Resting-state periodic and aperiodic brain oscillations from birth to preschool years: Aperiodic maturity predicts developmental course.

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Learn more: PMC Disclaimer | PMC Copyright Notice Dev Cogn Neurosci . 2026 Mar 14;79:101709. doi: 10.1016/j.dcn.2026.101709 Search in PMC Search in PubMed View in NLM Catalog Add to search Resting-state periodic and aperiodic brain oscillations from birth to preschool years: Aperiodic maturity predicts developmental course Heather L Green Heather L Green a Lurie Family Foundations MEG Imaging Center, Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA Find articles by Heather L Green a, 1 , J Christopher Edgar J Christopher Edgar a Lurie Family Foundations MEG Imaging Center, Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA b Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA Find articles by J Christopher Edgar a, b, 1 , Kylie Mol Kylie Mol a Lurie Family Foundations MEG Imaging Center, Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA Find articles by Kylie Mol a , Marybeth McNamee Marybeth McNamee a Lurie Family Foundations MEG Imaging Center, Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA Find articles by Marybeth McNamee a , Laura A Prosser Laura A Prosser c Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA h Division of Rehabilitation Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA Find articles by Laura A Prosser c, h , Mina Kim Mina Kim a Lurie Family Foundations MEG Imaging Center, Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA Find articles by Mina Kim a , Emily S Kuschner Emily S Kuschner a Lurie Family Foundations MEG Imaging Center, Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA d Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA Find articles by Emily S Kuschner a, d , Gregory A Miller Gregory A Miller e Department of Psychology, University of California, Los Angeles, Los Angeles, CA 90095, USA f Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095, USA g Department of Psychology and Beckman Institute, University of Illinois Urbana-Champaign, Champaign, IL 61820, USA Find articles by Gregory A Miller e, f, g , Yuhan Chen Yuhan Chen a Lurie Family Foundations MEG Imaging Center, Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA b Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA Find articles by Yuhan Chen a, b, ⁎ Author information Article notes Copyright and License information a Lurie Family Foundations MEG Imaging Center, Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA b Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA c Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA d Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA e Department of Psychology, University of California, Los Angeles, Los Angeles, CA 90095, USA f Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095, USA g Department of Psychology and Beckman Institute, University of Illinois Urbana-Champaign, Champaign, IL 61820, USA h Division of Rehabilitation Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA ⁎ Correspondence to: The Children’s Hospital of Philadelphia, 3401 Civic Center Blvd, Seashore House 1F, Room 116B, Philadelphia, PA 19104, USA. [email protected] 1 Shared first authors Received 2025 Aug 21; Revised 2026 Jan 28; Accepted 2026 Mar 10; Collection date 2026 Jun. © 2026 The Authors This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). PMC Copyright notice PMCID: PMC13068577  PMID: 41916012 Previous version available: This article is based on a previously available preprint posted on bioRxiv on May 12, 2025: " Resting state periodic and aperiodic brain oscillations from birth to preschool years: Aperiodic maturity predicts developmental course ". Abstract Background Establishing maturation trajectories of resting-state neural activity from infancy to preschool ages would improve opportunities for clinical assessment and intervention. This study evaluated the superiority of an adapted Dark-Room eyes-open condition over a standard eyes-open Video-On condition during magnetoencephalography as well as regional brain differences in resting-state activity in 107 young children. Associations between the resting-state measures and behavior were also examined. Methods Typically developing children, 2-to-68 months-old, viewed a video without audio alternated with eyes open in total darkness. Source-localized resting-state power spectra from seven brain regions (parietal-occipital and left and right frontal, temporal, and central cortex) yielded periodic measures (dominant frequency and power) and aperiodic measures (exponent and offset of 1/f neuronal activity). Results As hypothesized, a dominant frequency response was observed more often in the Dark-Room than in the Video-On condition, the former eliciting 36% more dominant oscillation activity. Maturation of this dominant frequency increased non-linearly with age. Also as hypothesized, maturation of aperiodic measures decelerated with age, and maturation rate differed by brain region. Finally, more mature aperiodic values predicted better adaptive behaviors and daily living skills. Conclusions (1) The use of an appropriate Dark-Room eyes-open task provides measures of young child resting-state periodic activity with excellent signal-to-noise ratio, better than the conventional video task. (2) Analyses in brain source space reveal regional differences in aperiodic activity and its maturation that typical scalp-space analyses have not addressed. (3) More mature aperiodic brain function measures predict higher developmental behavior scores, pointing to potential brain mechanisms supporting behavioral development. Keywords: Resting-state, Aperiodic and periodic activity, MEG, Dominant frequency, Infant, Development 1. Background Brain development during embryonic and early fetal periods primarily involves the migration of neurons to specific locations, followed by events that establish a regional identity for each neuron ( Kandel, 2021 ). Brain development during the late fetal period through infancy involves specification and refinement of local neural circuits and the formation of local and long-range connections between neurons ( Kolb and Fantie, 1997 ). Research on infant brain maturation is of interest given that the first months after birth are a peak period of neural reorganization, with neural maturation during this period associated with normal variation in developmental milestones as well as developmental delays, future mental illness, and cognitive impairment. Literature dating to the 1940s demonstrates changes in RS neural activity from birth through adulthood, with age-related decreases in delta and theta activity and age-related increases in alpha, beta, and gamma activity ( Gibbs and Knott, 1949 , Corbin and Bickford, 1955 , Matousek and Petersen, 1973 , Eeg-Olofsson, 1971 , Gasser, 1988 , Edgar, 2015 , Niedermeyer and Lopes da Silva, 