Aperiodic 1/f noise drives ripple activity in humans

Domain: neuroscience
Published: 2026-01-17
Editorial outcome: major_revision

Abstract
Sharp-wave ripples (SWR) are central for cognition and hallmark sleep in the rodent hippocampus. Recently, ripple-like activity was also observed in the human hippocampus and neocortex during wakefulness and sleep. However, ripple detection across brain states and cortical regions remains challenging. We demonstrate that putative ripples largely index noise originating from region-, state-, and demand-dependent modulation of cortical background activity. We establish the noise sensitivity for five common detection algorithms across three intracranial EEG studies during sleep and cognitive engagement. On average, 77% of awake ripples in the medial temporal lobe, including the hippocampus, reflect false positives within the 1/fχ noise floor. We also report task-related 1/fχ modulations that lead to spurious ripple activity, and demonstrate scenarios where ripple detections are less impacted by noise. Our results offer a simulation-based approach to estimate the false positive rate and demonstrate the importance of 1/fχ activity for state- and context-dependent cortical processing.

Introduction
Hippocampal sharp-wave ripples (SWR) constitute one of the central building blocks underlying cognition and memory in the mammalian brain 1. SWR reflects synchronized volleys of population activity in the frequency range from ~80–200 Hz that preferentially occur during slow wave sleep (SWS). During SWR, population activity patterns are reactivated and repeated in a process known as replay 2,3, which is thought to facilitate neuroplasticity underlying long-term memory storage 4–6. While SWR were mainly described in the CA1 region of the rodent hippocampus during sleep, recent studies described ripple-like signatures in the human hippocampus 7–12 as well as in neocortex 13–16, and have additionally been observed during wakefulness 10,16,17. Especially, wake ripples have recently been implicated in several cognitive functions, including memory retrieval, navigation, and planning 10,18,19. However, it has been fiercely debated whether awake ripples recorded from the neocortex constitute true functional homologs of the hippocampal SWR 20. In humans, ripples were mainly reported in epilepsy patients undergoing presurgical intracranial electroencephalography (iEEG) monitoring with implanted depth or subdural grid electrodes. Identification of ripples in this patient population is hampered by several factors, including the size of clinical macro-electrodes, which often span several hippocampal subfields, as well as pathological electrophysiological signatures of epilepsy, such as interictal spikes, sharp waves, and high-frequency oscillations 21–24. Recently, ripple-like signatures and replay were also reported from non-invasive source imaging using magnetoencephalography (MEG) in non-epileptic volunteers 25,26; however, it remains unclear if low-amplitude, high-frequency activity in the medial temporal lobe (MTL) can be precisely localized with MEG. Currently, there is no consensus about which features and characteristics define a ripple in humans and to what extent these signatures actually correspond to the canonical hippocampal SWR 20.

In a largely unrelated line of inquiry, it had recently been shown that electrophysiological ‘background’ activity, also termed aperiodic activity for the lack of a unique periodicity, indexes different brain states and cognitive demands. Aperiodic activity is also called 1/f noise as it follows a fχ scaling law. We defined 1/f noise as 1/fχ, which yielded a positive exponent. We adopted the common convention to report the exponent in slope form as the negative exponent 27,28. Spectral exponents typically range from −1 to −2 during cognitive engagement 29,30, from −2 to −4 during sleep 31–35, and have values below −4 during general anesthesia 27,35. Moreover, the spectral exponent follows a hierarchical organization with overall higher values in early sensorimotor areas as compared to the association cortex 36. Critically, 1/f noise exhibits spectral power at all frequencies, which raises the intriguing possibility that region-, state-, and task-specific changes in ripple rate largely reflect 1/f noise characteristics. Here, we adopt the term ‘1/f noise’ to highlight its relationship with well-known noise definitions (such as white, pink, and brown noise that correspond to χ=0, −1, and −2, respectively) and its detrimental impact on ripple detection.

To address this hypothesis, we combined three experiments conducted during sleep and wakefulness with noise simulations to test if, and to which extent, noise characteristics impact ripple detection. Using simulated 1/f noise with varying spectral exponents, we tested whether ripple detection is modulated as a function of the underlying 1/f noise. In the next step, using experimental data, we specifically tested if more ripples are evident in cortical regions that exhibit a higher spectral exponent (e.g., hippocampus and the medial temporal lobe; MTL) as compared to neocortical association areas (e.g., lateral prefrontal cortex; LPFC). In addition, we determined the relationship of 1/f noise and ripples during different cortical states (i.e., sleep, quiet wakefulness, and cognitive engagement). Given prominent, state-dependent 1/f differences wherein the exponents are high during cognitive engagement and lower during sleep, we predicted a higher ripple rate in the wake state as a result of increased 1/f noise. To test if state-specific changes in ripple detection reflect an altered 1/f noise characteristic, we conducted analyses on electrodes in the early visual and motor cortex – regions for which no strong prior evidence of ripple occurrence exists. We reasoned that ripple detections should be driven by the previously documented change in 1/f noise 37,38.

Results
1/f noise characteristics drive ripple event detections
Detection of putative ripple events relies on predefined sets of criteria that include band-pass frequency, duration, and amplitude. Numerous detection algorithms have been proposed wherein these criteria are variably applied. Here, we implemented five different algorithms that reflect commonly reported methodological approaches (Table 1). As we do not wish to highlight any specific algorithm or study in particular, we employed adaptions that aim to illustrate the range of possible algorithmic criteria and their impact on ripple detection in various noise scenarios. It is critical to highlight that most algorithms have not been benchmarked in the past; hence, their false positive rate remains unknown. To illustrate this issue, we first detected ripples on real data recorded during the longest consecutive slow-wave sleep period (SWS; duration = 23.5 min) as well as simulated data that matched the spectral exponent of the recording per 30s segment (χ=−2.01±.03; mean±SE; N = 47) for an exemplary single subject and channel (Fig. 1a). Both yielded physiologically plausible putative ripple events that satisfied commonly employed criteria, including the typical morphology in the time domain (Fig. 1b Left) and the isolated, short-lasted power increase in the ripple-band (80–120 Hz) in time-frequency representations (Fig. 1b Right) without concomitant broadband power changes. Given the short and transient nature of ripples, spectral representations of SWS typically do not exhibit a clear ripple peak, similar to pink or brown noise (Fig. 1c). However, it is critical to highlight that while noise signals are devoid of oscillatory activity, they still contain non-oscillatory, broadband spectral power in the ripple band (Fig. 1c). After identifying candidate events with a detection algorithm, the events will necessarily feature the selection characteristics—which in the case of ripple detection constitutes a clear and distinct peak in the ripple band (red trace in Fig. 1c).

To quantify the relationship between 1/f noise and ripple detections, we simulated multiple one-hour-long surrogate EEG traces that followed a fχ relationship with varying exponents χ, ranging from −4.0 to 0 in 0.1 steps. Note that the surrogate datasets (1/f noise time series) were simulated solely based on the pre-defined spectral exponent. Hence, we did not simulate ripple oscillations on top of the 1/f noise, indicating that all detections were solely obtained from the noise time series, devoid of any true oscillatory activity. We observed that the density of detected ripples (defined as the number of events per second; Hz) differed between algorithms, but always scaled with the spectral exponent (Fig. 1d, all p<0.0001, RM-ANOVA; effect sizes: detector (1): η²=0.99; (2): η²=0.96; (3): η²=0.08; (4): η²=0.95; (5): η²=0.99). Critically, all algorithms yielded physiologically plausible ripple waveform shapes as commonly reported (Fig. 1e). However, the precise relationship between spectral exponent and ripple density differed between the detection algorithms: Detector 1 and 2 exhibited an inverted u-shape curve (a direct result of the min. and max. amplitude criteria) where ripple events peaked for exponents of −1.6 (detector 1) and −1.8 (detector 2). Changing the amplitude cut-off introduced a ceiling effect for higher exponents that approached white noise (χ=0; detector 5). In contrast, detectors 3 and 4 exhibited an inverse relationship where negative exponents gave rise to more ripple detections. It is noteworthy that this negative relationship (and the overall higher number of detected events) resulted from their duration criteria (cf. Table 1). In sum, this set of findings demonstrates that ripple detection is modulated as a function of the underlying 1/f noise.

Spectral characteristics of aperiodic activity drive ripple detection during sleep
Having demonstrated that ripple detection depends on 1/f noise characteristics, we next determined how physiologic differences in 1/f activity shape ripple expression. In order to capture the region- and state-specific impact of 1/f noise on putative ripples, we assessed MTL and LPFC activity during wakefulness and SWS—conditions where clear 1/f differences have recently been described. Specifically, the spectral exponent is higher in early sensorimotor areas and the MTL as compared to association areas like the LPFC 39,40. Moreover, the spectral exponent decreases from wake to SWS from approximately −2 to −3, especially in the MTL, while this relationship might be reversed in other regions, such as the LPFC.

To capture 1/f activity and ripples with high spatiotemporal resolution, we analyzed intracranial EEG recordings from the MTL and LPFC during a whole night of sleep (N=14; 82 MTL bipolar contacts, 339 LPFC contacts) while pharmacoresistant patients underwent presurgical invasive monitoring with implanted depth electrodes. We compared the iEEG recordings during sleep to surrogates by simulating the noise profile throughout the night. Specifically, for every 30s segment of sleep iEEG, we simulated a corresponding segment with the same spectral exponent per channel. Spectral exponents were determined per 30s segment using FOOOF (“Methods”). The segments were subsequently concatenated, which yielded a 1/f matched surrogate iEEG trace devoid of any oscillatory activity. We then separately detected ripples on the experimental and surrogate data (Fig. 2a) for MTL and LPFC contacts (Fig. 2b). Since all detection algorithms exhibit their own idiosyncrasies, we conducted all subsequent analyses using the first detector. This detector was chosen for the main analyses as it utilizes several characteristics shared across detectors (c.f., Table 1) and yields a representative detection profile on 1/f noise (c.f., Fig. 1d, Supplementary Fig. 1). Note that qualitatively and quantitatively similar results were observed for the other detectors (SI Appendix). A separate detection of interictal epileptiform discharges (IEDs) was conducted for the experimental datasets (“Methods”), thus discarding any candidate ripple events in close temporal proximity of an IED (±2.5s); thereby minimizing erroneously considering IEDs as ripples.

