back to warm (34°C) carbogenated standard ACSF and incubated another 20–30 min. Slices were used for electrophysiology between 1 and 6 hr post-slicing.

Whole-cell recordings

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V1 and PFC were identified in acute slices using the shape and morphology of the white matter as a reference. Pyramidal neurons were visually targeted and identified by the presence of an apical dendrite and teardrop-shaped soma, and morphology was confirmed by post hoc reconstruction of biocytin fills. Borosilicate glass recording pipettes were pulled using a Sutter P97 micropipette puller, with acceptable tip resistances ranging from 3 to 5 MΩ. Cs + methanesulfonate-based internal recording solution was modified from Xue et al., 2014, and contained (in mM) 115 Cs-methanesulfonate, 10 HEPES, 10 BAPTA•4Cs, 5.37 biocytin, 2 QX-314 Cl, 1.5 MgCl2, 1 EGTA, 10 Na2-phosphocreatine, 4 ATP-Mg, and 0.3 GTP-Na, with sucrose added to bring osmolarity to 295 mOsm, and CsOH added to bring pH to 7.35. Inclusion criteria included Vm, Rin, and Rs cutoffs as appropriate for experiment type and internal solution; values are listed below.

All recordings were performed on submerged slices, continuously perfused with carbogenated 34°C recording solution. Neurons were visualized on an Olympus upright epifluorescence microscope using a 10× air (0.13 numerical aperture) and 40× water -immersion objective (0.8 numerical aperture) with infrared differential interference contrast optics and an infrared CCD camera. Data were low-pass filtered at 5 kHz and acquired at 10 kHz with Axopatch 200B amplifiers and CV-7B headstages (Molecular Devices, Sunnyvale, CA). Data were acquired using an in-house program written either in Igor Pro (Wavemetrics, Lake Oswego, OR) or MATLAB (Mathworks, Natick, MA), and all post hoc data analysis was performed using in-house scripts written in MATLAB.

mEPSC recordings

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For spontaneous mEPSC recordings, pyramidal neurons were voltage clamped to −70 mV in standard ACSF containing a drug cocktail of TTX (0.2 μM), APV (50 μM), picrotoxin (25 μM); 10 s traces were obtained and amplified (10–20×). Event inclusion criteria included amplitudes > 5 pA and rise times < 3 ms. Neurons were excluded from analysis if Rs >25 MΩ or Vm > −50 mV.

mEPSC analysis

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To reliably detect mEPSC events above noise and limit bias in selection, we used an in-house program written in MATLAB that employs a semi-automated template-based detection method contained in a GUI (Torrado Pacheco et al., 2021). In brief, the program first filters the raw current traces and then applies a canonical mEPSC event- shaped template to detect regions of best fit. Multiple tunable parameters for template threshold and event kinetics that aid in detection were optimized and then chosen to stay constant for all analyses. Briefly, sample traces were chosen, which had all of the mEPSC events manually annotated. This training dataset was used while we sampled much of the parameter space for template-matching parameters. These parameters include the mEPSC shape used for template, how the error around the template was calculated, the threshold cutoff, and a coefficient that balances detection of larger mini events vs. smaller ones to correct for larger error in larger events. After identification, putative events were then examined for hallmark features of mEPSC (rise times, decay kinetics, conforming to amplitude cutoff, not saturated) and some detected events (~4%) were excluded if they did not conform to these features. Finally, the automatic detection missed ~6% of events and these were manually added. Results did not differ substantially when the data were not manually corrected.

Array recording and stimulation

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The arrays were connected to an Intan RHD2216 amplifier board, which in turn was connected to a RHD2000 SPI interface cable (Intan, Los Angeles, CA). The cable connected to a custom-designed Hall effect-based active commutator (NeuroTek Innovative Technology Incorporated, Toronto, Ontario, Canada). The commutator fed this digitized data into the RHD2000 USB board (Intan). Data were recorded at 25 kHz continuously for up to 100 hr using the RHD2000 Interface Software (Intan). Spike extraction, clustering, and sorting were done using custom MATLAB and Python code (see below). Light for stimulation was produced using a fiber coupled 470 nm LED system (M470F3/LEDD1B, Thorlabs). Light irradiance was adjusted for each animal and never exceeded 18 mW/cm2. Stimulation was triggered using voltage pulses of varying magnitude from a NIDAQ board (controlled by custom MATLAB program) to the LED driver, set to modulation mode.

Semi-automated spike extraction, clustering, and sorting

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Spike extraction, clustering, and sorting was performed as previously described (Hengen et al., 2016; Torrado Pacheco et al., 2021). Spikes were detected in the raw signal as threshold crossings (−4 standard deviations from mean signal) and re-sampled at 3× the original rate. Principal component analysis was done on all spikes from each channel, and the first four principal components were used for clustering (Harris et al., 2000). A random forest classifier implemented in Python was used to classify spikes according to a model built on a dataset of 1200 clusters manually scored by expert observers. A set of 19 features, including ISI contamination (% of ISIs < 3 ms), similarity to regular spiking unit (RSU) and fast spiking unit (FS) waveform templates, amount of 60 Hz noise contamination and kinetics of the mean waveform. Cluster quality was also ensured by thresholding of L-ratio and isolation distance (Schmitzer-Torbert et al., 2005). Clusters were classified as noise, multi-unit or single-unit. Only single-unit clusters with a clear refractory period were used for FR analysis. We classified units as RSU or FS based on established criteria (mean waveform trough-to-peak and tail slope, Hengen et al., 2016). Only RSUs (putative excitatory neurons) were used for analysis. Some units were lost during the recording, presumably due to electrode drift or gliosis. To establish periods of time when units could be clearly detected and isolated, we used ISI contamination: when hourly % of ISIs < 2 ms was above 4%, the unit was considered to be poorly isolated; neurons were included for analysis for each behavioral epoch in which unit isolation quality met criterion. Using these criteria, 93% of cells were well isolated for greater than one-third of the full experiment time, and 80% of cells were well isolated for greater than half of full experiment time of 2–3 days. Results of the automated scoring were manually reviewed for final inclusion of neurons.

fEPSP analysis

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For fEPSP analysis, the data were bandpass filtered (high-pass = 5 Hz, low-pass = 450, filter order = 2). For each optogenetic stimulation, a section of data was extracted (100 ms prior to stimulation and 80 ms after) for each channel in the array. While all the channels were used for estimation of cortical layer placement, only channels in L4 to upper L5 where the response was largest and shortest latency, and channels where signal-to-noise was stable, were chosen for fEPSP analysis. Evoked events were rejected if the standard deviation was >200 µV in the last 5 ms before stimulation or if an evoked trough of >30 µV magnitude could not be found. Slopes were calculated for the rise of the waveform between 20% and 80% of the maximum amplitude. For the sandwich analysis (Figure 7), flanking periods of 1 hr before and after a given epoch were compared. Normalized change in amplitude was calculated as post/pre – 1. For time course analysis (Figure 6), fEPSP amplitudes were normalized to the first hour of a given epoch. For analysis of the rare epochs that spanned L-D transitions, we normalized the amplitude just after the transition to the amplitude just before the transition to account for L-D mediated changes in amplitude; results were not different if instead these epochs were excluded or were used without this normalization.

Evoked firing rate analysis

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For the evoked spike analysis (Figure 8), spike responses were determined for each RSU for each stimulation. Spike responses were measured as the number of spikes that occurred in the 20 ms interval post-stimulation above what was expected given the previous baseline firing rate. A baseline firing rate was determined in the 60 s prior to each stimulation for each cell, and the expected number of spikes given the baseline firing rate was subtracted from the actual spike number that occurred in the 20 ms post-interval: Response = Spike Count − (baseline FR * 0.02 s). To determine whether evoked spikes were above chance levels for a given cell, we performed a bootstrap analysis by randomly sampling times where no stimulations occurred and performed the same analysis. This produced a null distribution of ‘responses’ to no stimulation. Cells that had responses <95th percentile of randomly sampled responses were considered unresponsive to stimulation and excluded from analysis.

Statistical analysis

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All data analysis was performed using in-house scripts written in MATLAB. For each experiment, means ± SEM derived from individual cell measurements are provided within the Results section of the text, and n’s (number of cells and animals), p-values, and statistical tests are provided within figure captions. Generally, a Kruskal-Wallis test was performed on two to three condition experiments in Figure 3 as the distributions did not pass normality tests. Two-sample Kolmogorov-Smirnov test with a Bonferroni correction for multiple comparisons was used for comparisons between distributions. Wilcoxon signed-rank test was used to compare distributions that were not normally distributed in Figures 7 and 8. Figure 8E’s data was log normal and so a two-sample t-test after log transformation was used.

For slice experiments where expected means and variance were known, target sample sizes were determined beforehand using power analyses, using a detection threshold of ~10%. For our in vivo experiments, this information was not known so an a priori power analysis was not possible. A post hoc calculation based on sample size and variance shows that changes of >10% would have been detectable.

Code and data availability

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Data availability

Processed datasets and all figure data have been uploaded to Figshare (https://figshare.com/projects/Cary_et_al_2021_Elife_Submission/95867).

The following data sets were generated
    1. Cary BA
    2. Turrigiano GG
    (2021) Figshare
    ID figshare.com/projects/Cary_et_al_2021_Elife_Submission/95867. Cary et. al Elife Submission 2021.

References

Decision letter

  1. John R Huguenard
    Senior and Reviewing Editor; Stanford University School of Medicine, United States
  2. Anita Lüthi
    Reviewer; University of Lausanne, Switzerland

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

The authors carefully document sleep-wake behavior of young rats during the critical period for development of vision. Building on this, they use an innovative multimodal approach to study synaptic strength in relation to recent sleep-wake behaviors. Of note, they fail to find any relationship between sleep history and the state of excitatory synapses. These results challenge the synaptic homeostasis hypothesis, especially the tenet that sleep universally drives widespread downscaling of synaptic weights.

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting your work entitled "Stability of cortical synapses across sleep and wake" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Anita Lüthi (Reviewer #3).

Our decision has been reached after extensive consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.

Notably, this report documents surprisingly little evidence for sleep related changes in cortical synaptic plasticity, and therefore challenges the SHY hypothesis. The editors and reviewers appreciate the novel methodology of sleep state detection, careful synaptic analysis with miniature excitatory postsynaptic current recordings and in vivo multi-electrode array local field potential recordings. However, there are concerns about the difficulty in comparing the results of this study to others, given that the approach for picking sleep-related epochs for analysis may be problematic. This concern includes, but is not limited to the result that the sleep dense and wake dense periods appear to be as brief as a few hours (Figure 3A), which may be an insufficient duration to allow significant plasticity.

Given the significant strengths of the end-point analysis (timed recordings of minis and in vivo LFP), if you are interested, we would encourage submission of new manuscript that addresses with new experiments the issues of timing of sleep-related experimental epochs to allow for a more direct comparison to previous studies.

Reviewer #1:

The study by Cary and Turrigiano aims to test further the hypothesis that wakefulness is associated with increased synaptic strength and sleep leads to decreased synaptic efficacy. Previous studies on this topic differ widely with respect to the choice of experimental models and methodology, which prevents straightforward comparison between studies. To my opinion the current work is inconclusive as it stands, because it uses experimental models, approaches and data analyses not conventionally used in studies addressing sleep regulatory mechanisms, and does not take into account factors that have important influence on sleep dynamics, behaviour and brain activity.

1. In this study young long-Evans rats were used between postnatal day P25 and 31. Although literature on the development of sleep regulation at this age is limited, evidence suggests that sleep in young rats of a comparable age is more fragmented and a clear cut homeostatic dynamics of SWA is not yet well pronounced: https://journals.physiology.org/doi/abs/10.1152/ajpregu.1990.258.3.R634.

To this end, the first type of analyses I would have suggested is to plot individual hypnograms (as on Figures 2 and 5 in the paper cited above) and identify time points which are not simply preceded by wake-rich or sleep-rich intervals, but differ with respect to homeostatic sleep pressure. Generally, I felt the question asked by the authors could have been better addressed if the experiments were designed and data analyses were performed taking into account the context of daily sleep physiology.

2. It is an important limitation of the study that the animals were generally allowed only a few days of recovery after invasive surgery ("Animals were given 2-3 days of recovery before data collection"). Post op treatment is not mentioned but I would have expected that the animals receive some sort of analgesia for at least a few days as I presume is required by the office of Laboratory Animal Welfare. I would not be surprised if the recovery process or post-op treatment have a significant influence on sleep regulation and on some of the measures taken in this study.

3. The description of the surgical procedure is not entirely clear. It is stated that "For animals to undergo acute slice physiology, we implanted 3 EEG screws" but "For in vivo recording and stimulation experiments, a 16 channel silicon probe" was used. Did you record EEG also in the in vivo recording and stimulation experiments? If I understand correctly, the EEG screws were implanted in the frontal and parietal derivation, and the third screw was a reference above cerebellum. Have you based your "behavioral state classification" on EEG power from the frontal or from the parietal EEG?

4. The procedure for "behavioral state classification" is not very clear. It mentions power bands and their ratios but their choice is not justified. Please provide full absolute EEG power spectra. Please explain how "Large deflections in the EMG" were normalized. It is stated "All classifications made in real time can be manually verified post hoc." Please confirm whether all classifications were actually verified.

Further to this point, it is stated "This real time behavioral state classifier greatly improved our ability to explore the effects of sleep on synaptic strengths, because rodent sleep is both highly variable and fragmented (Figure 1A; see standard deviation Figure 2A)." What exactly is meant by variability here and I do not understand how did your classifier help to deal with that in the context of the question being addressed here?