2005 , Klimesch, 1999 , Cragg, 2011 , Petersen and Eeg-Olofsson, 1971 , Clarke, 2001 , Katada, 1981 , Matsuura, 1985 ). Scientists examining the maturation of RS neural activity often study the dominant frequency, referred to in older children and adults as the ‘peak alpha frequency’ and in infants as the infant ‘alpha’ rhythm or the ‘infant dominant frequency’ ( Lodder and van Putten, 2011 , Marcuse, 2008 , Bethlehem, 2022 , Cellier, 2021 , Stroganova et al., 1999 ). The dominant peak is observed at a lower frequency in infants and children than adults ( Freeman and Zhai, 2009 , Gibbs and Knott, 1949 , He, 2014 , He et al., 2019 , Klimesch, 1996 , Shen, 2023 , Tran, 2020 ), with dominant oscillation activity reflecting the excitability of a cortical region ( Haegens, 2011 , Foxe and Snyder, 2011 , Klimesch et al., 2007 , Riddle, 2020 ), associated with top-down cognitive control processes ( Buschman and Miller, 2007 , Hwang, 2014 , van Diepen, 2015 ), and with an increase in the dominant frequency across development likely reflecting the ability to process information more quickly ( Klimesch, 1999 , Klimesch, 1996 ). Evidence supporting this latter association comes from studies showing that the dominant frequency predicts processing speed in neurotypical children ( Shen, 2023 , Edgar, 2019 ). To date, challenges have hindered research on RS neural activity in infants and young children. In older child and adult electrophysiology RS studies, brain measures are often obtained in an eyes-closed condition, as the RS dominant peak is much stronger when the eyes are closed than open ( Klimesch, 1999 , Berger, 1929 , Adrian and Matthews, 1934 , Edgar, 2023 ). Infants and young children are unable to keep their eyes closed for an extended period on command, and as a result almost all infant/toddler RS studies acquire data while the child is at rest with eyes open, often while viewing visual stimuli ( Stroganova et al., 1999 , Carter Leno, 2021 , Schaworonkow and Voytek, 2021 , Karalunas, 2022 , Favaro, 2023 , Vandewouw, 2024 , Wilkinson, 2024 ). This strategy has limitations, as the dominant peak is much better identified in an eyes-closed than an eyes-open condition ( Cellier, 2021 , Schaworonkow and Voytek, 2021 , McSweeney, 2023 , Hill, 2022 , Isler, 2023 ). For example, several EEG studies examining school-age children found a significantly larger dominant response in an eyes-closed than an eyes-open task ( McSweeney, 2023 , Hill, 2022 , Isler, 2023 ). A meta-analysis by Freschl et al. ( Freschl, 2022 ) highlighted the difficulty in obtaining RS data in infants. A review of their Table 1 shows that, of the included 3882 subjects, only ∼5% were under 1 year old (with half of these from a single 1990 study), with most of the included infant studies reporting on eyes-open data. Table 1. (A) Mean ± Standard Deviation of VABS domain standard scores. (B) Mixed-effects model main effects. ABC Communication Daily Living Skills Socialization Motor A. VABS domain scores 97.28 ± 8.51 99.43 ± 7.93 97.23 ± 10.36 98.70 ± 8.33 98.91 ± 10.06 B. Mixed model results ABC Communication Daily Living Skills Socialization Motor Aperiodic Exponent F (1732) = 52.46, p = .0001** F (1732) = 2.87, p = .27 F (1732) = 77.34, p < .0001** F (1732) = 6.30, p = .01 F (1732) = 8.86, p = .004* Aperiodic Offset F (1732) = 28.34, p < .0001** F (1732) = 2.64, p = .10 F (1732) = 68.22, p < .0001** F (1732) = 0.89, p = .35 F (1732) = 0.28, p = .60 Open in a new tab * p < .05 ** p < .01 Studies from the 1940s to the 1990s suggest that obtaining RS alpha measures in a dark room with eyes open is a viable alternative to the traditional RS eyes-closed exam (e.g., ( Stroganova et al., 1999 , Adrian and Matthews, 1934 , Ellingson, 1956 , Mulholland, 1995 , Chapman et al., 1970 , Lehtonen and Lehtinen, 1972 , Stroganova, 1987 )). To explore this claim, recent studies from our group ( Edgar, 2023 , McNamee, 2026 ) compared RS alpha obtained in eyes-closed, eyes-open, and eyes-open dark-room conditions (Dark-Room task described below in Methods) in neurotypical children and in children with autism spectrum disorder 6.9–17.1 years old. High consistency was observed between eyes-closed and eyes-open Dark-Room alpha frequency and power measures (ICCs≥0.80), in contrast to RS alpha activity near noise levels in the eyes-open condition. The present study assesses infant RS activity via the use of our Dark-Room RS task optimized for infants and young children to evaluate its feasibility. Separate from the issue of data acquisition, this study addressed another methodological issue, regarding data analysis and interpretation. Scientists have recently called into question previous electrophysiology RS findings, noting that most RS power-spectrum analyses conflate two brain processes. RS neural activity exhibits aperiodic background activity (contributing power across all frequencies when subjected to standard power-spectrum analysis) that co-exists with periodic oscillations ( He, 2014 , Freeman and Zhai, 2009 , Donoghue, 2020a ). In particular, RS electrophysiology power spectra show a characteristic 1/f power distribution (higher power at lower frequencies). Accordingly, RS analyses need to distinguish the 1/f aperiodic activity (often referred to as non-sinusoidal ‘background noise’ ( Bedard et al., 2006 , Usher et al., 1995 )) but very likely meaningful ( Donoghue, 2020a , Donoghue et al., 2020b , Ostlund, 2022 ) from periodic oscillatory activity in order to understand RS neural activity (for examples, see ( Donoghue, 2020a , Bullock et al., 2003 )). A growing literature relies on the ‘specparam’ toolbox ( Donoghue, 2020a , Ostlund, 2022 ) to separately parameterize periodic and aperiodic activity ( Donoghue et al., 2020b , Ostlund, 2022 , He et al., 2019 ). Studies have observed an age-related flattening of RS power spectra from infancy to adolescence ( Tran, 2020 , Schaworonkow and Voytek, 2021 , Karalunas, 2022 , He et al., 2019 , Waschke et al., 2017 , Voytek, 2015 , Merkin, 2023 , Donoghue, 2025 ). The age-related change in the RS 1/f power-spectrum slope (aperiodic exponent) has been hypothesized to reflect changes in the neural-circuit excitatory-to-inhibitory balance ( Gao et al., 2017 ), with many studies investigating the aperiodic exponent as an indicator of neural-circuit imbalance in neurodevelopmental and psychiatric disorders (see review Donoghue, 2025 ). To date, most parameterization efforts to assess infant RS activity have been problematic, because most studies have conducted the analyses on EEG sensor data, thus assessing power spectra that reflect activity from multiple brain regions (for discussions of the limitations associated with this approach see ( Edgar et al., 2026; Hoechstetter, 2004 , Scherg and Berg, 1996 , Scherg and Picton, 1991 , Edgar, 2017 , Edgar, 2003 ). This blurring of activity from multiple brain sources has often been even more severe, because in most studies