The Interaction between Sleep and Development on Wake EEG Oscillations

Visual Abstract

Abstract

The amount of time previously spent awake or asleep strongly impacts the sleep electroencephalogram (EEG), especially slow waves during nonrapid-eye-movement (NREM) sleep. These effects on the sleep EEG meaningfully interact with age and to a lesser extent developmental disorders such as attention-deficit hyperactivity disorder (ADHD). We aimed to determine whether EEG oscillations during wakefulness were likewise affected by the interaction of sleep and development, using data collected from 163 participants aged 3–25 years old (62 female). We analyzed age- and sleep-dependent changes in two measures of oscillatory activity (amplitudes and density) and aperiodic activity (offsets and exponents). Finally, we compared wake EEG in children with ADHD (N = 58) to neurotypical controls, with habitual good sleep quality required for inclusion. We found that oscillation amplitudes exhibited the same dynamics as sleep slow waves: decreasing with age, decreasing after sleep, and the overnight decrease decreasing with age. Strikingly, wake oscillation densities in the alpha band decreased overnight in children but increased overnight in adolescents and adults. Aperiodic measures were affected by both sleep and age albeit with minimal interaction. No wake measure showed significant effects of ADHD, suggesting that previously reported differences in patients may reflect uncontrolled variability in sleep quality rather than disorder-specific effects. While these results do not disentangle homeostatic from circadian effects, they underscore the need to control for sleep/wake history and measurement scheduling in all EEG experiments, especially when focusing on children and adolescents.

Significance Statement

Most studies measuring EEG during wakefulness do not take into consideration prior sleep/wake history. Here, we show that wake EEG measures significantly differ when measured before or after sleep, and these effects are strongly dependent on age. Differences between pediatric populations may in fact be due to prior sleep quality or circadian rhythms rather than hypothesized group differences.

Introduction

The EEG is one of the few tools available to study the human developing brain already from birth (Korotchikova et al., 2009), with the potential as a prognostic and diagnostic tool for both typical development and disease. Sleep EEG, and in particular slow-wave activity (0.5–4 Hz) during nonrapid-eye-movement (NREM) sleep, is especially sensitive to brain maturation (Campbell and Feinberg, 2009) and developmental disorders such as attention-deficit hyperactivity disorder (ADHD; Furrer et al., 2019). This is because slow-wave activity reflects the overall synchronicity of the brain, which decreases with age following decreasing synaptic density across adolescence (Huttenlocher, 1979; Jenni and Carskadon, 2004; Campbell and Feinberg, 2009), and may be lower in ADHD due to reduced cortical thickness (Shaw et al., 2006).

In addition to development, sleep slow waves reflect the buildup and dissipation of homeostatic sleep pressure, increasing following wake and decreasing during sleep (Borbély, 1982). These changes in slow waves are hypothesized to reflect synaptic plasticity: Synaptic strength increases with wake and daytime learning and decreases during sleep (Tononi and Cirelli, 2003, 2014). Generally, plasticity decreases with brain maturation, and this is reflected in decreases in the overnight changes in slow-wave properties with age (Jaramillo et al., 2020). Thus, absolute slow-wave activity reflects overall synaptic density, and changes in slow-wave activity reflect plasticity. While these predictions have been repeatedly validated in sleep, their consequences on the wake EEG are less clear, due to limitations in prior analyses.

The EEG is made up of both “aperiodic” activity (the background signal) and “periodic” activity (oscillations) which combine together in the spectral domain as a characteristic 1/f curve with Gaussian bumps (Fig. 1A, Donoghue et al., 2020). Aperiodic activity is defined by its offset (reflecting the overall aperiodic power) and exponent (the steepness of the curve). Notably, aperiodic exponents become progressively steeper with increasing sleep depth (Lendner et al., 2020, 2023; Schneider et al., 2022) and become progressively shallower with increasing sleep duration, reflecting the dissipation of sleep pressure (Horváth et al., 2022; Bódizs et al., 2024). Brain maturation also strongly affects exponents, becoming shallower with age (Cellier et al., 2021; Hill et al., 2022; Tröndle et al., 2022), and interacting with sleep depth (Favaro et al., 2023). Lastly, differences in aperiodic exponents and offsets have been found in children with ADHD compared with neurotypical controls (Robertson et al., 2019; Ostlund et al., 2021).

Figure 1.

Wake EEG measures. A, Measures based on spectral power. Given the nature of power, it is traditionally analyzed log-transformed to have more normally distributed values. These are then aggregated into bands. Here, we focus on classical wake EEG bands: theta (4–7 Hz, yellow), alpha (8–11 Hz, orange), and low beta (12–16 Hz, red), with gaps between bands to avoid overlapping information. The example comes from a 15-year-old male participant, used for the entire figure. The EEG signal is composed of aperiodic “background activity” (gray signal in B) and oscillatory activity (colored signal in B). When plotting the power spectrum on a log–log scale, a line can be fitted to the aperiodic component of the signal, which can be subtracted from the whole spectrum, leaving behind only periodic power. The aperiodic line can then be quantified by its offset (where it intersects 0 on the log–log scale, i.e., the log power at 1 Hz) and its exponent (how tilted it is). Thus, the power spectrum provides four measures: log-transformed power, periodic power, exponent, and offset of the aperiodic signal. B, Cycle-by-cycle analysis is used to detect bursts of oscillations by identifying sections of the EEG signal that show primarily periodic activity (colored), relative to the aperiodic background activity (gray). Once bursts are detected, there are two main parameters to quantify them: density (how much of the signal in time contains an oscillation) and amplitude (average peak-to-peak voltage of each oscillation). Densities are expressed in percentage, and when pooling bursts detected in all channels, they can easily exceed 100%, as the same burst will appear in multiple channels and multiple bursts can co-occur. Amplitudes are in microvolts. The EEG trace was stitched together for illustrative purposes. N.B. Beta periodic power is lower than alpha periodic power (in A), but their densities (in B) are roughly the same; this is because periodic power is also influenced by the lower amplitudes of low beta compared with alpha. The densities of other participants are provided in Extended Data Figure 1-1.

Figure 1-1

Individuals’ distribution of oscillation densities by frequency. Each plot is from one participant, with frequency (Hz) on the x-axis and density (% recording) on the y-axis. Colors reflect the frequency bands used in Main Figure 6 and Figure 7: yellow for theta (4-7 Hz); orange alpha (8-11 Hz); red low beta (12-16 Hz). This figure only includes evening oddball recordings from neurotypical participants, sorted by age. Download Figure 1-1, TIF file ^{(756KB, tif)} .

Periodic activity, like aperiodic activity, can be estimated from spectral power by subtracting the aperiodic activity from overall power, giving periodic power (Fig. 1A). However, this ignores how oscillations can change in both amplitude and in density (Fig. 1B). The amplitudes of oscillations reflect the synchronicity of the oscillating neuronal population, determined both by the number of neurons in phase with each other and the strength of their synaptic connections. Whether an oscillation occurs at all (i.e., density) will instead depend on the activity of “pacemaker” interneurons which entrain a population of neurons to the same rhythm (Perkel et al., 1964; Le Bon-Jego and Yuste, 2007), and this pattern of rhythmic firing will be in service of some underlying function that will come and go as needed. In short, amplitudes reflect synchronicity and densities reflect activity.

Because oscillation amplitudes reflect synchronicity, they should reflect the same information as slow waves measured during sleep. Sleep slow-wave activity increases along a saturating exponential function relative to the prior duration of wakefulness, increasing rapidly after only a few hours awake and increasing less and less with further hours awake (Borbély, 1982; Dijk et al., 1987). In fact, wake oscillation amplitudes follow the same trajectory across 24 h awake (Snipes et al., 2023). Instead, alpha oscillation densities (8–12 Hz) decreased, supporting the specificity of the effect to amplitudes and masking the effect when measuring spectral power.

Given these prior results, we hypothesized that wake oscillation amplitudes specifically should behave like sleep slow waves also across development: Absolute amplitudes should decrease with age, and overnight changes in amplitude should decrease with age. Furthermore, given that children with ADHD have lower sleep slow-wave activity, they should likewise have lower wake amplitudes.

To answer these questions, we analyzed data collected from previous studies at the University Children's Hospital of Zurich, with high-density wake EEG recordings measured the evening before and morning after a night of sleep. The final dataset included 105 neurotypical participants and 58 patients with ADHD.

Materials and Methods

Datasets

The data for this manuscript was assembled from previous studies conducted between 2008 and 2021. The participant demographics of each dataset are in Table 1. In total, we included 163 participants between the ages of 3.5 and 24.7, 38% female, 7% left-handed. Of these, 36% were diagnosed with ADHD at the Department of Child and Adolescent Psychiatry and Psychotherapy at the University of Zurich, the outpatient clinic of the Child Development Center, and at private children's clinics in Oerlikon, Zurich. Patients were not excluded based on medication status and therefore were a mixture of medicated, previously medicated, and unmedicated (Table 2; Ringli et al., 2013; Furrer et al., 2019). Otherwise, all participants were screened by telephone such that they all were completely healthy, took no (other) medication, had no (other) comorbidities, and were good sleepers. All participants were recruited from canton Zurich, Switzerland, and recorded at the University Children's Hospital of Zurich, except for the children from 3.5 to 8 (Dataset2009, N = 11), who were recruited in Providence, RI, USA, and recorded at home. Sleep time was determined by their individual preferred sleep and wake-up time, which they had to maintain the week prior to each measurement. Wake measurements were done just before going to sleep and ∼30 min after waking up. A total of 115 participants had two sessions, spaced at least 1 week apart, both included in these analyses. Depending on the dataset, different paradigms were used involving different wake tasks (described below), and there could be 1–4 tasks at each time point (Table 1). Every set of tasks for each dataset was repeated in the evening and in the morning. In total, 1,243 recordings were included in these analyses lasting on average 4.2 min (task time + buffer − artifacts), with a standard deviation of 2.5 min. The processed data for all participants is provided as extended data.

Table 1.