5. The choice of wake-dense and sleep-dense epochs is, to my view, suboptimal. As well known, the animals may and indeed do spend extended periods of time in a relatively superficial sleep state, where I would not expect any systematic homeostatic changes to occur. The analyses the authors are performing would have been more informative if time intervals compared differ with respect to levels of sleep pressure.

6. For brain slice preparation, the choice of the visual cortex and prefrontal cortex is not well explained. When selecting brain areas for this study I would have looked for evidence of sleep homeostatic response and plasticity at this early age. It would strengthen the argument if such evidence is cited.

7. The approach to detect mEPSC events is not well described. It refers to "Multiple tunable parameters" that were "optimized", but not clear how this was done.

8. This sentence is not clear: "To establish "on" and "off" times for neurons, we used ISI contamination: when hourly % of ISIs < 2 msec was above 4%, unit was considered to be offline." Please clarify what was the purpose of this analysis.

9. Figure 1: Please clarify how the spectrogram was normalized.

10. It is stated that "…on average, the chance of being in a sleep dense epoch between ZT 0-4 was less than 50% (Figure 2B), indicating that circadian time is not a good predictor of sleep history for individual animals." And then later "We wondered whether circadian time might impact postsynaptic strengths independently of sleep/wake history. To test this, we entrained animals to an inverted light/dark cycle…". It is unclear how inverting the LD cycle helps the problem of the potential circadian influence on synaptic strength or any other variable. Does Figure 3A, where ZT0-12 are shaded, shows an example of an inverted LD cycle?

11. It is observed that "mEPSC amplitudes in L2/3 pyramidal neurons were identical after wake dense or sleep dense epochs". Please provide further information on how wake and sleep dense epochs were distributed across the light and the dark periods.

12. Given the pronounced effect of behavioural state on the evoked potentials, it is essential to ensure that the behavioural state is identical for all comparisons. Without knowing the behavioural state it is difficult to conclude whether the effects or lack thereof are related to fluctuations in arousal, movement or reflect changes in synaptic efficacy. I should point out that the figure 4F shows that most longest waking periods are associated with an increase in evoked responses from sleep before to sleep after (please compare hr9 vs hr13, hr34 vs hr38, hr57 vs hr62 or hr82 vs hr86).

Reviewer #2:

Cary and Turrigiano conclude from a series of experiments using minis, evoked responses, and firing rates that they are unable to replicate several findings previously reported by the Tononi and Cirelli group and several other laboratories in support of the synaptic homeostasis hypothesis. There are many reasons for these supposedly negative findings. Without conducting a more careful analysis using restrictive criteria to define sustained periods of sleep and waking, and without additional experiments to control for crucial factors such as level of arousal and brain temperature, these results are impossible to interpret. In short, before the authors can conclude that they are unable to replicate the findings by Vyazovskiy et al., and other labs, should conduct the experiments using the same, or similar, carefully controlled conditions.

Minis:

1) There are several reasons that can explain why the authors failed to see significant differences between sleep and waking; (1) the criteria to define sleep dense and wake dense periods (65% of total time for 4 consecutive hours) are not very restrictive; for instance, in Vyazovskiy et al., 2009 spontaneously awake rats were sacrificed during the dark phase after a long period of continuous waking (1 hour, interrupted by periods of sleep not longer than 4 min), and after spending at least 75 % of the previous 6 hours awake; similar criteria were used in de Vivo et al., 2017 in mice; Liu et al., used a lower cut-off, of 65%, but crucially, the spontaneous waking episodes considered for the study occurred during the first part of the dark phase, when mice and rats are much more active; here, mice were studied in the first part of the light period, and as expected and shown in Figure 2B, the probability of single animals to be awake > 65% for 4 consecutive hours was very close to zero; in fact, given figure 2A and B, it is difficult to understand how such long episodes of sustained wake could be found, how many of them, and in how many mice; a figure showing the raw sleep/wake data for all the mice used for the in vitro study (figure 3) should be shown; in line 117, the authors refer to figure 1B, but that figure only shows an example for 60 minutes, not 4 consecutive hours; again, based on figure 2, it is hard to imagine how the authors could find enough episodes in enough mice to perform the experiments; this is further confirmed by the 4 traces shown in figure 3A: none of the 4 examples show >65% waking time for the last consecutive hours; the same issue applies to the layer 2/3 results; also, the legend states that only 3 rats were used for the sleep group, although 4 traces are shown in figure 3A; none of the traces for the inverted wake group are shown, and they should, since results for the inverted waking group actually do show some changes (see below).

2) There are inconsistencies between the data presented by the authors and their conclusions. Firstly, in Figure 3G the authors did find an increased mEPSC amplitude in inverted wake vs sleep animals, however, they downplayed this point in the text by saying "minor shift"; In fact, it was significant according to K-S test (see Figure 3 G legend). The K-S test but not ANOVA is an appropriate statistical test used for examining the amplitude of mEPSCs, because the distribution of mEPSC amplitude is not normalized and the comparison among means from experimental groups is not appropriate. Secondly, In Figure S1F, the authors did show a very significant shift to the left in the cumulative distribution of mEPSC inter-event interval in the WD group as compared with the sleep group in L2/3 PFC neurons (SD-WD p<1e-5), which suggested a higher mEPSC frequency in the WD group than in the sleep group if measured with this parameter. In terms of absolute value, the mEPSC frequency was also higher in WD group than in the sleep group (although it was not significant). Therefore, the statement that synaptic strength was stable across sleep/wake periods is questionable at least in these L2/3 PFC neurons.

3) It is not clear whether the recording of mEPSCs from naturally wake and sleep rats was well controlled throughout the investigations. It seems that the preparation of slices from these two groups were performed at different times of the day. This means slices were cut at different time points for these two groups (Figure 3A is misleading). Therefore, the variation in slice conditions may mask the difference between groups. Although the authors did sample a big number of cells for each group, I am not sure whether this will help to limit the effect of variation resulting from the slice preparations. Note that Liu et al., took care of running paired experiments, in which one slice from a control animal and one from a waking/sleep deprived animal were always run in parallel the same day, to limit technical variability.

4) The rationale for selecting layer 4 should be better justified (line 124); firing rates vary across waking and NREM sleep across the entire thalamocortical system, not just in layer 4; thus, taken alone this is not a compelling reason to select layer 4 neurons; on the other hand, it is well known that after the end of the critical period, the thalamocortical synapses targeting layer 4 of primary somatosensory, auditory, and visual cortex lose most of their ability to undergo plastic changes under physiological conditions, and that ability can be reinstated only by specific manipulations such as prolonged unimodal or crossmodal sensory deprivation or peripheral nerve transection. Thus, it seems that the authors of this study chose to focus on synapses that are known to have little plasticity to test synaptic homeostasis hypothesis, whose main claim is that sleep is the price for plasticity during waking; in fact, if the results related to layer 4 could be trusted (but see all the issues related to selection of behavioral states and minis analysis), then they would actually be a nice confirmation of the main tenet of this hypothesis.

Evoked responses:

The analysis of the evoked responses is impossible to interpret because too many crucial details and control experiments were not performed.

1) First, the criteria to define prolonged periods of sleep and waking for the evoked responses analysis are not specified and cannot be deduced from Figure 5A, which has no time bar (same problem in Figure 6). In Vyazovskiy et al., a decrease in slope was present only after at least 2 hours of consolidated sleep, or more than one hour of continuous waking (most rats were awake for 2-4 hours). Vyazovskiy also stimulated only twice, before and after sleep or waking, while it seems that in the current study pulses were given every 20 to 40 secs continuously, for days.

2) Second, evoked responses are exquisitely sensitive to neuromodulatory conditions (arousal levels) and subtle changes in arousal could mask any subtle effect due to sleep/waking history. Vyazovskiy et al. took great care in controlling for this factor by delivering the stimuli under a very standardized quiet waking condition, which required 2 investigators watching the animal full time. As they state, "We did not attempt to record evoked responses continuously for several hours in freely behaving rats because it is impossible to maintain the animals in a standard quiet wakefulness for more than a few minutes." Moreover, Vyazovskiy et al. confirmed that changes in slope were present after controlling for response amplitude. Note that their major results were confirmed by comparing high vs low sleep pressure in all 4 behavioral states separately.

3) The classifier distinguished 3 states, but not active and quiet waking (line 87). This is a crucial limitation because waking responses vary due to arousal levels (see point 2), and differ between quiet and active waking. There is strong evidence from electrophysiological and calcium imaging data that the activity of V1 neurons is very sensitive to locomotion; thus pooling evoked responses across "waking" is inappropriate.

4) Third, evoked responses are exquisitely sensitive to brain temperature. Very small changes in brain temperature can affect evoked responses and mask any additional effect due to sleep and waking history. Vyazovskiy et al. controlled for this factor by conducting specific experiments in which brain temperature was also measured; in doing so, they could demonstrate that the changes in the slope of the evoked response did not correlate with changes in brain temperature. This issue is especially crucial in the current study, where light pulses were used to evoke the response. On a related matter, the intensity of the laser stimulation should be specified.

Firing rates:

The current negative findings relative to firing rates in V1 are at odds with the evidence provided by at least 3 different labs showing that mean firing rates decreases with sleep, including Vyazovskiy et al., in barrel cortex (Nature 2011), Grosmark et al., in the hippocampus (Neuron 2012), Watson et al., in frontal cortex (Neuron 2016), Miyawaki et al., in the hippocampus (Curr Biol 2016, Cell Reports 2019). As for the evoked responses, it is unclear whether the criteria used by Cary and Turrigiano to define prolonged periods of sleep and waking were stringent enough to match those used in other studies. Cary and Turrigiano cite one paper from their lab (line 57) showing that mean firing rates do not change during extended periods of sleep and waking. At the very least, it would be appropriate to quote all the other studies that found the opposite.

The authors state that many units were lost in the course of the several days of recordings. The exact number should be stated.

Reviewer #3:

General assessment. This paper asks how states of sleep and wakefulness regulate global synaptic strength at various cortical pyramidal neurons. A major proposition for this question is formulated in the well-known SHY hypothesis, for which there is mostly indirect molecular and structural evidence. Therefore, it is very important to test SHY with direct functional measures of synaptic strength. This paper does so using electrophysiological methods and is, therefore, an important contribution to a long overdue question.

The authors depart from a form of homeostatic plasticity that is known to be regulated by sensory experience and largely based on amplitude measurements of mEPSCs. When now applied to sleep and wakefulness, results are overall negative, thus questioning that SHY affects homeostatic plasticity. The experiments are well-done and the results are clear and striking.

Still, I would encourage the authors to consider a number of points in a revised version of their manuscript.

1) Insufficient information about animal husbandry is provided. All experiments are done in young rats around and shortly after weaning. Weaning changes metabolism and stress levels are high. Synapse growth and development progress rapidly. When were animals weaned relative to the day of surgery? How were they housed prior to and after surgery, and how was recovery from surgery monitored (weight loss and recovery, stress monitoring, etc.)? A time period of 2-3 days for recovery from surgery is very short (~1 week is typical). How much time was given for habituation to the tethering to the recording cables (~1 week is typical)? Is the sleep-wake behavior of the animals stable from 2-3 days after surgery?

2) More information on the sleep-wake behavior of these young rodents is also needed. Figure 2 suggests that the typical preference for sleep over wake during the light period found in adult is not there yet. Do these animals show homeostatic regulation of sleep? The SHY hypothesis implies slow-wave activity in the renormalization of synaptic strength. Slow-wave activity is proposed to be key for synaptic scaling during sleep. Therefore, it would be important to show some evidence that slow-wave activity varies with time-of-day and/or after sleep deprivation.

3) Experiments coincide with critical periods of the visual system. If animals are taken 7-10 days later, after the closure of the critical period, are effects of prior sleep-wake history still negative? What about thalamocortical projections for which the critical period closed much earlier, such as for the whisker-to-barrel system?

4) Regarding the sleep-wake monitoring and analyses, a major weak point is that scoring of vigilance states is done in 10 sec intervals, which is 2.5x the window commonly used. This means that the duration of NREMS bouts are overestimated because brief arousals will go undetected. There should be an estimation provided for the limited time resolution.

5) The authors only show "Time awake" prior to slice preparation. I suggest they instead show time spent in NREM and REM sleep prior to sacrifice. Possibly, then, animals should be selected based on how much time they spent in NREM only.

6) Major forms of homeostatic synaptic plasticity in sensory cortices (e.g. effects of monocular deprivation) develop over time scales of days. The authors show themselves in a previous work that after a full day of MD, mEPSC amplitude is reduced to only ~95% of control (Lambo, 2013). Similarly, in cultures, homeostatic scaling of excitatory or inhibitory synapses is typically observed after 24 h of receptor antagonism. In contrast, here, authors work with time intervals of 4h during which both sleep and wake are present, with mean bout length on the order of hundreds of seconds. This could mean that changes in mEPSC amplitude might simply not be detectable at this point. The mEPSC as a measure for synaptic strength could thus not be sensitive enough. The same considerations might apply for prefrontal cortex, for which the maturational profile is even less known.

7) More generally, one might wonder about whether mEPSCs are suitable for monitoring changes in synaptic strength. Miniature EPSCs reflect the response of a synapse to the release of a single vesicle. Thalamocortical synapses, however, activate their postsynaptic targets via multivesicular release. This might lead to more vigorous postsynaptic receptor recruitment. Therefore, to fully assess whether or not sleep-wake modify synaptic strength, it would be important to look at how synaptic strength quantified by action-potential-dependent vesicular release is affected. I suggest to use single-fiber stimulation to trigger vesicular release via single action potentials.