aperiodic measures have been obtained from power spectra via an average of EEG channels ( Karalunas, 2022 , Tröndle, 2022 ) or via two or more regional clusters of EEG sensors ( Cellier, 2021 , Carter Leno, 2021 , Schaworonkow and Voytek, 2021 , Favaro, 2023 , Hill, 2022 , Tröndle, 2022 , Rico-Picó, 2023 ). These EEG cluster studies have produced inconsistent results. In contrast to analysis in sensor space, EEG and MEG analysis in source space is designed to identify and distinguish regional sources. To our knowledge, only two pediatric studies have explored regional differences of the aperiodic exponent and offset measures in source space. Vandewouw et al. ( Vandewouw, 2024 ) undertook MEG source analysis to assess regional differences in age and aperiodic exponent and offset associations in 69 children and adults 1–38 years old. They found that across brain regions the aperiodic exponent decreased with age. McNamee et al. (2026) compared Dark-Room and Eyes-Closed MEG aperiodic measures in children 7.7–17.1 years old at 15 brain regions and showed good consistency between the Dark-Room and Eyes-Closed aperiodic exponent and offset parameter values across the brain regions. McNamee et al. also observed regional differences in the aperiodic values as well as regional differences in their maturation. With respect to brain development, a limitation of the above two studies is that McNamee et al. did not include young children, and in the Vandewouw et al. study there were no children under 1 year old and few children 1–3 years old. Given regional differences in infant maturation of brain structure and chemistry, regional differences in RS activity are almost certain in very young brains. The present study sought to identify the beginnings of such RS differences, here examining infants and young children (2 months–5 years old) and assessing RS activity in a relatively large sample (N = 107). The present study addressed the possibility of regional differences in RS activity during the first 5 years of life. Associations between RS activity and behavior were also examined. The primary hypothesis was that in infants and young children, a Dark-Room Task would provide more evaluable dominant oscillation data with a better SNR than the commonly used young-child eyes-open task (Video-On). Specifically, we hypothesized that in the parietal-occipital cortex, stronger dominant oscillation activity would be observed in the Dark-Room than the Video-On Condition. It was also hypothesized that maturation of aperiodic measures would decelerate with age, and maturation rate would differ by brain region. 2. Methods 2.1. Participants All infants and children were typically developing. Inclusion criteria included (1) no premature birth (<37 weeks gestation); (2) no concerns regarding developmental delay; (3) no history of seizure disorder; and (4) no known hearing or visual impairment. The study was approved by the Children’s Hospital of Philadelphia Institutional Review Board (IRB), and all families gave written consent. Of 107 enrolled children, evaluable MEG data was acquired from 102 children (infants<36 months N = 78, 31 females; young children 36–68 months N = 24, 10 females). Five infants were excluded due to excess movement artifact during imaging (N = 4) or technical difficulties (head position indicator (HPI) coil detached during exam N = 1). Thirty-three of the 102 infants (13 females) had evaluable MEG data from more than one timepoint (2 Timepoints N = 19; 3 Timepoints N = 10; 4 Timepoints N = 3; 6 Timepoints N = 1). For the 33 infants with longitudinal data, only a single timepoint was included in the cross-sectional analyses, with in-house code used to randomly select one timepoint for each infant. 2.2. RS dark-room paradigm For the RS Dark-Room eyes-open MEG task, over a 5-min period the child alternated between viewing an Inscapes video ( Vanderwal, 2015 ) without audio for 20 s and then resting with their eyes open for 30 s in total darkness, alternating 6 times. During the task, a parent and research assistant stayed in the room with the child. Fig. 1 A illustrates the Dark-Room paradigm. Fig. 1. Open in a new tab (A) Dark-Room RS task. Infant MEG data (children<36 months) were obtained using the 123-channel Artemis system. Preschool MEG data (children > 36 months) were obtained using the 275-channel CTF system. (B) Source-space RS measures were obtained from seven regions of interest (ROIs; left side of panel B), with periodic and aperiodic measures obtained using the specparam algorithm. The right side of panel B demonstrates how specparam models features of the power spectrum via performing a sequential decomposition into periodic and aperiodic components. The periodic RS dominant rhythm parameters (center frequency, amplitude, and bandwidth) were obtained from a midline parietal-occipital region. RS aperiodic parameters (aperiodic exponent and offset) were obtained from all seven ROIs. 2.3. MEG data acquisition For children under 36 months, MEG data were recorded in a magnetically shielded room using an Artemis123 system (Tristan Technologies, Inc., San Diego, CA, USA) with a sampling rate of 5000 Hz and a 0.1 Hz high-pass filter. Artemis123 was designed for use with children from birth to 3 years of age ( Roberts, 2014 , Edgar, 2015 ). The Artemis system has 123 first-order axial gradiometers and a helmet circumference of 50 cm, which corresponds to the median head circumference of 36-month-old children in the United States. Before MEG acquisition, a fabric cap with 4 HPI coils was placed on the child’s head. The child’s head shape, anatomical landmarks (nasion, right and left preauricular points), and locations of the HPI coils were digitized using a FastSCAN System (Polhemus, Colchester, VT). Head position was continuously monitored during MEG recording using the HPI coils. MEG data from children 36 months and older were acquired in the same room using a 275-channel CTF system (VSM MedTech, Coquitlam, BC) with synthetic third-order gradiometer noise correction applied. CTF MEG data were acquired with a sampling rate of 1200 Hz. Before data acquisition, 3 HPI coils were placed on the child’s head. The child’s head shape, anatomical landmarks, and HPI coils were digitized, and head position was monitored using the HPI coils. The Artemis123 and CTF MEG systems used the same hardware for stimulus presentation (projector, presentation computer). Children were scanned in the supine position, viewing the same visual stimuli at the same viewing distance and visual angle. Several strategies helped keep the children calm and engaged during the MEG exams ( Kuschner, 2021 , Chen, 2024 ). Before visiting the lab, each study family was sent a YouTube video that describes the visit and shows the family what to expect during their visit. The MEG exam was scheduled around each child’s nap schedule to ensure that the child was awake and alert during the MEG scan. Parents