Qualitatively similar results can be observed with the other detectors as well, revealing a biased relationship between 1/f noise and ripples, albeit that ripple detections differ slightly between distinct algorithms (cf. Fig. 1d). 1/f activity was estimated from FOOOF models with standard parameters (“Methods”). The overall goodness-of-fit of the aperiodic model was high (R2=.9874±.0008; median±SEM), indicating a successful parametrization of the underlying power spectra. This decomposition revealed prominent power differences in the ripple band between wakefulness and SWS (Fig. 2c Top) as well as between MTL and LPFC electrodes (Fig. 2c Bottom) as a result of altered 1/f characteristics. Specifically, the spectral exponent was lower in the LPFC (−2.85±.03, mean±SEM) as compared to the MTL (−2.36±.07), irrespective of state (Fig. 2d; t=6.43, p<.0001, two-sample t-test). In addition, spectral exponents during SWS differed from wakefulness in both regions. Statistical quantifications were conducted at the pseudo-population level using repeated-measures ANOVAs (RM-ANOVA) and were further supported by linear mixed-effect models (LME) that accounted for subjects and electrodes. At a pseudo-population level, the spectral exponent was lower during SWS as compared to wakefulness in the MTL (F1,81=18.65, p<.0001, η2=.19, RM-ANOVA), and highly comparable findings were observed when accounting for subjects and electrodes using LME models (p<.0001, 95% confidence interval [CI95]=[−.33, −.12], t=−4.35, β=−.23, LME). Notably, this relationship was reversed in the LPFC (F1,338=203.68, p<.0001, η2=.38, RM-ANOVA; p<.0001, [CI95]=[.35, .46], t=14.29, β=.40, LME).

To understand the relation between the spectral exponent and ripple detection, we analyzed ripple density as a function of the spectral exponent (from −6.0 to 0, in 0.1 steps). For MTL ripples, we observed a broad distribution across many spectral exponents (Fig. 2e Top Left), whereas ripples detected on 1/f noise-matched surrogates fell within a narrower, but substantially overlapping range of exponents (−2.7 to −.4). In total, 58% of empirically observed ripples fell within this noise range. Qualitatively and quantitatively similar results were evident in the LPFC (Fig. 2e Bottom Left), where 28% of ripples were within the noise floor (exponent range: −2.7 to −.4), as well as for different spectral parametrization approaches (Supplementary Fig. 2).

To quantify these observations, we determined ripple density as a function of region and state for both observed and surrogate data (Fig. 2e Right). For the MTL, we observed a significant decrease in ripple density (Fig. 2e Top Right) from wakefulness to SWS (experimental data: F1,81=13.61, p=.0004, η2=.14, RM-ANOVA; p=.0003, [CI95]=[−.01, 0], t=−3.71, β=−.01, LME; simulated data: F1,81=32.68, p<.0001, η2=.29, RM-ANOVA; p<.0001, [CI95]=[−.02, −.01], t=−5.75, β=−.02, LME). This decrease was most strongly observed for simulated as compared to experimental data (F1,162=12.28, p=.0006, η2=.07, RM-ANOVA; p=.0013, [CI95]=[−.02, −005], t=−3.25, β=−.01, LME).

For the LPFC, we observed significant changes in event density (Fig. 2e Bottom Right), which decreased from wakefulness to SWS for the experimental data (F1,338=8.87, p=.0031, η2=.03, RM-ANOVA; p=.0030, [CI95]=[−.004, −.001], t=−2.98, β=−.002, LME), whereas simulated data showed increased event density from wakefulness to SWS (F1,338=68.45, p<.0001, η2=.17, RM-ANOVA; p<.0001, [CI95]=[.01, .01], t=8.29, β=.01; LME). We consequently observed an interaction effect wherein conditional changes in ripple density were different between experimental and simulated data (F1,676=68.08, p<.0001, η2=.09, RM-ANOVA; p<.0001, [CI95]=[.01, .01], t=6.32, β=.01, LME).

To illustrate and summarize these results, we calculated the percent overlap of observed ripples relative to the surrogate distribution (Fig. 2f). In the MTL, we observed that the median proportion of wake ripples falling within the noise floor was 77%, while only 23% during SWS could be attributed to the 1/f characteristics. In the LPFC, 4% of ripple detections during wakefulness and 28% during SWS possibly reflected false positives. Statistical quantification confirmed that false positive ripple detections significantly decreased from wakefulness to SWS in the MTL (F1,81=21.16, p<.0001, η2=.21, RM-ANOVA; p<.0001, [CI95]=[−37.33, −15.0], t=−4.63, β=−26.17, LME), but showed the reversed pattern in the LPFC (F1,338=122.98, p<.0001, η2=.27, RM-ANOVA; p<.0001, [CI95]=[17.7, 25.3], t=11.11, β=21.54, LME). We consequently observed a significant interaction effect between the two regions (F1,419=97.28, p<.0001, η2=.19, RM-ANOVA; p<.0001, [CI95]=[38.24, 57.18], t=9.89, β=47.71, LME). Similar effects can be observed for all other detectors (Fig. 2g, Supplementary Fig. 3). In sum, these results indicate state- and region-dependent modulation of ripple density that follows a 1/fχ relationship.

To determine if ripple density was biased by 1/f noise, we employed partial correlations to control for the 1/f noise floor as defined by the surrogate ripple density. This analysis demonstrated that the positive correlation between the spectral exponent and ripple density observed for MTL channels (rs=.37, p=.006) can be accounted for when including surrogate ripple density as a confounder (r=.05, p=.6687). LPFC channels similarly showed that ripple density and spectral exponent values were significantly correlated (rs=.62, p<.0001), which was significantly reduced (p=.0009, comparison of correlation coefficients) when accounting for the surrogate ripple density (partial correlation: r=.44, p<.0001).

Collectively, this set of findings demonstrates that region- and state-specific differences in 1/f noise characteristics severely impact ripple detection, especially if the detector noise susceptibility (Fig. 1d) peaks within the experimental condition, as exemplified for MTL ripples. While the spectral exponent of the MTL during wakefulness matched the noise profile, the spectral exponent was lower during SWS and outside of the detector noise range; thus, explaining why more false positive ripples were detected during wakefulness as compared to SWS. Note that different detection algorithms exhibit varying detection patterns on 1/f noise, but give rise to qualitatively comparable results (Supplementary Fig. 1, Supplementary Fig. 3).

Wake ripples result from a spectral exponent increase during tasks
Modulation of 1/f noise is not limited to the wake-sleep cycle, but also occurs physiologically over the course of single trials during cognitive tasks, such as perception, attention, and memory encoding 29,41. Given the recent interest in wake ripples, we hypothesized that ripple detections often reflected altered 1/f noise characteristics rather than genuine ripple activity. As we did not intend to single out a specific study or paradigm, we sought to illustrate this issue in early visual and motor cortex – two regions where ripple activity has previously not been reported and where ripples are not typically thought to constitute key elements of their functional architecture. We specifically assessed two datasets where Miller and colleagues previously reported prominent 1/f (broadband) modulations during task engagement as compared to baseline intervals 37,38. We hypothesized that ripple detection should reflect the 1/f modulation through increased ripple detections during the task epoch. Moreover, we reasoned that if this ripple increase solely stems from a change in 1/f noise, then ripple detection on simulated, noise-matched surrogates should yield a comparable number of ripples. Ripple detection was conducted identically to our analysis on the Sleep dataset, and ripples in close temporal proximity to IEDs (±2.5s) were discarded.

To demonstrate the feasibility of this approach, we first simulated 1/f noise with varying exponents over several seconds that might correspond to different task epochs (Fig. 3a, Supplementary Fig. 1). As expected, ripple detection was systematically biased to the state with the highest spectral exponent. This bias was driven by the predefined amplitude threshold criterion in the ripple detection algorithm. This fixed threshold is derived from the mean and variance of the band-pass-filtered signal envelope across the entire recording; a methodological approach that is common to most detection algorithms (cf. Table 1).

Next, we analyzed intracranial recordings from subdural electrodes placed over occipital areas (Fig. 3b; 34 electrodes in 5 patients) while patients searched for a visual target 37. In this task, patients viewed a 5×4 colored grid wherein a white star indicated the target stimulus and a black arrow indicated the target direction (down, up, left, or right). Patients had to verbally report the color of the adjacent grid coordinate next to the target stimulus in the cued direction. Ripple detection yielded a plausible grand-average waveform (Fig. 3c) and, as hypothesized, both the spectral exponent (Fig. 3d Top; Supplementary Fig. 4a) and ripple density (Fig. 3d Center) increased during the task epochs as compared to the inter-trial interval (ITI). The overall goodness-of-fit of the FOOOF model to the power spectra was high (R2=.8793±.0059; median±SEM). We again simulated surrogate trials that matched the 1/f noise exponents during the ITI and task epochs (as outlined in Fig. 3a; for 1s segments without overlap). Ripple detection on the surrogate data also increased during the simulated task epoch (Fig. 3d Bottom), indicative of the fact that ripple detection reflected variations in the 1/f noise.