8) Looking at evoked fEPSPs in response to sensory or afferent stimulation has a long tradition but is not a very informative type of data. Evoked fEPSPs are not only composed of sources arising from the synaptic input, but also by the tendency of the network to switch between up and down states. This is particularly the case during nREM sleep because on-going oscillations are strong and excitatory input can switch the networks between states. So, neither amplitude nor slope of these responses, nor their stability across sleep periods, tell much about "synaptic strength".

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Stability of neocortical synapses across sleep and wake" for further consideration by eLife. Your revised article has been evaluated by John Huguenard (Senior Editor).

As you will see from the reviewers comments below, the manuscript has been improved but there are remaining issues that need to be addressed, as outlined below. Normally these would require additional experiments, but given pandemic conditions that limit feasibility for such, then at the very least the title, abstract and conclusions need to be moderated to reflect that the conclusions may be limited to an early developmental period. Further, upon careful inspection of the in vivo LFP data, I see that there is a potential confound that needs to be examined. While these are clearly thalamic-dependent LFP responses, as they are recorded in the cortex, and evoked by ChR2 activation of thalamocortical projections, what is not clear is how one might distinguish the specific thalamic fEPSP from the overall LFP response especially with optogenetic stimulation which can lead to a significant fiber volley (see PMID: 27489370).

Reviewer #1:

They authors start with a comprehensive documentation of sleep-wake behavior of young rats during the critical period for vision, followed by an ingenious approach to study synaptic strength as a function of spontaneous recent sleep-wake behaviors, followed by an in-depth analysis of in vitro and in vivo correlates of synaptic strength. I particularly liked the explicit way of the authors in motivating their choice of parameters regarding sleep-wake behavior, of animals during the critical period, and of the synapses studied in vitro or in vivo.

The authors carefully conclude that their results do not support the idea that sleep or wakefulness per se lead to global modifications of synaptic strength, at least not in visual and prefrontal cortex. Interestingly, however, the authors identified circadian variations specifically in prefrontal cortex, an observation that is worth pursuing in the future.

This manuscript is a long awaited and authoritative approach to challenge the SHY hypothesis with solid experimental quantification of functional synaptic strength.

I have a few additional comments to further improve some analysis and their documentation in this study:

1) The sleep-wake behavior of these young animals is clearly different than the one from adult animals. It is irregular and polyphasic and there is very little light-dark dependence (see Figure 1B). Therefore, it would be good to show hourly mean times spent in the different vigilance states wake, NREM and REM rather than only the 4h-slidingwindow means.

2) Beyond the mean times, the detailed architecture of sleep-wake behavior in the 4h-windows is also important. Why is this: even if you go for times enriched in wake or sleep, it is not the same whether this enrichment happens in many very brief bouts or in few relatively long bouts. This is particularly the case for sleep, for which fragmentation has a strong effect on plasticity/learning. Therefore, do the animals sleep in consolidated bouts, i.e. what is the mean NREM sleep bout duration? Is this duration variable between Sleep dense and wake dense, and as a function of circadian time?

3) The word "consolidated" has a strong meaning in the sleep field and as it refers to the mean duration of NREMS bouts – the less they are interrupted by microarousals, the more consolidated NREMS is said to be. It should not be used to describe an enrichment of mean times spent in sleep or wake over a 4-h period (see line 113).

4) Line 116: δ power is not the same as the size of slow waves and should not be equated. Slow waves are EEG or LFP graphoelements that at best make up a fraction in the power of the broad δ frequency band used here (0.5-4Hz). Please use these terms carefully and specify the frequency bands upon first use.

5) Line 119. The time-of-day-dependence of δ power is not typically referred to as an oscillation. Also, the way the data in Figure 1C analyzed should be checked. It must be done for equivalent amounts of time spent in NREM sleep epochs that are preceded and followed by other NREM sleep epochs. If it is not done like this, the amounts of δ power at different times of day are not weighed equivalently. Therefore, please divide the total time spent in NREMS in the light and in the dark phase into similar amounts and calculate δ power within these bouts. As more time is spent in NREMS in the light phase, subdivisions can be higher in the light than in the dark phase (e.g. 12 time points in the light and 6 in the dark phase). Literature from the Paul Franken lab can be consulted to do this properly.

6) Lines 168-172: Please explain more quantitatively. What is a long natural wake epoch during the early light phase? What was the criterion to add a novel object? What you do here strictly amounts to a sleep deprivation that should be documented in terms of its effects on quiet vs mobile wakefulness and the increase in the time spent in wakefulness. One could also argue that this is a period of environmental enrichment that has an impact on its own on visual plasticity. These animals might also show a greater increase in δ power upon the end of wakefulness due to greater amounts of sleep loss. These caveats should be quantified whenever possible and discussed.

7) Figure 3 —figure supplement 1. This figure indicates somewhat worrisomely that the increase in δ power at light onset compared to the end of the light period is extremely variable – from somewhere between >40%, which is very high, to ~17%, which is very low. In addition, the example recording shown in panel A is close to 50%, so where is that datapoint represented in panel B? What happened with these animals during the darkphase that their sleep pressure at light onset is so variable? Overall, I am not sure that this analysis is particularly helpful because it relates an endpoint measure of synaptic amplitudes to an unknown starting point measure.

8) Line 624. What are slice behavioral data?

9) The finding that evoked field potential amplitudes in visual cortex were larger in NREMS than in wakefulness during both the light and the dark phase is intriguing and in contrast with previous observations on evoked auditory field responses (see e.g. the literature from Yaniv Sela et al.,). Rather what has been seen is that the secondary outward components of the evoked responses are disproportionately increased during NREM sleep. Evoked field responses are, as already mentioned in my first review, also problematic because they can be contaminated because of on-going oscillatory activity. Can the authors better describe when these responses were elicited in response to on-going up- and downstates, for example? And how these components were removed to isolate the evoked field response?

Reviewer #2:

In the revised manuscript, the authors included important and interesting new analyses, but no new experiments have been undertaken, and the key issue remains that the data set, limited to results obtained in juvenile animals and analyzed in some novel, yet untested ways, is not ideal for reaching strong conclusions.

My main points I raised previously remain the same:

1. The experiments have been performed in juvenile animals, which prevents direct comparisons with most previous studies. I recommend that the title of the manuscript should state the age of animals to avoid misunderstanding.

2. The experiments were performed too soon after a major invasive surgical procedure, which is certainly expected to affect sleep and wake quality, because the immune system and thermoregulation can be still compromised especially in young animals.

3. The overall wake-sleep pattern is too fragmented for a straightforward comparison between effects of wake and sleep, and I still do not see evidence provided that homeostatic sleep pressure is increased during time periods referred to as "wake-dense" epochs, and decreases during "sleep-dense" epochs.

4. The chronic fEPSP experiment shows massive state- and light-dark-dependent instantaneous variations in the amplitude of responses, which makes it very difficult to extract any meaningful sleep-wake history dependent changes, and the potential direct influence of arousal, behaviour and brain temperature on evoked responses remains not addressed.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Stability of critical-period neocortical synapses across sleep and wake states" for further consideration by eLife. Your revised article has been evaluated by John Huguenard (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

We think this is an important paper providing experimental data regarding the sleep homeostatic hypothesis using an innovative approach. Please consider the following comments from reviewers in your final revision of this paper. We will not need to send your revision back to reviewers. Instead the reviewing editor will make the final call.

Reviewer #1:

The authors did an excellent job replying to my concerns. In my view, the study now makes abundantly clear that it studies critical period synapses. The study also has appropriately addressed remaining analytical concerns, with one exception:

This concerns the re-analysis of the data presented in the Reviewer Figure 1. I appreciate that the authors re-binned their data according to the light-and dark phase. However, this analysis is NOT about simple binning. It is about calculating δ power for similar times spent in NREMsleep. Therefore, it is about dividing the total amount of time spent in NREMS in different percentiles in the light and dark phase. Accordingly, the datapoints will not be placed in regular time intervals, but they will be displaced according to where the mean values of the time bin come to lie.

While I can understand that this can be considered as a detail, I encourage the authors to present their main figure according to the standards in the field.

My second remaining concern is that the authors should once again read the recommendations of the Senior Editor to use careful wording regarding the evidence against SHY given all that was discussed. For example, I still see the last sentence of the abstract "…strong evidence against the view that sleep drives widespread downscaling" unchanged.

Finally, the Supplement to Figure 5 seems not necessary to me.

Reviewer #2:

The change in the title is welcome, but no attempt has been made to address my other comments. My original comments were:

– The experiments were performed too soon after a major invasive surgical procedure, which is certainly expected to affect sleep and wake quality, because the immune system and thermoregulation can be still compromised especially in young animals.

The current version of the manuscript states, for example, "Animals were given 2-3 days of recovery before data collection, at which point sleep/wake behavior had stabilized and was comparable to unsurgerized animals." No data were provided to support this statement, and in my view this is unacceptable for any study that attempts to address physiological functions of sleep.

The overall wake-sleep pattern is too fragmented for a straightforward comparison between effects of wake and sleep, and I still do not see evidence provided that homeostatic sleep pressure is increased during time periods referred to as "wake-dense" epochs, and decreases during "sleep-dense" epochs.

I would like to refer the authors to extensive literature which describes modelling of sleep homeostasis (based on δ power, or firing rates), specifically studies that show individual examples of how Process S changes as a function of sleep-wake states. From those studies it is clear that the dynamics occuring as a function of sleep-wake states are expected to be relatively slow, and one cannot expect prominent changes during wake or sleep states unless these are consolidated.

– The chronic fEPSP experiment shows massive state- and light-dark-dependent instantaneous variations in the amplitude of responses, which makes it very difficult to extract any meaningful sleep-wake history dependent changes, and the potential direct influence of arousal, behaviour and brain temperature on evoked responses remains not addressed.

I do not have anything to add to this point, except I would like to emphasise that unless the major light-dark differences in the amplitude of evoked responses are explained, it cannot be considered a reliable measure of synaptic strength.

Altogether, I do not think this study provides data that support conclusions such as "our data show that synaptic downscaling is not a universal function of natural sleep across cortical cell types and brain regions".

https://doi.org/10.7554/eLife.66304.sa1

Author response

[Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]

Notably, this report documents surprisingly little evidence for sleep related changes in cortical synaptic plasticity, and therefore challenges the SHY hypothesis. The editors and reviewers appreciate the novel methodology of sleep state detection, careful synaptic analysis with miniature excitatory postsynaptic current recordings and in vivo multi-electrode array local field potential recordings. However, there are concerns about the difficulty in comparing the results of this study to others, given that the approach for picking sleep-related epochs for analysis may be problematic. This concern includes, but is not limited to the result that the sleep dense and wake dense periods appear to be as brief as a few hours (Figure 3A), which may be an insufficient duration to allow significant plasticity.

Given the significant strengths of the end-point analysis (timed recordings of minis and in vivo LFP), if you are interested, we would encourage submission of new manuscript that addresses with new experiments the issues of timing of sleep-related experimental epochs to allow for a more direct comparison to previous studies.

We have significantly extended our data and analyses to address the major points raised above, and have also performed extensive textual revisions to clarify our experimental logic and procedures, and to explain how our paradigms and results compare to previous studies.

The major changes to the manuscript are:

1. To address the timing and characteristics of sleep related epochs, we have performed a detailed analysis of the sleep patterns of the young (P25-31) Long-Evans rats used in these experiments. Consistent with previous reports we find modulation of the amplitude of slow wave sleep (SWA) at this age (new Figure 1C), and in general their sleep/wake amount and distribution between light and dark phases are quite similar to those reported previously for older Long-Evans rats (Figure 1; Frank and Heller, 1997; Frank et al., 2017).

2. We have added data showing that even very long periods of natural sleep and wake – up to 5 hr (sleep dense) or 13 hr (wake dense) – do not drive changes in thalamocortical synaptic efficacy in neocortical neurons. These data are presented in the new Figures 6-8. We note that the longest epochs we analyze are as long as these animals will naturally experience, so if an important function of sleep is to constitutively drive synaptic changes then they should occur over this time frame.

3. We provide additional analysis of our mEPSC data. We show that in addition to mEPSC amplitude being stable after sleep and wake dense epochs, this measure of postsynaptic strength does not correlate with the proportion of time spent awake during wake dense epochs (Figure 3 figure supplement 1E), or with the drop in δ power during sleep dense epochs (Figure 3 figure supplement 1B).

4. We note that the criteria we use for sleep and wake dense epochs, the age range of our animals, and the brain regions examined are comparable or overlapping with those used in a range of earlier studies (summarized in the attached reviewer Table 1 and now discussed in detail in the text).

5. We now extend our behavioral state classification to include REM, NREM, and active and quiet wake (Figure 2 —figure supplement 1), and quantify changes in fEPSP amplitude and evoked spikes separately for each of these 4 states (new Figures 6-8). Further, we find that L-D transitions rapidly modulate fEPSP amplitude, and we now account for this in our analysis (new Figure 5G).

6. We performed a number of additional analyses and extended and revised most of the figures to better support our major conclusions; these are explained in our point-by-point response to the reviewers below.

7. We have completely re-written the manuscript to better motivate and explain our approach and to incorporate these new data and analyses, and in particular we now strive to make clear how our data fit in with previous findings.

Reviewer #1:

The study by Cary and Turrigiano aims to test further the hypothesis that wakefulness is associated with increased synaptic strength and sleep leads to decreased synaptic efficacy. Previous studies on this topic differ widely with respect to the choice of experimental models and methodology, which prevents straightforward comparison between studies. To my opinion the current work is inconclusive as it stands, because it uses experimental models, approaches and data analyses not conventionally used in studies addressing sleep regulatory mechanisms, and does not take into account factors that have important influence on sleep dynamics, behaviour and brain activity.