were asked to bring a pacifier, bottle, and snacks (in the case of older infants and young children). During the RS task, and as detailed in the Methods section, periods of Dark-Room were alternated with Inscapes videos, the latter included to obtain Video-On data as well as to keep the child entertained and happy throughout the task. During the RS MEG exam, a parent and a research assistant remained with the child throughout the scan. The child was given breaks to play with toys or bubbles when needed. If a child became tired and fussy, they were provided a place to take a nap, with data collection resuming once the child was awake and rested. The above procedures were adapted from the INSPIRE program (formerly known as MEG-PLAN, Kuschner et al., 2021 ), these procedures developed for collecting auditory MEG data with children with developmental disabilities, and here adapted for use with typically developing infants and young children during a resting state exam. 2.4. MEG source analysis MEG data were analyzed using Brainstorm (( Tadel, 2011 ); http://neuroimage.usc.edu/brainstorm ). Digitized FastSCAN surface points representing the shape of the infant’s head ( > 10,000 points) were used to co-register each child’s MEG data to an age-appropriate infant or young child MRI template ( O'Reilly, 2021 , Richards, 2016 ) using an affine transformation that accommodated global scale differences between the child’s anatomy and the atlas. The Destrieux cortical parcellations were used ( Destrieux, 2010 ). MEG data were downsampled to 1000 Hz and band-pass filtered 0.3–55 Hz, with a 60 Hz notch filter. Heartbeat and eyeblink artifacts were removed via independent component analyses. Other artifacts (e.g., movement, muscle artifact) were visually identified and marked as artifact. Times when the child was not attending to the stimuli were manually removed (e.g., falling asleep). Average head movement across time for each child (displacement of the HPI locations compared to the starting head location) was computed for use in statistical analyses. Whole-brain RS activity maps were computed using Minimum Norm Estimates (MNE; ( Hämäläinen and Ilmoniemi, 1994 , Matsuura and Okabe, 1995 , Hauk, 2004 , Lin, 2006 )), with minimum-norm imaging estimating the amplitude of brain sources constrained to the cortex and with the current dipoles oriented normal to the local cortical surface ( Baillet et al., 2001 ). A MEG noise covariance matrix was obtained from an empty room recording obtained immediately prior to each child’s MEG scan. MNE solutions were computed with normalization as part of the inverse routine, based on the noise covariance. Forward solutions were computed using an overlapping spheres model. MNE source estimation was computed and mapped to an age-appropriate MRI template. 2.5. Obtaining source-level RS periodic and aperiodic parameter values Virtual time courses at each vertex in brain source space were derived from the MNE solution (∼15,000 vertices). At each vertex, an average RS power spectrum density was computed via Welch estimation for power density, using Hanning windows of 4 s and 50% overlap, computed separately for the Dark-Room and Video-On conditions. To compute regional periodic and aperiodic activity, and to limit the number of statistical analyses, power spectra were averaged across vertices within seven ROIs defined in the Destrieux cortical atlas for the Dark-Room and Video-On conditions ( Destrieux, 2010 ): parietal-occipital, left frontal, right frontal, left central, right central, left temporal, and right temporal ( Fig. 1 B right panel). Fig. 1 B shows how RS periodic and aperiodic measures were obtained using the specparam algorithm (formerly known as FOOOF; Donoghue, 2020a , Donoghue et al., 2020b , Ostlund, 2022 ). Specparam performs a series of decompositions of the power spectrum and divides the power spectrum into periodic and aperiodic components ( Fig. 1 B), so that the original spectrum is fitted with 1/f aperiodic activity, and then the aperiodic signal is subtracted. Specparam then sequentially estimates and fits peaks in the residual periodic spectrum as Gaussians and subtracts the Gaussians from the power spectrum to refit the 1/f aperiodic function. The final specparam model provides estimates of: (1) periodic measures including spectral peaks, with center frequency and power computed for each identified peak, and (2) aperiodic measures including the exponent (slope of the 1/f function) and the offset (vertical displacement of the 1/f function, see Fig. 2 A). Fig. 2. Open in a new tab (A) Dark-Room power spectrum from a 3-year-old child and specparam fits. Specparam was used to identify the periodic dominant frequency and power for the midline parietal-occipital ROI and aperiodic exponent and offset for each of the seven ROIs. (B) Top left: The midline parietal-occipital dominant frequency increased exponentially as a function of age. Top right: Although Dark-Room and Video-Off conditions did not differ in parietal-occipital dominant frequency, the dominant frequency was observed more often in the Dark-Room (94%) than the Video-On condition (83%). Bottom left: Parietal-occipital dominant power was not associated with age. Bottom right: Dominant power was 36% stronger in the Dark-Room than the Video-On condition. (C) Examples of dominant peaks after removing 1/f aperiodic activity from seven representative children 5 months to 5 years old. Each trace represents the dominant peak from a different child, showing inter-subject variability of dominant peak power as well as a consistent increase in the dominant peak frequency across age. ( D ) Midline parietal-occipital RS dominant rhythm spectrum for Dark-Room (blue) and Video-On (red) conditions for all subjects, after removing 1/f activity. The color indicates the age of each child (2–68 months) from deep blue/red (younger children) to light blue/red (older children). Studies have shown that infant and young child dominant frequencies fall into a frequency range between 3 and 12 Hz ( Cellier, 2021 , Schaworonkow and Voytek, 2021 , Rico-Picó, 2023 , Smith, 1938 , Lindsley, 1939 , Henry and Greulich, 1944 , Marshall et al., 2002 ). As recommended by Ostlund et al. ( Ostlund, 2021 ), choice of the specparam frequency range was guided by the data, with explicit justification of parameter choices. Across the present sample, power spectrum plots showed activity up to around 20 Hz and then essentially none (see Fig. 3 in the online supplement). Accordingly, the specparam frequency range was set at 1–20 Hz. Other specparam settings were: width for a detected peak between 1.5 and 8 Hz, maximum number of detectable peaks 3, minimum peak height 3 dB, proximity threshold 2 standard deviations from the peak model, and fixed aperiodic mode. Model fit for each child was evaluated via the R 2 between the original spectra and fitted spectra for each condition and each ROI. The fitting