Similar to analyses on our sleep dataset, statistical quantifications were conducted at the pseudo-population level using RM-ANOVAs and were supported by LMEs that accounted for subjects and electrodes. These analyses demonstrated a significant effect of condition, leading to increases during the task epoch for the spectral exponent (Fig. 3e Top; F2,66=13.58, p=.0008, η2=.29, RM-ANOVA; ITI: p=.0003, [CI95]=[−.58, −.18], t=−3.78, β=−.38, LME; ITI: p=.0004, [CI95]=[−.57, −.17], t=−3.70, β=−.37, LME), observed ripple density (Fig. 3e Bottom; F2,66=59.77, p<.0001, η2=.64, RM-ANOVA; ITI: p<.0001, [CI95]=[−.09, −.05], t=−7.88, β=−.07, LME; ITI: p<.0001, [CI95]=[−.09, −.05], t=−7.82, β=−.07, LME), and surrogate ripple density (F2,66=23.75, p<.0001, η2=.42, RM-ANOVA; ITI: p<.0001, [CI95]=[−.10, −.04], t=−5.06, β=−.07, LME; ITI: p<.0001, [CI95]=[−.10, −.04], t=−4.84, β=−.07; LME). Supplementary analyses demonstrate that modulation of the spectral exponent by task is irrespective of the frequency range used for spectral parameterization (Supplementary Fig. 5a,b).

To determine if the increase in ripple density during task execution was only driven by the relationship to 1/f noise (rs=.47, p=.0047), we employed partial correlations to control for the 1/f noise floor as defined by the surrogate ripple density. This analysis yielded a non-significant correlation (r=−.16, p=.3730), indicating that a change in ripple density can be explained by the spectral dynamics of 1/f noise.

In order to demonstrate that this relationship is not exclusive to the visual cortex, we sought to replicate this set of findings in a second dataset in yet another region where ripples typically do not occur physiologically. We analyzed intracranial EEG data recorded from the motor cortex (Fig. 3f; 99 electrodes across 18 patients) while patients were instructed to execute simple, repetitive movements of the hand (i.e., clenching and releasing a fist) or tongue (protrusion and retraction of the tongue) at approximately 1–2 Hz for a duration of 3s 38. After an ITI, patients received a visual cue that indicated the respective body movement. All analytical steps matched the Visual Search Task (Fig. 3b–e). Again, the overall goodness-of-fit of the FOOOF model to the power spectra was high (R2=.9428±.0026; median±SEM). We observed a qualitatively and quantitatively highly comparable set of findings as compared to the previously discussed Visual Search Task. Ripple detection yielded a plausible grand-average waveform (Fig. 3g) and significant increases were observed during the task epoch for the spectral exponent (F2,196=64.27, p<.0001, η2=.40, RM-ANOVA; ITI: p<.0001, [CI95]=[−.24, −.15], t=−8.07, β=−.20, LME; ITI: p<.0001, [CI95]=[−.25, −.15], t=−8.12, β=−.20; LME), observed ripple density (F2,196=21.33, p<.0001, η2=.18, RM-ANOVA; ITI: p<.0001, [CI95]=[−.02, −.01], t=−4.64, β=−.01, LME; ITI: p<0001, [CI95]=[−.02, −.01], t=−4.69, β=−.01; LME), and surrogate ripple density (F2,196=10.94, p=.0012, η2=.10, RM-ANOVA; ITI: p=.0012, [CI95]=[−.0101, −.0025], t=−3.27, β=−.01, LME; ITI: p=.0007, [CI95]=[−.0102, −.0027], t=−3.41, β=−.01; LME) (Fig. 3h, i, Supplementary Fig. 4b). Additional analyses demonstrate that modulation of the spectral exponent by task is irrespective of the frequency range used for spectral parameterization (Supplementary Fig. 5c,d). Correlational analyses again demonstrate that ripple density and spectral exponent values were significantly correlated (rs=.05, p=.0264), which resulted in a non-significant partial correlation when controlling for surrogate ripple density as a confounding variable (r=.01, p=.2934).

In sum, these results clearly demonstrate that ripples during wakefulness and cognitive engagement largely reflect false positive detections within 1/f noise and not genuine ripple oscillations. We purposefully decided to illustrate this relationship in two cortical areas and task conditions that have previously not been associated with ripples. However, since many cognitive tasks, such as associative memory tasks, rely on visual input and overt motor responses, the present results can be extrapolated to these related contexts. While several papers reported the presence of wake ripples during memory tasks 10,14,16–18, a largely unrelated line of inquiry focusing on aperiodic activity reported modulations of the spectral exponent during cognitive engagement. Our findings now offer a more parsimonious explanation for why ripple density increases during task epochs.

Methods
Participants
Study 1 (sleep). Sleep recordings were conducted in patients with pharmacoresistant epilepsy (N=14; Mage=36.79±13.28 years, mean±SD, range 19–58 years; 9 female) undergoing pre-surgical invasive monitoring to delineate the seizure onset zone. Intracranial depth electrodes (Ad-Tech) were implanted in all subjects, and two patients received additional subdural grid electrodes. Electrodes were localized in native space by two independent neurologists, and group-level topographical visualization was done in normalized MNI space using Matlab R2021a (MathWorks Inc.) and Fieldtrip (fieldtrip-20241025) 75 (ft_plot_mesh) (Fig. 2b). Specific localization was based on co-registered pre- and post-implantation MRIs as well as a high-resolution anatomical atlas as previously outlined 11,76–78. In line with our recent studies 9,15, only electrodes within the MTL (CA1, CA3/dentate gyrus, subiculum, entorhinal cortex, perirhinal cortex, parahippocampal gyrus, and temporal pole; N=82) or LPFC (medial and lateral prefrontal, as well as orbitofrontal cortices; N=339) were considered for analyses. Electrodes localized to the seizure onset zone were excluded. All recordings were conducted at the University of California, Irvine Medical Center, USA. Informed consent was obtained from all subjects prior to study participation. Data acquisition and analyses protocols were approved by the Institutional Review Board at the University of California, Irvine (protocol number: 2014–1522) and the Committee for Protection of Human Subjects at the University of California, Berkeley (Protocol number: 2010–02–783). The study was conducted in accordance with the 6th Declaration of Helsinki.

Study 2 (visual search task). Data were recorded from epilepsy patients (N=5) as part of their clinical monitoring to delineate the seizure onset zone. Intracranial platinum subdural grid and strip electrodes (Ad-Tech) were placed at frontal, parietal, temporal, and occipital cortex. Recordings were sampled at 1000 Hz, referenced to scalp and ground electrodes, and band-pass filtered (0.15–200 Hz). Analyses only included electrodes localized in the visual cortex. Group-level topographical visualization was done in normalized MNI space using Matlab and Fieldtrip (ft_plot_mesh) (Fig. 3b; Nelectrodes=34; VTPM atlas) 79. All patients participated in a purely voluntary manner, after providing informed written consent, under experimental protocols approved by the Institutional Review Board of the University of Washington (#12193). All patient data was anonymized according to IRB protocol, in accordance with HIPAA mandate. These data originally appeared in the manuscript titled Dynamic modulation of local population activity by rhythm phase in human occipital cortex during a visual search task published in Frontiers in Human Neuroscience in 2010 37.

Study 3 (motor task). Data were recorded from epileptic patients (N=19) as part of their clinical monitoring to delineate the seizure onset zone. One subject was excluded from analyses because of a shorter trial length (2s instead of 3s duration). Intracranial platinum subdural grid and strip electrodes (Ad-Tech) were placed independently following clinical criteria. Recordings were sampled at 1000 Hz, referenced to scalp and ground electrodes, and band-pass filtered (0.15 or 0.3–200 Hz). Analyses only included electrodes localized in the motor cortex. Group-level topographical visualization was done in normalized MNI space using Matlab and Fieldtrip (ft_plot_mesh) (Fig. 3f; Nelectrodes=99; Brainnetome atlas [area 4]) 80. All patients participated in a purely voluntary manner, after providing informed written consent, under experimental protocols approved by the Institutional Review Board of the University of Washington (#12193). All patient data was anonymized according to IRB protocol, in accordance with HIPAA mandate. It was made available through the library described in A Library of Human Electrocorticographic Data and Analyses by Kai Miller 81, freely available at https://searchworks.stanford.edu/view/zk881ps0522. All patient data was anonymized according to IRB protocol, in accordance with HIPAA mandate. These data originally appeared in the manuscript titled Spectral Changes in Cortical Surface Potentials during Motor Movement published in Journal of Neuroscience in 2007 38.

Procedures
Study 1 (sleep monitoring). EEG data were simultaneously recorded from intracranial electrodes located in the MTL and LPFC, as well as scalp electrodes (Fz, Cz, C3, C4, and Oz; localized in accordance with the international 10–20 system). Additional electrooculography (EOG; four electrodes placed on the left and right outer canthi) and electromyography (EMG) data were recorded. Data were recorded using a Nihon Kohden recording system (model JE120A; 256-channels), and were digitally sampled at 5000 Hz and analog filtered at .01 Hz. A seizure-free full-night recording was analyzed per patient, with recordings starting between 19:30-22:00 and lasting for 10–12h. An expert rater scored all recordings according to established criteria (Fig. 2a Top) 82. Average sleep duration was 447±123 min (mean±SD; range 181–649 min), of which 13% ± 9% was spent in SWS (range 2–36%). Further details regarding sleep recordings are described in detail in our previous study 15.

Study 2 (visual search task). Task details are described in detail by Miller et al 37. In short, the Visual Search Task presented subjects with numerous stimuli (2s) separated by Inter-Trial-Intervals (ITIs; 2s) that showed subjects a blank screen using a LCD monitor at 1m distance. Each stimulus showed (1) a 5×4 grid of colored squares (1×1 cm), (2) a white star located in the center of a random colored box, and (3) an arrow indicating a direction (up, down, left, & right). Subjects were instructed to verbally report the color of the box adjacent to the star following the arrow’s direction.