We respectfully disagree that our approach differs fundamentally from those used previously. Previous studies have used (1) changes in mEPSC amplitude/frequency (Liu et al., 2010; Khlghatyan et al., 2020), (2) changes in firing rates (Vyazovskiy et al., 2009; Miyawaki and Diba, 2016), (3) changes in evoked fEPSPs (Vyazovskiy et al., 2008), and (4) changes in synapse morphology or protein composition (de Vivo et al., 2017; Diering et al., 2017), to argue that sleep and wake drive opposite changes in synaptic strengths; here we use 3 of these 4 standard approaches in the same study. The major difference between our study and previous studies is that here we combined a careful analysis of the wake and sleep history of each animal with rigorous measurements of each of these correlates of synaptic efficacy. We appreciate the reviewer’s point that we originally did not provide some important details of our sleep/wake analysis, and we have now revised the manuscript to include these details, as described in our response to specific points below.

1. In this study young long-Evans rats were used between postnatal day P25 and 31. Although literature on the development of sleep regulation at this age is limited, evidence suggests that sleep in young rats of a comparable age is more fragmented and a clear cut homeostatic dynamics of SWA is not yet well pronounced: https://journals.physiology.org/doi/abs/10.1152/ajpregu.1990.258.3.R634.

To this end, the first type of analyses I would have suggested is to plot individual hypnograms (as on Figures2 and 5 in the paper cited above) and identify time points which are not simply preceded by wake-rich or sleep-rich intervals, but differ with respect to homeostatic sleep pressure. Generally, I felt the question asked by the authors could have been better addressed if the experiments were designed and data analyses were performed taking into account the context of daily sleep physiology.

Our experimental paradigm was explicitly designed to take circadian aspects of sleep physiology into account, but we did not make this point clearly in our original presentation of the data. As suggested, we now provide many hypnogram examples, and sleep histories for each animal (Figure 3, Figure 3 – supplement 2; Author response images 1-5), as well as an analysis of slow wave activity (SWA) across the circadian cycle (new Figure 1C). These show that LE rats of this age have sleep patterns that look similar to older animals: they sleep more during the light phase as expected, and show oscillations in SWA that are higher at the beginning of the light phase (when they have slept less) and decrease as they accumulate sleep, then rise again during the dark phase as they spend more time awake (Figure 1C). We agree these data are important for interpreting our results, and we now show that sleep-dense periods generally ensue when SWA is high, as expected. Further, we now provide an analysis showing that there is no correlation between the drop in sleep pressure (as assessed by slow wave power) during a sleep dense epoch, and mEPSC amplitude at the end of that epoch (new Figure 3 —figure supplement 1).

Author response image 1
Sleep history from start of EEG recording for animals used for slice experiments.

Unperturbed sleep patterns from initiation of EEG recordings, which started 2-3 days postsurgery. Dots show proportion of time spent awake for individual animals (averaged in 1 hr bins). Green line shows average across animals, shading = SEM. Sleep/wake behavior stabilized soon (roughly 4-8 hr) after the initiation of recording..

Author response image 2
Endpoint behavioral state proportions.

Top row (Last 4 hrs), represents the relative time spent in each behavioral state in the last 4 hrs prior to sacrifice for each individual animal used for ex vivo slice recording. The top row is separated in to three columns for each type of experiment. From left to right, sleep dense experiments (in light period), light period wake dense experiments, dark period wake dense experiments. Green and blue dotted lines represent wake (>65%) and sleep (<35%) dense thresholds, respectively. Bottom row (Last hour), is identical to the top row but for the last hours of time prior to sacrifice. Black bars represent mean and 95% confidence interval.

Author response image 3
Visual Cortex L4 hypnograms.

Here the hypnograms for each individual animal used in L4 visual cortex ex vivo slice experiments are plotted. Green bars indicate instances of wake, magenta indicates REM, and blue NREM. Grey rectangle indicates hours when lights are off (i.e. the dark period). Top plot, hypnograms for animals in the light period sleep dense experiment. Bottom plot, hypnograms from light period wake dense experiment.

Author response image 4
Visual Cortex L2/3 hypnograms.

Here the hypnograms for each individual animal used in L2/3 visual cortex ex vivo slice experiments are plotted. Green bars indicate instances of wake, magenta indicates REM, and blue NREM. Grey rectangle indicates hours when lights are off (i.e. the dark period). Top plot, hypnograms for animals in the light period sleep dense experiment. Middle plot, hypnograms from light period wake dense experiment. Bottom plot, hypnograms from dark period wake dense experiment.

Author response image 5
Prefrontal Cortex L2/3 hypnograms.

Here the hypnograms for each individual animal used in L2/3 prefrontal cortex ex vivo slice experiments are plotted. Green bars indicate instances of wake, magenta indicates REM, and blue NREM. Grey rectangle indicates hours when lights are off (i.e. the dark period). Top plot, hypnograms for animals in the light period sleep dense experiment. Middle plot, hypnograms from light period wake dense experiment. Bottom plot, hypnograms from dark period wake dense experiment.

We would also like to point out that studies in support of sleep-dependent regulation of synaptic strength in rodents have spanned a wide age range, including animals of a comparable (e.g. Liu et al., 2010; de Vivo et al., 2017; Spano et al., 2019) or even much younger age (de Vivo et al., 2019); sleep is thought to be especially important in young animals, so if a fundamental role of sleep is to reduce synaptic efficacy, we might expect to see it in young animals during this highly plastic phase of postnatal development.

2. It is an important limitation of the study that the animals were generally allowed only a few days of recovery after invasive surgery ("Animals were given 2-3 days of recovery before data collection"). Post op treatment is not mentioned but I would have expected that the animals receive some sort of analgesia for at least a few days as I presume is required by the office of Laboratory Animal Welfare. I would not be surprised if the recovery process or post-op treatment have a significant influence on sleep regulation and on some of the measures taken in this study.

Our standard protocol for these chronic recordings is to begin habituating animals to the recording chamber on the third day post surgery (e.g. Hengen et al., 2013, 2016; Torrado Pacheco, 2020). Animals are fully recovered at this point (eating and drinking normally, gaining weight, grooming normally, etc.; following NIH guidelines and our IACUC approved protocol), and this timeframe gives us the greatest recording stability for these chronic recordings. We have shown previously using this same surgery and recovery protocol that baseline firing rates are already stable after this recovery period (Hengen et al., 2016; Torrado Pacheco, 2020). We have now verified that sleep patterns in our animals have also stabilized prior to the initiation of experiments (Author response image 1).

3. The description of the surgical procedure is not entirely clear. It is stated that "For animals to undergo acute slice physiology, we implanted 3 EEG screws" but "For in vivo recording and stimulation experiments, a 16 channel silicon probe" was used. Did you record EEG also in the in vivo recording and stimulation experiments? If I understand correctly, the EEG screws were implanted in the frontal and parietal derivation, and the third screw was a reference above cerebellum. Have you based your "behavioral state classification" on EEG power from the frontal or from the parietal EEG?

The methods have been expanded and clarified throughout, and we now make clear that EEG was recorded for the sleep classification in slice experiments, while for the thalamocortical stimulation experiments we used LFPs from the implanted electrodes (Methods, lines 565-566). The EEG signal was recorded as the difference between the parietal and frontal screw, grounded to the cerebellum screw. This has been added to the methods.

4. The procedure for "behavioral state classification" is not very clear. It mentions power bands and their ratios but their choice is not justified. Please provide full absolute EEG power spectra. Please explain how "Large deflections in the EMG" were normalized. It is stated "All classifications made in real time can be manually verified post hoc." Please confirm whether all classifications were actually verified.

We have now re-written the Results section and figure legend to better explain and justify our classification procedure, and we have included additional details on the behavioral recording and classification in the Methods (lines 577-596); yes, all sleep state classifications were verified post-hoc, and we now provide a benchmark of our automatic classification against manual classification (revised Figure 2D).

Further to this point, it is stated "This real time behavioral state classifier greatly improved our ability to explore the effects of sleep on synaptic strengths, because rodent sleep is both highly variable and fragmented (Figure 1A; see standard deviation Figure 2A)." What exactly is meant by variability here and I do not understand how did your classifier help to deal with that in the context of the question being addressed here?

We apologize for not making our reasoning clear. There is individual variability between animals in exactly when they experience sleep dense or wake dense epochs (new Figure 1, compare animals 1 and 2). By classifying sleep in real time we can detect these epochs when they occur, rather than relying on the average behavior, which would result in mis-classification in some animals (new Figure 1). We have bolstered this point by showing individual hypnograms (Figure 3, Figure 3 – supplement 2), as well as examples of individual (Figure 1A) and average (Figure 1B-D) sleep-wake behavior.

5. The choice of wake-dense and sleep-dense epochs is, to my view, suboptimal. As well known, the animals may and indeed do spend extended periods of time in a relatively superficial sleep state, where I would not expect any systematic homeostatic changes to occur. The analyses the authors are performing would have been more informative if time intervals compared differ with respect to levels of sleep pressure.

To disentangle the impact of sleep and wake from circadian time it is necessary to compare sleep dense and wake dense epochs from a similar circadian period, where on average the sleep pressure will be similar; we also analyzed wake dense periods from the opposite circadian time when sleep pressure will on average be different (see new Figure 1, Figure 3). Most studies designed to test SHY have used a similar design. However, for each individual animal the sleep pressure at the beginning of a sleep dense epoch will differ (as it depends on previous sleep history). Therefore, to more directly address the question of whether sleep pressure is predictive of changes in synaptic strength we looked at the relationship between the change in slow wave amplitude across a sleep dense epoch and mEPSC amplitude at the end of the epoch. We found no relationship (new Figure 3 – supplement 1), consistent with the overall lack of impact of sleep on synaptic strengths.

6. For brain slice preparation, the choice of the visual cortex and prefrontal cortex is not well explained. When selecting brain areas for this study I would have looked for evidence of sleep homeostatic response and plasticity at this early age. It would strengthen the argument if such evidence is cited.

Visual cortex was chosen because there is extensive plasticity during the visual system critical period, and we have shown previously that synaptic up and downscaling can be induced at this age in V1 (Hengen et al., 2016; Torrado Pacheco et al., 2020). Prefrontal cortex was chosen because a previous study reported sleep/wake dependent changes in mEPSC amplitude in this brain area, from animals of a comparable age (Lui et al., 2010). We have now made this rationale more explicit in the Results section (lines 220-223).

7. The approach to detect mEPSC events is not well described. It refers to "Multiple tunable parameters" that were "optimized", but not clear how this was done.

We now describe our method clearly in the methods section; the approach we use has been published previously, and allows us to do largely automated detection (Miska et al., 2018; Torrado Pacheco et al., 2020).

8. This sentence is not clear: "To establish "on" and "off" times for neurons, we used ISI contamination: when hourly % of ISIs < 2 msec was above 4%, unit was considered to be offline." Please clarify what was the purpose of this analysis.

9. Figure 1: Please clarify how the spectrogram was normalized.

For the chronic evoked-spike recordings we need to determine when during the experiment we have well-isolated units; how these “on” and “off” times are determined is now more clearly explained in the spike extraction, clustering, and sorting subsection of methods (lines 720-726). The spectrogram heatmap color was normalized to the maximum power value, such that all power values for each frequency were represented as a fraction of the highest power in the 1 hour segment of data shown (this has been added to the legend).

10. It is stated that "…on average, the chance of being in a sleep dense epoch between ZT 0-4 was less than 50% (Figure 2B), indicating that circadian time is not a good predictor of sleep history for individual animals." And then later "We wondered whether circadian time might impact postsynaptic strengths independently of sleep/wake history. To test this, we entrained animals to an inverted light/dark cycle…". It is unclear how inverting the LD cycle helps the problem of the potential circadian influence on synaptic strength or any other variable. Does Figure 3A, where ZT0-12 are shaded, shows an example of an inverted LD cycle?

We did not explain our reasoning clearly in the original version of the manuscript. Our purpose was to compare sleep dense and wake dense periods that occurred during the light cycle (within a 5 hr circadian window) to compare the impact of sleep and wake largely independently of circadian time; we also wished to compare wake dense epochs that occurred during opposite circadian times, to probe for any influence of circadian cycle on mEPSC amplitude. We inverted the L-D cycle in this later group of animals in order to keep all other experimental variables (time of slicing and recording) the same for the experimenter. We have now clarified this in the methods section (lines 537-542).

11. It is observed that "mEPSC amplitudes in L2/3 pyramidal neurons were identical after wake dense or sleep dense epochs". Please provide further information on how wake and sleep dense epochs were distributed across the light and the dark periods.

The sleep/wake history and endpoint (including when in the L or D cycle) for each animal for the slice experiments is now shown in Figure 3, and Figure 3 – supplement 2 (Author response images 2-5 for full details). Measurements were taken within two windows: either ZT 3-8 (after sleep or wake dense epochs), or ZT 13-16 (after wake dense epochs).

12. Given the pronounced effect of behavioural state on the evoked potentials, it is essential to ensure that the behavioural state is identical for all comparisons. Without knowing the behavioural state it is difficult to conclude whether the effects or lack thereof are related to fluctuations in arousal, movement or reflect changes in synaptic efficacy. I should point out that the figure 4F shows that most longest waking periods are associated with an increase in evoked responses from sleep before to sleep after (please compare hr9 vs hr13, hr34 vs hr38, hr57 vs hr62 or hr82 vs hr86).