All participants' demographics, split by dataset

	N #	Female %	Left-handed %	ADHD %	Age range (years)	Mean age (years)	Paradigm	Sessions #	Recordings #
Dataset2008	38	32	0	0	8.7–23.4	14.1 (3.8)	Adaptation	2	7.3/8
Dataset2009	11	73	0	0	3.5–8.0	5.6 (1.5)	Oddball	1	1.8/2
Dataset2010	28	21	14	100	9.7–16.3	12.7 (1.9)	Adaptation	1	2.8/4
Dataset2016	18	44	0	0	18.4–24.7	21.6 (2.1)	Oddball	2	3.9/4
Dataset2017	42	43	17	36	8.1–17.6	12.2 (2.7)	Attention	2	12.7/16
Dataset2019	26	38	0	58	8.8–16.8	11.4 (2.0)	Attention	2	10.1/12
All	163	38	7	36	3.5–24.7	13.2 (4.4)

The year for each dataset indicates the beginning of data collection. N indicates the number of participants. After the mean age of each dataset, the standard deviation is provided in parentheses. “Paradigm” indicates which set of wake tasks was recorded. “Sessions” indicate the number of sessions expected for each dataset, although in practice due to dropouts, some participants only completed one. “Recordings” indicate the average number of recordings per participant compared with the total number of recordings expected by the experimental paradigm. Recordings were missing either because they contained too many artifacts or were omitted entirely during data collection.

Table 2.

ADHD demographics, split by patient status

	N #	Female %	Left-handed %	Age range (years)	Mean age (years)	Oddball %
Medicated in the past (1)	6	17	0	9.7–14.8	12.5 (2.2)	33
Unmedicated (2)	14	21	14	9.5–15.3	11.3 (1.6)	64
Medication the day before (3)	22	23	14	8.7–16.3	12.3 (2.2)	45
Medication the day of (4)	13	15	0	9.0–16.1	12.3 (2.0)	54
All patients	55*	20	9	8.7–16.3	12.1 (2.0)	51
Controls (5)	105	47	6	3.5–24.7	13.8 (5.2)	58

The indices in parentheses provide the subgroup categories in Extended Data 1. For each patient group, the “oddball” column indicates how many came from a dataset performing the oddball task (rather than the attention tasks). *for three patients, medication status was missing; therefore, the true total is 58.

Informed consent was obtained from all adult participants, and from the legal guardians of all children aged below 14 years, as well as from adolescent participants aged 14–18 years. All studies were approved by the local ethics committees and performed according to the Declaration of Helsinki.

Oddball and motor adaptation paradigms

Ninety-five participants (Dataset2008, Dataset2009, Dataset2010, Dataset2016) performed an auditory oddball task during their wake EEG. The task lasted 4 min and was performed in the evening just before going to bed and in the morning ∼30 min after waking up. The task involved 300 tones at ∼80 dB, with an interstimulus interval of 0.8 s. A random 10% of stimuli were targets to which the participant had to push a button in response. For the youngest children (Dataset2009), the 4 min task was split into two segments, and for the adults (Dataset2016), the task was extended to 6 min.

Sixty-six of these participants (Dataset2008, Dataset2010) also performed a half-hour visuomotor adaptation task (Ghilardi et al., 2000) followed by a second oddball. One dataset (Dataset2008) also included a second session with a control visuomotor task (no adaptation), counterbalanced with the motor adaptation task. The motor tasks were not included in this analysis, because they further differed from evening to morning. For more details on the adaptation task, see Wilhelm et al. (2014). The youngest (Dataset2009) and oldest (Dataset2016) participants only conducted one oddball and no motor task. Dataset2016 also had two sessions, one night with phase-targeted auditory stimulation during NREM sleep and the other sham. The experimental conditions did not affect the wake EEG (data not shown).

The sleep data from these participants has been published (Kurth et al., 2010; Buchmann et al., 2011; Ringli et al., 2013; Wilhelm et al., 2014; Volk et al., 2018; Furrer et al., 2019, 2020; Jaramillo et al., 2020), as has a subset of the wake EEG data (Fattinger et al., 2017).

Attention paradigm

Sixty-eight participants (Dataset2017, Dataset2019) performed three tasks with a focus on attention. These were studies investigating the relationship between slow waves, behavior, and MR spectroscopy (Volk et al., 2019; Jaramillo et al., 2020). This included two sessions to compare the effects of phase-targeted auditory stimulation on slow waves in sleep (sham and stimulation; data currently unpublished). The wake tasks were part of the test battery for attentional performance (Zimmermann and Fimm, 2012), which included 2 min of a visual go/no-go task (respond to 1 stimulus, withhold response to another), 5.5 min of a visual and acoustic alertness task, and then 2 1.5 min fixation recordings. For one dataset (Dataset2019), the go/no-go task was adapted using diagram images (extended to 12 min), but only one fixation recording was measured (lasting 2 min).

EEG recordings and preprocessing

All datasets were measured using 128-channel Electrical Geodesics Inc. (EGI) Geodesic Sensor nets and EGI amplifiers. Wake recordings were done with Cz reference, 1,000 Hz sampling rate, and impedances kept below 50 kΩ. All analyses were performed in MATLAB 2023b, with the EEGLAB toolbox v2023.1 (Delorme and Makeig, 2004), the FOOOF/specparam toolbox v1.1.0 (Donoghue et al., 2020), and custom scripts on Windows 10.

EEG data was first mean-centered, then low-pass filtered at 40 Hz (EEGLAB's pop_eegfiltnew function) and notch-filtered at either 50 or 60 Hz (Dataset2009) along with subsequent harmonics. The data was downsampled to 250 Hz and then high-pass filtered over 0.5 Hz (Kaiser filter, stopband = 0.25 Hz, stopband attenuation = 60, passband ripple = 0.05).

Artifacts were removed with a fully automated procedure. Movement and other large artifacts were detected in data filtered between 1 and 40 Hz, in 3 s segments. A segment was labeled a “major artifact” if it exceeded 500 µV or a “minor artifact” if the correlation with neighboring channels was below 0.3. Major artifacts were always removed, by either removing all data in all channels during those 3 s or removing the entire channel with such an artifact, depending on which (channel or segment) removed the least amount of clean data. Minor artifacts were removed in a similar way, removing iteratively either the channel with the most artifactual segments or the segments with the most artifactual channels, until all channels and all segments had at most 30% of the data containing a minor artifact. Flat channels were removed using EEGLAB's clean_artifacts function. The missing Cz channel was added (as a vector of zeros), and then the data was average referenced. Physiological artifacts (blinks, eye movements, muscle tone, heartbeat) were removed with independent component analysis (ICA), with components calculated separately as described in the next section. After these were removed, a second pass was conducted using EEGLAB's clean_windows function (MaxBadChannels = 0.3, PowerTolerances = [-inf, 12]), then bad segments/channels still containing amplitudes over 140 µV were removed, and finally EEGLAB's clean_channels_nolocs was applied (MinCorrelation = 0.5, IgnoredQuantile = 0.1, MaxBrokenTime = 0.5). Recordings for which >25 channels were removed, or which had <1 min of data, were excluded from analysis. In a last step, EEG channels were interpolated, for a total of 123 channels, excluding the external electrodes (49, 56, 107, 113) and the face electrodes (126, 127).

Automatic detection and removal of physiological artifacts using ICA

For ICA, a separate copy of the EEG data was preprocessed as previously described; however, the data was filtered between 2.5 and 100 Hz and downsampled to 500 Hz. Automatically detected bad channels and bad time windows were removed, an empty Cz channel was added, and then the data was rereferenced to the average of all channels. EEGLAB's runica function was run with principal component analysis rank reduction. Then, components were automatically classified with EEGLAB's iclabel, as either brain, muscle, eye, heart, line, channel noise, or other. This function provides a probability score for each label from 0 to 1, so the label with the largest score for each component was taken. Components classified as muscle, eye, or heart were removed. Of the remaining noise classifications (line, channel, other), due to poor classification accuracy, an additional step was implemented. Spectral power was calculated for each component (pwelch, 4 s Hanning windows, 50% overlap) and then smoothed over 5 Hz (lowess) to facilitate model fitting. Unlike for the main analyses, the periodic signal was not of interest; therefore, stronger smoothing was possible. The “specparam” algorithm (Donoghue et al., 2020; Ostlund et al., 2022) was applied to the power spectrum between 8 and 30 Hz to extract aperiodic exponents. Components for which the spectral exponent was shallower than 0.5 (so almost flat or even tilted positive) were considered noise and therefore excluded, as they reflected either muscle activity or other nonphysiological signals. Using the manually labeled components in an independent adult dataset (Snipes et al., 2022), we confirmed that this procedure was sufficiently comparable to human detection of artifactual components. We further confirmed that the outcome matched human component classification in a small subset of the children's data. However, considering the trend toward 0.5 exponents observed, for future datasets with older participants, we would recommend a lower threshold.

For the Dataset2009 cohort of <8-year-olds, given how little data there was and how many more movement artifacts, we chose to apply the same manual artifact rejection as in Snipes et al. (2022) to preserve as much data as possible.

Burst detection

Bursts of oscillations were detected using cycle-by-cycle analysis (Cole and Voytek, 2019) implemented in MATLAB (Snipes et al., 2023). Bursts were detected with the same thresholds as in Snipes et al. (2024). Briefly, the EEG was narrow-bandpass filtered in overlapping ranges (2–6 Hz, 4–8 Hz …), from which zero-crossings were detected. Then, in the broadband filtered data (0.5–40 Hz), peaks were identified between the zero-crossings, and a cycle was considered an oscillation from positive-to-positive peak. A minimum number of consecutive cycles must meet a set of criteria (monotonicity, period consistency, amplitude consistency, shape consistency, etc.) for this to be considered a burst. Importantly, amplitude itself is never used as a threshold, as this would create a greater dependency between amplitude and density (such that a decrease in an amplitude threshold would result in an automatic increase in density).

Three sets of criteria were used. The first aimed to detect bursts relying on many low-threshold criteria (frequency in range of narrowband filter; PeriodConsistency = 0.5; AmplitudeConsistency = 0.4; FlankConsistency = 0.5; ShapeConsistency = 0.2; MonotonictyInTime = 0.4; MonotonicityInAmplitude = 0.4; ReversalRatio = 0.6; MinCycles = 4). The second had fewer criteria with intermediate thresholds but a higher minimum number of cycles (PeriodConsistency = 0.6; AmplitudeConsistency = 0.6; MonotonicityInAmplitude = 0.6; FlankConsistency = 0.6; MinCycles = 5). The third set had fewer criteria but stricter monotonicity thresholds (frequency in range of narrowband filter; PeriodConsistency = 0.7; FlankConsistency = 0.3; MonotonicityInAmplitude = 0.9; MinCycles = 3). These criteria were chosen a priori based on manual tuning of the burst detection on an independent dataset of wake EEG in adults during sleep deprivation.

After bursts were detected in each channel separately, they were grouped into clusters when they occurred simultaneously in multiple channels with roughly the same frequency. The frequency of bursts was calculated as the inverse of the average distance between negative peaks (1/period). Bursts for which the shorter one overlapped at least 50% of the longer one, and were within 1 Hz of each other, were considered part of the same burst cluster. Bursts identified separately in each channel were used for all the topographies; otherwise, burst clusters were used to reduce the effect of burst globality (spread across the scalp) on measures of density.