Study 3 (motor task). Task details are described in detail by Miller et al 38. In short, patients were required to perform simple, repetitive motor tasks of the hand (flexion and extension of all fingers) or tongue (opening of mouth with protrusion and retraction of the tongue) at ~1–2 Hz (i.e., repetitive motion rather than tonic contraction). Trials (3s) were separated by inter-trial-intervals (ITIs; 3s) during which subjects were at rest and presented with a blank screen. Movement was performed contralateral to the side of cortical grid placement. Trial on- and offset were visually indicated by the presence or absence of a written word indicating the type of movement within a 10×10 cm presentation window at a distance of 75–100 cm from the patient.

Data processing
Intracranial EEG from the Sleep dataset were demeaned, detrended, rereferenced to a bipolar montage, and downsampled to 500 Hz. Simultaneously recorded scalp EEG data utilized for sleep scoring were demeaned, detrended, referenced to the common average over all scalp electrodes, low-pass filtered (50 Hz), and downsampled to 500 Hz. Data from the Visual Search Task and Motor Task were rereferenced to the common average. All data preprocessing and analyses were conducted using Matlab R2021a (MathWorks Inc.). Data preprocessing, filtering, and segmentation were conducted using Fieldtrip (fieldtrip-20241025) 75 and EEGlab (eeglab2020_0) 83 in addition to custom code.

Simulations
Colored noise were simulated using the colored_noise function 84. Here, white noise is filtered to create colored noise characterized with a defined spectral exponent of its power spectrum density. Data were iteratively simulated (N=100) for a range of spectral exponents (−4 to 0 in .1 steps) at 1000 Hz, followed by minimum standard EEG preprocessing steps where the data were demeaned, detrended, band-pass filtered (.5–150 Hz), and z-normalized. Data were simulated with a constant spectral exponent (Fig. 1a) or approximated the spectral exponent of the recorded iEEG data per segment over time (Fig. 2A: 30s segments without overlap; Fig. 3a, d, h; 1s segments without overlap). Simulated data were created per segment per channel and subsequently concatenated. It is critical to highlight that no simulated ripples were added to the surrogate datasets.

Event detections
Candidate ripple events were detected on the continuous data using five detection algorithms inspired by recently published algorithms that were utilized in human intracranial studies (Table 1).

Detector 1. Data were band-pass filtered (80–120 Hz; Butterworth), Hilbert-transformed to extract the absolute instantaneous amplitude, smoothed (10 Hz low-pass filter; Butterworth), and z-normalized. Candidate ripple events were detected when the Hilbert envelope exceeded the threshold of 2SD above the mean for a duration of 25–200ms. Candidate events with an amplitude between 2–4SD, frequency > 80 Hz, peak-to-peak amplitude differential <2, and no overlap with IEDs ±2.5s relative to the ripple trough were considered for analysis. Events were aligned to the nearest trough in the broadband data.

Detector 2. Data were high-pass filtered (80 Hz; FIR, filter order 3) and subsequently low-pass filtered (100 Hz; FIR, filter order 3). The absolute signal envelope was extracted using the Hilbert transform, smoothed (20ms smoothing), and z-normalized. Candidate events were detected when the signal envelope exceeded the 99% of R.M.S. for a minimum duration of 38ms (i.e., minimum 3 cycles at 80 Hz) and maximum of 100ms. duration. Candidate events were aligned to the nearest trough in the broadband data, and those within temporal proximity of IEDs (±1.5s) were rejected.

Detector 3. Data were band-pass filtered (80–120 Hz; Butterworth; two-pass, filter order 2), after which the absolute signal envelope was extracted using the Hilbert transform. Candidate events were detected when the signal envelope exceeded 2SD above the mean for a minimum of 25ms. Events ±15ms were merged. Events were aligned to the nearest trough in the broadband data.

Detector 4. Data were band-pass filtered (70-180 Hz; FIR; two-pass, filter order 5). Signal envelope was extracted using the Hilbert transform. Extreme values (>4SD) were clipped to 4SD, after which the clipped envelope was squared and smoothed (40 Hz low-pass Kaiser FIR filter). Mean and SD of the transformed data were utilized for subsequent event detection on the original (squared but unclipped) signal envelope. Candidate events were detected when the squared signal envelope exceeded 4SD above the mean for a duration of 20–200ms. Events ±30ms were merged. Events were aligned to the nearest trough in the broadband data.

Detector 5. Data were band-pass filtered (80–120 Hz; Butterworth), Hilbert-transformed to extract the absolute instantaneous amplitude, smoothed using a 10 Hz low-pass filter (Butterworth), and z-normalized. Candidate ripple events were detected when the Hilbert envelope exceeded the threshold of 2SD above the mean for a duration of 20–200ms. Candidate events with an amplitude between 2–4SD, frequency > 80 Hz, and no overlap with IEDs ±1s relative to the ripple trough were considered for analysis. Events were aligned to the nearest trough in the broadband data.

IED detection. IED detection was conducted based on established protocols 22. Preprocessing included band-pass filtering between 25 and 80 Hz, extraction of the signal envelope using the absolute Hilbert transform, and z-normalization. Events were detected when the envelope exceeded a threshold of 2SD for a duration of 20–100ms, and were discarded when the raw signal envelope did not exceed 2SD. IEDs in close temporal proximity (1s) were concatenated.

Data analyses
Spectral decomposition of simulated data (Fig. 1c) was conducted on segmented data (30s segments; 70% overlap) using a Hanning window (from 2–128 Hz in 1 Hz steps with a ¼ octave frequency smoothing) as implemented by Fieldtrip (ft_freqanalysis). The spectral exponent of the power spectral densities was determined between 20 and 45 Hz in log-log space with spectral parameterization 36 using linear fits (aperiodic mode = fixed) as implemented by Fieldtrip (ft_freqanalysis). This fitting range aligns with previous studies 33,35,85, has been shown to dissociate regional state-dependent dynamics between the MTL and PFC 40, and avoids potential artifacts caused by spectral knees or line noise. Goodness-of-fit was assessed using R2 values. Continuous estimates of the spectral exponent were conducted following segmentation of the data (Fig. 2a: 30s segments, 90% overlap; Fig. 3a, d, h: 1s segments, 90% overlap). Average spectral exponents per sleep stage (30s segments, no overlap) were calculated per channel. Condition-average spectral exponents (Fig. 3e, i) were calculated by segmenting the data per condition (Visual Search Task: 2s segments; Motor Task: 3s segments) without overlap.

Ripple grand averages (Figs. 1b, e, 3c, g) were derived following ripple detection (see above). Broadband data were segmented relative to the ripple trough (±2.5s) and amplitudes were z-normalized relative to baseline (−2.5 to −2s relative to the ripple trough).

Event density over time (Figs. 2a; 3d, h, Middle & Bottom) delineates the number of events per second (1s moving window in .1s steps). Event density per condition (Fig. 3e, i Bottom) shows the average event density per condition (Inter-Trial-Interval [ITI] or Task conditions).

Average event density per exponent bin (ranging from −8 to 0 in .1 steps) across the night for events detected on recorded iEEG data or simulated data matching spectral exponent per segment (Fig. 2e Left) and sleep stage (Fig. 2d). We subsequently determined the percentage of detected ripples within the detection noise floor as a function of the spectral exponent of 1/f noise (Fig. 2f) by dividing the number of ripples detected on experimental data by those detected on surrogate simulated data.

Electrode coverage (Figs. 2b, 3b, f) shows electrode positions in MNI space on a standardized brain. Note that each electrode is represented by a sphere whose diameter does not represent the actual size of the electrode. For the Sleep dataset, electrode localization was determined in native space. Following localization, electrodes were subset for the medial temporal lobe (MTL; CA1, CA3/dentate gyrus, subiculum, entorhinal cortex, perirhinal cortex, parahippocampal gyrus, and temporal pole) and prefrontal cortex (PFC; medial and lateral prefrontal, as well as orbitofrontal cortices). Electrode locations (Figs. 2b, 3b, f) were mirrored for visualization purposes only to be shown on a single hemisphere. For the Visual Search dataset, electrode locations were determined using Fieldtrip (generate_electable_v3). Analyses only included electrodes localized in the visual cortex as determined by the VTPM atlas (Fig. 3b, N=34) 79. For the Motor Task dataset, electrode coordinates were transformed from Talairach to MNI coordinates using Fieldtrip (tal2mni). Electrode locations were determined using Fieldtrip (generate_electable_v3). Analyses only included electrodes localized in the motor cortex as determined by the Brainnetome atlas (Fig. 3f, N=99) 80.

Distributions (Fig. 2d, N=200) reflect the average number of segments (30s) per spectral exponent per channel over a night of sleep. Data are shown on group level, and separated for iEEG electrodes localized to the MTL or PFC.

