Peer review of: Rapid fast-delta decay following prolonged wakefulness marks a phase of wake-inertia in (2020)

Reviewers' comments: 

Reviewer #1 (Remarks to the Author): 

This manuscript provides important novel insights into the relationship between sleep-wake history 
and EEG dynamics during sleep. As the authors emphasize, the “increase in delta power” after 
sleep deprivation became synonymous to sleep homeostatic process, which is an oversimplified 
view and not an accurate representation of how sleep is regulated or the meaning of SWA and its 
underlying neurophysiological mechanisms. The results presented here are consistent with 
previous literature, including both animal and human work, and the study also presents new 
results and re-analysis of existing data, which altogether make a strong case for heterogeneity 
within the traditional slow wave frequency band. Understanding the neurophysiological 
mechanisms underlying the EEG dynamics within the slow frequency range will undoubtedly shed 
new lights on sleep homeostasis. 
This study is timely and important, and the data analyses and presentation are superb. However, I 
have several comments/suggestions for revision, mostly concerning authors’ interpretation of their 
own and others’ results. 
Major comments 
1. The title of the manuscript is somewhat misleading: it sounds as it calls into question the 
validity of EEG slow-wave dynamics as a measure for sleep homeostasis, while instead it clarifies 
the relative contribution of different frequencies within the traditional EEG slow-wave frequency, 
which have, as many studies have shown previously, different dynamics after sleep deprivation. 
For example, using a modelling approach, it has been shown that the time constants of Process S 
are greater for slow frequencies in rats: https://www.ncbi.nlm.nih.gov/pubmed/17683787. The 
shift of power towards slower frequencies (shown previously and by Hubbard et al.), is expected to 
result in a slower decrease in slower frequencies and a faster decrease in the faster part of SWA 
range. These observations do not raise any issues of “validity” of SWA dynamics as a proxy of 
sleep homeostatic process, but they help to clarify the underlying mechanisms. The study of 
Hubbard et al. highlights important limitations of the overall approach of relying on “global” EEG 
recording and spectral analysis, rather than showing that SWA is not an adequate measure of 
Process S. 
2. The observation of faster delta waves nested within slower, “multipeak” waves is interesting, 
and consistent with the findings from human studies, that different “peaks” within a multipeak 
wave could reflect slow waves originating from different sources. Have you analysed the effects of 
sleep-wake history, such as baseline vs recovery after sleep deprivation, on the phenomenon of 
“nesting“? One approach to disentangle the contribution of slower and (nested) faster delta waves 
into the spectral dynamics after SD could be to filter the signal in delta1 band and subtract it from 
the original signal (and also do the same for the delta2 band), and calculate the effects of sleep 
deprivation on spectra calculated from the resulting signals (original minus delta1 and original 
minus delta2). 
3. The effects of optogenetic silencing of CMT neurons on LFP spectral power in the cingulate 
cortex are interesting, but what happens in other brain areas after CMT silencing? Is the selective 
upregulation of delta2 band in this case site-specific and localised to the cingulate cortex? Do you 
have EEGs in other cortical areas in this study? Further, you show that the initial sleep after SD is 
characterised by enhanced delta2 band and increased brain temperature and higher EMG tone. 
Does optogenetic silencing of CMT induce these other changes in addition to the increase in delta2 
band in the cingulate cortex? 
4. Line 569: Contrary to the author’s interpretation, the change is SW slope has not been 
interpreted as an independent SW-feature. It has been shown that SW slope reflects how 
synchronised is the transition between neuronal population ON to population OFF period. If the 
entire local network transitions swiftly and synchronously into an OFF period, the latter is more 
likely to be longer, and, in turn, the length of an OFF period has been associated with SW 
amplitude. Therefore, I would argue that the key underlying phenomenon for the homeostatic 
changes in EEG or LFP-derived SWA changes is the level of synchrony across the neuronal 
population (reflected in the slope), and SW amplitude, if anything, is a secondary feature. The 

bottomline is that to my opinion it is very difficult to reach strong conclusions about the meaning 
of specific features of slow waves, or to understand how they relate to each other, using “global” 
EEG signals only. 
5. Generally, I am not convinced that there are two distinct classes of SWs. The shift in spectral 
power between high and low sleep pressure condition (shown previously and also demonstrated 
here) instead suggests a possibility that characteristics of SWs (but more importantly the 
underlying network dynamics) can change across time – perhaps different characteristics changing 
at different rates. This may result in faster and slower frequencies within the SW frequency band 
to behave differently, but it does not mean that they are regulated independently or reflect distinct 
neurophysiological processes. I would interpret the slowing of the spectral peak not as decreased 
incidence of delta2 waves, but as a change in network dynamics/synchrony, which results in delta2 
waves gradually becoming delta1 waves. 
6. The section titled “Implications for models on sleep regulation and function” provides a 
stimulating discussion, but it does not provide a clear solution to the problem of equating SWA 
dynamics with sleep homeostatic process (which I totally agree with the authors is an important 
issue in the field). The paper shows elegantly, building on previous work, that there is a 
heterogeneity within the traditional, and, of course (!) arbitrary frequency band. Different aspects 
of delta band dynamics may indeed reflect different aspects of the elusive homeostatic process, 
but subdividing SWs into delta1 and delta2 does not puts the problem to rest. 
Minor comments 
7. Line 723: It is stated "However, this increase in δ1 after SD in the mouse could relate to 
aspects other than extended wakefulness, such as the intervention to keep mice awake." Please 
elaborate. Is there any experimental evidence available for this statement? 
8. The choice of terminology for UP/DOWN states is suboptimal as it was derived from filtering 
EEG/LFP and not from neuronal activity. 

