Converging evidence in rats and mice shows that many cortical and hippocampal excitatory synapses are stronger after wake than after sleep (Cirelli and Tononi, 2021). Excitatory synaptic strength is thought to increase on average during wake due to incidental learning leading to net synaptic potentiation, and to decrease during sleep due to synaptic renormalization that is both widespread and selective (Tononi and Cirelli, 2014; Cirelli and Tononi, 2021). The net decrease in synaptic strength afforded by sleep is likely to serve an essential function, maintaining the energetic cost of synaptic activity under control, promoting memory consolidation, and preserving the brain’s ability to learn by avoiding synaptic saturation (Diekelmann and Born, 2010; Tononi and Cirelli, 2014).

In rodents the evidence for an overall increase in synaptic strength during wake and a net decrease during sleep comes from multiple sources. Electrophysiologically, experiments in mice and rats found that the slope and amplitude of cortical evoked responses, as well as the amplitude and/or frequency of miniature excitatory postsynaptic currents, are higher after wake than after sleep (Vyazovskiy et al., 2008; Liu et al., 2010; Bjorness et al., 2020; Bridi et al., 2020; Khlghatyan et al., 2020). The molecular evidence was provided by studies in synapse-rich preparations obtained from cortex, hippocampus, and the whole forebrain. These experiments showed that the expression of the glutamatergic AMPA receptors is higher after several hours of sustained wake relative to several hours of consolidated sleep, independent of whether the animals were spontaneously awake at night or sleep deprived during the day (Vyazovskiy et al., 2008; Diering et al., 2017; Miyamoto et al., 2021).

Ultrastructural evidence has been obtained using serial block face scanning electron microscopy (SBEM) (Cirelli and Tononi, 2020). It was found that the average size of the axon-spine interface (ASI), a structural measure of synaptic strength (Desmond and Levy, 1988; Buchs and Muller, 1996; Cheetham et al., 2014), is larger after wake than after sleep in the axospinous synapses of the mouse primary motor and sensory cortex (de Vivo et al., 2017), as well as in the hippocampal CA1 region (Spano et al., 2019). By contrast, synapse number does not change between sleep and wake except for the nonperforated synapses of CA1, whose density increases after sleep deprivation (Spano et al., 2019). Thus, the effects of sleep and wake in cortex and hippocampus are not so much due to changes in synapse number, but to changes in the surface expression, subunit composition, and phosphorylation levels of the AMPA receptors, which ultimately control the trafficking of these receptors in and out of the synaptic membrane (Cirelli and Tononi, 2021).

In the mammalian brain the great majority of neurons and synapses are housed in the cerebellum, which remains highly plastic even during adulthood (Imamizu et al., 2000; Hansel et al., 2001). Cerebellar granule cells account for approximately 60 out of 86 billions in the human brain (Herculano-Houzel, 2010) and the glutamatergic parallel fiber synapse, between the parallel fibers of the granule cells and the spiny dendrites of the Purkinje cells, is the most abundant type of cerebellar synapse, with each Purkinje cell in the rat receiving ~175,000 parallel fiber inputs (Napper and Harvey, 1988a). These synapses undergo long-term depression in response to the paired stimulation of parallel fibers and climbing fibers, a form of plasticity that has classically been linked to motor learning (Ito, 2001). Presynaptic and postsynaptic forms of long-term potentiation are also present at these and other cerebellar synapses and contribute to different forms of learning (Gao et al., 2012; D’Angelo et al., 2016; Romano et al., 2018). However, very little if anything is known about how sleep and wake modulate cerebellar plasticity in general (Canto et al., 2017) and, more specifically, whether they affect the number and strength of the parallel fiber synapses. Here, we used SBEM to investigate whether the parallel fiber synapses of the mouse cerebellar posterior vermis undergo ultrastructural changes after several hours of sleep, spontaneous wake, and sleep deprivation.