The reviewer raises an excellent set of points that prompted us to undertake a significant additional analysis of the impact of behavioral state and L-D periods on fEPSPs. The increase in evoked responses that the reviewer noticed was actually due to the transitions between L and D (not noted on the original figure). We have now separated our fEPSP measurements by behavioral state and by ZT time (new Figure 58) so that this effect can be clearly seen. For all 4 behavioral state classifications (REM, NREM, active wake and quiet wake) there is a clear and rapid change in fEPSP amplitude at L-D transitions, and no other significant changes as a function of ZT time (new Figure 5G). We now analyze the impact of time asleep or awake on fEPSP amplitude and slope independently for each behavioral state, and take the L-D transition into account; consistent with our earlier data, we find that fEPSPs do not change as a function of time spent awake or asleep (new Figure 6, 7).

Reviewer #2:

Cary and Turrigiano conclude from a series of experiments using minis, evoked responses, and firing rates that they are unable to replicate several findings previously reported by the Tononi and Cirelli group and several other laboratories in support of the synaptic homeostasis hypothesis. There are many reasons for these supposedly negative findings. Without conducting a more careful analysis using restrictive criteria to define sustained periods of sleep and waking, and without additional experiments to control for crucial factors such as level of arousal and brain temperature, these results are impossible to interpret. In short, before the authors can conclude that they are unable to replicate the findings by Vyazovskiy et al., and other labs, should conduct the experiments using the same, or similar, carefully controlled conditions.

We have revised this manuscript to include substantial new analyses to address the specific concerns of the reviewer. We wish to emphasize here that we have used similar measures of synaptic efficacy, and experimental conditions that are as well (or in some cases better) controlled, than previous studies.

1) There are several reasons that can explain why the authors failed to see significant differences between sleep and waking; (1) the criteria to define sleep dense and wake dense periods (65% of total time for 4 consecutive hours) are not very restrictive; for instance, in Vyazovskiy et al., 2009 spontaneously awake rats were sacrificed during the dark phase after a long period of continuous waking (1 hour, interrupted by periods of sleep not longer than 4 min), and after spending at least 75 % of the previous 6 hours awake; similar criteria were used in de Vivo et al., 2017 in mice; Liu et al. used a lower cut-off, of 65%, but crucially, the spontaneous waking episodes considered for the study occurred during the first part of the dark phase, when mice and rats are much more active; here, mice were studied in the first part of the light period,

The duration of time, and criteria for defining “sleep dense” and “wake dense” vary substantially across studies that have been used to support sleep and wake-driven changes in synaptic strengths and firing rates. We have included a table with the major such studies, listing these and other experimental details for ease of comparison with our work (Author response Table 1). Most comparable for our mEPSC recordings were the experiments of Liu et al., (2010) which used >65% sleep or wake over 4 hours, and reported differences in mEPSC amplitude/frequency between groups. Other studies with comparable criteria to those used for our mEPSC recordings were Diering et al., (2017) (averaged 69% sleep in 4 hours and 81% wake in 4 hours); Vyazovskiy et al., (2008) (4 hours for fEPSP experiments); and Vyazovskiy et al., (2009) and Watson et al. (2016), which found effects of sleep and wake on firing rate after periods of an hour or less. We note that we include data from wake dense epochs during the dark phase (Figure 3C-G, D. Wake condition).

Study
Citation
Sleep
Definition
Wake
Definition
Method for
Classification
Liu et al., 2010>65% 4 hours>65% 4 hoursVideo coding
Vyazovskiy et al.,
2008
>75% 6 hours –
biochemical experiments
Up to 4 hours –
Cortical field potential
– no explicit threshold
>75% 6 hours –
biochemical experiments
Up to 4 hours –
Cortical field potential
– no explicit threshold
EEG and EMG
De Vivo et al., 2017>75% 7 hours (light period)>70% 7 hours (dark period)
Sleep Dep. 7 hours (light period)
Video coding
Diering et al., 2017~69% 4 hours (light period) – No Explicit
Threshold
~81% 4 hours (dark period) – No Explicit
Threshold
Video coding
Watson et al., 2016Avg. 2/3 hour
– No Explicit Threshold
Avg. 1/3 hour
– No Explicit Threshold
LFP, EMG, video/motion detector
Miyawaki & Diba,
2016
>30 min with interruptions <1 min>15 min with interruptions <1 minLFP/EEG, EMG
(3/4 animals)
Vyazovskiy et al.,
2009
46/60 min in last hour50/60 min in last hour
Sleep Dep. 4 hours (light period)
EEG, LFP, EMG

While we used a threshold for selection of >65% sleep dense or wake dense for our slice experiments, the average values were higher: 69% for sleep dense, 76% wake for light period wake dense (L. Wake), and 71% for the dark period wake dense animals (D. Wake; Author response image 2; we have added these values to the Results section, lines 159-161). Additionally, we required that the last hour be especially wake or sleep dense (>70% awake/asleep). Furthermore, we had significant variation in wake densities in our dataset (up to 92% wake dense), and we now show that there is no relationship between wake density and mEPSC amplitude across experiments (Figure 3 —figure supplement 1E). As in previous studies we include data on wake dense epochs during the dark phase, and again see no impact of wake on mEPSC amplitudes. Thus our inability to replicate the findings of Liu et al., (2010) cannot be attributed to the duration of our sleep dense and wake dense epochs, but are likely due to other issues with that study, such as lack of information on layer and cell type (as discussed in the Discussion section, lines 389-396, 407412).

Finally, we now show that fEPSPs and evoked spike rates are stable even across the longest sleep and wake dense epochs these animals naturally experience (~5 or 13 hr, respectively; new Figure 6), and this conclusion holds when we use more stringent criteria for sleep and wake dense states (>75% dense, new Figure 6-8). If the longest periods of consolidated sleep and wake these animals experience are not sufficient to drive detectible oscillations in synaptic strength, then the impact of sleep and wake states on synaptic strengths is unlikely to be functionally important.

and as expected and shown in Figure 2B, the probability of single animals to be awake > 65% for 4 consecutive hours was very close to zero; in fact, given figure 2A and B, it is difficult to understand how such long episodes of sustained wake could be found, how many of them, and in how many mice; a figure showing the raw sleep/wake data for all the mice used for the in vitro study (figure 3) should be shown; in line 117, the authors refer to figure 1B, but that figure only shows an example for 60 minutes, not 4 consecutive hours; again, based on figure 2, it is hard to imagine how the authors could find enough episodes in enough mice to perform the experiments; this is further confirmed by the 4 traces shown in figure 3A: none of the 4 examples show >65% waking time for the last consecutive hours; the same issue applies to the layer 2/3 results; also, the legend states that only 3 rats were used for the sleep group, although 4 traces are shown in figure 3A; none of the traces for the inverted wake group are shown, and they should, since results for the inverted waking group actually do show some changes (see below).

We think the reviewer misunderstood what we were plotting in the original figures, and the point we were making about individual variability. We now provide additional data and have revised our figures and description to clarify what we mean. While a given animal has a high probability of experiencing a sleep or wake dense epoch at some point during the day (Figure 1A), when this probability is averaged across animals it can be seen that it fluctuates over ZT but remains pretty low at any given time (new Figure 1B).

This is precisely why circadian time cannot be used as a proxy for sleep dense and wake dense periods, as many studies nonetheless do (discussed in Frank and Cantera, 2014). On the other hand, by following individual animals over several days and quantifying their S-W behavior in real time, we were able to find a sleep or wake dense epoch that met our criteria and ended within a selected circadian window in every animal. Finally, we now show the endpoint analysis for each animal used in the slice experiments in revised Figure 3, Figure 3 – supplement 2, Author response images 2-5 (3-6 animals/condition were used, for a total of 31 animals for the slice experiments). We also illustrate more conventional hypnograms for most animals used in the slice experiments (new Figure 3, Figure 3 – supplement 2; for all hypnograms, Author response images 3-5).

2) There are inconsistencies between the data presented by the authors and their conclusions. Firstly, in Figure 3G the authors did find an increased mEPSC amplitude in inverted wake vs sleep animals, however, they downplayed this point in the text by saying "minor shift"; In fact, it was significant according to K-S test (see Figure 3 G legend). The K-S test but not ANOVA is an appropriate statistical test used for examining the amplitude of mEPSCs, because the distribution of mEPSC amplitude is not normalized and the comparison among means from experimental groups is not appropriate.

We do not believe we are being inconsistent. We see no difference in the mean mEPSC amplitude between conditions for any cell type or brain area, and a very small difference in the distribution of mEPSC amplitudes between the Sleep Dense in Light and Wake Dense in Dark in PFC L2/3 (but not in V1). Demonstrating differences in the population mean by cell is the gold-standard measure in the synaptic plasticity field for supporting any contention that there is a change in quantal amplitude between conditions, and whether we use parametric or non-parametric statistics for this comparison none of these differences are statistically significant. Looking at the cumulative distribution of quantal amplitudes by selecting a number of events from each cell can tell you if there are subtle differences in the shape of the amplitude distribution between conditions, but relying on this approach to detect amplitude differences when one does not see it in the cell means is problematic. The KS test is extremely sensitive to very small differences, because it relies on cumulative differences between distributions with many samples (individual mEPSC amplitude or inter-event intervals; we select 60 events per cell to avoid biasing the contribution of any one cell). Further, it is debatable whether one should consider each of these events from a neuron as an independent measurement, as the KS test assumes. Finally, we note that the difference in the median value in the cumulative amplitude distribution in PFC L2/3 is ~5%, much smaller than (for example) the differences reported using anatomical measures (~18%, de Vivo et al., 2017). For all these reasons we consider this to be a minor shift in the amplitude distribution with no significant change in the mean amplitude, which is how we report these data.

Importantly, there is no difference in the cumulative distributions between Wake Dense and Sleep Dense conditions measured during the same circadian period (light period); this modest difference is only detected between conditions at opposite circadian times. We thus interpret this as a possible modest effect of circadian time on the mEPSC amplitude distribution in PFC L2/3 pyramidal neurons. Because we see no such effect in visual cortex it appears to be cell-type specific, as has been reported for other circadian effects on quantal amplitude and synaptic transmission (Bridi et al., 2020). Taken together our data suggest a very modest and cell-type specific effect of circadian time, but not sleep or wake history, on the distribution of quantal amplitudes. We have attempted to clearly lay out our interpretation of these data in the Results section (lines 226-230).

Secondly, In Figure S1F, the authors did show a very significant shift to the left in the cumulative distribution of mEPSC inter-event interval in the WD group as compared with the sleep group in L2/3 PFC neurons (SD-WD p<1e-5), which suggested a higher mEPSC frequency in the WD group than in the sleep group if measured with this parameter. In terms of absolute value, the mEPSC frequency was also higher in WD group than in the sleep group (although it was not significant). Therefore, the statement that synaptic strength was stable across sleep/wake periods is questionable at least in these L2/3 PFC neurons.

Indeed, we reported and discussed differences in mEPSC frequency between conditions in PFC, and we have now moved the mEPSC frequency data to the new Figure 4 and discuss these data more fully. Again there were no significant differences in in the cell averages in any condition, but the cumulative distributions were different in PFC; see our discussion above for relative merits of these two measures. In PFC L2/3 frequency was higher after L. Wake dense then after D. Wake or L. Sleep dense conditions; this suggests that sleep and wake history alone cannot explain this effect.

It is also unclear what these mEPSC frequency differences say about changes in synaptic strength. Many variables can affect spontaneous release frequency without impacting evoked synaptic strength (see revised section of Discussion addressing this, lines 427-438; Choy et al., 2018; Sharma and Vijayaraghavan, 2003; Zhou et al., 2000; Liu and Tsien, 1995). Third, previous studies have arrived at opposite conclusions as to whether sleep deprivation modulates mEPSC frequency in PFC (Liu et al., 2010 found changes; Khlghatyan et al., 2020 did not). Finally, morphological measures in support of SHY document changes in the size but not the number of synaptic contacts (de Vivo et al., 2017), which should correlate with changes in mEPSC amplitude rather than frequency. Thus our hypothesis going into these experiments was that we should see changes in mEPSC amplitude as a function of time spent asleep or awake. We agree this effect on mEPSC frequency is potentially interesting, and we now discuss these points more thoroughly (lines 427-443) – but they do not contradict our statement that all three of our functional measures that are direct correlates of synaptic efficacy (mEPSC amplitude, fEPSP amplitude, and ability to evoke spikes) are unaffected by preceding periods of sleep and wake.

3) It is not clear whether the recording of mEPSCs from naturally wake and sleep rats was well controlled throughout the investigations. It seems that the preparation of slices from these two groups were performed at different times of the day. This means slices were cut at different time points for these two groups (Figure 3A is misleading). Therefore, the variation in slice conditions may mask the difference between groups. Although the authors did sample a big number of cells for each group, I am not sure whether this will help to limit the effect of variation resulting from the slice preparations. Note that Liu et al. took care of running paired experiments, in which one slice from a control animal and one from a waking/sleep deprived animal were always run in parallel the same day, to limit technical variability.

We show the endpoint of all slice experiments in ZT time in the new Figure 3 and Figure 3 —figure supplement 2. The endpoints for all L. Wake dense and L. Sleep dense experiments were within ~4 hours of each other in ZT time and are on average ~8 hours different from the D. Wake dense condition. For the D. Wake condition animals were on an inverted LD cycle so that all slice experiments were performed at a similar time of day for the experimenter. We note also that the mean and variance in our mEPSC amplitude measurements across conditions are quite similar, and are very close to published values from other studies using similar recording and selection criteria (Torrado Pacheco et al., 2020; Lambo and Turrigiano, 2013; Bridi et al., 2020); in contrast, the baseline values in the Liu study were quite variable (for a discussion see Timofeev and Chauvette, 2017). Finally, we do not think that sacrificing animals at precisely the same ZT time when they have had different prior sleep/wake histories (due to individual variability) is a more controlled approach.