procedure was very successful. Models with R 2 < 0.80 were excluded (N = 1). As the dominant oscillation periodic activity is primarily generated in parietal-occipital brain regions ( Berger, 1929 , Haegens, 2014 , Salmelin and Hari, 1994 ), assessment of periodic neural activity in the parietal-occipital ROI was of primary interest. Parietal-occipital aperiodic activity was also examined. As shown in Fig. 4 , six other brain regions were identified to compare aperiodic findings between brain regions, with six other ROIs created to provide a generally lobar assessment of aperiodic neural-circuit activity: left and right frontal, left and right temporal, and left and right central ROIs (see Fig. 1 B). Using in-house software, the dominant peak was identified as the largest peak in the parietal-occipital ROI between 2 and 12 Hz. Supplementary Figure 1 A shows several scenarios encountered when identifying the dominant peak. Specparam identified multiple peaks under 20 Hz (maximum 3 peaks) in 49% and 47% of children in the Dark-Room and Video-On conditions, respectively (see Supplementary Figure 1B ). Fig. 4. Open in a new tab Given no main effect of condition, regionally specific developmental changes in aperiodic measures are shown only for the Dark-Room condition. (A) Top left: Aperiodic exponent by age. Bottom left: Illustration of the shift in the aperiodic exponent with age. Right: The relationship between the aperiodic exponent and age for each ROI. (B) Top left: Aperiodic offset by age. Bottom left: Illustration of the shift in the aperiodic offset with age. Right: The relationship between the aperiodic offset and age for each ROI. Asterisk and brackets indicate where there were significant differences (ps < 0.05) between the parietal-occipital region and the six other brain regions . 2.6. Developmental milestones Developmental milestones were assessed via parent report using the Vineland Adaptive Behavior Scales (VABS-3; ( Sparrow et al., 2016 )) for 53 children. Medical records were used to confirm typical development for 49 children whose caregivers did not complete the VABS-3. To evaluate associations between RS periodic and aperiodic neural activity and behavior, VABS-3 adaptive behavior composite (ABC) standard scores as well as four subdomain standard scores ( M =100, SD =15) were used: communication, daily living skills, socialization, and motor. 2.7. Statistical analyses Analyses were conducted using JMP Pro Version 18 (SAS Institute, Inc.). To evaluate associations between age and RS periodic activity (midline parietal-occipital dominant frequency and power), the Akaike information criterion (AIC) was used to determine whether a linear or non-linear model provided a better fit. To determine whether Dark-Room or Video-On provided better RS periodic signals, one-way ANOVAs tested the effect of condition on the periodic dominant frequency and power measures. A Fisher’s Exact Test evaluated whether a dominant peak was observed more often in the Dark-Room than the Video-On condition. To assess changes in RS aperiodic activity (exponent, offset) as a function of age, AIC was used to determine whether a linear or non-linear model provided a better fit. To determine if there were regional differences in associations between age and aperiodic values, mixed-effect models were run separately for exponent and offset, with log age, ROI, head movement, condition, and log age X ROI interaction entered as fixed effects and subject entered as a random effect. Log age was used because of the nonlinear maturation of the aperiodic measures observed in the present sample (see Results). MEG machine was not included as a fixed effect in the LMMs, as all infant and young toddler MEG data (<40 months) were obtained in the Artemis machine, and all MEG data from older children ( > 40 months) were obtained in the CTF machine, reflecting growth in head size. As such, as age and machine were completed confounded, only age was included in the LMMs. Finally, relationships between RS measures and behavior were assessed. For RS periodic measures, each VABS-3 ABC and subdomain standard score were entered as a dependent variable in separate analyses, with periodic dominant frequency (or power), condition, and periodic dominant frequency (or power) X condition interaction entered as independent variables. Age was entered as a random effect, allowing more nuanced analysis of age-related differences while accounting for the variability across subjects (i.e. allowing the age slope to vary across subjects). Mixed-effect models were run separately for dominant frequency and power, with a family-wise corrected threshold of p = .005 ( p = .05 divided by 10 tests). That is a conservative threshold, because the 10 tests were not independent. For RS aperiodic measures, mixed-effect models were performed with each VABS-3 ABC and subdomain standard score entered as a dependent variable, and age entered as a random effect. Aperiodic measures (exponent or offset), ROI, condition, and their interactions were entered as independent variables, with family-wise correction again applied. 3. Results Across infant (Artemis123) and young child (CTF) samples, the mean (SD) amount of data collected for each condition was: Dark-Room (Artemis123) 125 s (61), Dark-Room (CTF) 168 s (32), Video-On (Artemis123) 88 s (37), and Video-On (CTF) 112 s (22). The total amount of artifact-free data was: Dark-Room (Artemis123) 102 s (56), Dark-Room (CTF) 162 s (32), Video-On (Artemis123) 73 s (35), and Video-On (CTF) 105 s (24). As detailed in the Supplementary Figure 2 and Supplementary Table 1 , to evaluate whether variables related to subject and data acquisition quality predicted specparam model fit ( R 2 ), a mixed-effect model was conducted with R 2 entered as the dependent variable, head movement, condition, age, ROI, and length of artifact-free data entered as fixed effects, and subject entered as a random effect. The specparam model fits were very good, with no difference in model fit between the conditions, and with model fit not associated with age or head movement. The only factor associated with specparam model fit was ROI ( p < .0001), with midline parietal-occipital ROI having the best model fit ( Supplementary Table 1 ). Supplementary Figure 3 shows an example of periodic RS activity at all seven ROIs in an 11-month-old child. Whereas in both conditions a large parietal-occipital dominant peak was quite evident, periodic peaks, if any, were much smaller and more difficult to identify in all other regions. 