Oscillation measures

“Oscillation amplitudes” were calculated as the average negative-to-positive peak voltage difference for all cycles involved in all bursts, with units in microvolts (µV). “Oscillation densities” were calculated as the percentage of the recording occupied by bursts (sum of all the bursts' durations divided by the duration of the recording). This accounts for both the duration and overall quantity of bursts in the recording. The average duration and number of bursts can be affected by the background aperiodic signal which can break up sustained oscillations into smaller bursts (Tal et al., 2020); therefore, burst density was preferable. When calculating across multiple channels, oscillation density could easily exceed 100%, as burst clusters in different frequency ranges often co-occur. When combining densities across multiple frequency bands, bursts were pooled rather than averaged (sum of the durations of all the bursts of any frequency, divided by the duration of the recording).

The choice of frequency bands

Only bursts between 2 and 16 Hz were detected. Below 4 Hz very few bursts could be identified; therefore, only bursts above 4 Hz were included in the analysis. Bursts over 16 Hz could be detected, but with higher false-positive rates, as determined by visual inspection. The choice of cutoff at 16 Hz was done arbitrarily a priori to capture alpha (8–12 Hz) with generous padding. The division of bands for later analyses was done using conventional bands (Pernet et al., 2020) with 1 Hz gaps to reduce overlapping information due to the drift in peak frequencies across individuals and ages: theta (4–7 Hz), alpha (8–11 Hz), and low beta (12–16 Hz). The inclusion of low beta was done a posteriori based on results in Extended Data Figure 6-2.

Many researchers advocate for the use of an individual alpha frequency (IAF) to define frequency ranges, especially when analyzing data across development (Klimesch, 1999; Bazanova and Vernon, 2014; Ostlund et al., 2022; Tröndle et al., 2022). This is done by selecting the peak alpha frequency separately for each individual and defining the band around this peak. The shift in IAF with age makes a strong case for such an approach (Freschl et al., 2022). The main problem with using IAF is that it assumes the largest oscillation will be functionally the same for all participants. Given that participants displayed large heterogeneity in the number and amplitude of frequency peaks within the 2–16 Hz range (Extended Data Fig. 1-1), and the vast majority were old enough that the peak alpha frequency was larger than 8 Hz (Freschl et al., 2022), we preferred to use fixed bands with gaps. The completely distinct topographies across the three bands for all age groups support this decision for this dataset.

Spectral power analysis

Spectral power was calculated using MATLAB's pwelch function, with 4 s Hanning windows and 50% overlap. When average power across channels was calculated, edge channels were excluded (total count: 98). To dissociate periodic and aperiodic spectral power, we used the MATLAB extension of specparam [formerly known as FOOOF (Donoghue et al., 2020)]. Spectra were smoothed over 2 Hz, and the aperiodic signal was fitted between 2 and 35 Hz (frequencies sufficiently separated from the 0.5–40 Hz filter range of the preprocessed data). Otherwise, the default settings were used (peak width, 0.5–12; max number of peaks, inf; minimum peak height, 0; peak threshold, 2; aperiodic mode, fixed).

Spectral and aperiodic measures

Power was calculated by averaging the log-transformed power values between 4 and 16 Hz. Aperiodic offsets were provided by specparam as the log power value at 1 Hz of the aperiodic signal, and exponents as the x value in the 1/f^x model that describes the steepness of the aperiodic signal. The values are such that positive exponents refer to a downward descending aperiodic signal, and the larger the value, the steeper the descent. Periodic power was the log-transformed power after the aperiodic signal was subtracted. Fitting model parameters r-squared and mean absolute error (MAE) were similarly analyzed to evaluate whether the model fit could account for the results (Ostlund et al., 2022), with the results provided in Extended Data Figure 3-1. Overall, the specparam model fit was very good, with an average MAE of 0.028 (interquartile range: 0.019, 0.035) and r-squared values of 0.998 (0.997, 0.999).

Statistics

Statistics were performed using the MATLAB Statistics and Machine Learning Toolbox. For all analyses, statistical significance was determined for p < 0.05. Given the heterogeneous datasets pooled together for this analysis, we chose to conduct linear mixed-effects models (MATLAB function fitlme) to model the relationship between age, sleep, ADHD, and EEG measures. Each model used is provided in proximity to the corresponding results section, for clarity.

For each fixed factor of the model, β estimates, t-values, p-values, and degrees of freedom (df) are reported in the text. β estimates of continuous variables (e.g., age) indicate by how much the EEG measure (e.g., density) changes for each unit of the continuous variable (e.g., 1 year) when all other variables are 0. Similarly, the β estimates of categorical variables (e.g., group) indicate how much the EEG measure changes from that category (e.g., ADHD) to the baseline category (e.g., controls), for all other factors set to 0. Lastly, t-values allow a comparison of the magnitude of the effect of each factor when comparing models with different measuring units.

To determine the topography of the effects, we ran linear mixed-effects models for each channel separately, correcting for multiple comparisons using false discovery rates (FDR; Benjamini and Yekutieli, 2001). We plotted the FDR-corrected p-values and corresponding β estimates to identify regions showing significant overnight changes in each outcome variable.

We ran models with fixed factors Task, Time, Age, Group, and Sex, the interaction between Time and Age, and nested mixed factors Participant and Session. “Task” compared the levels “oddball” versus “go/no-go,” “alertness,” and “fixation.” “Time” compared the time of recording (evening versus morning). “Group” compared neurotypical participants versus those with ADHD. “Sex” compared females versus males. Depending on the analysis and subset of the data, different factors were included or not, and so the exact model is specified before each analysis and in each figure caption.

As a simpler analysis and sanity check, we conducted Pearson's correlations between age and each measure. In each figure, r-values are provided as effect sizes. We further ran Pearson's correlations between measures to evaluate the relative importance of the four main measures with spectral power and periodic power (see Results, Comparison of oscillation and aperiodic measures to spectral measures). Both sets of correlations used FDR correction for multiple comparisons.

To determine whether differences in r-values were statistically significant, we ran Steiger's test for comparing two dependent correlations sharing one variable (Steiger, 1980; Lee and Preacher, 2013).

All data used for the channel-averaged statistical models are provided in Extended Data 3 and the full model outputs in Extended Data 4.

Code accessibility

The code is available in Extended Data 1.

Data 1

MATLAB code used to preprocess, analyze and plot the wake EEG data used throughout the manuscript. Includes the primary repository children-wake/ as well as the required toolboxes: chART, fooof_mat, & Matcycle. Download Data 1, ZIP file ^{(18.7MB, zip)} .

Data 2

MAT file containing channel-averaged spectra for each recording (‘SpectraAverage’), the vector of frequencies (‘Frequencies’) and recording information table (‘Metadata’). Download Data 2, ZIP file ^{(3.4MB, zip)} .

Data 3

CSV table providing participant information and EEG summary values for each recording, used for the statistics and Figure 2. Download Data 3, CSV file ^{(416.1KB, csv)} .

Data 4

Text file with the full statistical models using Extended Data 3. Download Data 4, TXT file ^{(20.5KB, txt)} .

Data 5

MAT file containing the per-channel EEG outcome measures for each recording, saved in a struct (‘Topographies’), the channel information (‘Chanlocs’) and recording information (‘Metadata’). Used for Figures 4, 5, and 8. Download Data 5, ZIP file ^{(6MB, zip)} .

Data 6

MAT file containing the per-channel EEG outcome measures (Density, Amplitude, Power, PeriodicPower) split by frequency band (theta, alpha, beta) in recording x channel x band matrices, saved in a single struct (‘TopographiesBand’). Also included are the channel information (‘Chanlocs’), band limits (‘Bands’), and recording information (‘Metadata’). Used for Figures 6 and 7. Download Data 6, ZIP file ^{(9.6MB, zip)} .

Results

Effects of age, sleep, sex, and ADHD on wake EEG measures

The following linear mixed-effects model was applied to each wake EEG measure, averaged across channels: $\mathrm{Measure}\sim \mathrm{Task}+\mathrm{Time}*\mathrm{Age}+\mathrm{Group}+\mathrm{Sex}+(1|\mathrm{Participant})+(1|\mathrm{Participant}:\mathrm{Session})$. The full outputs of the model are provided in Extended Data 4. Average spectra are provided in Figure 2, and the relationship between age and each measure is provided in Figure 3.

Figure 2.

Log–log power spectra, averaged across channels. Thick lines indicate group averages, thin lines indicate individuals (averaged across multiple recordings and/or channels), separately in the evening (blue) and morning (yellow). ADHD and controls are pooled. The x- and y-axes are plotted on logarithmic scales. A, Auditory oddball tasks, split by age group, from Dataset2008, Dataset2009, Dataset2010, and Dataset2016. B, Go/no-go, alertness, and fixation recordings from Dataset2017 and Dataset2019. The peak of activity ∼30 Hz reflects iota oscillations (Snipes, 2025), shown here to be largely task-independent. The individual recording spectra are provided in Extended Data 2.

Figure 3.

Wake EEG measures correlated with age. Only auditory oddball recordings were included, pooling both neurotypical and ADHD participants. Each dot represents the value for a single participant. For participants with multiple sessions, values across sessions were first averaged. Pearson's correlations were done for each figure, with r-values provided in the corner. If the p-value was <0.05, a correlation line was drawn (FDR correction for multiple comparisons). Amplitudes and densities of oscillations were obtained from burst clusters, pooling all frequencies (4–16 Hz). Exponent values are such that larger values indicate a steeper aperiodic signal; units are “arbitrary” (a.u.), in that exponents are scale-free measures. Power spectra were calculated for each channel and then averaged across all channels, excluding the outermost ring of channels. Power and periodic power were then calculated by averaging values from 4 to 16 Hz. The same figure for the specparam fitting estimates is provided in Extended Data Figure 3-1. The correlations between each measure with the other are provided in Extended Data Figure 3-2. The data are provided in Extended Data 3, and the statistics of the linear mixed-effects models are provided in Extended Data 4.