4) The rationale for selecting layer 4 should be better justified (line 124); firing rates vary across waking and NREM sleep across the entire thalamocortical system, not just in layer 4; thus, taken alone this is not a compelling reason to select layer 4 neurons; on the other hand, it is well known that after the end of the critical period, the thalamocortical synapses targeting layer 4 of primary somatosensory, auditory, and visual cortex lose most of their ability to undergo plastic changes under physiological conditions, and that ability can be reinstated only by specific manipulations such as prolonged unimodal or crossmodal sensory deprivation or peripheral nerve transection. Thus, it seems that the authors of this study chose to focus on synapses that are known to have little plasticity to test synaptic homeostasis hypothesis, whose main claim is that sleep is the price for plasticity during waking; in fact, if the results related to layer 4 could be trusted (but see all the issues related to selection of behavioral states and minis analysis), then they would actually be a nice confirmation of the main tenet of this hypothesis.

We do not understand the reviewer’s reasoning here. First, SHY posits a global regulation of excitatory synapses, meaning it should be apparent in different brain regions and cell types. We thus chose two brain regions and two distinct excitatory cell types in order to cast a broad net; this included a brain region previously implicated in SHY (Liu et al., 2010). Second, the reviewer is incorrect that we chose a period of time at which thalamocortical synapses onto L4 excitatory neurons are no longer plastic. Our experiments were performed during the classic visual system critical period (which closes after P33) precisely to encompass the period of most pronounced V1 plasticity; we and others have directly demonstrated plasticity of both thalamocortical and intracortical excitatory synapses at this age (see e.g. Miska et al., 2018, Cooke and Bear, 2010; Wang et al., 2013; Kirkwood et al., 1996). We mentioned this in the original version of the manuscript but have now made our rationale more explicit.

Evoked responses:

The analysis of the evoked responses is impossible to interpret because too many crucial details and control experiments were not performed.

We have added additional data and analysis to address many of the specific issues raised below, and have completely revised our presentation of these data (see new Figures 5-8).

1) First, the criteria to define prolonged periods of sleep and waking for the evoked responses analysis are not specified and cannot be deduced from Figure 5A, which has no time bar (same problem in Figure 6). In Vyazovskiy et al., a decrease in slope was present only after at least 2 hours of consolidated sleep, or more than one hour of continuous waking (most rats were awake for 2-4 hours). Vyazovskiy also stimulated only twice, before and after sleep or waking, while it seems that in the current study pulses were given every 20 to 40 secs continuously, for days.

In the original manuscript we showed fEPSPs measured across 2 (sleep dense) or 3 (wake dense) hours in state (original Figure 5E); we now include data for much longer periods of time, up to ~5 hours for sleep and ~13 hours for wake dense epochs (new Figure 6-8). The advantage of our approach – continuously sampling fEPSPs at very low frequency during natural periods of waking and sleeping – is that we can follow the kinetics of any changes that might occur with time in a state, and can separately assess the impact of behavioral state, light-dark transitions, and circadian time. Additionally, because we use an optogenetic approach to label a specific set of synapses, we are sampling from a defined and consistent set of synapses across animals. We are now able to show that fEPSP amplitude (Figure 6, 7) and slope (Figure 6 —figure supplement 1) are stable across 65% sleep and wake dense periods that last as long as ~5 hr (SD) and ~13 hr (WD); we see the same stability if we use the more stringent criteria of >75% dense, where we find epochs that extend >2.5 hr for sleep and 8 hr for wake dense. Thus we now examine comparable or longer periods of time, with greater resolution, than in the mentioned study.

2) Second, evoked responses are exquisitely sensitive to neuromodulatory conditions (arousal levels) and subtle changes in arousal could mask any subtle effect due to sleep/waking history. Vyazovskiy et al., took great care in controlling for this factor by delivering the stimuli under a very standardized quiet waking condition, which required 2 investigators watching the animal full time. As they state, "We did not attempt to record evoked responses continuously for several hours in freely behaving rats because it is impossible to maintain the animals in a standard quiet wakefulness for more than a few minutes." Moreover, Vyazovskiy et al., confirmed that changes in slope were present after controlling for response amplitude. Note that their major results were confirmed by comparing high vs low sleep pressure in all 4 behavioral states separately.

3) The classifier distinguished 3 states, but not active and quiet waking (line 87). This is a crucial limitation because waking responses vary due to arousal levels (see point 2), and differ between quiet and active waking. There is strong evidence from electrophysiological and calcium imaging data that the activity of V1 neurons is very sensitive to locomotion; thus pooling evoked responses across "waking" is inappropriate.

The reviewer raises an excellent point – we agree that it is important to take behavioral state into account when measuring fEPSPs. Rather than trying to deliver stimuli only in particular states (as in Vyazovskiy above), we instead sampled continuously (at low frequency) while carefully monitoring behavioral state (using LFP and Video); thus we obtained interleaved samples from each state over time. In our original analysis we did not break out fEPSP measurements by the state they were measured in. We agree this is an issue because fEPSP amplitude is rapidly modulated by behavioral state. Furthermore, rodents quickly cycle through different states, for example even a sleep dense epoch is interrupted by short bouts of wake. To address this set of issues we now plot fEPSPs measured within a specific state; for sleep dense epochs we separately plot fEPSPs measured during REM or NREM, and for wake dense epochs we separately plot fEPSPs measured during active and quiet wake. These data are shown in the new figures Figure 6, 7; it can be seen that this does not change our conclusion that fEPSPs remain stable across even very long sleep dense and wake dense epochs.

4) Third, evoked responses are exquisitely sensitive to brain temperature. Very small changes in brain temperature can affect evoked responses and mask any additional effect due to sleep and waking history. Vyazovskiy et al., controlled for this factor by conducting specific experiments in which brain temperature was also measured; in doing so, they could demonstrate that the changes in the slope of the evoked response did not correlate with changes in brain temperature. This issue is especially crucial in the current study, where light pulses were used to evoke the response. On a related matter, the intensity of the laser stimulation should be specified.

Vyazovskiy et al., 2008 showed that there was no significant increase in temperature across an extended waking period, nor did they find that the slope of electrically evoked EPSPs was correlated with brain temperature; thus we do not expect brain temperature to be a confounding variable in our experiments. Since we do not see changes in fEPSP slope with time spent awake or asleep, we do not in any case see how a change in brain temperature could explain our results; one would have to hypothesize that changes in temperature are affecting fEPSPs in such a way as to precisely compensate for a gradual change in fEPSPs due to time spent awake or asleep. The light pulses were 1 ms every 20-40 seconds at a maximum intensity of 18 mW/cm2; this duration and intensity of stimulation is not sufficient to change brain temperature (Owen et al., 2019).

Firing rates:

The current negative findings relative to firing rates in V1 are at odds with the evidence provided by at least 3 different labs showing that mean firing rates decreases with sleep, including Vyazovskiy et al., in barrel cortex (Nature 2011), Grosmark et al., in the hippocampus (Neuron 2012), Watson et al., in frontal cortex (Neuron 2016), Miyawaki et al., in the hippocampus (Curr Biol 2016, Cell Reports 2019). As for the evoked responses, it is unclear whether the criteria used by Cary and Turrigiano to define prolonged periods of sleep and waking were stringent enough to match those used in other studies. Cary and Turrigiano cite one paper from their lab (line 57) showing that mean firing rates do not change during extended periods of sleep and waking. At the very least, it would be appropriate to quote all the other studies that found the opposite.

We present data on evoked rather than spontaneous firing, but we nonetheless have now included a detailed discussion of the findings from various labs on spontaneous firing rates in different brain areas as a function of time awake or asleep (lines 483-490). We note that the results of the studies cited above do not all agree, and have found different effects (over different timescales) of REM, NREM, and wake on firing rates; this suggests that the impact of sleep and wake on spontaneous firing likely depends critically on brain area, rather than reflecting a global function of sleep and wake states. Our lab now has two separate datasets from V1 where we were able to follow the firing of individual neurons over long periods of time in freely behaving animals as they cycle between many bouts of sleep and wake; we see small differences in firing between states that are expressed rapidly during state transitions, but no significant change across sleep or wake states (Hengen et al., 2016; Torrado Pacheco et al., 2020). We note that rather than measuring ongoing firing (which arises from many internal and external sources) as for these earlier studies, here we probe the ease of evoking spikes using thalamocortical stimulation. Consistent with the other measures of functional synaptic strength we examine, we find that the ability to evoke spikes changes rapidly by state but does not depend on time spent asleep or awake. The criteria for defining prolonged periods of sleep or wake for the evoked firing were the same as for evoked fEPSPs.

The authors state that many units were lost in the course of the several days of recordings. The exact number should be stated.

This number is now included in the methods section (lines 724-726). 93% of cells were well isolated for >1/3 of the full experiment time, and 80% of cells were well isolated for >1/2 of full experiment time. This means that each unit included in our analysis was held across many sleep/wake epochs.

Reviewer #3:

This paper asks how states of sleep and wakefulness regulate global synaptic strength at various cortical pyramidal neurons. A major proposition for this question is formulated in the well-known SHY hypothesis, for which there is mostly indirect molecular and structural evidence. Therefore, it is very important to test SHY with direct functional measures of synaptic strength. This paper does so using electrophysiological methods and is, therefore, an important contribution to a long overdue question.

The authors depart from a form of homeostatic plasticity that is known to be regulated by sensory experience and largely based on amplitude measurements of mEPSCs. When now applied to sleep and wakefulness, results are overall negative, thus questioning that SHY affects homeostatic plasticity. The experiments are well-done and the results are clear and striking.

Still, I would encourage the authors to consider a number of points in a revised version of their manuscript.

1) Insufficient information about animal husbandry is provided. All experiments are done in young rats around and shortly after weaning. Weaning changes metabolism and stress levels are high. Synapse growth and development progress rapidly. When were animals weaned relative to the day of surgery? How were they housed prior to and after surgery, and how was recovery from surgery monitored (weight loss and recovery, stress monitoring, etc.)? A time period of 2-3 days for recovery from surgery is very short (~1 week is typical). How much time was given for habituation to the tethering to the recording cables (~1 week is typical)? Is the sleep-wake behavior of the animals stable from 2-3 days after surgery?

We have now added details of our animal husbandry, which followed closely our previously published procedures and timelines (e.g. Hengen et al., 2013, 2016; Torrado Pacheco et al. 2019, 2020). Rats were weaned at P21 and from then were housed with littermates. Surgeries were performed between P22-P26. Animals were housed with littermates during recovery; post-surgical monitoring was approved by the Brandeis IACUC and followed NIH guidelines. All animals received two days of post-operative care comprised of daily injection of Meloxicam and Penicillin. Recovery was rapid and animals were fully recovered (based on normal eating/drinking/weight gain, grooming, and play with littermates) by 2-3 days post-surgery, again consistent with our previous work in these critical period animals. Animals were continuously monitored over several days in their home-cage recording chamber and sleep patterns and fEPSPs generally stabilized within 24 hr of initiation of recording, and prior to data acquisition, as can be seen from our analysis of the ensuing three days of continuous monitoring (Author response image 1).

2) More information on the sleep-wake behavior of these young rodents is also needed. Figure 2 suggests that the typical preference for sleep over wake during the light period found in adult is not there yet. Do these animals show homeostatic regulation of sleep? The SHY hypothesis implies slow-wave activity in the renormalization of synaptic strength. Slow-wave activity is proposed to be key for synaptic scaling during sleep. Therefore, it would be important to show some evidence that slow-wave activity varies with time-of-day and/or after sleep deprivation.

We performed a number of additional analyses of the Sleep/Wake behavior of these young LE rats, and now include these data in the new Figure 1. They indeed have more total sleep in the light phase and more total wake in the dark phase, as reported previously for LE rats of around this age (Frank and Heller, 1997); even in older rats sleep and wake are distributed across both the light and dark phases (Endo et al., 1997; adult WKY rats, Leemburg et al., 2010).

The reviewer raises an excellent set of questions around the amplitude of slow waves and their role in driving plasticity, that we have now dug into. First, we show that our animals exhibit a substantial circadian oscillation in slow wave amplitude, that follows the expected pattern (new Figure 1C). Second, because SHY predicts that synaptic downscaling should be tied to the amplitude of slow waves (i.e. a larger decrease in SWA should correspond to more dramatic downscaling), we looked at the relationship between the change in slow wave amplitude during sleep dense periods and mEPSC amplitude/frequency. We found no relationship, suggesting again that neither mEPSC amplitude or frequency is constitutively downscaled by slow wave sleep.

3) Experiments coincide with critical periods of the visual system. If animals are taken 7-10 days later, after the closure of the critical period, are effects of prior sleep-wake history still negative? What about thalamocortical projections for which the critical period closed much earlier, such as for the whisker-to-barrel system?

The reviewer raises a very interesting set of questions that will be important to examine in future studies. Here we focused on testing some of the core tenets of SHY during a highly plastic period of time when we know we can induce robust synaptic up- and downscaling (Hengen et al., 2016; Torrado Pacheco et al., 2020). We also tested two different brain areas and two different cell types, and used three different measures of functional plasticity. We think our data as they stand provide strong evidence against the idea that sleep serves the global and universal function of constitutively downscaling synaptic strengths. We also acknowledge that this may not be true for all cell types in all brain areas at all developmental times, and now explicitly state this (lines 497-501).

4) Regarding the sleep-wake monitoring and analyses, a major weak point is that scoring of vigilance states is done in 10 sec intervals, which is 2.5x the window commonly used. This means that the duration of NREMS bouts are overestimated because brief arousals will go undetected. There should be an estimation provided for the limited time resolution.