3.1. RS periodic measures As shown in Fig. 2 B, a dominant peak was observed more often in the Dark-Room (94% of children) than in the Video-On condition (83% of children; Fisher’s Exact Test p = .01). Comparison of a linear model (Dark-Room: AIC=300; Video-On: AIC=286) versus a nonlinear exponential growth model with 3 parameters (3 P; Dark-Room: AIC=223; Video-On: AIC=234) showed that the nonlinear 3P model better represented the relationship between age and dominant frequency ( Fig. 2 B). For the Dark-Room and Video-On conditions, the midline parietal-occipital dominant frequency rapidly increased during the first 2 years of life, with less rapid changes observed from 3 to 5 years old. The dominant frequency was ∼2.6 Hz at 2 months and ∼9.5 Hz at 68 months. Fig. 2 C illustrates the development of RS dominant peaks after removing the 1/f aperiodic activity from seven children 5 months to 5 years old. Each trace represents the dominant peak from a different child, showing inter-subject variability of dominant peak power as well as a consistent increase in the dominant peak frequency across age. The Dark-Room condition elicited higher dominant oscillation power ( M =0.68 dB/Hz, SD =0.23) than the Video-On condition ( M =0.50 dB/Hz, SD =0.19; F (1179)= 28.86, p < .0001, 95% CI (-0.11, −0.24); Fig. 2 B), with the Dark-Room condition providing a 36% increase in dominant oscillation activity. No association between age and dominant power was observed . Fig. 3 provides an example of how dominant peaks develop from a child scanned longitudinally from 3 to 39 months, with dominant frequency ranging from 3.97 Hz at 3 months to 7.25 Hz at 3 years. Fig. 3. Open in a new tab Maturation of dominant peak in one child. Dark-Room periodic activity is shown in blue and Video-On periodic activity in red. Blue arrows show the Dark-Room dominant frequency (DF). 3.2. RS aperiodic parameter values Comparison of a linear model (Dark-Room: AIC=635; Video-On: AIC=563) versus a nonlinear 3P exponential growth model (Dark-Room: AIC=567; Video-On: AIC=547), showed that the nonlinear 3P model better represented the relationship between age and the aperiodic exponent. As shown in Fig. 4 A, rapid development of the Dark-Room aperiodic exponent was observed during the first year of life. A mixed-effects model showed main effects of log age ( F (1, 102.3)= 57.29, p < .0001) and ROI ( F (1, 1213.2)= 2.79, p = .01), as well as a log age X ROI interaction ( F (1, 1212.4)= 8.50, p < .0001). Simple-effect analyses of the ROI main effect showed that, compared to the midline parietal-occipital ROI, larger aperiodic exponent values were observed in the left central, left frontal, and left temporal regions ( ps <.0001) and right central and right frontal regions ( ps <.01). Comparison of a linear model (Dark-Room: AIC=1174, Video-On: AIC=1059) versus a nonlinear 3P exponential growth model (Dark-Room: AIC=1150; Video-On: AIC=1050) showed that the nonlinear 3P model best represented the relationship between age and aperiodic offset. Fig. 4 B shows rapid aperiodic offset changes during the first months of life. Mixed-effect model analyses with aperiodic offset as the dependent variable showed effects of ROI ( F (1, 1210.8)= 3.76, p = .001) and a log age X ROI interaction ( F (1,1210.0)= 5.85, p < .0001). Simple-effect analyses of the ROI main effect showed that, compared to the midline parietal-occipital ROI, larger aperiodic offset values were observed in left frontal, left central, left temporal regions ( ps <.0001) and in right frontal, right central, and right temporal regions ( ps <.01). Head movement and condition did not account for significant variance in the aperiodic exponent or offset. Given between-child differences in the distance between their head and the MEG sensors (e.g., different head size in younger versus older children), the LMMs were rerun including an SNR measure (norm of the lead field). The aperiodic findings were unchanged after accounting for between-child differences in the distance from their head to the MEG sensors, demonstrating that the findings were not due to between-child differences in head position within the helmet. 3.3. Associations between periodic and aperiodic parameter values and behavior No association was observed between behavior ratings and dominant frequency or power in either condition ( p s > 0.05 after family-wise correction). Table 1 A presents VABS-3 ABC and domain standard scores (N = 53, 4 subjects excluded as multivariate outliers). RS aperiodic exponent and offset measures accounted for significant variance in domain-specific developmental scores. Table 1 B shows that the aperiodic exponent accounted for significant variance in ABC, daily living skills, and motor domain scores. Aperiodic offset accounted for significant variance in ABC and daily living skills scores. For both aperiodic measures, smaller aperiodic values were associated with higher ABC and daily living skill scores. No associations were found between aperiodic measures and communication (language) and socialization scores. No interactions were observed between the fixed-effect variables. As an example, Fig. 5 scatterplots show the associations between the aperiodic measures averaged across ROI and condition and VABS-3 daily living skill score, with R 2 and p -values based on zero-order correlations. Fig. 5. Open in a new tab A more mature aperiodic measure predicts higher ABC and daily living skills scores. 4. Discussion Findings provide insight into the maturation of RS neural activity during the first years of life, replicating some recent reports while also pointing to region differences and behavior relationships not previously addressed. The expected age-related increase in the midline parietal-occipital dominant frequency was observed, with much more rapid maturation of the dominant rhythm in younger than older infants. The advantage of the Dark-Room condition over the Video-On condition was confirmed, with the dominant peak more often detected in the Dark-Room (94% of children) than the Video-On condition (83% of children), and with 36% higher dominant oscillation activity in the Dark-Room than the Video-On condition. Aperiodic findings mirrored those from most previous studies, with exponent and offset values decreasing as a function of age. Findings support the small but growing literature in this area and document both regional differences in aperiodic measures and regional differences in relationships between age and aperiodic measures. In addition, aperiodic measures were found to be associated with behavior ratings, with more mature aperiodic activity associated with higher adaptive behavior and daily living skills scores. Findings thus demonstrate that (1) the use of an eyes-open Dark-Room task provides measures of young child RS periodic activity with excellent SNR, (2) an understanding of the development of infant RS activity is better achieved via obtaining measures in brain source space in order to detect regional differences in aperiodic activity, and (3) a more mature aperiodic value predicts higher developmental scores on behavioral tasks. 