Three groups of mice were selected based on the sleep/wake behavior during the last 6–8 hr before the brains were collected, according to strict criteria as in previous studies (see Materials and methods for details). They included a spontaneous waking (W) group of mice that were mostly awake for the first 6–8 hr of the dark period (n=4), an extended waking (EW) group of animals that were sleep deprived during the first 6–8 hr of the light period (n=4), and a sleep (S) group of mice that spent most of the first 6–8 hr of the light phase asleep (n=6; Figure 1A). These 14 mice were the same used to assess ultrastructural sleep/wake synaptic changes in CA1 (Spano et al., 2019), and 8 of them were also used for the analysis of primary motor and sensory cortex (de Vivo et al., 2017). As previously reported (Spano et al., 2019), in the last 6–8 hr W mice were awake for ~86% of the time, EW mice were kept continuously awake, and S mice slept ~84% of the time (Spano et al., 2019). The S mice were compared to both wake groups to identify ultrastructural changes driven by the sleep/wake behavior while controlling for time-of-day effects (day vs. night) and the effects of sleep deprivation (spontaneous vs. forced wake) (Figure 1A).

Figure 1

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Figure 2

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In each mouse we used SBEM to acquire stacks of ~500 images (~10,000 μm³) in the posterior vermis of lobule VI (Figure 1B), a region that contains mostly zebrin II positive Purkinje cells (Cerminara et al., 2015). We specifically targeted the superficial part of the molecular layer, approximately 20–30 µm from the pial surface (Figure 1C), where the distal tips of the dendritic branchlets are located (Ichikawa et al., 2016). Our 3D reconstructions showed that roughly two-thirds of the segmented dendrites (66 out of 96) were terminal branches (Figures 1D and 2). In these dendritic tips most synapses are established by the parallel fibers, with few if any climbing fiber synapses (Ichikawa et al., 2016). Spiny dendritic segments were randomly selected within each stack and all their protrusions, also called spines (see Materials and methods), were manually segmented by trained annotators blind to experimental condition. The length and diameter of the dendrites were balanced across groups (see Materials and methods; Table 1). Overall, across all mice 96 dendritic branches were segmented (S=43; W=29; EW = 24) (Figure 2) for a total of 7388 spines. Of these spines, 6853 had a synapse, and in 6618 of them the ASI could be fully traced and measured (N of ASIs, S=3004; W=1766; EW = 1848; at least 358 ASIs/mouse) (Table 2).

Density of spines and synapses

All dendritic branches were very spiny (Figures 2 and 3A) with an average spine density across all mice of around 2 per dendrite surface area (Table 2). The great majority of these spines (92.7% across all mice) contained a synapse and in almost all cases only one synapse was present in each spine, in line with previous studies (Harris and Stevens, 1988; Napper and Harvey, 1988a; Napper and Harvey, 1988b ). First, we tested for sleep/wake effects on the absolute density of all spines and separately for spines with and without a synapse. By applying linear mixed effect (LME) models with condition as a fixed effect and mouse as a random intercept (Materials and methods, Table 3), we found that the absolute density of spines did not change across experimental groups (p=0.1774; N of spines per dendrite surface area; mean ± std; W=1.83 ± 0.30; EW = 2.16 ± 0.44; S=1.97 ± 0.51) (Figure 3B). The absolute density of synapses also did not change across experimental groups (p=0.1476; N of synapses per dendrite surface area; mean ± std; W=1.76 ± 0.28; EW = 2.04 ± 0.41; S=1.78 ± 0.46) (Figure 3C and Table 2). On the other hand, the density of spines without a synapse (‘naked’ spines), which account for ~7% of all spines across mice, showed a significant effect of condition (p=0.0023; mean ± std; W=0.08 ± 0.06; EW = 0.13 ± 0.07; S=0.19 ± 0.10). Post hoc tests found a significant difference between S and W mice, with the observed density of spines without a synapse being 58% higher in the S group relative to the W group (p=3e-5; Figure 3D). A similar trend was present between S and EW mice (23% higher in S; p=0.0906; Figure 3D). Using an LME model to test for changes in the proportion of spines with and without a synapse relative to all spines, we also found a significant effect of condition (p=0.0014): in S mice the spines lacking a synapse represented ~10% of all spines, roughly double the proportion in W (p=2e-5) and EW (p=0.0138) mice (Figure 3E, left). Conversely, spines with a synapse represented ~95% of all spines in W and EW mice and significantly less, ~90%, in S mice (S<W, p=0.0001; S<EW, p=0.0139; Figure 3E, right). There were several types of spines without a synapse, including single spines (Figure 3F), branched spines with two spines both lacking a synapse (Figure 3G), branched spines with one spine with and the other without a synapse (Figure 3H), and spines lacking a synapse coming off the head of a spine with a synapse (Figure 3I). S mice showed a higher proportion of spines without a synapse of all types, but only in a few cases the comparison with the wake groups was statistically significant (Figure 3J–M).