We have now clarified our classification procedures in the revised manuscript. For the real-time sleep/wake classification we used a 10 s interval, to allow us to rapidly determine when a sleep or wake dense epoch had occurred. We subsequently went back and performed a more stringent classification of all sleep/wake behavior with resolution down to 1s. This was sufficient to allow us to detect microarousals and (for example) accurately determine the state in which fEPSPs were elicited. We now add additional detail to the methods (lines 609-611) to clarify this point.

5) The authors only show "Time awake" prior to slice preparation. I suggest they instead show time spent in NREM and REM sleep prior to sacrifice. Possibly, then, animals should be selected based on how much time they spent in NREM only.

We now include additional information on sleep behavior prior to sacrifice in the new Figure 3, Figure 3 —figure supplement 2 (Endpoint state proportions, Author response image 2; all hypnograms, Author response images 3-5). The time spent in NREM during sleep dense epochs is pretty tightly clustered across animals (Author response image 2, Sleep Dense) and is quite different from NREM time in Wake dense epochs during either the L or D phase.

6) Major forms of homeostatic synaptic plasticity in sensory cortices (e.g. effects of monocular deprivation) develop over time scales of days. The authors show themselves in a previous work that after a full day of MD, mEPSC amplitude is reduced to only ~95% of control (Lambo2013). Similarly, in cultures, homeostatic scaling of excitatory or inhibitory synapses is typically observed after 24 h of receptor antagonism. In contrast, here, authors work with time intervals of 4h during which both sleep and wake are present, with mean bout length on the order of hundreds of seconds. This could mean that changes in mEPSC amplitude might simply not be detectable at this point. The mEPSC as a measure for synaptic strength could thus not be sensitive enough. The same considerations might apply for prefrontal cortex, for which the maturational profile is even less known.

The reviewer raises the important question of how sensitive our approach is. We can detect 10-20% mEPSC amplitude changes induced during up- or downscaling, measured ex vivo using the same approach we use here (Lambo and Turrigiano, 2013, Hengen et al., 2013; Torrado Pacheco et al., 2020). Given the variance and sample size, both our mEPSC recordings and fEPSP recordings are capable of detecting an effect size of <10%, which is smaller than that predicted by other studies supporting SHY (Liu et al., 2010; de Vivo et al., 2017). It is possible that sleep and wake constitutively drive such subtle changes in synaptic strength that they are too small to measure over naturally occurring periods of sleep and wake, but then we are not sure what this would mean in terms of the function of these changes. In particular if constitutive changes driven by sleep are too small to induce a detectible functional change in synaptic transmission (as in our evoked spike measurements), then we would suggest they are unlikely to be very important for brain function.

7) More generally, one might wonder about whether mEPSCs are suitable for monitoring changes in synaptic strength. Miniature EPSCs reflect the response of a synapse to the release of a single vesicle. Thalamocortical synapses, however, activate their postsynaptic targets via multivesicular release. This might lead to more vigorous postsynaptic receptor recruitment. Therefore, to fully assess whether or not sleep-wake modify synaptic strength, it would be important to look at how synaptic strength quantified by action-potential-dependent vesicular release is affected. I suggest to use single-fiber stimulation to trigger vesicular release via single action potentials.

We appreciate the reviewer’s point that mEPSCs do not monitor all aspects of synaptic function. We recorded mEPSCs for a number of reasons: (1) this is a classic assay for homeostatic up and downscaling, and we can detect such changes ex vivo when we induce synaptic scaling with visual deprivation or eye reopening; (2) a previous study reported sleep-induced changes in mEPSC amplitude (Liu et al., 2010); and (3) changes in synapse area and in AMPAR accumulation after periods of sleep have been reported recently (Diering et al., 2017; de Vivo et al., 2017), and if these changes are functionally meaningful then they should correlate with changes in mEPSC amplitude. We also used two other functional measures of synaptic efficacy that are sensitive to both pre- and postsynaptic changes in transmission – namely evoked thalamocortical transmission and ability to evoke spikes. These approaches allow us to monitor thalamocortical efficacy in vivo in freely behaving animals, so we think there are major advantages to this over ex vivo measurements of single-fiber efficacy taken at a single point in time. While no measure alone captures all aspects of excitability that might be modulated by sleep and wake, taking these three measures together provides quite a comprehensive survey of synaptic function.

8) Looking at evoked fEPSPs in response to sensory or afferent stimulation has a long tradition but is not a very informative type of data. Evoked fEPSPs are not only composed of sources arising from the synaptic input, but also by the tendency of the network to switch between up and down states. This is particularly the case during nREM sleep because on-going oscillations are strong and excitatory input can switch the networks between states. So, neither amplitude nor slope of these responses, nor their stability across sleep periods, tell much about "synaptic strength".

We respectfully disagree with the reviewer that the amplitude/slope of the thalamocortically evoked fEPSP within L4 is not informative. Many studies have validated that the initial rapid downward deflection arises primarily from activation of synaptic inputs within L4 (Khibnik et al., 2010; Cooke and Bear, 2010), and changes in this amplitude induced by monocular deprivation correlate nicely with changes in thalamocortical EPSCs evoked ex vivo (Miska et al., 2018), using a similar approach to that used here. We have now carefully separated these evoked responses by behavioral state to control for the amplitude differences between states, and regardless of the state in which they are measured we see no sleep or wake-driven changes in fEPSPs. Again, we think each of the approaches we use to probe for functional changes in synaptic strength have some advantages and some disadvantages, but taken together they paint a consistent and compelling picture.

[Editors’ note: what follows is the authors’ response to the second round of review.]

As you will see from the reviewers comments below, the manuscript has been improved but there are remaining issues that need to be addressed, as outlined below. Normally these would require additional experiments, but given pandemic conditions that limit feasibility for such, then at the very least the title, abstract and conclusions need to be moderated to reflect that the conclusions may be limited to an early developmental period. Further, upon careful inspection of the in vivo LFP data, I see that there is a potential confound that needs to be examined. While these are clearly thalamic-dependent LFP responses, as they are recorded in the cortex, and evoked by ChR2 activation of thalamocortical projections, what is not clear is how one might distinguish the specific thalamic fEPSP from the overall LFP response especially with optogenetic stimulation which can lead to a significant fiber volley (see PMID: 27489370).

We thank the editor and reviewers for the thoughtful response to our revised manuscript, and have now made a number of additional changes to address the remaining concerns. The major changes we have made include:

1. We have ensured that the title (line 1), abstract (line 13), and Results section (lines 98108) all emphasize that we examine one developmental period of time, corresponding to the highly plastic visual system critical period. We note that previous studies have spanned a wide age range including earlier, comparable, and later developmental stages.

2. We have performed a number of additional analyses and made textual changes to address the remaining comments of Reviewer 1, detailed below in our response to the points raised.

3. Regarding the contribution of the fiber volley to the fEPSP: the editor is correct that the fiber volley contributes to the early voltage deflections during the fEPSP, whether the responses are evoked by electrical or optical stimulation. We have taken care to minimize the impact of the fiber volley on our estimate of fEPSP magnitude in the following ways. First, we previously characterized the thalamocortical evoked response in great detail in slice recordings from L4 after thalamocortical axons were labeled identically to our protocol here, and found that brief illumination (as we use here) produced a rapid stimulus artifact followed several ms later by a monosynaptic EPSC (Miska et al., 2018 supplemental Figure 1). The initial (predominantly monosynaptic) phase of the in vivo fEPSP we characterize here follows a similar highly stereotyped sequence: there is an immediate stimulus artifact, an early deflection that corresponds to the fiber volley (and is relatively small in L4), and then a rising phase that corresponds temporally to the monosynaptic current we observe in vitro (New Figure 5 – supplement 1). We also note that this optogenetically evoked response looks very similar to previously characterized electrically evoked fEPSPs and visual evoked potentials in L4 (Cooke and Bear, 2010; Niell and Stryker, 2008). Second, our measurement of the rising phase of the fEPSP (slope, measured between 20 and 80% of peak) is thought to be relatively insensitive to contamination by the fiber volley, so is often the standard measure reported for quantifying fEPSP magnitude (Schuman, 1996). This was a reason for measuring and reporting both the fEPSP slope as well as amplitude. Third, the Hass and Glickfield, (2016) study shows that high-frequency optogenetic stimulation in vitro does not evoke a reliable fiber volley, which would greatly complicate interpretation of evoked events.

However, in our study we confined our analysis to very low frequency stimulation (1/201/40 Hz) to avoid this issue. Finally, in addition to measuring the slope, we also measured the first negative peak, which occurs well after the fiber volley (which peaks ~1 ms post stim.; Hass and Glickfield, 2016), but is potentially contaminated by variable polysynaptic events. Notably both measures (slope and amplitude) give the same results for all analyses we performed: namely, that fEPSP varies by behavioral state, but does not change across sleep or wake states. We discuss these points in the Results section (lines 270-282).

Reviewer #1:

They authors start with a comprehensive documentation of sleep-wake behavior of young rats during the critical period for vision, followed by an ingenious approach to study synaptic strength as a function of spontaneous recent sleep-wake behaviors, followed by an in-depth analysis of in vitro and in vivo correlates of synaptic strength. I particularly liked the explicit way of the authors in motivating their choice of parameters regarding sleep-wake behavior, of animals during the critical period, and of the synapses studied in vitro or in vivo.

The authors carefully conclude that their results do not support the idea that sleep or wakefulness per se lead to global modifications of synaptic strength, at least not in visual and prefrontal cortex. Interestingly, however, the authors identified circadian variations specifically in prefrontal cortex, an observation that is worth pursuing in the future.

This manuscript is a long awaited and authoritative approach to challenge the SHY hypothesis with solid experimental quantification of functional synaptic strength.

I have a few additional comments to further improve some analysis and their documentation in this study:

1) The sleep-wake behavior of these young animals is clearly different than the one from adult animals. It is irregular and polyphasic and there is very little light-dark dependence (see Figure 1B). Therefore, it would be good to show hourly mean times spent in the different vigilance states wake, NREM and REM rather than only the 4h-slidingwindow means.

We now plot hourly means of all states in Figure 1 – supplement 1 (these new plots reveal similar dynamics). However, we do want to stress that our data are actually quite similar to published data from older Long Evans rats in terms of time spent asleep in L vs D, average length of sleep bouts, and other characteristics (Frank and Heller, 1997), and we now make this point clearly in the Discussion section. We also note that there is huge variation in sleep patterns across mammalian species, suggesting that if there is a universal function of sleep it is unlikely to depend critically on the amount or degree of fragmentation.

2) Beyond the mean times, the detailed architecture of sleep-wake behavior in the 4h-windows is also important. Why is this: even if you go for times enriched in wake or sleep, it is not the same whether this enrichment happens in many very brief bouts or in few relatively long bouts. This is particularly the case for sleep, for which fragmentation has a strong effect on plasticity/learning. Therefore, do the animals sleep in consolidated bouts, i.e. what is the mean NREM sleep bout duration? Is this duration variable between Sleep dense and wake dense, and as a function of circadian time?

The mean NREM durations in the SD 4h-window was 161.53 ± 10.4 s, vs. durations of 120.97 ± 7.8 s in the WD window during the same circadian period. Thus sleep bouts were longer during SD periods than WD periods. These values are comparable to those reported previously for Long Evans rats (Frank and Heller, 1997). We now include these values in the Results section, lines 167-168.

3) The word "consolidated" has a strong meaning in the sleep field and as it refers to the mean duration of NREMS bouts – the less they are interrupted by microarousals, the more consolidated NREMS is said to be. It should not be used to describe an enrichment of mean times spent in sleep or wake over a 4-h period (see line 113).

We have changed our language to be more precise (lines 118, 136-137, 140, 178).

4) Line 116: δ power is not the same as the size of slow waves and should not be equated. Slow waves are EEG or LFP graphoelements that at best make up a fraction in the power of the broad δ frequency band used here (0.5-4Hz). Please use these terms carefully and specify the frequency bands upon first use.

We now specify the frequency band upon first use and make clear that we are using δ power as a proxy for slow wave activity (as others have done; Vyazovskiy et al., 2008; Dijk, 2009) (lines 121-123).

5) Line 119. The time-of-day-dependence of δ power is not typically referred to as an oscillation.

We removed the term “oscillation”, lines 124-125

Also, the way the data in Figure 1C analyzed should be checked. It must be done for equivalent amounts of time spent in NREM sleep epochs that are preceded and followed by other NREM sleep epochs. If it is not done like this, the amounts of δ power at different times of day are not weighed equivalently. Therefore, please divide the total time spent in NREMS in the light and in the dark phase into similar amounts and calculate δ power within these bouts. As more time is spent in NREMS in the light phase, subdivisions can be higher in the light than in the dark phase (e.g. 12 time points in the light and 6 in the dark phase). Literature from the Paul Franken lab can be consulted to do this properly.

We redid this analysis using the approach of the Franken lab (1 hr bins during the light period, and 2 hr bins during the dark). The resulting variations in δ power across the L and D cycles look almost identical to our original analysis. We include this plot for the reviewer/editor, and describe this additional analysis in the methods section. We did not include this as a supplemental figure as it seemed redundant, but are happy to include if the editor feels this is important.

6) Lines 168-172: Please explain more quantitatively. What is a long natural wake epoch during the early light phase? What was the criterion to add a novel object? What you do here strictly amounts to a sleep deprivation that should be documented in terms of its effects on quiet vs mobile wakefulness and the increase in the time spent in wakefulness. One could also argue that this is a period of environmental enrichment that has an impact on its own on visual plasticity. These animals might also show a greater increase in δ power upon the end of wakefulness due to greater amounts of sleep loss. These caveats should be quantified whenever possible and discussed.