4.1. The maturation of midline parietal-occipital periodic neural activity As shown in Fig. 2 D, midline parietal-occipital dominant peaks were observed at all ages, with a dominant frequency observed at 2.6 Hz at 2 months. The infant RS literature shows mixed results regarding the age when the dominant peak is first observed, as well as the pattern of age-related changes in dominant oscillation frequency and power. Factors likely accounting for mixed young child findings include: (1) examining RS activity using an RS eyes-open task ( Carter Leno, 2021 , Schaworonkow and Voytek, 2021 , Karalunas, 2022 , Vandewouw, 2024 , Wilkinson, 2024 , Rico-Picó, 2023 , Levin, 2017 ) rather than a RS Dark-Room task ( Stroganova et al., 1999 ), (2) examining RS periodic activity with ( Carter Leno, 2021 , Schaworonkow and Voytek, 2021 , Karalunas, 2022 , Wilkinson, 2024 , Rico-Picó, 2023 ) rather than without ( Stroganova et al., 1999 , Levin, 2020 ) having removed power contributed by 1/f aperiodic activity, and (3) examining RS activity in sensor space ( Stroganova et al., 1999 , Carter Leno, 2021 , Schaworonkow and Voytek, 2021 , Karalunas, 2022 , Wilkinson, 2024 , Rico-Picó, 2023 , Levin, 2017 ) rather than source space ( Vandewouw, 2024 ). As discussed below, present findings demonstrate advantages of both obtaining Dark-Room data and analyzing RS activity in source space. Studies have reported an age-related increase in the dominant frequency in infants as young as 2 months ( Cellier, 2021 , Hill, 2022 , McSweeney, 2023 , Stroganova et al., 1999 , Vandewouw, 2024 , Wilkinson, 2024 , for meta-analysis see Freschl et al., 2022 ). This pattern is often but not always observed. For example, Rico-Pico ( Rico-Picó, 2023 ) observed a quadratic relationship between the dominant frequency and age, with an early increase, then a plateau, and then a decrease. Wilkinson et al. ( Wilkinson, 2024 ) reported an age-related increase in the dominant frequency in children only 6 months and older (see Fig. 2 in Wilkinson et al., 2024 ). In the present study, a focus on midline parietal-occipital RS neural generators provided periodic dominant peaks that were easily detected in almost all infants, with a comparison of the Dark-Room and Video-On power spectra used to identify the dominant peak when multiple periodic peaks were observed. As shown in Fig. 2 D and Supplementary Figure 1 , a stronger periodic peak in the Dark-Room than the Video-On condition allowed the periodic dominant peak to be easily identified, with the frequency of the dominant peak increasing as a function of age. As shown in Fig. 3 , this age effect was observed at the level of an individual infant. Present findings thus provide strong evidence for the maturation of the dominant oscillation, shifting from delta and theta ranges to the low alpha range during the first years of life. Present findings also demonstrate that the periodic dominant peak would often be difficult to detect in a Video-On condition, with the peak smaller and sometimes absent. There is an ongoing conversation in the literature with respect to defining the infant and young child RS dominant oscillation. As reviewed in Stroganova et al. ( Stroganova et al., 1999 ), some consider the delta- and theta-band activity observed in children a precursor of adult eyes-closed alpha activity ( Smith, 1939 , Smith, 1941 , Markland, 1990 ). Others, however, propose that RS alpha activity undergoes a developmental change different from that of delta and theta waves ( Katada, 1981 , Walter, 1950 ). As in Stroganova et al. ( Stroganova et al., 1999 ), the present study adopted a “functional topography” approach ( Kuhlman, 1980 ), with the determination of a child dominant peak based on two main criteria: spatial distribution (larger in posterior than anterior brain regions) and functional reactivity (changes as a function of visual input, emerging given a homogeneous visual field ( Lehtonen and Lehtinen, 1972 ). Present findings support this functional topography approach. First, Supplementary Figure 3 demonstrates regional differences in the dominant peak, with largest dominant peaks observed in parietal-occipital regions and absent in frontal regions. Such findings suggest that analysis strategies that average RS activity across EEG sensors risk diminishing or averaging out the dominant oscillation response. Second, as demonstrated in Supplementary Figure 1 , for children with multiple periodic peaks a comparison of Dark-Room and Eyes-Closed power spectra facilitated identification of the periodic peak as the peak is functionally reactive to visual input (the greater synchronization of oscillatory activity given a homogenous visual field) and thus confirmed the Dark-Room periodic peak as the dominant peak. Differences between study findings are likely due to whether the RS dominant frequency and power were identified via a functional topography approach used in our study, or via averaging periodic spectrum across all EEG sensors. The latter approach could result in dominant peaks observed in fewer infants, as well as a shift of dominant frequency range (see Supplementary Figure 3 ). With respect to the RS dominant power findings, there was only a trend finding of an age-related increase in midline parietal-occipital periodic dominant power ( p = .08 for Dark-Room and p = .09 for Video-On condition). Dominant power findings in the literature are mixed, with some studies reporting an increase with age ( Stroganova et al., 1999 , Wilkinson, 2024 , Tröndle, 2022 ) and others reporting no association with age ( Cellier, 2021 , Vandewouw, 2024 , Hill, 2022 ). Such differences again are likely due to the differences noted above in how infant RS data are obtained and analyzed. Present findings suggest an age-related increase in dominant power, with a small effect size requiring a large sample for reliable confirmation. In sum, present findings demonstrate that a RS Dark-Room paradigm and infant source-space RS analyses are well suited to assess the maturation of midline parietal-occipital RS periodic neural activity in infants and young children. 