Statistical analyses. Condition and data type contrasts (Fig. 2e, f) were compared at the pseudo-population level using repeated-measures ANOVAs (RM-ANOVA) and were supported by Linear Mixed Effects models (LME) wherein subject and channel were included as independent variables to control for subject-specific effects. Effect sizes are reported as partial eta-squared (η2) for the RM-ANOVAs and as beta coefficients (β) for the LMEs. Spearman correlational analyses were conducted for the spectral exponent and ripple density (Figs. 2e, 3e, i) using conditional differences (i.e., Wake – SWS and Task – ITI, respectively). Similarly, follow-up partial correlations for the spectral exponent and ripple density were conducted using conditional differences, wherein surrogate ripple density was considered as a confounding variable.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript addresses the important question of whether sharp-wave ripples detected in human intracranial EEG data result from noise. The analyses seek to determine how aperiodic 1/f^x noise components and choice of ripple detection algorithm affects ripple detection. This includes characterization of various factors (brain state, brain region, level of engagement) and their influence on underlying 1/f^x characteristics which drive ripple detection. The authors use three human iEEG datasets to address these questions. Recordings were taken from MTL and LPFC of epileptic patients receiving presurgical monitoring for intractable epilepsy during sleep, occipital cortex during a visual search task, and motor cortex during a motor task. These physiological recordings were used to create 1/f^x aperiodic noise matched simulated datasets with physiologically plausible ripple morphology. Analyses of slow wave sleep (SWS) and 1/f^x-matched simulated data by five ripple detector algorithms show that each detector algorithm reports differing ripple densities as a function of the spectral exponent of the simulated dataset. Subsequent analyses focused on the effect of physiological differences (brain region identity and brain state) on ripple detection with the understanding that these physiological differences affect 1/f^x activity. Comparisons of iEEG recordings taken from sleep and awake states in the MTL and LPFC demonstrated the proportion of ripples in the noise floor differed by both brain region and state. The highest proportion of ripples within the noise floor was 77% from MTL recordings during wakefulness. To further the point that these 1/f^x aperiodic noise characteristics drive ripple detection, analyses were performed using iEEG recordings from occipital and motor cortex. These regions should not have SWRs during the tasks that altered 1/f^x. Nonetheless, task engagement modulated the spectral exponent and subsequently the 1/f^x noise which was related to the reported ripple density throughout the task. In total, these findings illustrate that 1/f^x aperiodic noise components which may vary depending on brain region, brain state, and cognitive engagement with a task, affect ripple detection.\nThis study and its findings present compelling evidence for the case that 1/f^x aperiodic noise affects ripple detection. This is highly significant for the interpretation of reported SWRs in human iEEG datasets, which have likely reflected little if anything more than noise. The analyses are logical, rigorously executed, and straightforward to interpret. I have no major concerns and few minor comments.\n1. Although the manuscript attempts to walk the fine line of criticizing previous characterizations of ripple oscillations in the human iEEG literature without calling out any specific study, such analyses could be useful. That is, it would help readers to appreciate the significance of these findings if specific examples of how these detectors have been used by previous studies were mentioned and, ideally, if prior data could be re-analyzed in light of these findings.\n2. If nothing else, some explicit discussion of relevance to previous recommendations for SWR detection (e.g., Liu et al. 2022) should be included.\n3. The choice to report the results of detector 1 needs to be justified. There is variability among ripple detection algorithms making the choice of detector 1 appear spurious. Would it be possible to better summarize results from all tested detectors?\n4. It could be useful to clarify the definition of 1/f^x as 1/f^x where χ =−α. The current indication is slightly confusing as it uses conventions from two different bodies of literature. A sufficient alternative would be to represent 1/f^x in slope form.\n5. The presence of interictal epileptiform discharges is acknowledged in the methods for event detection, but how IEDs are detected is not explained. Moreover, it is not immediately obvious that IEDs are accounted for during the detection of candidate ripple events which may present as an important detail given that in some conditions, ripple detection is performed during SWS where these epileptic phenomena are common.\n6. The authors should report the units of ripple density. It is suggested in the methods section, but ambiguous in the plots and main text.\n7. Figures 1E and 1E need a legend to describe which color corresponds to each detector. This is difficult to discern given the current format, including caption.", "reviewer_2": "Summary:\nThis paper investigated hippocampal sharp-wave ripples (SWR), which have been implicated in cognition in previous literature and more recently identified during wake states. Specifically, the authors looked at the performance of five different ripple detection algorithms on recorded data, as well as simulated data generated from the 1/f^x relationship, in order to understand the impacts of aperiodic ‘noise’ on ripple detection. Despite differences in density of detected ripples across the detection algorithms, the density of identified ripples always scaled with the spectral exponents. Next, authors demonstrate both regional- and state-specific differences in aperiodic noise, highlighting the necessity for future investigations to consider this noise floor during ripple detection to avoid false positives. These results are replicated in two regions where ripples have not previously been identified. Again we see the rate of ripple detection vary as a function of aperiodic noise. In sum, these results suggest that ripples identified during wakefulness and cognitive performance are likely false positives, reflecting aperiodic activity, rather than true ripple oscillations.\nThis is likely to be a very widely read, impactful paper. The results are quite striking and compelling, and this will certainly agitate some folks. Well done!\nMajor Comments:\nFrom the methods, it is not clear whether or not simulated data was generated from spectral exponents alone, or if ripples were also simulated. We’re fairly certain that no ripples were injected into the simulations, but rather the detected ripples in the simulation are just due to the stochastic nature of high frequency events. If this is correct, we believe that the authors should make an even bigger deal of this point! It’s of course unusual to include in the methods something that you did not do, but in this case we feel that clarification on this is important because the detection algorithm's performance depends on whether ripples were present. If ripples were not simulated and the detection algorithms still identified these timepoints, this significantly impacts the interpretation of ripple detection.\nIn the Introduction, the authors state that “We specifically tested if more ripples are evident in cortical regions that exhibit a higher spectral exponent (e.g., hippocampus and the medial temporal lobe; MTL) as compared to neocortical association areas (e.g., lateral prefrontal cortex; LPFC).” This and the following about ripples in different states, as written, are not evidence that ripple detection is impacted by aperiodic activity. Rather, they suggest that ripples might just show regional and/or state differences. We believe that the authors frame these decisions more strongly later in the paper, where they explain that these choices included regions where we don’t have strong prior evidence for ripples (such as M1). The authors should lean on this framing more in the Introduction, as well.\nMinor Comments:\n* The authors state that the data were referenced to a bipolar montage. This is somewhat unclear, as bipolar montages are typically applied in intracranial recordings, but are uncommon in scalp EEG. Clarification on the referencing process for the scalp EEG is needed.\n* The authors estimated the spectral exponent from the 20-45 Hz range. But we recommend that they perform a sensitivity analysis, re-running a few key statistical comparisons but using different frequency ranges, to show that their results are relatively robust to the spectral fitting range.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Point-by-point responses\nReviewer comments are highlighted in bold and our responses in blue. The relevant\nparagraph(s) that were modified or added to the revised manuscript are indented.\nParticularly important changes are highlighted in red.\nReviewer #1 (Remarks to the Author):\nThis manuscript addresses the important question of whether sharp-wave ripples\ndetected in human intracranial EEG data result from noise. The analyses seek to\ndetermine how aperiodic 1/f^x noise components and choice of ripple detection\nalgorithm affects ripple detection. This includes characterization of various factors\n(brain state, brain region, level of engagement) and their influence on underlying\n1/f^x characteristics which drive ripple detection. The authors use three human\niEEG datasets to address these questions. Recordings were taken from MTL and\nLPFC of epileptic patients receiving presurgical monitoring for intractable epilepsy\nduring sleep, occipital cortex during a visual search task, and motor cortex during\na motor task. These physiological recordings were used to create 1/f^x aperiodic\nnoise matched simulated datasets with physiologically plausible ripple\nmorphology. Analyses of slow wave sleep (SWS) and 1/f^x-matched simulated data\nby five ripple detector algorithms show that each detector algorithm reports\ndiffering ripple densities as a function of the spectral exponent of the simulated\ndataset. Subsequent analyses focused on the effect of physiological differences\n(brain region identity and brain state) on ripple detection with the understanding\nthat these physiological differences affect 1\\/f^x activity. Comparisons of iEEG\nrecordings taken from sleep and awake states in the MTL and LPFC demonstrated\nthe proportion of ripples in the noise floor differed by both brain region and state.\nThe highest proportion of ripples within the noise floor was 77% from MTL\nrecordings during wakefulness. To further the point that these 1/f^x aperiodic noise\ncharacteristics drive ripple detection, analyses were performed using iEEG\nrecordings from occipital and motor cortex. These regions should not have SWRs\nduring the tasks that altered 1/f^x. Nonetheless, task engagement modulated the\nspectral exponent and subsequently the 1/f^x noise which was related to the\nreported ripple density throughout the task. In total, these findings illustrate that\n1/f^x aperiodic noise components which may vary depending on brain region, brain\nstate, and cognitive engagement with a task, affect ripple detection.