Figure 3-1

Specparam fitting estimates correlated with age. Only auditory oddball recordings are included, pooling both neurotypical and ADHD participants, the same as in Main Figure 3. Each dot represents a single participant. For participants with multiple sessions, values across sessions were first averaged. Pearson’s correlations were done for each figure, with r values provided in the corner. If the p-value was less than .05, a correlation line was drawn (without correcting for multiple comparisons). The same linear mixed effects model was applied to the fitting estimates of the specparam model, to evaluate whether any potential differences in model fitting could explain the aperiodic results. Mean absolute errors had a trending effect of age (beta = 0.000, t = 1.69, p = .091, df = 1234), curiously increasing with age, and a significant decrease the morning after sleep (beta = -0.007, t = -3.75, p < .001, df = 1234). There was no significant effect of ADHD (beta = 0.001, t = 0.36, p = .717, df = 1234) or sex (beta = 0.000, t = 0.09, p = .930, df = 1234), and the Time * Age interaction was only trending (beta = 0.000, t = 1.75, p = .080, df = 1234). R-squared values significantly decreased with age (beta = -0.000, t = -3.72, p < .001, df = 1234), significant increased after sleep (beta = 0.001, t = 2.96, p = .003, df = 1234), had no significant effect of ADHD (beta = -0.000, t = -0.87, p = .384, df = 1234), sex (beta = -0.000, t = -0.47, p = .639, df = 1234), or Time * Age interaction (beta = -0.000, t = -0.90, p = .366, df = 1234). Given that model fitting varied systematically with the factors of interest, it is possible some of the effects observed for aperiodic exponents and offsets are attributable to differences in model fitting. However, the main effects of aperiodic exponents and offsets were substantially larger than these effects of the model fits. Download Figure 3-1, TIF file ^{(427.5KB, tif)} .

Figure 3-2

Correlations between outcome measures. Each dot represents the data of a recording (n = 1243). Pearson’s r values are provided correlating all measures from both datasets, and significant correlations (p-value < .05, FDR corrected for multiple comparisons) have linear fits plotted separately for each dataset. Given that there are repeated measures from the same participants, these correlations violate assumptions of independence, but were conducted nevertheless to provide a simple metric to compare the relationship between different measures. Download Figure 3-2, TIF file ^{(5.3MB, tif)} .

Oscillation amplitudes significantly decreased with age (β = −0.783, t = −11.33, p < 0.001, df = 1,234) and the morning following sleep (β = −3.977, t = −15.90, p < 0.001, df = 1,234), with a significant positive interaction (β = 0.143, t = 7.74, p < 0.001, df = 1,234), such that amplitudes decreased less overnight with increasing age (Fig. 3, bottom row). Amplitudes were significantly lower in males than females (β = −1.476, t = −2.38, p = 0.017, df = 1,234), and were not significantly different in participants with ADHD (β = −0.574, t = −0.89, p = 0.373, df = 1,234). As can be seen in Figure 3, the relationship between age and amplitudes was quite robust, both as absolute values (r_eve = −0.66, r_mor = −0.63) and overnight changes (r = 0.56). Overall, amplitudes changed in the expected directions for both development and sleep pressure, except for the lack of an effect of ADHD.

Oscillation densities significantly decreased with age (β = −3.540, t = −2.11, p = 0.035, df = 1,234) and the morning after sleep (β = −77.506, t = −10.39, p < 0.001, df = 1,234), with a significant positive interaction between age and time of recording (β = 4.878, t = 8.85, p < 0.001, df = 1,234). Unlike amplitudes, the correlation between age and density was weak (r_eve = −0.28, r_mor = 0.01). Instead, the correlation between age and overnight change in density was quite strong (r = 0.57), such that oscillation densities decreased overnight in children under 15 and increased in young adults (Fig. 3). There was no effect of ADHD (eta = −0.154, t = −0.01, p = 0.992, df = 1,234) or sex (β = −16.446, t = −1.10, p = 0.271, df = 1,234). Overall, oscillation densities behaved independently from amplitudes, especially in the direction of overnight changes in adolescents and adults.

Aperiodic exponents became significantly shallower with age (β = −0.027, t = −9.23, p < 0.001, df = 1,234) but significantly steeper overnight (β = 0.099, t = 5.51, p < 0.001, df = 1,234), with a trending negative interaction between time of recording and age (β = −0.003, t = −1.91, p = 0.056, df = 1,234). The increased overnight steepness was driven primarily by a decrease in higher-frequency power (Fig. 2), whereas decreasing steepness with age is driven by decreases in low-frequency power (Favaro et al., 2023). The correlations between exponents and age were as robust as for oscillation amplitudes (r_eve = −0.60, r_mor = −0.66), but the correlation with overnight change was weak (r = −0.15, not significant). There was no significant effect of ADHD (β = 0.028, t = 1.07, p = 0.284, df = 1,234) or sex (β = 0.041, t = 1.63, p = 0.102, df = 1,234). This means that aperiodic exponents also change independently from oscillation amplitudes, and do not reflect the direction of changes expected for sleep pressure.

Aperiodic offsets also significantly decreased with age (β = −0.067, t = −16.59, p < 0.001, df = 1,234), but with no significant effect of time (β = 0.029, t = 1.65, p = 0.100, df = 1,234), and a significant negative interaction (β = −0.004, t = −3.11, p = 0.002, df = 1,234), such that offsets decreased more overnight with age. The correlations between age and offsets were the strongest of all measures (r_eve = −0.81, r_mor = −0.83); however, the correlation between age and overnight change in offset was negligible (r = −0.05). Again, there was no effect of ADHD (β = 0.021, t = 0.55, p = 0.579, df = 1,234) or sex (β = −0.003, t = −0.07, p = 0.944, df = 1,234). Overall, offsets correlated with age in the same direction as amplitudes, changed overnight in the same direction, but the overnight change was not larger in children compared with adults, counter to what would be expected for sleep pressure.

Curiously, fitting parameters of the specparam algorithm showed both significant age and time of recording effects, with the model fit improving in the morning and worsening with age (Extended Data Fig. 3-1). Therefore, differences in fit quality may partially contribute to the effects observed for aperiodic measures. However, the changes in model fitting go in the opposite direction one would expect of data quality (i.e., worse in the morning and better in adults). This suggests that some other aspect of the EEG signal that changes with age and time can affect the fit quality of the specparam algorithm.

In summary, of the four EEG measures, only amplitudes followed the same trajectories expected for both development and sleep pressure. The absolute values of all four measures had a negative correlation with age and differed primarily in the overnight response and the relationship between age and overnight response. Oscillation densities in particular showed a strong effect of age on overnight changes, reversing direction between childhood and adolescence. No measure showed any relationship with ADHD, and only amplitudes were affected by sex. Therefore, in later analyses we did not include these factors, and pooled patients and controls for greater statistical power.

Comparison of oscillation and aperiodic measures to spectral measures

To quantify the extent to which spectral measures were influenced by oscillation and aperiodic measures, we directly correlated each measure to the other (Extended Data Fig. 3-2) and then controlled for time of the recording, task, and age using linear mixed-effects models: $\mathrm{Measure}1\sim \mathrm{Measure}2+\mathrm{Time}*\mathrm{Age}+\mathrm{Task}+(1|\mathrm{Participant})+(1|\mathrm{Participant}:\mathrm{Session})$ (Table 3).

Table 3.

Mixed-effects models between wake EEG measures

	Amplitude	Density	Exponent	Offset	Power	Periodic power
Amplitude		b = 10.00 t = 13.2 p < 0.001	b = −0.00 t = −0.5 p = 0.618	b = 0.02 t = 8.7 p < 0.001	b = 0.07 t = 39.3 p < 0.001	b = 0.02 t = 20.6 p < 0.001
Density	b = 0.01 t = 12.0 p < 0.001		b = 0.00 t = 14.7 p < 0.001	b = 0.00 t = 14.5 p < 0.001	b = 0.00 t = 28.2 p < 0.001	b = 0.00 t = 46.9 p < 0.001
Exponent	b = −0.76 t = −1.9 p = 0.063	b = 167.38 t = 14.9 p < 0.001		b = 0.88 t = 61.9 p < 0.001	b = 0.18 t = 4.7 p < 0.001	b = 0.17 t = 12.5 p < 0.001
Offset	b = 3.05 t = 7.6 p < 0.001	b = 160.06 t = 14.5 p < 0.001	b = 0.84 t = 61.0 p < 0.001		b = 0.63 t = 17.5 p < 0.001	b = 0.12 t = 8.4 p < 0.001
Power	b = 7.54 t = 40.5 p < 0.001	b = 181.32 t = 29.9 p < 0.001	b = 0.10 t = 5.8 p < 0.001	b = 0.32 t = 19.4 p < 0.001		b = 0.22 t = 30.2 p < 0.001
Periodic Power	b = 13.77 t = 19.3 p < 0.001	b = 666.33 t = 46.7 p < 0.001	b = 0.61 t = 12.1 p < 0.001	b = 0.46 t = 8.5 p < 0.001	b = 1.73 t = 27.9 p < 0.001

The model was $\mathrm{Measur}{\mathrm{e}}_{\mathrm{column}}\sim \mathrm{Measur}{\mathrm{e}}_{\mathrm{row}}+\mathrm{Task}+\mathrm{Sleep}*\mathrm{Age}+(1|\mathrm{Participant})+(1|\mathrm{Participant}:\mathrm{Session})$. The degrees of freedom for all models were df = 1,235.

Using FDR to correct for multiple comparisons, all measures significantly correlated with each other, except periodic power which did not correlate with either aperiodic exponents or offsets. Power was most related to amplitudes, also in the mixed-effects model (correlation r = 0.90, mixed-effects t-value = 39.3). Power was also highly related to offsets (r = 0.78, t = 17.5) but less so with densities (r = 0.61, t = 28.2) and exponents (r = 0.48, t = 4.7). The difference in r-values between the amplitude–power correlation and the others was statistically significant (z > 12.43, p < 0.001). Periodic power was highly correlated with densities (r = 0.85, t = 46.9) and somewhat with amplitudes (r = 0.43, t = 20.6). Again, the difference in r-values was statistically significant (z = 23.96, p < 0.001).

Topography of wake EEG measures by age

Figure 4 provides the average topographical maps of each measure for five age bins, averaging (or pooling for densities) all frequencies from 4 to 16 Hz, from the oddball task. Amplitudes, densities, exponents, and offsets all showed unique topographies from each other. Across ages, for each measure there were primarily changes in magnitude more so than major regional differences. However, oscillation amplitudes in the youngest cohort began as a single midline occipital spot, which spread bilaterally in the 7–10-year-olds. Prominent central bilateral peaks also appeared in the 7–10-year-olds. Oscillation densities similarly started as a single midline occipital spot, but these became more lateral–parietal in the 14–18-year-olds. Like amplitudes, two small bilateral central peaks emerged in the densities of 7–10-year-olds, which merged with the primary occipital–parietal cluster in the 14–18-year-olds. Furthermore, a frontal peak gradually emerged with age. Exponents were steepest in midline channels, whereas offsets showed both a frontal midline and occipital peak. As with the correlations between measures, power topographies most resemble amplitudes, and periodic power resembles densities.

Figure 4.