Reviewer #2 (Remarks to the Author): 

Reassessing the validity of slow-wave dynamics as a proxy for NREM sleep homeostasis 

By Hubbard et al. 

In this intriguing manuscript, the authors have (re-) analysed EEG data obtained in mice an man, 
to investigate the changes in occurrence of slow-waves in the NREM sleep EEG, as the authors 
noted changes in frequencies of delta waves in mice after sleep deprivation. Subsequently the 
authors were able to distinguish two types of delta waves (1: 0.75-1.75 Hz and 2: 2.5-3.5 Hz), 
which show two distinct time courses after sleep deprivation. A similar distinction could be made in 
the human EEG albeit with both frequencies being slower compared to mice. The dynamics of the 
delta 2 activity paralleled that of other physiological parameters changing in relation to sleep 
deprivation. Some ways of expressing and some claims in the manuscript, like for instance the 
way the two process model of sleep regulation is represented in the manuscript, the claim that the 
authors ‘discovered’ two types of delta waves, and the way the relationship between amplitude, 
incidence and slope of slow-waves are related do not justice to what has been written about this 
subject in the past and probably need correction. The detailed analysis and the conclusions about 
the existence of a transient NREM sleep state are of interest and therefore merit more attention. 
However, in my opinion the conclusions drawn are a bit over the top and the data dot not support 
the claims made. 

Major comments 
The consequences for the existing models of sleep regulation are not really fleshed out in the 
manuscript. In addition, it is a bit worrying that the authors seem to revert their attention to 
‘popular’ versions of these models instead of discussing how their data may be a challenge to the 
models as they are described in the original manuscripts. 

On page 36 the authors shortly summarize the two process model and particularly the homeostatic 
component of this model, but also build a kind of straw man of the model by writing “Its 
acceptance is so wide-spread that EEG delta power has now become the equivalent to the 
underlying sleep homeostatic process and any interventions affecting the manifestation of SWs are 
taken as a proof of altered sleep homeostasis.” However, this is not how the model originally was 
described by the authors. SWA merely reflects the level of the sleep homeostatic process and in 
the more elaborate model (Achermann Brain Res Bull 1993) the level of SWA is thought to 
represent the decrease rate of process S instead of process S itself. Next to that, there is a long 
list of changes in SWA, either induced pharmacologically or pathological (see for an overview 
Deboer 2015 Curr Topics in Behavioral Neurosci) in which it is very clear that they do not 
represent or reflect the level of the sleep homeostatic process. Therefore, the way the homeostatic 
process is represented on page 36 does not do justice to how it was originally conceptualized by 
the authors of which the model is now being challenged. I agree with the authors that there is a 
large group in sleep research who live with the described misconception, but this does not mean 
that we need to strengthen this. This is also true for the last sentence of the discussion (page 38): 
“…would allow us to refocus attention on the regulation and function of time-spent-in-NREMS and 
REM sleep, as their rebounds after SD have not been taken into consideration in the two-process 
model,…” I think this is not true. I agree that REM sleep has been mostly forgotten in the two 
process model, but particular when it comes to NREM sleep this is nonsense. The amount of NREM 
sleep in sleep homeostatic processes has been taken into consideration in all the manuscripts 
where authors not simply analysed changes in SWA, but included the durations of NREM sleep in 
this analysis by calculating slow-wave energy. Particularly in rodent studies this has been 
repeatedly applied, also by some of the authors of the present manuscript, to analyse sleep 
homeostatic responses. When it comes to the well-known ‘human’ version of the two process 
model, I agree, again that REM sleep is forgotten, but that in this model it is very clear where the 
time-spent-in-NREMS is. It is, obviously, the interaction between S and C that determines the 
sleep time (NREM and REM), but I think the authors know that as well as I do. I want to urge the 
authors to not criticize the ‘popular’ version of the two-process model. This straw man has been 
built before and does not do justice to the model. Please keep it fair and go back to the original 
publications. 
In addition to that, one can ask the question whether the finding that the sleep homeostatic 
response (in sleep intensity) consists of two types of waves, disqualify the combined activity of 
these two waves to be a good marker for sleep homeostasis? The model has proven its value and 
has shown (over the last 3-4 decades) that there is a close relationship between power density in 
the slow-wave range, sleep homeostatic modelling and predictions about sleep depth and duration. 
The finding that this homeostatic response in delta power may be shaped by two types of delta 
waves is an interesting finding, but this does not mean that the model per se is challenged. 

Also the claim in the abstract that the authors ‘discovered’ two (new) types of delta waves is not 
completely true as they also acknowledge later in the manuscript. Similar frequency ranges have 
been used to separate delta activity in a fast and slow component in several previous studies. 
Basically the authors only confirm, what was suggested in the literature for the last two decades, 
that there exist two distinct NREM sleep delta frequencies, each with its own time course of change 
after a sleep deprivation (see for instance Huber et al J Neurophys 2000). The existence of these 
two types of waves in the EEG is supported by electrophysiological data as reviewed by Amzica 
and Steriade already in 1998 in Electroencephalogr Clin Neurophysiol (which is more than 20 years 
ago). 

A similar dis-representation as is done for the two-process model and the two types of delta waves 
is then presented on page 37 for the way the analysis of slow-wave amplitudes and slopes is done 
to support the synaptic homeostasis hypothesis. The finding that the amplitude and incidence fully 
accounts for the change in slow-wave slopes is not very surprising and seems to suggest the 
authors do not understand that the analysis of slow-wave slopes in the articles analysing the 
changes in synchronization of firing rate of cortical neurons to explain the changes in shape of the 
slow-waves (and subsequently in other publication draw conclusion on neuronal connectivity based 

on the slope of the waves) is done on waves with the same amplitude. In these papers waves with 
the same amplitude are compared before and after sleep deprivation and the authors find that the 
slope of the waves (with the same amplitude) increases after sleep deprivation, and they take that 
as a support of the notion that neuronal connectivity is changing. The comparison of waves with 
the same amplitude is very important in this. The latter is completely ignored by the authors in the 
present manuscript. And it is, in my view, then a bit peculiar to mix waves of different amplitudes 
and come up with the statement that amplitude (and incidence) completely account for the change 
in slope. It seems that the authors completely miss the point here. 

Minor comments 
Page 3 line 74, the first paper modelling sleep homeostasis in the mouse is missing (Huber et al 
Brain Res 2000). 
Delta 2 also more increased in the dark, is that sleep deprivation? The effect here seem to last 
longer than after sleep deprivation. (fig 4A) 
Line 171-172 I am a bit confused here. I don’t think the lowest values for the SW period were 
present in the first hour of recovery (red circles [in fig 1D?]. They seem to be very average 
compare to the hours following (they do not have the highest values or the lowest values in the 
graph). Also in figure S2E period does not seem to be affected, as is also confirmed in the legend 
of the figure. However, in the main text it is claimed repeatedly that period is shorter. 
Line 290: “We found that delta1 power was unaffected [by] time spent in SD…” 

Reviewer #3 (Remarks to the Author): 

Title: Reassessing the validity of slow-wave dynamics as a proxy for NREM sleep homeostasis 

Author: Jeffrey Hubbard et al. 

The topic of how cortical oscillations can be used to assess sleepiness and “sleep need” following 
sleep loss (i.e., NREM sleep homeostasis) is one of long-standing interest to specialists in the field 
of sleep research. Given the topic is clearly important the evaluation of this manuscript rides on 
whether the findings represent a sufficiently important advance to warrant publication in the high 
impact journal Nature Communications. 

Summary of findings and potential impact: The paper identifies two types of delta waves in mice 
and concludes that only the faster waves (2.5 to 3.5Hz in mice) respond to sleep loss with 
increased amplitude during subsequent recovery sleep, whereas the slower wave (0.75 – 1.75 Hz 
in mice) do not. Studies in humans confirm the presence of 2 similar types of slow waves that 
respond the same way. The findings indicate that only the faster type of delta waves have 
contributed to the past findings supporting the the long-held idea that slow wave power (with a 
freq range that includes both types of the waves the authors describe here) is a valid proxy for 
NREM sleep homeostasis. The biological substrate supporting unique generation of these 2 types of 
slow waves is supported by their differential modulation by inhibition of the centromedial 
thalamus. The association of high delta waves in NREM sleep immediately after sleep deprivation 
with other physiological measures that are more closely associated with waking (temperature, 
muscle tone, heart rate, etc.) is an interesting paradoxical observation that bears further 
investigation although the explanations given for these data are plausible. Overall, the findings 
could usher in a new era of electrophysiological studies that more precisely explore cortical slow 
waves as a measure of sleepiness and the homeostatic sleep drive. 

Critique: This is an interesting and well conducted study that warrants publication in Nature 
Communications. The discussion section is particularly well written and easy to follow, clearly 
conveying the importance of the findings for the field. The discussion section was critical to this 
reviewer’s final positive decision. All the other sections of the manuscript including the figures are 

very complex, difficult to read, understand, and to extract the important findings (for a 
reviewer/reader who does not study electrophysiological measures of sleep homeostasis). 

Consequently, rewriting the manuscript for clarity and ease of communication is warranted prior to 
publication in this high impact journal for a broad audience of specialists in sleep research.