Table 3

Spine density (all spines) − sqrt(#/area)
Random effects	Standard error
Mouse (intercept)	0.0618
Residual	0.1426
Fixed effects	Level	Estimate	Standard error
Intercept		1.4681	0.0426
Condition	EW (reference)	0	0
	W	–0.1140	0.0590
	S	–0.0757	0.0542
Spine density (spines with synapse) − sqrt(#/area)
Random effects	Standard error
Mouse (intercept)	0.0605
Residual	0.1349
Fixed effects	Level	Estimate	Standard error
Intercept		1.4241	0.0412
Condition	EW (reference)	0	0
	W	–0.0994	0.0569
	S	–0.1002	0.0522
Spine density (branched synapses) − sqrt(#/area)
Random effects	Standard error
Mouse (intercept)	0.0350
Residual	0.1760
Fixed effects	Level	Estimate	Standard error
Intercept		0.5920	0.0401
Condition	EW (reference)	0	0
	W	–0.0934	0.0547
	S	–0.2281	0.0503
Spine density (spines without synapse) − sqrt(#/area)
Random effects	Standard error
Mouse (intercept)	0.0387
Residual	0.1027
Fixed effects	Level	Estimate	Standard error
Intercept		0.3443	0.0286
Condition	EW (reference)	0	0
	W	–0.0791	0.0395
	S	0.0760	0.0363
Proportion of spines without synapse − sqrt(# without/# total)
Random effects	Standard error
Mouse (intercept)	0.0270
Residual	0.0642
Fixed effects	Level	Estimate	Standard error
Intercept		0.2331	0.0189
Condition	EW (reference)	0	0
	W	–0.0386	0.0240
	S	0.0674	0.0262
Proportion of synapses with spinula – sqrt(# with spinula/# total)
Random effects	Standard error
Mouse (intercept)	0.1285
Residual	0.0898
Fixed effects	Level	Estimate	Standard error
Intercept		0.4276	0.0669
Condition	EW (reference)	0	0
	W	–0.1730	0.0944
	S	0.0733	0.0862

Figure 3

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Overall, these results show that the proportion of spines lacking a synapse is higher after sleep than after wake while the opposite is true for the spines that harbor a synapse, and both single and branched spines likely contribute to these differences. These changes occur without net changes in the total number of spines or synapses, most likely because spines lacking a synapse account for a small minority of all spines.