Topography averages of wake EEG measures. Each plot is a schematic of the EEG viewed from above, with the nose on top. Lighter colors indicate greater magnitude over a given location for that measure (rows). Only neurotypical participants and oddball recordings were included, and participants were grouped into age bins (columns). Multiple oddball recordings from different sessions and times of day were first averaged for each participant. The number of participants included is indicated in the top right corner of each plot. Acronyms: y.o., years-old; a.u., arbitrary units. The data are provided in Extended Data 5.

To determine the topography of overnight changes in EEG measures, we performed linear mixed-effects models for each channel, with the model $\mathrm{Measure}\sim \mathrm{Time}+\mathrm{Task}+(1|\mathrm{Participant})+(1|\mathrm{Participant}:\mathrm{Session})$, dividing participants into four age bins (the youngest aged 3–7 years were excluded as they were too few, with too few recordings). Figure 5 plots the β estimates for the effect of Time.

Figure 5.

Topographies of wake EEG overnight changes. A linear mixed-effects model was run for each measure, each age group, and each channel: Measure ∼ Time + Task + (1|Participant) + (1|Participant:Session). Color reflects β estimates for the fixed effect “Time,” such that red indicates an overnight increase in that measure. The factor “Task” was not included for the 18–25-year-old group, as these participants only performed oddballs. White dots indicate channels for which the β estimate was statistically significant, corrected for multiple comparisons with false discovery rate (FDR). Black dots indicate remaining channels. Data includes both patients and neurotypical controls. Degrees of freedom (df) are provided for each plot. For each column, the sample sizes were N = 29, 67, 36, and 23. The data are provided in Extended Data 5.

Amplitudes showed widespread overnight decreases across all age groups; however, the decrease was largest in occipital channels for the youngest group, and slightly more frontotemporal in young adults. The overnight density topographies resembled the average density topographies from Figure 4, in terms of location of the effects. The youngest group showed the largest overnight decrease in the same midline occipital spot where there were the most oscillations (Fig. 4), and adults showed the largest increase in the same bilateral occipital–parietal areas where they had the largest densities.

The overnight increase in the steepness of exponents was widespread but peaked in an occipital spot in all age groups, with additional bilateral frontal spots in <18-year-olds. These topographies do not correspond to the average topography of exponents from Figure 4. Offsets revealed widespread decreases, with localized increases in the same occipital locations for which exponents increased the most. This suggests that aperiodic offsets generally decrease, although the increase in exponent steepness contrasts this effect.

Power and periodic power again showed similarities to amplitudes and densities, respectively; however, while densities increased in the 14–18-year-olds, periodic power decreased. Likewise, the overnight increase in periodic power for >18-year-olds was more occipital and lateral than the increase in densities, and the larger decrease in amplitudes in occipital regions of 7–10-year-olds was less evident in power than for amplitudes.

Frequency-specific wake EEG changes with age and sleep pressure

In the previous analyses, we had pooled all frequencies between 4 and 16 Hz. Subsequently, we explored how oscillation and spectral EEG measures changed for each frequency, this time averaging channels (Extended Data Fig. 6-1). We found that oscillation amplitudes and spectral power showed gradual changes across both age and frequency, whereas average density and periodic power had distinct peak values between 8 and 11 Hz, with the peak shifting upward with age, a well-known property of alpha oscillations during development (Smith, 1938; Freschl et al., 2022; Tröndle et al., 2022). Overnight densities (and periodic power) showed decreases in higher frequencies (>11 Hz, i.e., low beta) and increases in alpha. These increases only began between 8 and 10 years of age, they were strongest in adults, and the range shifted to higher frequencies with age.

Given the dissociation between alpha and beta for density, we explored the topographic changes in density, split by both age and frequency band. In Figure 6, we plot average values. Theta oscillations were the overall rarest. They were most prevalent in the youngest children, and with age, the peak in theta density gradually shifted upward and decreased in magnitude. Alpha density instead started as a midline occipital cluster in the 3–7-year-olds. Alpha densities in the occipital spot decreased in the 7–10-year-old cohort, with bilateral central spots instead becoming more pronounced. With age, these three peaks morphed into a continuous occipital–parietal cluster, while in the remaining channels, the overall density of alpha increased. Lastly, low-beta oscillations showed yet another topography. They were almost completely absent in the youngest group and started to appear in the 7–10-year-olds as lateral occipital peaks and a frontal midline spot. Gradually, the lateral peaks converge toward the midline and in adults became partially overlapping with alpha, on average right-lateralized.

Figure 6.

Average topographies of oscillation densities, split by frequency band. Recordings are the same as in Figure 4. Theta is 4–7 Hz, alpha is 8–11 Hz, and low beta is 12–16 Hz. Each band has a different color scale range. The age by frequency spectra are provided in Extended Data Figure 6-1. The data are provided in Extended Data 6.

Figure 6-1

Average spectra across ages. From the oddball task, pooling controls and ADHD participants. Average spectra from the other tasks are provided in main Figure 2. A: Average values, such that lighter colors indicate greater magnitude for a given frequency and age. B: Difference values between morning and evening recordings, such that red indicate a greater magnitude in the morning. The measurement unit of each figure is the same as that of A. Download Figure 6-1, TIF file ^{(1.4MB, tif)} .

The overnight changes in density split by frequency are presented in Figure 7, based on the model $\mathrm{Density}\sim \mathrm{Time}+\mathrm{Task}+(1|\mathrm{Participant})+(1|\mathrm{Participant}:\mathrm{Session})$. Like in Figure 5, these reflect the β estimates for the fixed effect of Time.

Figure 7.

Topographies of overnight density changes, split by frequency band. Participants and plot information are the same as in Figure 5, with color indicating the β estimates for the linear mixed-effects model, such that red indicates an overnight increase in density and white dots statistically significant channels, FDR corrected for multiple comparisons. The model was Density ∼ Time + Task + (1|Participant) + (1|Participant:Session). The data are provided in Extended Data 6.

The overnight changes in theta density were small (∼1%), however quite variable by region and age. In the 7–10-year-olds, theta density generally increased overnight except in a central spot, exactly where the largest theta densities were seen in Figure 6, which instead decreased. This continued for the 10–14-year-olds. In the 14–18-year-olds, there were no significant effects; however, the midline theta spot, now more frontal, showed a nonsignificant decrease. In adults, only some scattered theta increases were observed.

For alpha, the main occipital spot in the 7–10-year-olds decreased overnight. Already in the 10–14-year-olds, a bilateral central alpha rhythm started to increase overnight, with still some slight decreases in the occipital spot. The overnight increases spread to the entire scalp in adolescents and adults, peaking in occipital–parietal areas, especially right lateralized. For low beta, across all ages there were decreases, with the peak shifting across age bins.

Effect of ADHD on the wake EEG topography

In our initial mixed-effects models pooling channels and frequencies, we found no significant effects for ADHD, which is why subsequent analyses and figures no longer included a Group effect. However, we nevertheless conducted mixed-effects models to determine the effect of ADHD for each channel: $\mathrm{Measure}\sim \mathrm{Task}+\mathrm{Time}*\mathrm{Age}+\mathrm{Group}(1|\mathrm{Participant})+$ $(1|\mathrm{Participant}:\mathrm{Session})$. We found no significant effects when correcting for multiple comparisons (Fig. 8). However, amplitudes were on average lower in participants with ADHD, and frontal exponents were steeper compared with controls.

Figure 8.

Effects of ADHD on EEG measures. Red indicates larger values in patients compared with controls. The scale for each topography is the same as for Figure 5. The model was Measure ∼ Time * Age + Task + Group + (1|Participant) + (1|Participant:Session). White dots would have indicated statistically significant channels, following FDR correction for multiple comparisons. The data are provided in Extended Data 5.

Discussion

In this study, we compared four distinct wake EEG measures and their relationship to development, time of recording, and ADHD. Specifically, we hypothesized that oscillation amplitudes would behave like sleep slow waves, because both measures likely reflect neuronal synchronization due to synaptic density and plasticity. Our predictions were met on all accounts except for the sensitivity of wake amplitudes to ADHD. Of the four measures, only amplitudes decreased overnight in all ages, and the overnight decrease was largest in younger children (Fig. 3). Aperiodic offsets showed similar development and overnight changes, but they did not display larger overnight decreases in younger children, a key indicator of higher neural plasticity. Although overnight changes could reflect circadian influences, our previous wake EEG findings during sleep deprivation (Snipes et al., 2023) provide converging evidence that wake oscillation amplitudes are modulated by sleep pressure.

While oscillation amplitudes were the only wake measure that followed all the patterns expected for sleep pressure, every EEG measure reflected development. Average amplitudes, exponents, and offsets were all strongly anticorrelated with age, with offsets showing the strongest relationship (Fig. 3). The correlation between average densities and age was weak, but we found distinctive regional patterns across age groups (Fig. 6). Overnight changes in both oscillation amplitudes and density were robustly correlated with age (Figs. 3, 7), whereas overnight changes in exponents and offsets were not as affected by age, as evidenced by the low correlations in Figure 3. Finally, no measure showed significant effects of ADHD (Fig. 8).

Oscillation amplitudes

Like for slow waves during sleep, the decrease in wake oscillation amplitudes with age could be explained by decreasing synaptic density in the cortex across adolescence (Huttenlocher, 1979). The overnight decrease in wake amplitudes could be explained by sleep's role in reducing net synaptic strength (Tononi and Cirelli, 2014; Cirelli and Tononi, 2022), and such plastic changes are more pronounced in children than adults (Jaramillo et al., 2020). The larger decrease in occipital regions in children compared with adults may be because primary sensory and motor areas obtain peak cortical thickness earlier in children, followed by adjacent secondary areas and finally frontal association areas (Shaw et al., 2008), resulting in an overall posterior–anterior maturation trajectory also for slow-wave activity (Kurth et al., 2010). Therefore, younger children show larger overnight decreases in amplitudes in occipital areas because these areas undergo higher plastic changes at that maturational stage. Finally, even the significant sex difference was comparable to sleep slow-wave activity (Mourtazaev et al., 1995), with wake oscillation amplitudes higher in females than males (although the effect was relatively small). This effect could be due to females' smaller heads and thinner skulls (Dijk et al., 1989). However, Schaworonkow and Voytek (2021) did not find any changes in wake oscillation amplitudes from 1–7-month-old infants, despite this period corresponding to the fastest growth in head circumference (Roche et al., 1986). Therefore, it is unlikely that the more modest increases in head size from childhood to adolescence can entirely explain the large decrease in amplitudes we observed here.