\nThis study and its findings present compelling evidence for the case that 1/f^x\naperiodic noise affects ripple detection. This is highly significant for the\ninterpretation of reported SWRs in human iEEG datasets, which have likely\nreflected little if anything more than noise. The analyses are logical, rigorously\nexecuted, and straightforward to interpret. I have no major concerns and few minor\ncomments.\nWe thank the reviewer for the positive evaluation and are grateful for the helpful\ncomments. Below, we address all remaining queries.\ncharacterizations of ripple oscillations in the human iEEG literature without calling\nout any specific study, such analyses could be useful. That is, it would help readers\nto appreciate the significance of these findings if specific examples of how these\ndetectors have been used by previous studies were mentioned and, ideally, if prior\ndata could be re-analyzed in light of these findings.\nWe thank the reviewer for their remarks, and have expanded the Discussion section with\nthe following new paragraph.\nDiscussion (p. 14): In this study, we evaluated five different ripple detectors.\nThese detectors, or close variants thereof, have been used to study hippocampal\n1–4 as well as neocortical 5–9 ripples during NREM sleep and wakefulness. In\naddition, hippocampal ripples were reported during memory encoding and retrieval\n10–13, wherein differences in ripple rate between remembered and forgotten items\nduring encoding or retrieval provide strong behavioral evidence. In light of the here\nreported association of 1/f noise and ripple detection, it might be conceivable that\nripples as previously reported mainly index altered excitability dynamics and local\ncortical activation. This in accordance with a large body of literature that shows\nthat increased high-frequency band activity (sometimes termed broadband or high-\ngamma) precisely indexes local cortical activation 14–16. One conceivable approach\nfor future studies is to closely examine oscillatory signatures (such as ripples) as\nwell as aperiodic markers to infer to which extent these two markers overlap or\nreflect dissociable signatures. In the context of memory formation, some evidence\nsuggests that these reflect dissociable signatures 17.\nR1.2. If nothing else, some explicit discussion of relevance to previous\nrecommendations for SWR detection (e.g., Liu et al. 2022) should be included.\nOur findings provide further empirical support to address several challenges identified by\nLiu and colleagues 18. Specifically, our results demonstrate how background activity (the\n1/f noise structure) is a critical factor that should be taken into account. We have expanded\nthe Discussion section to clarify this point, and emphasize how simulation-based\napproaches might enable estimating the detection noise floor to obtain a false-positive\nrate. We additionally discuss how our findings connect to previous recommendations for\nripple detection.\nDiscussion (p. 13): The gold standard for SWR detection in rodents are high-\ndensity laminar recordings that enable the visual identification of the distinct sharp-\nwave component arising in CA3 and the ripple component in CA1 19. Hence, most\ndetection algorithms that are employed in rodents take advantage of detecting\nmultiple waveform characteristics 20,21. As virtually all intracranial EEG recordings in\nthe human brain are obtained from epileptic patients, it is not feasible to detect sharp\nwave components as these also hallmark interictal discharges. Hence, it proves\nchallenging to detect ripple events based on visual inspection, as even experts often\ncannot reach consensus 22. To overcome these challenges, it is critical to move\ntowards a consensus for the recording, detection, and reporting of ripples 18, for\nwhich the authors provided a series of recommendations. An important step is the\ncomparison to ripples detected during NREM sleep. The present study\ndemonstrates a challenging aspect of this control analysis, as even simulated data\nmay contain physiologically-plausible ripple events that resemble the typical\n2\nmorphology in the time and frequency domain (Fig. 1). A second recommendation\nis accounting for EEG background activity. We demonstrate how background noise\ncharacteristics differ between states as well as regions (Fig. 2), which has not been\naccounted for in previously employed detection approaches. Crucially, changes in\nbackground activity may also give rise to spurious ripple detections in the neocortex\n(Fig. 3). Such false positive ripple detections result from fixed amplitude and\nduration thresholds applied to the signal envelope of the band-pass filtered EEG\nrecordings – a methodology utilized by many detection algorithms (c.f. Table 1).\nThese amplitude thresholds are often based on the standard deviation of the\nbackground activity across the entire recording, thus, not accounting for possible\ndifferences between brain states. We now demonstrate how noise floors can be\nestimated per state and region, and offer an additional approach to establish a false-\npositive detection rate. Note that the choice of frequency band, ripple duration, and\nvarious other detection criteria constitute additional sources of detection bias that\nlikely account for the variability across studies. Further recommendations, such as\nsimultaneous recording of micro- and macro-LFP in the hippocampus, localization\nto hippocampal subfields and layers, and investigating the effect of anti-seizure\nmedication on ripple occurrence and sleep, provide avenues for future studies to\ndemonstrate how ripple detection can be improved.\nR1.3. The choice to report the results of detector 1 needs to be justified. There is\nvariability among ripple detection algorithms making the choice of detector 1\nappear spurious. Would it be possible to better\nsummarize results from all tested detectors?\nWe agree with the reviewer that the choice for utilizing\ndetector 1 for the main analyses requires justification.\nWe chose to utilize detector 1 for all main analysis as it\nimplements many of the characteristics observed\nacross all detectors (i.e., filtering frequency, amplitude\nthreshold, and event duration; c.f. Table 1), and its\ndetection profile on 1/f noise closely matched two other\ndetectors (Fig. 1D; detectors 2 and 5). The detector\nadditionally demonstrated a relatively low noise floor\nfor ripple detection in the medial temporal lobe during\nNREM sleep (Fig. 2F).\nWe now demonstrate how the noise profile of each\nmethodology affects ripple detection on simulated data\nFig. 1 | Detecting ripple oscillations in\nwith varying exponents for every detector (Fig. S1), as 1/f noise. ... (D) Ripple density (Hz) as a\nwas originally only shown for detector 1 (Fig. 3A). This function of the spectral exponent of the\nreplication has been added to the Supplementary simulated data (mean ± SD; 1 h duration;\n100 iterations) for five different ripple\nMaterials.\ndetectors (different colors).\nThe Results section now provides additional context and illustrate qualitatively similar\nresults as previously observed for detector 1.\nprofile of each detector affects ripple detection on simulated data with variable 1/f characteristics. Upper row:\nSimulated signal with varying exponents (black) per 3 second segments. Middle row: Ripple-band activity\n(grey line; 80-120 Hz; z-normalized). Bottom row: Ripple density (Hz) per detector across identical simulated\ndatasets and iterations (100 iterations; mean ± SEM).\nResults (p. 5): Since all detection algorithms exhibit their own idiosyncrasies, we\nconducted all subsequent analyses using the first detector. This detector was\nchosen for the main analyses as it utilizes several characteristics shared across\ndetectors (c.f., Table 1) and yields a representative detection profile on 1/f noise\n(c.f., Fig. 1D; SI Appendix, Fig. S1). Note that qualitatively and quantitatively\nsimilar results were observed for the other detectors (SI Appendix).\nResults (p. 6-7): Collectively, this set of findings demonstrates that region- and\nstate-specific differences in 1/f noise characteristics severely impact ripple\ndetection, especially if the detector noise susceptibility (Fig. 1D) peaks within the\nexperimental condition as exemplified for MTL ripples. While the spectral exponent\nof the MTL during wakefulness matched the noise profile, the spectral exponent\nwas lower during SWS and outside of the detector noise range; thus, explaining\nwhy more false positive ripples were detected during wakefulness as compared to\nSWS. Note that different detection algorithms exhibit varying detection patterns on\n1/f noise, but give rise to qualitatively comparable results (SI Appendix Fig. S1;\nFig. S3).\nResults (p. 9): To demonstrate the feasibility of this approach, we first simulated\n1/f noise with varying exponents over several seconds that might correspond to\ndifferent task epochs (Fig. 3A; SI Appendix Fig. S1). As expected, ripple detection\nwas systematically biased to the state with the highest spectral exponent. This bias\nwas driven by the predefined amplitude threshold criterion in the ripple detection\nalgorithm. This fixed threshold is derived from the mean and variance of the band-\npass-filtered signal envelope across the entire recording; a methodological\napproach that is common to most detection algorithms (cf. Table 1).\nWe additionally acknowledge that the main analyses had to be replicated for all detectors,\nas this further emphasizes how ripple detection is affected by the noise distribution of the\ndata. Here, the noise floor of ripple detection is defined as the percentage of detected\nripples on simulated data relative to those detected on simulated surrogate datasets. We\nhave replicated our main finding for the Sleep dataset, wherein we show ripple density\nand noise floor estimates across wakefulness and slow-wave sleep (SWS) for detector 1\n(Fig. 2E-F), and demonstrate that this pattern can be observed for all detectors as outlined\n4\nin the revised supplemental material (see below; Fig. S2). Note that despite the different\ndetection characteristics (c.f., Fig. 1D), similar detection patterns were observed for the\nother detectors (Fig. S2C).\nTo make these results accessible, we now provide a summary in the main manuscript (see\nbelow: Fig. 2G), color-coding the median values to demonstrate similar state and regional\nchanges in the noise floor per detector. We also provide all details in the supplements\n(Fig. S3).\nResults (p. 6): Similar effects can be observed for all other detectors (Fig. 2G; SI\nAppendix Fig. S3). In sum, these results indicate state- and region- dependent\nmodulation of ripple density that follows a 1/fx relationship.\nFig. 2 | Different 1/f noise characteristics explain region- and state-specific ripple variability. ... (G)\nNoise-floor estimates across states and regions for all five ripple detectors. Note that the majority of detection\nalgorithms demonstrate the same state and regional differences. Colors depict median values per detector,\nstate, and region (wherein the first row corresponds to the values displayed in panel F).\nof detection algorithm. (A) Region- and state-specific relationship between noise and ripples. Left:\nDistribution of ripple events (y-axis) as a function of the spectral exponent (x-axis) in the medial temporal lobe\n(MTL; red) per detection algorithm, highlighting a clear relationship similar to the distribution reported in Figure\n1D. Ripple detection in noise-matched epochs (superimposed grey distribution) highlights MTL ripples within\nthe noise range and therefore, likely do not constitute genuine ripple events. Right: Distribution of ripple events\nrelative to the spectral exponent in the lateral prefrontal cortex (LPFC; same conventions as in the left panels).