We have now added additional details to the methods section to document how we extended wakefulness (lines 606-612). In brief, we waited until animals had experienced ~50% wakedense in the previous 4 hr, and then encouraged further wakefulness by moving, removing, or adding new toys or stirring the bedding; this procedure was generally initiated 1.25-2.25 hr before slicing, and maintained until animals had reached criterion for wake density. It is important to note that all animals experience the removal and addition of new toys and changes to bedding regularly, so although the frequency of these manipulations is higher during this wake extension they are familiar procedures to the animals. In general, our environment is enriched compared to the standard home cage environment. We sacrificed animals immediately after wake encouragement without allowing them to sleep, so cannot say whether they would have experienced higher δ power upon entering NREM. That said, based on our analysis of the full circadian cycle in these and other conspecific animals, we predict the δ power would be higher.

7) Figure 3 —figure supplement 1. This figure indicates somewhat worrisomly that the increase in δ power at light onset compared to the end of the light period is extremely variable – from somewhere between >40%, which is very high, to ~17%, which is very low. In addition, the example recording shown in panel A is close to 50%, so where is that datapoint represented in panel B? What happened with these animals during the darkphase that their sleep pressure at light onset is so variable? Overall, I am not sure that this analysis is particularly helpful because it relates an endpoint measure of synaptic amplitudes to an unknown starting point measure.

The differences in δ power drop during the light phase between animals reflects the fact that individual animals show a lot of variability in when they sleep – as we document carefully in this study (e.g. Figure 1). This is an important take-home message of our study. We would note that individual variability is generally not carefully examined in studies of rodent sleep, so it is difficult to compare our data to previous studies. In the example in A, average δ power is ~30% (not 50%) when averaged over the time window indicated.

Overall, I am not sure that this analysis is particularly helpful because it relates an endpoint measure of synaptic amplitudes to an unknown starting point measure.

One of the predictions of SHY is that slow waves drive synaptic downscaling, and in turn downscaling should reduce slow waves (and thus δ power) (Tononi and Cirelli, 2014; Tononi, 2009). That was our rationale for comparing the change in δ power across the preceding sleep period for each animal, with the synaptic strengths at the end of the sleep period. We also included this analysis to ensure that a change in synaptic strength did not become apparent in animals that experienced a large drop in δ power. For these reasons we think it is worth keeping this analysis as a supplemental figure.

8) Line 624. What are slice behavioral data?

We have clarified our language here (lines 638-639)

9) The finding that evoked field potential amplitudes in visual cortex were larger in NREMS than in wakefulness during both the light and the dark phase is intriguing and in contrast with previous observations on evoked auditory field responses (see e.g. the literature from Yaniv Sela et al.,). Rather what has been seen is that the secondary outward components of the evoked responses are disproportionately increased during NREM sleep.

In Sela et al., 2020, they find that auditory stimuli produce comparable firing rate responses between states in auditory cortex and somewhat diminished responses in perirhinal cortex. They further find that a subset of neurons termed “late-responding” (>40 ms post stimulus) in auditory cortex show a reduction in response during NREM. Larger cortical evoked potentials in NREM have been found by others using optogenetic stimulation (Matsumoto et al., 2020), or auditory stimuli (Hall and Borbely, 1970; see also Nir et al., 2015 for a discussion of the mixed results on this topic). We find that all aspects of the response (amplitude, slope, second outward component) are larger in NREM as compared to wake, in accord with the majority of studies in cortex.

Evoked field responses are, as already mentioned in my first review, also problematic because they can be contaminated because of on-going oscillatory activity. Can the authors better describe when these responses were elicited in response to on-going up- and downstates, for example? And how these components were removed to isolate the evoked field response?

We have addressed potential variability in fEPSPs during NREM in three ways. First, we calculated the coefficient of variation of responses in all 4 vigilance states, and CV was not larger in NREM than in other states (NREM ~0.3; other states ~0.3-0.4). This suggests that slow waves do not much impact our fEPSP measurements. Second, we measured fEPSPs separately in REM (where there are no up and down states) and NREM for the same sleep states, and in neither case do we see a relationship between time spent asleep and fEPSP slope or amplitude (Figure 6B, C). And third, we used a “sandwich” approach to detect changes across sleep states, where we compared fEPSPs in wake states before and after a prolonged period of sleep. These later two analyses do not depend on measurements during NREM and yield the same result.

Reviewer #2:

In the revised manuscript, the authors included important and interesting new analyses, but no new experiments have been undertaken, and the key issue remains that the data set, limited to results obtained in juvenile animals and analyzed in some novel, yet untested ways, is not ideal for reaching strong conclusions.

My main points I raised previously remain the same:

1. The experiments have been performed in juvenile animals, which prevents direct comparisons with most previous studies. I recommend that the title of the manuscript should state the age of animals to avoid misunderstanding.

We have modified the title, abstract, and conclusions to emphasize that we examine one developmental period of time, corresponding to the highly plastic visual system critical period. We note that previous studies have spanned a wide age range including earlier, comparable, and later developmental stages to that used here.

2. The experiments were performed too soon after a major invasive surgical procedure, which is certainly expected to affect sleep and wake quality, because the immune system and thermoregulation can be still compromised especially in young animals.

3. The overall wake-sleep pattern is too fragmented for a straightforward comparison between effects of wake and sleep, and I still do not see evidence provided that homeostatic sleep pressure is increased during time periods referred to as "wake-dense" epochs, and decreases during "sleep-dense" epochs.

4. The chronic fEPSP experiment shows massive state- and light-dark-dependent instantaneous variations in the amplitude of responses, which makes it very difficult to extract any meaningful sleep-wake history dependent changes, and the potential direct influence of arousal, behaviour and brain temperature on evoked responses remains not addressed.

We addressed each of these points very carefully in the previous revisions to the manuscript. Briefly, we showed that sleep/wake behavior is fully recovered prior to the initiation of experiments. Other aspects of animal health (grooming, weight gain, play with siblings, and measures of activity in V1) are also back to baseline. The sleep wake patterns of these juvenile LE rats are approaching adult patterns (Frank and Heller, 1997), and evidence for SHY has been provided using much younger animals with very immature sleep patterns (de Vivo et al., 2019).

Finally, we have used a variety of approaches to analyze our fEPSP data that are independent of the rapid state-dependent changes in amplitude, and they provide a consistent picture.

[Editors’ note: what follows is the authors’ response to the second round of review.]

Reviewer #1:

The authors did an excellent job replying to my concerns. In my view, the study now makes abundantly lear that it studies critical period synapses. The study also has appropriately addressed remaining analytical concerns, with one exception:

This concerns the re-analysis of the data presented in the Reviewer Figure 1. I appreciate that the authors re-binned their data according to the light-and dark phase. However, this analysis is NOT about simple binning. It is about calculating δ power for similar times spent in NREMsleep. Therefore, it is about dividing the total amount of time spent in NREMS in different percentiles in the light and dark phase. Accordingly, the datapoints will not be placed in regular time intervals, but they will be displaced according to where the mean values of the time bin come to lie.

While I can understand that this can be considered as a detail, I encourage the authors to present their main figure according to the standards in the field.

We understand the reviewer’s point. We went back through publications from the Franken lab (as suggested by the reviewer) and adopted their approach, so that δ is calculated across the same amount of NREM sleep. The results are very similar to both of our previous methods of calculating the drop in δ power. We have now replaced Figure 1C with the new version of this figure, and modified the methods section accordingly.

My second remaining concern is that the authors should once again read the recommendations of the Senior Editor to use careful wording regarding the evidence against SHY given all that was discussed. For example, I still see the last sentence of the abstract "…strong evidence against the view that sleep drives widespread downscaling" unchanged.

Upon reflection, we think this final phrase in the abstract is unnecessary and have simply deleted it. We have considered our wording throughout and have modified our phrasing in places to avoid overstating our case (for example in the final sentence of the introduction).

Finally, the Supplement to Figure 5 seems not necessary to me.

We agree and have removed this figure.

Reviewer #2:

The change in the title is welcome, but no attempt has been made to address my other comments. My original comments were:

We made numerous changes to the manuscript in the previous round of revisions to address the concerns of the reviewer. On several major points we disagree with the reviewer and outline our reasons below.

– The experiments were performed too soon after a major invasive surgical procedure, which is certainly expected to affect sleep and wake quality, because the immune system and thermoregulation can be still compromised especially in young animals.

The current version of the manuscript states, for example, "Animals were given 2-3 days of recovery before data collection, at which point sleep/wake behavior had stabilized and was comparable to unsurgerized animals." No data were provided to support this statement, and in my view this is unacceptable for any study that attempts to address physiological functions of sleep.

We used a protocol for these chronic recordings that we have established and published on extensively, comprising several independent datasets all of which show that animals have recovered both behaviorally and by electrophysiological measures prior to the start of recordings (Hengen et al., 2013, 2016; Torrado Pacheco et al., 2019, 2021). Animals are habituated to the recording chamber on the third day post-surgery and are fully recovered at this point (eating and drinking normally, gaining weight, grooming normally, etc.; following NIH guidelines and our IACUC approved protocol). This timeframe gives us the greatest recording stability for these continuous chronic recordings. In addition to showing that activity in V1 is stable by this point (citations above), we show here that evoked responses are stable over the several days of recordings used in our analysis (Figure 5D). Finally, we verified that sleep patterns in our animals have also stabilized prior to the initiation of experiments; these data were provided as Reviewer Figure 1 in our original re-submission and we now include them as Figure 1 supplement 2. Further, our data on sleep/wake distributions look very similar to previously published data from Long Evans rats of comparable ages (e.g. Frank and Heller, 1997; Frank et al., 2017). Taking all of this together, we do not think the length of recovery after surgery is a confounding factor in our data.

The overall wake-sleep pattern is too fragmented for a straightforward comparison between effects of wake and sleep, and I still do not see evidence provided that homeostatic sleep pressure is increased during time periods referred to as "wake-dense" epochs, and decreases during "sleep-dense" epochs.

As we pointed out previously, the degree of sleep fragmentation we see is exactly what is expected in rodents of this age. We can find and analyze long periods of sleep and wake that are as “dense” as those in the literature in support of SHY, and yet see no sign of constitutive downscaling. We do provide evidence that sleep pressure (measured in the usual way, as a change in δ power, Figure 1C) indeed oscillates as expected across periods of sleep and wake. Finally, we point out that we can readily detect the gating of homeostatic plasticity by sleep and wake in animals of the same age (Hengen et al., 2016; Torrado Pacheco et al., 2021).

I would like to refer the authors to extensive literature which describes modelling of sleep homeostasis (based on δ power, or firing rates), specifically studies that show individual examples of how Process S changes as a function of sleep-wake states. From those studies it is clear that the dynamics occuring as a function of sleep-wake states are expected to be relatively slow, and one cannot expect prominent changes during wake or sleep states unless these are consolidated.

The reviewer does not acknowledge that in our revised manuscript we show that even periods of time as long as 5 hours (sleep) and 12 hours (wake) do not drive detectible changes in synaptic strength. If these periods of time are not sufficient, then constitutive sleep and wake-dependent synaptic changes will simply not be detectable in rodents (or in many other animals that do not exhibit long consolidated sleep/wake states). It is also worth noting that previous experiments in support of SHY have claimed to see changes driven by as little as 4 hr of sleep or wake in rodents of comparable ages.

– The chronic fEPSP experiment shows massive state- and light-dark-dependent instantaneous variations in the amplitude of responses, which makes it very difficult to extract any meaningful sleep-wake history dependent changes, and the potential direct influence of arousal, behaviour and brain temperature on evoked responses remains not addressed.

The modulation of evoked transmission by vigilance state is an interesting and expected phenomenon, and one cannot measure plasticity in vivo without taking it into account. Our experimental design in fact allowed us to deal rigorously with these state-dependent neuromodulatory changes, by allowing us to track responses across sleep and wake and independently analyze those that were measured within a given state (NREM or REM for sleep, active or quiet wake for wake). Finally, we used several independent analytic approaches that all gave the same answer, and we found the same results whether we measured mEPSCs ex vivo, or evoked fEPSPs and spikes in vivo. Each measure has potential downsides but we feel that together they provide coherent and compelling evidence for stability of these measures across sleep and wake states.

https://doi.org/10.7554/eLife.66304.sa2

Article and author information

Author details

  1. Brian A Cary

    Department of Biology, Brandeis University, Waltham, United States
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1759-164X
  2. Gina G Turrigiano

    Department of Biology, Brandeis University, Waltham, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    turrigiano@brandeis.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4476-4059

Funding

National Eye Institute (EY025613)

  • Gina G Turrigiano

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Alejandro Torrado Pacheco for his invaluable help with the surgery and implementation of in vivo recording and expertise in handling extracellular data and the programming involved in behavioral analysis. We thank Raul Ramos for his technical expertise and aid in imaging of brain slices. Supported by NIH EY025613 (GGT).

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All animals were housed, cared for, surgerized, and sacrificed in accordance with Brandeis IBC and IACAUC protocols (#15005 and #18002). All surgery was performed under isoflurane anesthesia. All surgerized animals received two days of post-operative care including daily injection of Meloxicam and Penicillin to reduce discomfort/inflammation and risk of infection. Rats were always housed and recorded with at least one littermate.

Senior and Reviewing Editor

  1. John R Huguenard, Stanford University School of Medicine, United States

Reviewer

  1. Anita Lüthi, University of Lausanne, Switzerland

Publication history

  1. Received: January 16, 2020
  2. Accepted: June 20, 2021
  3. Accepted Manuscript published: June 21, 2021 (version 1)
  4. Version of Record published: July 12, 2021 (version 2)

Copyright

© 2021, Cary and Turrigiano

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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