While wake oscillation amplitudes behave largely like sleep slow waves, the effects are reduced. In adults the overnight decrease in wake amplitudes was near 0 µV (Fig. 3), which is not the case for either slow-wave slopes or amplitudes during sleep (Riedner et al., 2007; Jaramillo et al., 2020). One possible explanation for this is that the evening wake recordings fell within the wake maintenance zone. This is a circadian time window just before bedtime characterized by increased alertness (Strogatz et al., 1987; Shekleton et al., 2013). Oscillation amplitudes are significantly reduced in this window, counteracting the otherwise monotonic buildup in amplitudes that occurred throughout the day (Snipes et al., 2023). In adults the contrasting effect of the wake maintenance zone may be sufficient to equalize evening and morning amplitudes. Overall, wake oscillation amplitudes may reflect the same information as sleep slow waves but may be more affected by circadian or other factors. Therefore, wake amplitudes may be affected by the same underlying neuronal changes as sleep slow-wave activity, but they are neither sufficiently specific nor sensitive to replace this gold standard measure of sleep pressure.

Oscillation densities

As could be expected, the densities of oscillations were the most complex EEG measure, with effects differing depending on age, time, topography, and oscillation frequency.

Theta

Theta oscillations were most prevalent in early childhood and decreased progressively with age, supporting previous results measuring relative theta power (Somsen et al., 1997). We further found that the peak in theta densities steadily drifted more frontally across childhood and adolescence. This frontal theta in adults is known to originate from the midline prefrontal cortex and to be anticorrelated to the default mode network (Scheeringa et al., 2008; Michels et al., 2010; Ishii et al., 2014). Therefore, this drift in theta may reflect the steady maturation of frontal cortices and the default mode network in particular (Fan et al., 2021).

There is still unresolved contradictory evidence on the role of theta in adults (Snipes et al., 2022), without including the question of theta during development. On the one hand, theta is often associated with cognitive effort (Buzsáki, 2005; Mitchell et al., 2008; Cavanagh and Frank, 2014; Meyer et al., 2019). On the other hand, it is also associated with sleepiness (Smith, 1938; Finelli et al., 2000; Snipes et al., 2022) and fatigue (Wascher et al., 2014; Tran et al., 2020; Arnau et al., 2021). A possible resolution to this paradox is that there are distinct oscillations that originate from different circuits with different functions and just happen to occur at the same frequency. Our results in Figure 7 would support this, as the peak source of theta shows overnight decreases, whereas theta from the rest of the cortex instead shows overnight increases, even as the theta peak drifts more frontally. Alternatively, theta could reflect a general form of “idling rhythm” (Scheeringa et al., 2008; Snipes et al., 2022, 2024), originating from disengaged cortical areas, and what changes from evening to morning is which circuits tend to idle. This would make theta functionally comparable to alpha (Laufs et al., 2003), differing only by source and frequency.

Alpha

Alpha begins in young childhood as a midline spot (Fig. 6). Two lateral central peaks become more defined at 7–10 years of age. These likely reflect sensorimotor mu rhythms which appear when motor activity is absent or even suppressed (Pineda, 2005; Pfurtscheller et al., 2006) and are already present in infants (Berchicci et al., 2011). We find that with age, they become topographically indistinguishable from occipital alpha, at least when recorded during an oddball task. These lateral central peaks are the first to show overnight increases in 10–14-year-olds, while the overnight decrease in the occipital midline spot becomes less prominent. The overnight increase in alpha densities then spreads over bilateral parietal and occipital areas across adolescence and adulthood. This dissociation between overnight decreases in childhood and increases in adulthood, as well as the slight differences in topography, could suggest that occipital alpha is in fact qualitatively distinct in children and adults. However, these rhythms were previously considered functionally equivalent because also in infants alpha power increases with eyes closed compared with eyes open (Stroganova et al., 1999). More research is needed on the sources of these oscillations.

It is also possible that this dissociation in overnight density changes is driven by some other difference with age, such as a longer window of sleep inertia in young children, longer sleep duration, or a shifted circadian rhythm compared with adults. Melatonin in the morning is elevated in children under 10, whereas older children and adolescents have morning melatonin levels comparable with the rest of the day (Attanasio et al., 1985). In adults, alpha power fluctuates with circadian rhythm and is therefore synchronized to melatonin levels (Cajochen et al., 2002). Therefore, it is possible that the dissociation of decreasing/increasing alpha densities originates from children and adults being at different phases of their alpha circadian rhythm in the morning. More research is needed into the circadian effects on the EEG across development.

Beta

Finally, we found overnight decreases in low beta densities across ages. There is ample literature on “beta bursts” from sensorimotor areas (Jones et al., 2009; Feingold et al., 2015; Shin et al., 2017; Little et al., 2019; Wessel, 2020; Lundqvist et al., 2024); however, these are likely qualitatively distinct from the beta oscillations we observed. First, the topography of sensorimotor beta rhythms was found to be either bilateral central in infants or frontal in adults (Rayson et al., 2023), unlike the bilateral occipital topographies in Figure 6. Second, sensorimotor beta was found to occur in “bursts” of power of <150 ms long (Sherman et al., 2016) and could thus correspond to only 1–3 cycles of beta (Jones, 2016; van Ede et al., 2018; Rayson et al., 2023). We detected bursts that were at least three cycles long, only up to 16 Hz, and therefore always longer than 187 ms. Therefore, the low-beta bursts we captured with cycle-by-cycle analysis are likely distinct from the previously investigated sensorimotor beta activity. It remains an open question what the functional role of these low-beta oscillations is, and why they may be less common the morning after sleep.

Aperiodic offsets and exponents

Our results on offsets and exponents replicate previous findings: They decrease linearly with age (Cellier et al., 2021; Hill et al., 2022; Tröndle et al., 2022) and originate from broad, primarily midline sources (Favaro et al., 2023). As in Lendner et al. (2023), we found exponents becoming increasingly steeper after sleep, extending this finding across ages. We further found, unusually, that exponents and offsets change in opposite directions, with offsets decreasing after a night of sleep. For aperiodic activity measured during sleep, both exponents and offsets decrease across the night, and the decrease decreases with age (Horváth et al., 2022). This indicates that changes in aperiodic activity related to sleep/wake history do not reflect the same information when measured during wake or during sleep. It is possible that during sleep the decrease in exponents reflects the decrease in sleep pressure, whereas during wake the increase in exponents could reflect something like lingering sleep inertia in the morning. Notably, the change in aperiodic exponents with age and sleep depth affects primarily lower frequencies (Favaro et al., 2023), whereas the overnight wake EEG changes affect primarily higher frequencies (Fig. 2). This means that the pivot point (the frequency at which the aperiodic signal rotates) of the exponent differs, and this likely has biological significance. Future studies should, as in Bódizs et al. (2021), specifically identify such pivot points, also to better dissociate changes in offset and exponent.

Second to oscillation amplitudes, aperiodic offsets showed the closest resemblance to sleep slow-wave activity, decreasing with age and decreasing overnight, in line with increases in offsets following sleep deprivation (Bai et al., 2024). However, offsets did not show the characteristic decrease in overnight changes with age that was observed for sleep slow waves and wake amplitudes (Fig. 3). This could suggest that offsets also reflect changes in synaptic strength and plasticity but may be less sensitive or specific than wake amplitudes, especially to overnight changes.

Spectral power versus oscillation burst detection

As explained by previous papers, changes in spectral power do not differentiate between changes in oscillation amplitudes or densities (Quinn et al., 2019; Tal et al., 2020; Zich et al., 2020; Donoghue et al., 2022; Snipes et al., 2023). With this study, we again demonstrate that amplitudes and densities meaningfully change independently, both across sleep and development. However, we also found that the changes in amplitude were largely captured by changes in average power, whereas changes in density were largely captured by changes in periodic power. This suggests that power and periodic power can be used as proxies for amplitudes and densities. Previous studies of development have likewise found differences between power and “relative power” (similar to periodic power) across development (Somsen et al., 1997). These results may be reinterpreted as the dissociation between changes in amplitude and density.

Arguing against this approach, however, is the topography of overnight changes in 14–18-year-olds: Oscillation densities increased overnight, whereas periodic power decreased, likely reflecting the greater influence of the overnight decrease in amplitudes. Similarly, the overnight increase in densities is more central than the overnight increase in periodic power. Therefore, differences between power and periodic power may suggest different influences of oscillation amplitudes and densities, but to know for certain, oscillations should be measured directly.

ADHD

Despite a relatively large sample size (N = 58), we did not observe any significant effects of ADHD on our EEG measures. We did find exponents to be steeper on average in patients, supporting the results of Robertson et al. (2019) and contrasting those of Ostlund et al. (2021). One explanation could be that our participants were performing tasks for most recordings, and the differences between patients and controls may mostly emerge in resting EEG. It's also possible our analysis did not reach significance because our participants were a combination of both medicated and unmedicated patients, and it is known that medication will reduce the effects of ADHD on the EEG (Furrer et al., 2019; Karalunas et al., 2022).

Another important difference with previous studies is that our participants were screened for habitual good sleep quality. This was originally intended to ensure both groups had comparable sleep in the laboratory. However, generally ∼40–55% of children with ADHD report sleep deficits (Corkum et al., 1998; Holmberg and Hjern, 2006; Konofal et al., 2010; Becker et al., 2019; Xian et al., 2025), so it is possible that poor sleep quality in patients contributed to differences in the wake EEG observed in other studies (Clarke et al., 2020; Pinggal et al., 2026). In other words, because of habitual lower sleep quality and increased daytime sleepiness, patients with ADHD could have presented with higher theta or steeper exponents compared with controls, but this effect is not specific to the pathology of ADHD.

Regardless of the reason, given that we do not see any systematic differences between patients and controls, none of the wake EEG measures we tested make for a reliable intrinsic marker of ADHD which could potentially be used to aid diagnosis. Instead, research investigating such markers in these and other patient populations should take special care to control for sleep/wake history and sleep quality, as these may have a greater impact on the EEG. Instead, within the same participants, we observed significant differences in sleep slow-wave activity from controls (Furrer et al., 2019), suggesting sleep is more sensitive to developmental deficits.

Limitations

The primary limitation of this study is its inability to disentangle the effects of sleep pressure from those of circadian rhythms. To do so would require substantially more intensive protocols, such as sleep deprivation, sleep restriction, or shifting sleep windows over several days. However, future studies could collect more wake recordings throughout the day to provide an indication as to whether the changes in oscillation amplitude are circadian or homeostatic (or both).

Another important limitation of this study is the scarcity of datasets under 8. It is known that in young children there is a switch from primarily synaptic growth to primarily synaptic pruning, peaking in different cell populations and regions at different ages (Shaw et al., 2008; Petanjek et al., 2011; Cao et al., 2020), which is also reflected in sleep slow-wave activity peaking around this age (Feinberg and Campbell, 2013). Wilkinson et al. (2024) found an increase with age in both offsets and exponents across 0–3-year-old infants, and McSweeney et al. (2023) found a quadratic relationship between age and offsets/exponents, such that they peaked at 5–7 years old (see also Antúnez et al., 2025). This would suggest more complex relationships between age and EEG measures than the linear trends observed here.