4.2. Aperiodic RS neural activity Present findings showed regional differences in the aperiodic measures as well as associations with age. Specifically, a nonlinear age-related decrease in the RS aperiodic measures was observed, with the aperiodic offset decreasing rapidly after birth and plateauing at around 10 months, and the aperiodic exponent decreasing rapidly after birth and plateauing around 20 months. Whereas most infant studies report age-related decreases for the aperiodic exponent ( Cellier, 2021 , Favaro, 2023 , Hill, 2022 , Rico-Picó, 2023 , Schaworonkow and Voytek, 2021 , Tröndle, 2022 , Vandewouw, 2024 , but see Isler et al., 2023 ) and offset ( Cellier, 2021 , Favaro, 2023 , He et al., 2019 , Hill, 2022 , Tröndle, 2022 , Vandewouw, 2024 , but see Isler et al., 2023 ), neonate mice and human neonates show an increase in the 1/f exponent ( Chini et al., 2022 ). Chini et al. ( Chini et al., 2022 ) hypothesized that the neonate-to-infant change in the direction of the 1/f exponent is explained by the decline of brain GABA levels ( Hermans, 2018 ) and cortical inhibition ( Lissemore, 2018 ). In children 1–7 months, it is also hypothesized that a wave of interneuronal death around this age ( Wong, 2018 ) might induce an early shift from an E:I ratio decrease to an E:I ratio increase. This change might also reflect maturational changes in the integration of inhibitory interneurons into cortical circuity, with inhibitory circuits dominated by somatostatin-positive inhibitory interneurons during the first postnatal weeks ( Tuncdemir, 2016 , Marques-Smith, 2016 ) and with parvalbumin-positive inhibitory interneurons later integrated ( Tuncdemir, 2016 , Marques-Smith, 2016 , Guan, 2017 ). After the first weeks of life, an age-related decrease in the aperiodic exponent and offset values (flattened RS power spectrum; Fig. 4 A & 4B bottom left plot) has been hypothesized to be associated with an age-related increase in the neural-circuit E:I ratio, with changes to the neural-circuit E:I due to less neural noise in older than younger children, given an age-related increase in inhibitory GABAergic and an age-related decrease in glutamatergic signaling ( Gao et al., 2017 ). A relatively rapid decrease in the E:I ratio during the first years of life has been hypothesized to facilitate efficient information processing ( Hill, 2022 ). The present finding of a steeper RS power spectrum is consistent with the hypothesis of more inhibition-dominant RS neuronal background in younger than older children. The above hypotheses are supported by recent findings ( Pearce, 2025 ), presented at the 2025 Society of Psycholophysiology conference showing that higher midline parietal-occipital GABA concentration (obtained using magnetic resonance spectroscopy) is associated with a less steep RS aperiodic exponent. In the present study, the association between age and the aperiodic measures differed by brain region, suggesting regionally specific changes in the E:I balance from birth to 5 years. These findings replicate our recently published older child study in which age x region interactions were observed for both the aperiodic exponent and the offset ( McNamee et al., 2026 ). Such findings indicate the need to develop nuanced models of the maturation of neural activity in children. As an example, regional differences in the aperiodic measures are likely associated with regional differences in the maturation of gray and white matter, with some brain areas maturing relatively quickly (e.g., visual system ( Otten, 2025 ), e.g., face processing ( Chen, 2021 )), whereas other brain regions mature more slowly (e.g., auditory system ( Chen, 2024 , Chen, 2023 )). 4.3. RS aperiodic parameter values predict behavior In the present study, more mature aperiodic exponent and offset values were associated with higher adaptive behavior, daily living skills, and motor ability scores. Although aperiodic activity was observed to be regionally specific, the associations between aperiodic measures and behavior were observed in all ROIs. This may reflect a general pattern, with a lower aperiodic exponent/offset likely reflecting less inhibitory and more excitatory activity (i.e., an increased E:I ratio) in the thalamocortical network in response to sensory input, thus aiding in the development of better daily living skills and motor ability. The observed associations indicate moderate effects, with the aperiodic measures accounting for 9–17% of the variance in behavior scores. 4.4. Limitations and Future Directions Although brain structure and chemistry are likely associated with RS neural activity, to date no infant multimodal neuroimaging study has examined such associations from infancy to preschool. Of interest are studies assessing associations between brain structure (gray and white matter), brain chemistry (GABA and glutamate), and RS neural activity, these studies informing our mechanistic understanding of neural-circuit activity in children. It is also of note that other child measures such as BMI, sleep quality, nutrition, and factors related to socioeconomic status likely influence neural activity. Although exploring the impact of these measures on RS neural activity was not within the scope of the present study, studies examining associations between these and other child measures are of interest. 5. Conclusions In summary, present findings demonstrate that (1) the use of an appropriate Dark-Room eyes-open task provides measures of young child RS periodic activity with an excellent SNR, (2) an understanding of the development of infant RS activity is best achieved via obtaining measures in brain source space in order to detect regional differences in aperiodic activity, and (3) a more mature aperiodic value predicts higher developmental behavior scores. Ethical Information All families provided written informed consent for participating in the following study protocols approved by the Institutional Review Board (IRB) at the Children’s Hospital of Philadelphia: IRB 18–015212, IRB 15–012129, IRB 23–021618. CRediT authorship contribution statement Heather L. Green: Writing – review & editing, Writing – original draft, Validation, Investigation, Formal analysis, Data curation, Conceptualization. Emily S. Kuschner: Project administration, Investigation. Gregory A. Miller: Writing – review & editing, Methodology, Conceptualization. Yuhan Chen: Writing – review & editing, Writing – original draft, Visualization, Software, Resources, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Kylie Mol: Project administration, Investigation. Marybeth McNamee: Project administration, Investigation. Laura A. Prosser: Funding acquisition. Mina Kim: Writing – review & editing, Project administration, Investigation. J. Christopher Edgar: Writing – review & editing, Writing – original draft, Visualization, Supervision, Resources, Investigation, Funding acquisition, Conceptualization. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This study was supported in part by NIH grant R01MH107506 (JCE), NICHD grant R01HD093776 (JCE), NICHD grant R01HD119165 (YC), NICHD grant 3UG1HD068244–13S1 (YC), and NICHD grant K08HD114880 (HLG). Footnotes Appendix A Supplementary data associated with this article can be found in the online version at doi:10.1016/j.dcn.2026.101709 . Appendix A. Supplementary material Supplementary material mmc1.docx (358.1KB, docx) Data availability Data will be made available on request. References Adrian E.D., Matthews H.C. The Berger rhythm: potential changes from the occipital lobes in man. Brain. 1934;47(4):355–385. doi: 10.1093/brain/awp324. [ DOI ] [ PubMed ] [ Google Scholar ] Baillet S., Mosher J.C., Leahy R.M. Electromagnetic brain mapping. IEEE Signal Process. Mag. 2001;18(6):14–30. [ Google Scholar ] Bedard C., Kroger H., Destexhe A. Does the 1/f frequency scaling of brain signals reflect self-organized critical states? Phys. Rev. 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