Branched spines and branched synapses

How can we account for the sleep/wake difference in the proportion of spines with and without a synapse? To address this question we focused on the branched synapses, which are defined as two (or sometimes more) distinct synapses housed in two (or sometimes more) spines that share the same neck, called branched spines (Figure 4A). In the cerebellum, like in the cerebral cortex and in the CA1 stratum radiatum, most synapses are harbored in single spines but in all three regions a sizable fraction of synapses and spines, around 15%, are branched. While the density of single synapses was similar across the three groups (mean ± std; W=1.47 ± 0.25; EW = 1.66 ± 0.34; S=1.62 ± 0.41), a significant effect of condition was found for the density of branched synapses (p=0.0013). The absolute density of branched synapses was twofold higher in both wake groups relative to the sleep group (mean ± std; W=0.29 ± 0.18; EW = 0.38 ± 0.21; S=0.16 ± 0.13), and post hoc tests confirmed that these differences were significant (S<EW, p=2e-5; S<SW, p=0.0141; W=EW, p=0.2010) (Figure 4B). The absolute density of branched spines, which almost always harbor synapses, also showed a significant effect of condition (p=0.0179), although post hoc tests found a significant difference only between S and EW mice (Figure 4C). In relative terms, branched synapses accounted for 15.1 ± 3.6% of all spines in W mice, 16.9 ± 6.3% in EW mice, and 7.6 ± 1.2% of all spines in S mice (mean ± SD per mouse; Figure 4D). In almost all cases (93%) the branched synapses contacted boutons belonging to two different parallel fibers (Figure 4E); in rare cases there were three branched synapses, each contacting a different fiber (Figure 4F). In only 7% of cases the branched synapses contacted two boutons of the same fiber (Figure 4G) or the same bouton (Figure 4H). For almost all branched synapses it was possible to track the axon far enough in the stack to see that the same parallel fiber established another synapse with a different dendrite (~2/3 cases) or the same dendrite (~1/3 cases).

Figure 4

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In sum, we found that the absolute number of branched synapses is twice higher after wake (both spontaneous and extended) than after sleep, on average by ~0.2 synapses per dendrite surface area. Correspondingly, the proportion of branched synapses over all spines decreases from an aggregate of ~16% in the wake conditions to ~8% after sleep. Conversely, the absolute number of spines (unbranched or branched) lacking a synapse (‘naked’ spines) is higher after sleep, on average by ~0.1 spines per dendrite surface area and, correspondingly, the proportion of naked spines is ~10% after sleep compared to ~5% after wake. Altogether, the decrease in branched synapses after sleep can account for the overall increase in the proportion of naked spines after sleep (taking into account the variability across dendrites). These results suggest that, in the course of sleep, branched spines lose one synapse, or both synapses, or are converted into single spines after losing both the synapse and the housing spine, while the opposite changes occur in the course of wake.

Spine morphology and ASI

The spines were highly homogeneous in shape, mostly club-shaped, and their synapses were usually located on the side of the spine head rather than on its tip (Figure 5A), in agreement with previous reports (Spacek and Hartmann, 1983; Harris and Stevens, 1988; Napper and Harvey, 1988a). Most spines contained tubules and vesicles of the smooth endoplasmic reticulum (SER), as previously described (Harris and Stevens, 1988; Martone et al., 1993), while only a few spines housed elements of the non-SER such as coated vesicles and multivesicular bodies (Table 2). The ASI size did not vary greatly within or across dendritic branches (range from 0.0053 to 1.1131 μm²). In the same mice, we previously found that ASI sizes follow a log-normal distribution in the cerebral cortex (de Vivo et al., 2017) and a bimodal distribution in the CA1 stratum radiatum of the hippocampus (Spano et al., 2019). In the cerebellum, the distribution of ASI sizes was unimodal, with mainly small/medium ASIs, but not enough very large ASIs to be considered a log-normal distribution (Figure 5B). Fitting an LME model for ASI (square-root transformation) with condition and dendrite diameter as fixed effects and mouse and dendrite as random effects revealed no effect of condition, neither on the mean ASI (p=0.0664; post hoc tests: S=EW p=0.2182; S=W p=0.4861; SW <EW p=0.0279) (Figure 5C) nor on the cumulative ASI (sum of ASIs per dendrite area; p=0.9098) (Table 4). The average ASI value was significantly lower in branched synapses compared to non-branched synapses (p=0.0002).

Figure 5

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Table 4

ASI − sqrt(ASI)
Random effects	Standard error
Dendrite (intercept)	0.0134
Mouse (intercept)	0.0164
Residual	0.1219
Fixed effects	Level	Estimate	Standard error
Intercept		0.3797	0.0227
Dendrite diameter	Continuous (linear)	–1e-7	2e-5
Condition	EW (reference)	0	0
	W	0.0331	0.0129
	S	0.0197	0.0118
ASI (with branched) – sqrt(ASI)
Random effects	Standard error
Dendrite (intercept)	0.0132
Mouse (intercept)	0.0168
Residual	0.1217
Fixed effects	Level	Estimate	Standard error
Intercept		0.3812	0.0227
Dendrite diameter	Continuous (linear)	7e-7	2e-5
Condition	EW (reference)	0	0
	W	0.0333	0.0132
	S	0.0184	0.0120
Branched	No (reference)	0	0
	Yes	–0.0217	0.0052
ASI density (sum ASI/surface area)
Random effects	Standard error
Mouse (intercept)	0.0425
Residual	0.0608
Fixed effects	Level	Estimate	Standard error
Intercept		0.3159	0.0247
Condition	EW (reference)	0	0
	W	–0.0042	0.0349
	S	–0.0131	0.0316

Other features that were measured inside each spine, including the presence of mitochondria and of components of the endoplasmic reticulum, did not change across experimental groups (Table 2). The only exception was the proportion of synapses with a spinula, which was higher in S mice than in W mice but highly variable in EW mice (effect of condition, p=0.0386; post hoc tests, S>W, p=0.0115; S>EW, p=0.6708) (Table 2).

Sleep and wake profoundly affect synaptic activity and plasticity in the cerebral cortex and the hippocampus, with electrophysiological, molecular, and ultrastructural evidence pointing to overall excitatory synaptic strength being higher after wake and lower after sleep (Tononi and Cirelli, 2014; Cirelli and Tononi, 2020; Cirelli and Tononi, 2021). The cerebellum contains the largest number of neurons and excitatory synapses in the brain – most of them between parallel fibers and Purkinje cells – and is known to be highly plastic. However, it was hitherto completely unknown whether sleep and wake can affect cerebellar synapses, and specifically the number and strength of the parallel fiber synapses. Here, we addressed this question by applying serial electron microscopy to obtain the full 3D reconstruction of more than ~7000 of spines located on the distal dendrites of Purkinje cells.

While the vast majority of spines carry a synapse, we found that a sizable minority do not. Remarkably, these ‘naked’ spines account for only ~5% of all spines after spontaneous or forced wake (~0.1 spines per dendrite surface area) but for ~10% of all spines after sleep (~0.2 spines per dendrite surface area). Most spines contacted by a parallel fiber carry a single synapse (Napper and Harvey, 1988a; Napper and Harvey, 1988b), but a fraction of spines share the same neck and carry two or occasionally three synapses (branched synapses on branched spines). By investigating branched synapses, we then found that they also decreased from wake to sleep, and that the reduction in the absolute number of branched synapses can account for the overall change in the proportion of naked spines. Specifically, there were on average ~0.3 branched synapses per dendrite surface area after sleep deprivation (~17% of all spines), but only ~0.1 after sleep (~8% of all spines). Thus, in the course of sleep branched spines may lose one synapse or both synapses, or they may be converted to single spines after losing both the synapse and the housing spine, whereas the opposite changes occur in the course of wake. These changes occur with no significant changes in the total number of spines and parallel fiber synapses with behavioral state, presumably because naked spines and branched synapses constitute a minority of all spines and synapses.

We also found that sleep and wake do not affect the size of the ASI, a structural measure of synaptic strength (Desmond and Levy, 1988; Buchs and Muller, 1996; Cheetham et al., 2014). This is in contrast with our findings in cortex and hippocampus, where sleep and wake lead to changes in the average strength of the synapses rather than in their number (Cirelli and Tononi, 2021). Thus, the effects of sleep and wake point in the same direction in all three brain regions that we have examined so far, with more and/or stronger synapses after wake than after sleep. However, the underlying mechanisms are quite distinct. In the cerebellum sleep and wake affect the number of branched synapses and the relative number of spines that carry a synapse but not the average synaptic strength. In cerebral cortex and hippocampus instead, sleep and wake mainly affect the size of the ASI but not the number of synapses. These regional differences are notable, since the current study utilized the same 14 mice previously used for the analysis of CA1 (Spano et al., 2019) and 8 of them were also used for the analysis of the primary motor and sensory cortex (de Vivo et al., 2017), but they are not unexpected. Specifically, the lack of changes in ASI size is less surprising if one considers the striking difference in the distribution of synaptic sizes between cortex and hippocampus on one hand, and the cerebellum on the other hand. In the first two regions the distributions are log-normal and bimodal, respectively, meaning that synaptic sizes (and strengths) cover a wide range and include large synapses. By contrast, the sizes of the parallel fiber synapses follow a unimodal distribution, meaning that these synapses tend to be all of the same small-medium size. This narrow range of sizes strongly suggests that, in general, plastic changes in these synapses are implemented more via all or none changes (adding or removing a synapse) than through graded changes in size.

Limitations and conclusions

Several studies have shown that the cerebellar cortex is involved in the early phases of cerebellar learning, while later stages involve the cerebellar and vestibular nuclei downstream (D’Angelo et al., 2016; Canto et al., 2017). Because of the labor-intensive nature of ultrastructural studies, we could only examine structural changes in synapses across sleep and wake in a restricted portion of cerebellar cortex that contains a relatively homogeneous population of Purkinje cells. In the area that we targeted, the vermal lobule VI, most or all Purkinje cells express the molecular marker zebrin II (Cerminara et al., 2015). Zebrin positive and zebrin negative Purkinje cells differ in many structural and functional parameters, from the dominant type of climbing fiber input that they receive to the level of intrinsic excitability and spiking activity (Zhou et al., 2014; Cerminara et al., 2015; Viet et al., 2022). The extent to which the current results extend to zebrin negative Purkinje cells is unknown. On the other hand, zebrin negative cells express stronger intrinsic and synaptic plasticity relative to zebrin positive cells (Wadiche and Jahr, 2005; Viet et al., 2022).

In the cerebral cortex, we previously reported that the sleep/wake changes in ASI size are accompanied by several changes in the peripheral astrocytic processes surrounding many cortical synapses. Specifically, these processes contain more numerous but smaller glycogen granules after spontaneous and forced wake compared to sleep, suggesting increased glycogen turnover to meet the high energy demand of wake (Bellesi et al., 2018). Moreover, after a few hours of sleep deprivation astrocytic processes get closer to the synaptic cleft, likely reflecting an increased need for glutamate clearance (Bellesi et al., 2015), and astrocytic phagocytosis of synaptic elements is increased (Bellesi et al., 2017). Bergmann glia, the astrocytic cells of the cerebellum, establish extensive contacts with parallel fiber synapses and their distal processes respond to the electrical stimulation of the parallel fibers with localized increases in calcium levels (Grosche et al., 1999). Calcium signaling in Bergmann glia can, in turn, modulate the activity of neighboring Purkinje cells (Wang et al., 2012), in line with strong neuron-glia crosstalk. Moreover, contrary to cortical astrocytes, Bergmann glia express AMPA receptors, which are required for the normal development of the parallel fiber synapses (Saab et al., 2012). Bergmann glia is also routinely involved in the phagocytosis of synaptic and extrasynaptic elements, a process that increases after motor learning (Morizawa et al., 2022). Thus, given the multiple ways Bergmann glia can modulate cerebellar activity (De Zeeuw and Hoogland, 2015), and specifically the parallel fiber synapses that are the focus of the current study, future experiments should test whether sleep and wake affect the structure and function of these cells.