A further limitation of the data is its lack of uniformity, pooling multiple experimental paradigms and tasks, and medicated and unmedicated patients. Our primary findings remain significant also for more uniform subsets of the data (e.g., Fig. 3 is only oddball), but it's possible that different tasks would generate different topographies, and significant effects of ADHD would have emerged from entirely unmedicated patients. Future studies may find significant effects by using strictly unmedicated patients or carefully controlling treatment.

Additionally, if one assumes that aperiodic and oscillatory signals are a linear sum of each other in the time domain of the EEG signal, it is possible that the aperiodic changes explain part of the changes in oscillation amplitudes. A better understanding of the relationship between oscillations and the aperiodic signal is needed to validate such an assumption, and then precise simulations would be needed to determine the magnitude of this effect. However, in practice we have demonstrated that oscillation amplitudes can behave differently from aperiodic measures, and therefore it is at least useful to measure them independently.

Finally, our data is limited to EEG. Future studies and analyses would greatly benefit by comparing these measures to structural and functional brain changes observable with MRI, as well as cognitive and behavioral outcome measures related to development. This would bridge the gap between a purely basic research finding and practical applications.

Conclusions

We have found that overnight changes in wake oscillations provide distinct markers of brain maturation. Both absolute amplitudes and overnight changes in amplitudes decrease linearly with age, the effect being more occipital in younger children. Overnight changes in oscillation density, especially of alpha oscillations, dissociate children from adolescents and adults by switching from an overnight decrease to an increase in density. Understanding the reason behind this effect would likely provide important information on brain development around puberty and adolescence. At the same time, these strong interactions can become confounds in studies where sleep/wake history and circadian rhythms differ. Both sleep and wake research should take into consideration the dynamics of the EEG across time, especially in young populations.

Synthesis

Reviewing Editor: Adrien Peyrache, McGill University

Decisions are customarily a result of the Reviewing Editor and the peer reviewers coming together and discussing their recommendations until a consensus is reached. When revisions are invited, a fact-based synthesis statement explaining their decision and outlining what is needed to prepare a revision will be listed below. The following reviewer(s) agreed to reveal their identity: Róbert Bódizs, Leila Tarokh.

This manuscript addresses an important and understudied question: how prior sleep and developmental stage shape wake EEG dynamics. The work has been evaluated by two Reviewers and they both highlight the richness of the dataset, the modern analytic approach, and the broad methodological relevance for cognitive and developmental neuroscience. In particular, the Reviewers commend the innovative EEG measures that bridge time-series and frequency-spectrum indicators, noting that these methods are up-to-date and impactful.

While the overall assessment is positive, the Reviewers raise several specific concerns that should be addressed to strengthen the manuscript. These include clarifying aspects of the methodological description, providing stronger justification for certain analytic choices, and refining the interpretation of developmental differences. Attention to these points will enhance the clarity and robustness of the findings.

In light of the enthusiasm for the study and the manageable nature of the concerns, the authors are invited to revise the manuscript accordingly.

Major concerns

1) The type of multiple comparisons correction that was performed should be clarified. This is unclear from the methods section.

2) Medication dosage for the ADHD cohort should be included as a factor in the analysis.

3) While it is claimed that this study provides evidence for oscillatory activity associated with changes in sleep pressure, the impact of circadian rhythms from sleep pressure can not be disentangled. It should be made clear throughout the manuscript and in the abstract.

4) The different age groups have variable total sleep time and this should be discussed and/or potentially included in an analysis. Does TST between the morning and evening session moderate the effect? This may help disentangle the influence of age from total sleep time

5) Along the same lines as the above comment, are there data on the number of hours awake before the evening EEG recording or habitual total sleep time? Were participants on a schedule? This would be important information to include, perhaps in a table.

6) Overnight effects are almost exclusively interpreted as the effects of decreased homeostatic sleep pressure in the morning as compared to the evening. Although this interpretation is a viable one, authors should be more explicit on the potential effects of circadian rhythms on EEG. For example cortisol levels are known to be high in the morning and low in the evening, an effect which is almost entirely time-of-day-dependent (i.e. only tangentially related to sleep). It is evident that cortisol levels affect the resting state EEG indicators (see for example: https://doi.org/10.1210/jcem.83.12.5326 ). Authors should be much more explicit on this possibility (Discussion, Limitations).

7) Authors repeatedly discuss the assumed effect of sleep on (resting state) wake EEG in terms of spectral power (parameters), as well as oscillation amplitudes and densities. When doing so they should consider available data derived from sleep deprivation studies. These studies revealed a frequency-independent increase in EEG power, as well as a shift in aperiodic offset (https://doi.org/10.1038/s41597-024-03268-2 , https://doi.org/10.1152/jn.00304.2024 ). Although the evidence is conflicting (see: https://doi.org/10.3390/brainsci13060949 ), this aspect has to be treated and discussed in detail, as it is crucially relevant to the core questions of the current study. Also sleep deprivation is a more powerful method when studying the effects of sleep per se on the EEG.

8) Authors claim that aperiodic activity reflects E:I balance. Although this is a very popular hypothesis, it is only a hypothesis (and a model-based one) and not the only one. Authors should consider alternative hypotheses on network bistability or up-down state transitions, which are not fully equivalent with the E:I theory (e.g. Baranauskas et al., https://doi.org/10.1152/jn.00470.2011 ).

9) Fig. 1 caption: "The EEG signal is composed of aperiodic "background activity" (gray signal in B) and oscillatory activity (colored signal in B)." Although very didactic, this statement is most probably not entirely correct in mathematical terms. Periodic and aperiodic components of a time series cannot be fully separated in time. Aperiodic activity persists during periods of enhanced oscillatory-type segments and vice versa. A less didactic, but more realistic description would emphasize the prevailing presence of periodic and aperiodic activities in colored and grey signals, respectively.

10) Linear mixed effects model used: Measure ~ Task + Time ∗ Age + Group + Sex + (1|Participant) + (1|Participant: Session). What about the main effects of Time and Age? Were these assessed? It seems like they were, as authors present results of this type. The "Time * Age" term only refers to their interaction in my interpretation. Please complete the formula or reflect/argue in your response!

11) In some parts of the paper statements should be deduced based on the nominal differences of correlation coefficients. E.g.: "but less so with exponents (r = .54, t = 4.7)". Please provide a statistical comparison of the correlation coefficients in such cases.

12) The paper would benefit from a clear graphical representation of the aperiodic differences. Many statements which are more or less qualitative (e.g. morning increases in spectral slope steepness are mainly due to decreased high frequency EEG power) are much easier to understand and confirm with a parallel graphical representation. The figure which is crucial in this regard and represents the spectra with the fitted slopes is in the Supplementary part of the paper now. This should rather be a main figure of the article.

13) Correlations between various EEG measures and parameters are reported e.g. in Fig. 2.2. in the supplementary material. Indeed band-limited indicators are missing from these types of reported results. These correlations should be reported as well (as a table or as scatterplots). The frequency-specific backgrounds of aperiodic measures (offset, slope) could be reported in the form of a multiple regression or even a principal component analysis with all EEG measures as inputs and components with an eigenvalue above 1 as outputs. This latter step would provide the reader with an overall picture of the whole study, which is now particularly detailed and divergent, and consequently not easy to summarize. While this step could reveal some results which suggest that some of the reported measures are redundant, yet there is a growing need to extract meaningful information from large data sources like the ones reported in this study.

Minor concerns

1) Pearson's ρ - Why not Pearson's r? Rho (ρ) is usually maintained for Spearman-type of rank correlations.

2) Figure 5-1: Spectrograms, averaged across channels - These are not spectrograms indeed. See for example: https://doi.org/10.1097/WNP.0000000000000736

Author Response

Response to reviewers We thank the reviewers and editor for their time and recommendations. Below we provide a point-by-point response detailing the revisions made. Due to the already lengthy manuscript, we could not incorporate all the reviewers suggested additions; where this was the case, we explain our rationale.

References to pages and line numbers apply to the track-changes copy of the paper.

Major concerns 1) The type of multiple comparisons correction that was performed should be clarified. This is unclear from the methods section.

We applied false discovery rate (FDR) correction where appropriate. This is now clarified in the Methods (page 15) and in the captions of the relevant figures.

2) Medication dosage for the ADHD cohort should be included as a factor in the analysis.

Unfortunately, we did not have access to precise medication dosages, and treatment regimens were heterogeneous. In exploratory analyses, we stratified patients and controls into the five subgroups shown in Table 2 (same day medication, medication free, etc. as done in previous work (Furrer et al., 2019)) and re-ran the statistical models accordingly. These analyses did not reveal any significant subgroup effects so were not included.

Given the length of the manuscript and the absence of significant findings, we chose not to include these additional analyses in the main text. Instead, we provide the complete data table used for the statistical analyses, including subgroup labels, to enable independent exploration. We emphasize treatment heterogeneity as a key limitation in the Discussion.

3) While it is claimed that this study provides evidence for oscillatory activity associated with changes in sleep pressure, the impact of circadian rhythms from sleep pressure can not be disentangled. It should be made clear throughout the manuscript and in the abstract.

We have now added caveats regarding the circadian vs sleep pressure interpretation in the following places:

- Abstract page 1 - Significance statement page 2 - beginning of discussion page 24 - Moving this to be the first limitation in the discussion, page 32 While this particular dataset does not disentangle homeostatic from circadian effects, our previous study with sleep deprivation more directly identified the link between wake oscillation amplitudes and sleep pressure using sleep deprivation (Snipes et al., 2023). This is why we lean more towards a homeostatic rather than circadian hypothesis in this paper, but indeed we can't rule one out.

4) The different age groups have variable total sleep time and this should be discussed and/or potentially included in an analysis. Does TST between the morning and evening session moderate the effect? This may help disentangle the influence of age from total sleep time Total sleep time correlates with both age and overnight changes in the wake EEG, and even more so N3 duration, but when it is used as a factor in mixed effects models, the relationship between N3 duration and overnight changes in amplitude is just trending, with amplitude age effects remaining intact (and no relationship persists with TST).

In our view, only dedicated modelling which takes into account both slow wave density and NREM sleep time would do justice to the question of whether these EEG markers in children go beyond what would be expected from purely sleep homeostatic models, and this is outside the scope of this paper. Furthermore, we found curious relationships between oscillation densities and sleep stages in the adult populations, however, this did not relate to development. To reduce the length of the manuscript and simplify the dataset, we preferred not to include any sleep data, as it raises many more unrelated questions and paths to explore.

5) Along the same lines as the above comment, are there data on the number of hours awake before the evening EEG recording or habitual total sleep time? Were participants on a schedule? This would be important information to include, perhaps in a table.

Participants were asked to maintain a regular sleep-wake rhythm the week before each recording (page 6 line 16-17). Unfortunately, the exact hours awake for each individual is only inconsistently available from the different datasets, often as unprocessed actigraphy data. Generally, younger children would have been awake for fewer hours prior to the evening recording compared to adults, as they overall sleep more. Thus, their higher amplitudes in the evening and larger overnight change are even more remarkable.

6) Overnight effects are almost exclusively interpreted as the effects of decreased homeostatic sleep pressure in the morning as compared to the evening. Although this interpretation is a viable one, authors should be more explicit on the potential effects of circadian rhythms on EEG. For example cortisol levels are known to be high in the morning and low in the evening, an effect which is almost entirely time-of-day-dependent (i.e. only tangentially related to sleep). It is evident that cortisol levels affect the resting state EEG indicators (see for example: https://fra01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1210%2Fjcem.83.12.5326%26data=05%7C02%7Csophia.snipes%40icm-institute.org%7Ccf2ae32ec6444c785b4808de3cc0825d%7C9df7cf1718fa41508b00dc5e754777d8%7C0%7C0%7C639014994704991858%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C%26sdata=J4dIvea7tFmwrDMsDm%2F9443CGz8OIseDRdo4FLB8c4A%3D%26reserved=0 ). Authors should be much more explicit on this possibility (Discussion, Limitations).

The cited study is quite interesting, however, their cortisol peak occurred approximately 10 minutes after the EEG changes, suggesting that the brain dynamics may be driving the cortisol effect, or a third system drives both. For this reason, we consider the general umbrella term of "circadian rhythms" to be appropriate. As recommended by a previous point, we have emphasized more throughout the text the alternative explanation of such circadian rhythms.

7) Authors repeatedly discuss the assumed effect of sleep on (resting state) wake EEG in terms of spectral power (parameters), as well as oscillation amplitudes and densities. When doing so they should consider available data derived from sleep deprivation studies. These studies revealed a frequency-independent increase in EEG power, as well as a shift in aperiodic offset (https://fra01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1038%2Fs41597-024-03268-2%26data=05%7C02%7Csophia.snipes%40icm-institute.org%7Ccf2ae32ec6444c785b4808de3cc0825d%7C9df7cf1718fa41508b00dc5e754777d8%7C0%7C0%7C639014994705007118%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C%26sdata=6D6Y0lHKz0HKJki%2BuaU%2BHa%2B6DC%2FmggNJFTHuPW6N3Oc%3D%26reserved=0 , https://fra01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1152%2Fjn.00304.2024%26data=05%7C02%7Csophia.snipes%40icm-institute.org%7Ccf2ae32ec6444c785b4808de3cc0825d%7C9df7cf1718fa41508b00dc5e754777d8%7C0%7C0%7C639014994705025739%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C%26sdata=k4M2fl2HimnOlQTKQOmUUCanPIT%2BhfztiTvhhIk3c3M%3D%26reserved=0 ). Although the evidence is conflicting (see: https://fra01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.3390%2Fbrainsci13060949%26data=05%7C02%7Csophia.snipes%40icm-institute.org%7Ccf2ae32ec6444c785b4808de3cc0825d%7C9df7cf1718fa41508b00dc5e754777d8%7C0%7C0%7C639014994705067979%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C%26sdata=hMRbd%2BykQw2aR8ZmSVQuuprKh09oP7X47yExIftksCA%3D%26reserved=0 ), this aspect has to be treated and discussed in detail, as it is crucially relevant to the core questions of the current study. Also sleep deprivation is a more powerful method when studying the effects of sleep per se on the EEG.

Thank you for pointing us to these papers, they are indeed relevant to the discussion, and the first is now included in page 30. Due to space constraints, we did not include the second study, because we find that the methodological differences between the two studies can explain the conflicting results: the second paper used eyes closed instead of eyes open resting data. Eyes closed wake during sleep deprivation is always problematic, because participants can very easily just slip into sleep. In our previous work we showed that eyes closed resting EEG diverges from other resting state recordings during sleep deprivation (Snipes et al., 2023). In our study, participants remained standing during the eyes closed condition specifically to reduce the likelihood of sleep, although signs of N1 still emerged under maximal sleep pressure, and these sleep intrusions reduced overall alpha activity. The fact that the second study the reviewers provide showed such a dramatic reduction in alpha power during sleep deprivation is in line with our previous results, and can be easily explained as participants drifting to sleep during the eyes-closed sleep deprivation recordings. Given this, we find the first study provides more reliable information on the changes in wake EEG following sleep deprivation compared to the second, as it maintained constant the vigilance state of the participants by keeping eyes open.

And indeed, we agree that sleep deprivation is a more powerful method for studying the effects of sleep on the EEG, which was the main focus of our earlier paper (Snipes et al., 2023), applying the same burst analyses on wake EEG following sleep deprivation. These two papers provide converging evidence that the changes we observe in wake are driven by sleep homeostatic effects.

8) Authors claim that aperiodic activity reflects E:I balance. Although this is a very popular hypothesis, it is only a hypothesis (and a model-based one) and not the only one. Authors should consider alternative hypotheses on network bistability or up-down state transitions, which are not fully equivalent with the E:I theory (e.g. Baranauskas et al., https://fra01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1152%2Fjn.00470.2011%26data=05%7C02%7Csophia.snipes%40icm-institute.org%7Ccf2ae32ec6444c785b4808de3cc0825d%7C9df7cf1718fa41508b00dc5e754777d8%7C0%7C0%7C639014994705084138%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C%26sdata=zTx0xLtCLi9uS1G0o2fK%2Bk%2BxhUky2VqmWhLo1j6QMTI%3D%26reserved=0 ).

We do not wish to claim that aperiodic activity reflects E:I balance; we had only indicated that it was an explanation given by others. In fact, we are skeptical of this interpretation, and therefore have now deleted the reference to this hypothesis entirely. While aperiodic measures are quite popular at the moment, these are not the focus of our paper or expertise as much as oscillatory activity, and we would rather not get involved in that debate at this time.

9) Fig. 1 caption: "The EEG signal is composed of aperiodic "background activity" (gray signal in B) and oscillatory activity (colored signal in B)." Although very didactic, this statement is most probably not entirely correct in mathematical terms. Periodic and aperiodic components of a time series cannot be fully separated in time. Aperiodic activity persists during periods of enhanced oscillatory-type segments and vice versa. A less didactic, but more realistic description would emphasize the prevailing presence of periodic and aperiodic activities in colored and grey signals, respectively.

We have now specified in the legend that the colored portion represents "primarily" oscillatory activity.

10) Linear mixed effects model used: Measure ~ Task + Time ∗ Age + Group + Sex + (1|Participant) + (1|Participant: Session). What about the main effects of Time and Age? Were these assessed? It seems like they were, as authors present results of this type. The "Time * Age" term only refers to their interaction in my interpretation. Please complete the formula or reflect/argue in your response! Main effects were always included in the model. In Wilkinson-Rogers notation, using "*" indicates that both the main effects and the interaction are included, whereas using ":" is only including the interaction in the model (https://ch.mathworks.com/help/stats/wilkinson-notation.html). So Y ~ a*b is the same as Y ~ a + b + a:b. Thus, we preferred writing the equation more succinctly using the asterisk.

11) In some parts of the paper statements should be deduced based on the nominal differences of correlation coefficients. E.g.: "but less so with exponents (r = .54, t = 4.7)". Please provide a statistical comparison of the correlation coefficients in such cases.

We have added Steiger's z-test for comparing r values. This is explained on page 15 line 3, and the results are on page 19. Furthermore, we now use the whole dataset for this analysis, as there was no reason to use only a subset (the difference is significant regardless).

12) The paper would benefit from a clear graphical representation of the aperiodic differences. Many statements which are more or less qualitative (e.g. morning increases in spectral slope steepness are mainly due to decreased high frequency EEG power) are much easier to understand and confirm with a parallel graphical representation. The figure which is crucial in this regard and represents the spectra with the fitted slopes is in the Supplementary part of the paper now. This should rather be a main figure of the article.

This figure has now been moved to the main text as Figure 2. To maintain the same number of figures in the main paper, we have moved what was previously Figure 5 to be extended data Figure 6-1.

13) Correlations between various EEG measures and parameters are reported e.g. in Fig. 2.2. in the supplementary material. Indeed band-limited indicators are missing from these types of reported results. These correlations should be reported as well (as a table or as scatterplots). The frequency-specific backgrounds of aperiodic measures (offset, slope) could be reported in the form of a multiple regression or even a principal component analysis with all EEG measures as inputs and components with an eigenvalue above 1 as outputs. This latter step would provide the reader with an overall picture of the whole study, which is now particularly detailed and divergent, and consequently not easy to summarize. While this step could reveal some results which suggest that some of the reported measures are redundant, yet there is a growing need to extract meaningful information from large data sources like the ones reported in this study.

In (now) Extended Figure 6-1 we show how densities but not amplitudes show band-specific effects, and this also held for other (non-reported) exploratory analyses. For this reason, we did not do any further frequency specific analyses.

While we appreciate the suggestion for using a single analysis with all EEG measures, we preferred to keep the statistics as simple and accessible as possible, hoping for a broader readership among pediatric and clinician researchers, who are often turned off by such analysis-heavy papers.

The reviewers are right to point out that there is a lot more meaningful information in this dataset then what we have exposed here. Therefore, we have provided the full processed data as a mat file, containing the spectra and topographies used for our analyses.

This is now stated explicitly in the manuscript (page 6, line 18).

Minor concerns 1) Pearson's ρ - Why not Pearson's r? Rho (ρ) is usually maintained for Spearman-type of rank correlations.

This was a mistake, and has now been changed consistently to r.

2) Figure 5-1: Spectrograms, averaged across channels - These are not spectrograms indeed. See for example: https://fra01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1097%2FWNP.0000000000000736%26data=05%7C02%7Csophia.snipes%40icm-institute.org%7Ccf2ae32ec6444c785b4808de3cc0825d%7C9df7cf1718fa41508b00dc5e754777d8%7C0%7C0%7C639014994705102813%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C%26sdata=u2QmOINCYysn4JVTHAhWIxN3u%2FipnLmO2gJNs5R1ZbI%3D%26reserved=0 Thank you for pointing this out! It has been replaced with spectra.