\n(B) Mean event density per state. Left: We observed more ripples in the MTL during wakefulness than during\nSWS, as a direct result of higher spectral exponents during wakefulness (cf. top panel Fig. 2C). Given the\noverall lower exponents during SWS, it is more likely to detect true ripples during SWS than during\nwakefulness. Boxplots represent median, 1st and 3rd quartiles, and extreme values. Right: Mean event density\nper state in the LPFC. Same conventions as in the left panels. (C) Percentage of ripples within the noise floor.\nNote that especially ripple detection in the MTL during wakefulness exhibit high percentages of false positives.\nSame conventions as in panel B.\npercent overlap of observed ripples relative to the surrogate distribution (Fig. 2F).\nIn the MTL, we observed that the median proportion of wake ripples falling within\nthe noise floor was 77%, while only 23% during SWS could be attributed to the 1/f\ncharacteristics. In the LPFC, 4% of ripple detections during wakefulness and 28%\nduring SWS possibly reflected false positives. Statistical quantification confirmed\nthat false positive ripple detections significantly decreased from wakefulness to\nSWS in the MTL (F = 21.16, p < .0001, η2 = .21, RM-ANOVA; p < .0001, [CI ]\n1,81 95\n= [-37.33, -15.0], t = -4.63, β = -26.17, LME), but showed the reversed pattern\n162\nin the LPFC (F = 122.98, p < .0001, η2 = .27, RM-ANOVA; p < .0001, [CI ] =\n1,338 95\n[17.7, 25.3], t = 11.11, β = 21.54, LME). We consequently observed a significant\n676\ninteraction effect between the two regions (F = 97.28, p < .0001, η2 = .19, RM-\n1,419\nANOVA; p < .0001, [CI ] = [38.24, 57.18], t = 9.89, β = 47.71, LME). Similar\n95 838\neffects can be observed for all other detectors (Fig. 2G; SI Appendix Fig. S3). In\nsum, these results indicate state- and region- dependent modulation of ripple\ndensity that follows a 1/fx relationship.\nR1.4. It could be useful to clarify the definition of 1/f^x as 1/f^x where χ =−α. The\ncurrent indication is slightly confusing as it uses conventions from two different\nbodies of literature. A sufficient alternative would be to represent 1/f^x in slope\nform.\nWe acknowledge that this description caused confusion, and have added an explicit\nstatement wherein we define 1/f noise.\nIntroduction (p. 2): In a largely unrelated line of inquiry, it had recently been\nshown that electrophysiological ‘background’ activity, also termed aperiodic activity\nfor the lack of a unique periodicity, indexes different brain states and cognitive\ndemands. Aperiodic activity is also called 1/f noise as it follows a fχ scaling law. We\ndefined 1/f noise as 1/fχ, which yielded a positive exponent. We adopted the\ncommon convention to report the exponent in slope form as the negative exponent\n23,24.\nmethods for event detection, but how IEDs are detected is not explained. Moreover,\nit is not immediately obvious that IEDs are accounted for during the detection of\ncandidate ripple events which may present as an important detail given that in some\nconditions, ripple detection is performed during SWS where these epileptic\nphenomena are common.\nThe detection of interictal epileptiform discharges (IEDs) and removal of ripple candidates\nin close temporal proximity are important topics that were not fully addressed in the\nmanuscript. We have now added the methodology on IED detection to the Methods\nsection.\nMethods (p. 23): IED detection. IED detection was conducted based on\nestablished protocols 25. Preprocessing included band-pass filtering between 25\nand 80 Hz, extraction of the signal envelope using the absolute Hilbert transform,\nand z-normalization. Events were detected when the envelope exceeded a\nthreshold of 2 SD for a duration of 20-100 ms, and were discarded when the raw\nsignal envelope did not exceed 2 SD. IEDs in close temporal proximity (1 s) were\nconcatenated.\nThe reviewer correctly points out that epileptic phenomena are common in our\nexperimental datasets. It should therefore be clear that IED detection was conducted on\nall experimental datasets, and that candidate ripple events in close temporal proximity to\nIEDs (± 2.5s) were discarded. We acknowledge that this detail has to be emphasized in\nboth Methods and Results sections, as it was only reported in the detector methodology\n(Methods). We have revised the Results section to make this correction step more\napparent.\nResults (p. 5): A separate detection of interictal epileptiform discharges (IEDs)\nwas conducted for the experimental datasets (Methods), thus, discarding any\ncandidate ripple events in close temporal proximity of an IED (± 2.5 s);\nthereby\nminimizing erroneously considering IEDs as ripples.\nResults (p. 9): We hypothesized that ripple detection should reflect the 1/f\nmodulation through increased ripple detections during the task epoch. Moreover,\nwe reasoned that if this ripple increase solely stems from a change in 1/f noise,\nthen ripple detection on simulated, noise-matched surrogates should yield a\ncomparable number of ripples. Ripple detection was conducted identically to our\nanalysis on the Sleep dataset, and ripples in close temporal proximity to IEDs (±\n2.5 s) were discarded.\nmethods section, but ambiguous in the plots and main text.\nRipple density is defined as the number of events per second, and can therefore be\nreported in Hz. The original figures utilized the symbol ρ, which we have now replaced\nwith Hz as a more accurate report. We have revised the text and figures of the main\nmanuscript as well as the Supplementary Materials.\nResults (p. 4): We observed that the density of detected ripples (defined as the\nnumber of events per second; Hz) differed between algorithms, but always scaled\nwith the spectral exponent (Fig. 1D; all p < 0.0001, RM-ANOVA; effect sizes:\ndetector (1): η² = 0.99; (2): η² = 0.96; (3): η² = 0.95; (4): η² = 0.08; (5): η² = 0.99).\nR1.7. Figures 1E and 1E need a legend to describe which color corresponds to each\ndetector. This is difficult to discern given the current format, including caption.\nThe reviewer correctly points out that it was not possible to identify the ripple detectors\nbased on color in Figures 1D and 1E. We added a legend to Fig. 1D.\nSummary:\nThis paper investigated hippocampal sharp-wave ripples (SWR), which have been\nimplicated in cognition in previous literature and more recently identified during\nwake states. Specifically, the authors looked at the performance of five different\nripple detection algorithms on recorded data, as well as simulated data generated\nfrom the 1/f^x relationship, in order to understand the impacts of aperiodic ‘noise’\non ripple detection. Despite differences in density of detected ripples across the\ndetection algorithms, the density of identified ripples always scaled with the\nspectral exponents. Next, authors demonstrate both regional- and state-specific\ndifferences in aperiodic noise, highlighting the necessity for future investigations\nto consider this noise floor during ripple detection to avoid false positives. These\nresults are replicated in two regions where ripples have not previously been\nidentified. Again we see the rate of ripple detection vary as a function of aperiodic\nnoise. In sum, these results suggest that ripples identified during wakefulness and\ncognitive performance are likely false positives, reflecting aperiodic activity, rather\nthan true ripple oscillations. This is likely to be a very widely read, impactful paper.\nThe results are quite striking and compelling, and this will certainly agitate some\nfolks. Well done!\nWe thank the reviewer for the positive evaluation of our manuscript.\nMajor Comments:\nR2.1. From the methods, it is not clear whether or not simulated data was generated\nfrom spectral exponents alone, or if ripples were also simulated. We’re fairly certain\nthat no ripples were injected into the simulations, but rather the detected ripples in\nthe simulation are just due to the stochastic nature of high frequency events. If this\nis correct, we believe that the authors should make an even bigger deal of this point!\nIt’s of course unusual to include in the methods something that you did not do, but\nin this case we feel that clarification on this is important because the detection\nalgorithm's performance depends on whether ripples were present. If ripples were\nnot simulated and the detection algorithms still identified these timepoints, this\nsignificantly impacts the interpretation of ripple detection.\nWe thank the reviewer for this excellent remark. Indeed, the simulated data were\ngenerated solely based on the spectral exponents derived from the experimental datasets.\nThe reviewer correctly identifies that no ripples were added to the simulations. We are\nappreciative of the encouragement, and have revised the Methods, Results, and\nDiscussion sections to emphasize this point.\nMethods (p. 22): Colored noise were simulated using the “colored_noise” function\n26. Here, white noise is filtered to create colored noise characterized with a defined\nspectral exponent of its power spectrum density. Data were iteratively simulated\n(N = 100) for a range of spectral exponents (-4 to 0 in .1 steps) at 1000 Hz, followed\nby minimum standard EEG preprocessing steps where the data were demeaned,\ndetrended, band-pass filtered (.5 – 150 Hz), and z-normalized. Data were\nsimulated with a constant spectral exponent (Fig. 1A) or approximated the spectral\n10\nexponent of the recorded iEEG data per segment over time (Fig. 2A: 30 s\nsegments without overlap; Fig. 3A, D, H; 1 s segments without overlap). Simulated\ndata were created per segment per channel and subsequently concatenated. It is\ncritical to highlight that no simulated ripples were added to the surrogate datasets.\nResults (p. 3): To quantify the relationship between 1/f noise and ripple detections,\nwe simulated multiple one-hour long surrogate EEG traces that followed a fχ\nrelationship with varying exponents χ, ranging from -4.0 to 0 in 0.1 steps. Note that\nthe surrogate datasets (1/f noise time series) were simulated solely based on the\npre-defined spectral exponent. Hence, we did not simulate ripple oscillations on\ntop of the 1/f noise, indicating that all detections were solely obtained from the\nnoise time series devoid of any true oscillatory activity.\nDiscussion (p. 11-12): In simulations and intracranial human recordings across\nthree experiments, we demonstrate that putative ripples largely result from different\n1/f noise characteristics that systematically vary as a function of cortical region,\nstate, and task demands. It is critical to highlight that all detection algorithms\nidentified ripples for simulated datasets that solely consisted of colored noise with\npre-defined spectral exponents. Our results provide a framework based on\nsurrogate 1/f noise simulations to infer a state-specific false positive rate.\nMoreover, these findings reveal the noise characteristics of various detection\nalgorithms and highlight the need to account for context-dependent 1/f activity\nduring ripple detection.