Very little is known about the effects of sleep and wake on cerebellar physiology (Canto et al., 2017), and the available evidence is mostly confined to the Purkinje cells. Both their simple spike activity, which is driven by the excitatory inputs from parallel fiber synapses, and their complex spike activity, which reflects the excitatory input from a single climbing fiber, are lower during non-rapid eye movement (NREM) sleep than during wake or REM sleep (Mano, 1970; Marchesi and Strata, 1971; Hobson and McCarley, 1972; Harlay et al., 1974; Canto et al., 2017). The response of Purkinje cells to microiontophoretically applied glutamate is also smaller in NREM sleep than in wake (Andre and Arrighi, 2001). This effect is not accounted for by changes in spontaneous firing rate but may be associated with the decreased level of acetylcholine and noradrenaline in NREM sleep, which is observed across the brain, including in the cerebellum (Andre and Arrighi, 2001). However, whether the sleep-wake cycle is also accompanied by changes in the number or strength of synaptic connections was completely unknown. As shown here through serial electron microscopy, the cerebellum, like the cerebral cortex and the hippocampus, also shows ultrastructural synaptic changes consistent with an overall upregulation during wake and down-selection during sleep (Tononi and Cirelli, 2014). The selective changes in branched synapses across wake and sleep also suggest that, during wake, coincidences of firing over parallel fibers may translate into the formation of synapses coincident over the same branched spine, which may be especially effective at driving the soma of Purkinje cells. During sleep, off-line activity over parallel fibers may instead lead to the pruning of branched synapses that were formed due to spurious coincidences (Figure 6). While speculative, this interpretation is consistent with electrophysiological, anatomical, and modeling studies showing that (1) many parallel fiber synapses are silent and (2) given the exorbitant number of these synapses, information storage is promoted when only some of them are effective (eloquent).

Figure 6

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All data generated in this study are included in the manuscript and supporting files (source data file 1 for figures 3 and 4; source data file 2 for figure 5).

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Author details

1. Sophia S Loschky

   Department of Psychiatry, University of Wisconsin-Madison, Madison, United States

   Contribution

   Investigation

   Contributed equally with

   Giovanna Maria Spano

   Competing interests

   No competing interests declared

2. Giovanna Maria Spano

   Department of Psychiatry, University of Wisconsin-Madison, Madison, United States

   Contribution

   Investigation

   Contributed equally with

   Sophia S Loschky

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2626-754X

3. William Marshall

   1. Department of Psychiatry, University of Wisconsin-Madison, Madison, United States
   2. Department of Mathematics and Statistics, Brock University, St. Catharines, Canada

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

4. Andrea Schroeder

   Department of Psychiatry, University of Wisconsin-Madison, Madison, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Kelsey Marie Nemec

   Department of Psychiatry, University of Wisconsin-Madison, Madison, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Shannon Sandra Schiereck

   Department of Psychiatry, University of Wisconsin-Madison, Madison, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

7. Luisa de Vivo

   Department of Psychiatry, University of Wisconsin-Madison, Madison, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5676-9279

8. Michele Bellesi

   Department of Psychiatry, University of Wisconsin-Madison, Madison, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

9. Sebastian Weyn Banningh

   Department of Psychiatry, University of Wisconsin-Madison, Madison, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

10. Giulio Tononi

    Department of Psychiatry, University of Wisconsin-Madison, Madison, United States

    Contribution

    Conceptualization, Writing - original draft, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3892-4087

11. Chiara Cirelli

    Department of Psychiatry, University of Wisconsin-Madison, Madison, United States

    Contribution

    Conceptualization, Supervision, Investigation, Writing - original draft, Writing - review and editing

    For correspondence

    ccirelli@wisc.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2563-677X

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