\nR2.2. In the Introduction, the authors state that “We specifically tested if more\nripples are evident in cortical regions that exhibit a higher spectral exponent (e.g.,\nhippocampus and the medial temporal lobe; MTL) as compared to neocortical\nassociation areas (e.g., lateral prefrontal cortex; LPFC).” This and the following\nabout ripples in different states, as written, are not evidence that ripple detection is\nimpacted by aperiodic activity. Rather, they suggest that ripples might just show\nregional and/or state differences. We believe that the authors frame these decisions\nmore strongly later in the paper, where they explain that these choices included\nregions where we don’t have strong prior evidence for ripples (such as M1). The\nauthors should lean on this framing more in the Introduction, as well.\nIt is correct to state that our investigation for ripple occurrence in different states and\ncortical regions in the experimental data does not directly provide evidence that ripple\ndetection is impacted by aperiodic activity. The Introduction section now highlights our\ninitial analyses on simulated datasets, as well as the final analyses conducted during\nwakefulness, wherein we evaluated state-specific changes in ripple detection in light of\nthe 1/f noise characteristics of background activity. These analyses were conducted on\nelectrodes in the early visual and motor cortex – regions where one would not expect\nripples.\nconducted during sleep and wakefulness with noise simulations to test if, and to which\nextent, noise characteristics impact ripple detection. Using simulated 1/f noise with varying\nspectral exponents, we tested if ripple detection is modulated as a function of the\nunderlying 1/f noise. In a next step using experimental data, we specifically tested if more\nripples are evident in cortical regions that exhibit a higher spectral exponent (e.g.,\nhippocampus and the medial temporal lobe; MTL) as compared to neocortical association\nareas (e.g., lateral prefrontal cortex; LPFC). In addition, we determined the relationship of\n1/f noise and ripples during different cortical states (i.e., sleep, quiet wakefulness, and\ncognitive engagement). Given prominent, state-dependent 1/f differences wherein the\nexponents are high during cognitive engagement and lower during sleep, we predicted a\nhigher ripple rate in the wake state as a result of increased 1/f noise. To test if state-specific\nchanges in ripple detection reflect an altered 1/f noise characteristic, we conducted\nanalyses on electrodes in the early visual and motor cortex – regions for which no strong\nprior evidence of ripple occurrence exists. We reasoned that ripple detections should be\ndriven by the previously documented change in 1/f noise 27,28.\nMinor Comments:\nR2.3. The authors state that the data were referenced to a bipolar montage. This is\nsomewhat unclear, as bipolar montages are typically applied in intracranial\nrecordings, but are uncommon in scalp EEG. Clarification on the referencing\nprocess for the scalp EEG is needed.\nWe acknowledge that the preprocessing of the scalp EEG was not clearly defined.\nRereferencing to a bipolar montage was only applied in the preprocessing of the\nintracranial EEG data, whereas the simultaneously acquired scalp EEG was rereferenced\nto the common average over all available scalp electrodes. The differentiation in data\npreprocessing has now been made more explicit, and additional information on\npreprocessing of the scalp EEG data has been added to the Methods section.\nMethods (p. 21): Intracranial EEG data from the Sleep dataset were demeaned,\ndetrended, rereferenced to a bipolar montage, and downsampled to 500 Hz.\nSimultaneously recorded scalp EEG data utilized for sleep scoring were\ndemeaned, detrended, referenced to the common average over all scalp\nelectrodes, low-pass filtered (50 Hz), and downsampled to 500 Hz. Data from the\nVisual Search Task and Motor Task were rereferenced to the common average.\nR2.4. The authors estimated the spectral exponent from the 20-45 Hz range. But we\nrecommend that they perform a sensitivity analysis, re-running a few key statistical\ncomparisons but using different frequency ranges, to show that their results are\nrelatively robust to the spectral fitting range.\nOur results demonstrate that the spectral exponent of the power spectral density,\ndetermined between 20 and 45 Hz in log-log space with spectral parameterization 29,\nidentifies region-, state-, and demand-dependent modulation of cortical background\nactivity (Fig. 2; Fig.3). This specific frequency range was chosen as it is generally devoid\nof narrow-banded oscillations, follows a clear 1/f decay, avoids line noise in both Europe\nand the US, and has been shown to reliably differentiate cortical states and regions 30.\nMoreover, this frequency range substantially overlaps with the canonical low gamma\nrange that may track cortical excitability 31.\nfor spectral estimation, we repeated the analyses for the sleep data (Fig. 2) using different\nfrequency ranges for spectral parameterization (1-40 Hz; 20-45 Hz; 30-100 Hz; and 2-128\nHz). All models utilized a linear fit (aperiodic mode = fixed).\nModel fits showed high goodness-of-fit estimates (R2 ≥ .97), wherein the best fit\nwas observed when the full spectrum was considered (1-40 Hz: R2 = .9830 ± .0004, mean\n± SEM; 20-45 Hz: R2 = .9817 ± .0008; 30-100 Hz: R2 = .9846 ± .0013; 2-128 Hz: R2 =\n.9908 ± .0002). Spectral exponents from the original fit (20-45 Hz) were significantly\ncorrelated with the 1-40 Hz range (all p ≤ .0001; rho = .09 ± .05 mean ± SEM), 30-100 Hz\nrange (all p ≤ .0001; rho = .36 ± .04), and the 2-128 Hz range (all p ≤ .0004; rho = .25 ±\n.05).\nAlthough all model fits differentiate between sleep states (Fig. S2A), the 2-128 Hz\nrange did not capture the regional differences observed for the narrower frequency range\nand showed an overall narrower distribution of spectral exponents across the night (Fig.\nS2B) as also demonstrated recently 30. Note that all models can be employed to estimate\na noise floor for ripple detection (Fig. S2B), albeit that the narrower distribution for the 2-\nboundaries. Combined, these simulations reveal a general pattern where ripple detection\non experimental data has a wider distribution of exponent values as compared to\nsimulated data.\nIn sum, our original approach (linear FOOOF model fit between 20-45 Hz) provides\na very high goodness-of-fit (R2 = .9817 ± .0008; mean ± SEM), omits the spectral knees\nin low and high frequency ranges, avoids line noise or notch filtering artifacts, delineates\nregional differences, and can be successfully utilized to identify the noise floor for ripple\ndetection. These findings are referred to in the main manuscript.\nSpectral exponents (y-axis) as a function of state (x-axis) and region (medial temporal lobe [MTL], red; lateral\nprefrontal cortex [LPFC], blue). Data are displayed as probability density functions. Model fits within the 20-45\nHz frequency range successfully differentiate between cognitive states and additionally capture regional\ndifferences. In contrast, a model fit across the entire spectrum (2-128 Hz) only differentiates between states.\n(B) Distribution of ripple density (y-axis) as a function of the spectral exponent (x-axis) in the MTL (red) and\nLPFC (blue). Simulation-based inference of ripple density (superimposed grey distribution) can be established\nregardless of the model’s frequency range. Note how the distribution of spectral exponents is narrower for the\nmodel fit across the entire spectrum (Bottom) as compared to the alternative fits (Top & Middle), resulting in\nall events falling within the noise floor boundaries.\n(Fig. 2E Bottom Left), where 28% of ripples were within the noise floor (exponent range: -\n2.7 to -.4), as well as for different spectral parametrization approaches (SI Appendix Fig.\nS2).\nTo determine the robustness of our findings, we repeated spectral\nparameterization of our wake datasets (Fig. 3D, H, Top) using multiple spectral fitting\nranges (1-40 Hz; 20-45 Hz; 30-100 Hz; and 2-128 Hz). Again, condition-specific changes\nof the spectral exponent values were assessed.\nFor the Visual Search Task, we demonstrate that the spectral exponent increases\nduring the task epoch as compared to inter-trial intervals (ITIs; Fig. S5A). Within the time\ndomain, the spectral exponent increased during task epochs as compared to the inter-trial\ninterval (ITI) (Fig. S5A). Statistical quantification was conducted at the pseudo-population\nlevel using RM-ANOVAs, and now includes frequency range as an additional factor,\ndemonstrating significant main effects of condition (Fig. S5B; F = 7.82, p = .0085, η2 =\n2,66\n.19, RM-ANOVA) and frequency range (F = 21.73, p < .0001, η2 = .40, RM-ANOVA),\n3,99\nas well as an interaction effect between condition and frequency range (F = 4.92, p =\n6,198\n.0114, η2 = .13, RM-ANOVA).\nA qualitatively and quantitatively highly comparable set of findings was observed\nfor the Motor Task. In the time domain, the spectral exponent increases during the task\nepoch as compared to inter-trial intervals (ITIs) (Fig. S5C). Statistical quantifications\ndemonstrate significant main effects of condition (Fig. S5B; F = 105.51, p < .0001, η2\n2,196\n= .52, RM-ANOVA) and frequency range (F = 339.60, p < .0001, η2 = .78, RM-ANOVA),\n3,294\nas well as an interaction effect between condition and frequency range (F = 58.67, p\n6,588\n< .0001, η2 = .37, RM-ANOVA).\nIn sum, these effects show that the spectral exponent is modulated by task\ncondition irrespective of frequency range used for spectral decomposition. Yet, the\nmagnitude of the effect differs between frequency ranges, and depends on the model\nparameters used for spectral parameterization. Specifically, the frequency range\nconsidered, as well as the inclusion of a spectral knee, may account for the current\nvariability 24,32. These findings have been added to the Supplementary Information (Fig.\nS5) and are referred to in the Results section.\nFig. S5 | Task-locked modulation of 1/f spectral exponent can be observed for multiple frequency\nrange. (A) Time-resolved spectral exponent values per frequency range (colored traces) during execution of\na visual search task. Note the modulation during task execution as compared to the pre and post inter-trial\nintervals (ITI) per frequency range considered for spectral parameterization. (B) Statistical quantification. The\nspectral exponent (median ± SEM) significantly increases during the task epoch as compared to the ITI.\nSame\nconventions as in panel A. Offset of the x-axis is for visualization purposes only. (C-D) Noise modulation during\nmovement execution (same conventions as in panels A-B).\nspectral exponent by task is irrespective of the frequency range used for spectral\nparameterization (SI Appendix Fig. S5A-B).\nResults (p. 10): Additional analyses demonstrate that modulation of the\nspectral exponent by task is irrespective of the frequency range used for spectral\nparameterization (SI Appendix Fig. S5C-D).\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed\nreports. This is part of the Nature Communications initiative to facilitate training in\npeer review and to provide appropriate recognition for Early Career Researchers\nwho co-review manuscripts.\nWe thank the reviewer for their co-review of our work and the helpful comments.\n16"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors adequately addressed all of the issues I raised. I have no other substantial concerns.", "reviewer_2": "The authors have done a fine job responding to all of our comments. We have no further concerns, and we are excited to see this paper get published.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}, {"round": 3, "reviewer_comments": {"reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter