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Longitudinal Trajectories of Aperiodic EEG Activity in Early to Middle Childhood

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Title: Longitudinal Trajectories of Aperiodic EEG Activity in Early to Middle Childhood
Language: English
Authors: Dashiell D. Sacks (ORCID 0000-0003-2842-1301), Viviane Valdes, Carol L. Wilkinson, April R. Levin, Charles A. Nelson, Michelle Bosquet Enlow
Source: Child Development. 2025 96(5):1688-1699.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 12
Publication Date: 2025
Sponsoring Agency: National Institute of Mental Health (NIMH) (DHHS/NIH)
Contract Number: MH078829
Document Type: Journal Articles
Reports - Research
Descriptors: Brain, Cognitive Processes, Cognitive Development, Child Development, Young Children, Brain Hemisphere Functions, Mothers, Anxiety
DOI: 10.1111/cdev.14261
ISSN: 0009-3920
1467-8624
Abstract: Aperiodic electroencephalography (EEG) activity is hypothesized to index biological mechanisms that underpin brain functioning. This longitudinal study characterized the developmental trajectories of the aperiodic slope (i.e., aperiodic exponent) and offset from infancy to 7 years of age in a US community sample (N = 391, 46.5% female, predominantly White; data collection 2013-2023). The study further examined whether differential developmental trajectories resulted in differential associations between child aperiodic activity and maternal anxiety symptoms. Developmental trajectories for slope and offset were nonlinear and characterized by relative increases in early childhood and a subsequent decrease or stabilization by Age 7, with variation by brain region and sex. Maternal anxiety was negatively associated with slope at 3 years and positively associated with slope at 7 years. Implications for child brain development are discussed.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1481836
Database: ERIC
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  Value: <anid>AN0187573602;cdv01sep.25;2025Aug29.07:30;v2.2.500</anid> <title id="AN0187573602-1">Longitudinal Trajectories of Aperiodic EEG Activity in Early to Middle Childhood </title> <p>Aperiodic electroencephalography (EEG) activity is hypothesized to index biological mechanisms that underpin brain functioning. This longitudinal study characterized the developmental trajectories of the aperiodic slope (i.e., aperiodic exponent) and offset from infancy to 7 years of age in a US community sample (N = 391, 46.5% female, predominantly White; data collection 2013‐2023). The study further examined whether differential developmental trajectories resulted in differential associations between child aperiodic activity and maternal anxiety symptoms. Developmental trajectories for slope and offset were nonlinear and characterized by relative increases in early childhood and a subsequent decrease or stabilization by Age 7, with variation by brain region and sex. Maternal anxiety was negatively associated with slope at 3 years and positively associated with slope at 7 years. Implications for child brain development are discussed.</p> <p>Keywords: aperiodic offset; aperiodic slope; development; EEG; longitudinal; maternal anxiety</p> <hd id="AN0187573602-2">Introduction</hd> <p>A large body of research has focused on delineating the properties and functions of electroencephalography (EEG) power spectral density, with the goal of elucidating biological indicators (biomarkers) of brain functioning. EEG is portable and affordable relative to other neuroimaging methods. Thus, EEG is an appealing modality for investigating such biomarkers, which may facilitate early identification and intervention strategies for improving developmental outcomes. Traditional analytical methods typically involve averaging absolute power within putative, predefined frequency bands, including delta, theta, alpha, beta, and gamma. However, the EEG power spectrum also contains an aperiodic component, which is characterized by a variable 1/f<sups><emph>x</emph></sups> like distribution in which power decreases as frequency increases. This aperiodic activity has traditionally been viewed as "background noise." Consequently, canonical analytical methods tend to conflate aperiodic activity with periodic activity or actively attempt to reduce or eliminate aperiodic activity from analyses (Donoghue et al. [<reflink idref="bib11" id="ref1">11</reflink>]; Levin et al. [<reflink idref="bib21" id="ref2">21</reflink>]). Recent methodological advances, particularly spectral parameterization (SpecParam; previously fitting oscillations and one‐over f) enable parameterization of the power spectrum into distinct variables that represent both periodic and aperiodic components.</p> <p>The aperiodic component is believed to reflect nonoscillatory neuronal spiking activity (Gao et al. [<reflink idref="bib13" id="ref3">13</reflink>]; Manning et al. [<reflink idref="bib23" id="ref4">23</reflink>]; Miller et al. [<reflink idref="bib28" id="ref5">28</reflink>]) and is defined by the aperiodic "offset" and "slope." The offset indexes the uniform shift in power across EEG frequencies; the slope indexes the rate at which power increases as frequency decreases, defined by the exponent <emph>x</emph> in the 1/f<sups><emph>x</emph></sups> distribution (commonly represented as the slope of the linear fit in log–log space). Aperiodic activity, particularly slope, has started to receive attention in various studies, with more recent hypotheses proposing that aperiodic activity reflects mechanistic underpinnings of brain activity (He et al. [<reflink idref="bib16" id="ref6">16</reflink>]). Specifically, the slope has been described as the "signal to noise ratio" of neural activity and as a potential index of the balance between synaptic excitation and inhibition (excitatory–inhibitory [E‐I] balance); (Donoghue et al. [<reflink idref="bib11" id="ref7">11</reflink>]; Gao et al. [<reflink idref="bib13" id="ref8">13</reflink>]). According to this conceptualization, flattened or reduced slope is hypothesized to reflect increased excitatory activity over inhibitory activity. In contrast, steeper or increased slope is hypothesized to reflect increased inhibitory activity over excitatory activity. However, further research is required to establish these primarily theoretically driven assumptions.</p> <p>Cross‐sectional studies investigating slope across the lifespan, starting from 4 years of age and through late adulthood, report reduced or "flattened" slope in older participants compared to younger participants (Hill et al. [<reflink idref="bib17" id="ref9">17</reflink>]; Merkin et al. [<reflink idref="bib27" id="ref10">27</reflink>]; Voytek et al. [<reflink idref="bib38" id="ref11">38</reflink>]). Earlier studies by Rico‐Picó et al. ([<reflink idref="bib30" id="ref12">30</reflink>]) and Schaworonkow and Voytek ([<reflink idref="bib33" id="ref13">33</reflink>]) reported a similar trend of flattening slope throughout infancy, although these studies parameterized slope across narrower 1–20 Hz and 1–10 Hz frequency ranges to address concerns with high levels of muscle noise. In contrast, a recent multisample, cross‐sequential study by Wilkinson et al. ([<reflink idref="bib39" id="ref14">39</reflink>]) reported the opposite trend during early childhood from 2 to 44 months of age. Specifically, slope (parameterized between 2 and 55 Hz) increased or "steepened" during early childhood, particularly during infancy. Furthermore, McSweeney et al. ([<reflink idref="bib26" id="ref15">26</reflink>]) fit mixed models both with and without an added quadratic term to investigate slope trajectories (cross‐sectionally) across 4‐ to 11‐year‐olds. They found the model with an added quadratic term provided a significantly better fit, suggesting nonlinear developmental trends throughout this age period. Of the aforementioned studies, Wilkinson et al. ([<reflink idref="bib39" id="ref16">39</reflink>]) was the only study to test sex effects. Significant nonlinear age‐by‐sex interactions were reported, with sex differences emerging in models after approximately the first year of life. Steepening slope in early childhood could reflect increases in brain volume and synaptogenesis that occur uniquely during early development. If slope is associated with E‐I balance, increasing slope in early childhood may also reflect the development in inhibitory networks that occurs during early childhood. Whereas excitatory neurons are well established by birth, GABAergic (Gamma‐Aminobutyric Acid) inhibitory neurons continue to migrate from ventral subregions of the brain to the cortices in early childhood (Chini et al. [<reflink idref="bib9" id="ref17">9</reflink>]; Paredes et al. [<reflink idref="bib29" id="ref18">29</reflink>]).</p> <p>The signal‐to‐noise ratio hypothesis suggests that the association between slope and brain functioning may be monotonic, with a steeper slope reflecting an enhanced signal‐to‐noise ratio. However, if slope indexes E‐I balance, the optimal slope may follow a more parabolic or inverted U‐shape. Such a pattern would be aligned with the complex systems approach, which suggests that there are nonlinear dynamics in biological systems, as opposed to the linear relations often observed in simpler systems (Cohen et al. [<reflink idref="bib10" id="ref19">10</reflink>]). In line with this framework, there may be an optimal homeostatic range, such that slopes that are either too flat or too steep are associated with cognitive impairment. Robertson et al. ([<reflink idref="bib31" id="ref20">31</reflink>]) further posit that there may be a range of cognitively optimal slopes at different developmental stages. Developmental trajectories of steepening or flattening slope throughout childhood may be associated with different optimal slope ranges. Furthermore, cortical maturation occurs in a regionally differential, posterior–anterior pattern (Gerván et al. [<reflink idref="bib15" id="ref21">15</reflink>]; Shaw et al. [<reflink idref="bib35" id="ref22">35</reflink>]), which suggests the potential for regionally based differences in slope trajectories. A recent systematic review by Stanyard et al. ([<reflink idref="bib37" id="ref23">37</reflink>]) found that, across studies, there was a posterior‐to‐anterior shift in aperiodic slope during development. Although some previous research that characterized linear trajectories of slope reported similar trends across regions (Hill et al. [<reflink idref="bib17" id="ref24">17</reflink>]), this research may have been limited in its ability to capture more nuanced differences by taking a linear approach and relying on a cross‐sectional design.</p> <p>If slope is characterized by nonlinear developmental trajectories and dynamics, differential associations with neuropsychiatric outcomes and environmental influences known to affect brain development may be observed at different stages of development. Recent studies at different ages in childhood and adulthood have demonstrated associations between both steeper and flatter slope and various neurodevelopmental and mental health disorders (Karalunas et al. [<reflink idref="bib18" id="ref25">18</reflink>]; Robertson et al. [<reflink idref="bib31" id="ref26">31</reflink>]). Research is required to investigate whether these differences may relate to developmental dynamics. Early childhood marks a period of substantial vulnerability to environmental influences for brain development. Significant developmental processes, including axonal and dendritic growth, synaptic stabilization, and synaptic pruning, are ongoing throughout childhood (Chen and Baram [<reflink idref="bib8" id="ref27">8</reflink>]).</p> <p>Caregivers are one of the most salient environmental influences during early development, with caregiver stress and psychopathology a known risk factor for differences in offspring neurodevelopment (Chen and Baram [<reflink idref="bib8" id="ref28">8</reflink>]). However, to date, limited research has investigated associations between caregiver characteristics and aperiodic EEG, with existing studies focusing only on infancy. Karalunas et al. ([<reflink idref="bib18" id="ref29">18</reflink>]) reported associations between family history of ADHD and infant EEG slope, and Brandes‐Aitken et al. ([<reflink idref="bib6" id="ref30">6</reflink>]) found that maternal hair cortisol, a biological proxy for chronic stress, was associated with EEG slope in 9–15‐month‐old infants. Here, we investigated whether any differences in aperiodic activity trajectories by developmental stage influenced the nature of associations between aperiodic activity and maternal anxiety across the first 7 years of life. Maternal anxiety has been associated with both structural and functional neurodevelopment in past studies (see e.g., [Adamson et al. [<reflink idref="bib1" id="ref31">1</reflink>]], for a review focused on prenatal anxiety across neuroimaging modalities), including EEG studies using EEG power spectral derivatives in early to middle childhood (e.g., [Sacks et al. [<reflink idref="bib32" id="ref32">32</reflink>]]).</p> <hd id="AN0187573602-3">The Current Study</hd> <p>The extant literature suggests there may be a shift in trajectory during early childhood from increasing (steepening) to decreasing (flattening) slope (Hill et al. [<reflink idref="bib17" id="ref33">17</reflink>]; McSweeney et al. [<reflink idref="bib26" id="ref34">26</reflink>]; Voytek et al. [<reflink idref="bib38" id="ref35">38</reflink>]; Wilkinson et al. [<reflink idref="bib39" id="ref36">39</reflink>]). Longitudinal research is required to investigate developmental shifts during childhood. It is important for such investigations to examine the influence of sex on slope trajectories, given reported sex differences in early childhood (Wilkinson et al. [<reflink idref="bib39" id="ref37">39</reflink>]). Investigations should also consider potential differences by brain region, given typical regional differentiation in patterns of brain development throughout childhood (Gerván et al. [<reflink idref="bib15" id="ref38">15</reflink>]; Shaw et al. [<reflink idref="bib35" id="ref39">35</reflink>]). Research is also needed to understand how trajectories of aperiodic EEG may influence the nature of associations with early environmental exposures, such as maternal psychopathology symptoms. The primary objective of the current study was to investigate the developmental trajectories of aperiodic activity across the first 7 years of life in a longitudinal community sample of typically developing children. Specifically, the aims were to: (<reflink idref="bib1" id="ref40">1</reflink>) characterize the developmental trajectories of EEG power spectral density slope and offset from infancy to middle childhood in whole scalp and frontal, central, temporal, and posterior regions; (<reflink idref="bib2" id="ref41">2</reflink>) examine potential sex differences in developmental trajectories; and (<reflink idref="bib3" id="ref42">3</reflink>) explore whether shifting developmental trajectories from infancy to 7 years influence the nature and magnitude of associations with maternal anxiety, a known influence on child neurodevelopmental outcomes. Aims 1 and 2 were primarily confirmatory, whereas Aim 3 was exploratory.</p> <hd id="AN0187573602-4">Method</hd> <p></p> <hd id="AN0187573602-5">Participants</hd> <p>Participants were recruited at infancy from a registry of local births in the Greater Boston Area, MA, United States of America, comprising families who had indicated willingness to participate in developmental research. Families in the current analyses participated in a prospective longitudinal study to examine the early development of emotion processing. Variables included in the current analyses were selected a priori from a larger battery of assessments as relevant to address each aim. Exclusion criteria included known prenatal or perinatal complications, maternal use of medications during pregnancy that may significantly impact fetal brain development (i.e., anticonvulsants, antipsychotics, opioids), pre‐ or post‐term birth (±3 weeks from due date), developmental delay, uncorrected vision difficulties, and neurological disorder or trauma. After enrollment, families were no longer followed, and their data were excluded from analyses if the child was diagnosed with an autism spectrum disorder or a genetic or other condition known to influence neurodevelopment. By design, families were enrolled in the parent study when the children were 5, 7, or 12 months old, with a subsample followed at 3 years, 5 years, and 7 years. In the current analyses, <emph>N</emph> = 391 children provided data at infancy; of these 391 participants, <emph>N</emph> = 187 provided follow‐up data at 3 years, <emph>N</emph> = 160 at 5 years, and <emph>N</emph> = 106 at 7 years. This study builds on Wilkinson et al. ([<reflink idref="bib39" id="ref43">39</reflink>]), which reported trajectories of aperiodic activity from infancy through age 3 years, by investigating a subset of participants through age 7 years.</p> <hd id="AN0187573602-6">Procedures</hd> <p>Parents (almost exclusively the child's mother [> 98%]; hereafter referenced as "mother") were asked to complete questionnaires via an online survey prior to or during each in‐person study visit (infancy, 3 years, 5 years, 7 years). Questionnaires relevant to the current analyses included assessments of sociodemographic characteristics collected at a baseline assessment in infancy and maternal anxiety symptoms collected at all time points. During each in‐person study visit, EEG data were collected. The Institutional Review Board at Boston Children's Hospital approved all study methods and procedures, and parents provided written informed consent prior to the initiation of study activities. Initial infancy study visits began in April 2013 and ended in April 2017. Data collection for the 7‐year visits continued through October 2023.</p> <hd id="AN0187573602-7">Measures</hd> <p></p> <hd id="AN0187573602-8">Sociodemographic Characteristics</hd> <p>Sociodemographic characteristics collected included child age, sex assigned at birth (hereafter "sex"), ethnicity, parental education, and annual household income.</p> <hd id="AN0187573602-9">Maternal Anxiety Symptoms</hd> <p>Maternal anxiety symptoms were measured at infancy and 3, 5, and 7 years using the Trait Anxiety form of the Spielberger State–Trait Anxiety Inventory (STAI‐T; Spielberger, 1983). The STAI is a 20‐item self‐report questionnaire designed to measure anxiety proneness. Respondents are asked to rate the frequency of general mood states on a 4‐point scale, ranging from "almost never" to "almost always." Item scores were summed to create a total score (possible range: 20–80), with a higher score indicating greater anxiety. The STAI‐T has been established to have good internal consistency (<emph>α</emph> = 0.90), and test–retest coefficients from 0.73 to 0.86 (Barnes et al. [<reflink idref="bib4" id="ref44">4</reflink>]). Internal consistency estimates for this sample were: <emph>α</emph> = 0.89 at infancy, <emph>α</emph> = 0.89 at 3 years, <emph>α</emph> = 0.91 at 5 years, and <emph>α</emph> = 0.91 at 7 years.</p> <hd id="AN0187573602-10">Electroencephalography (EEG)</hd> <p></p> <hd id="AN0187573602-11">EEG Acquisition</hd> <p>Continuous scalp EEG was recorded using a 128‐channel HydroCel Geodesic Sensor Net (HGSN; Electrical Geodesic Inc.) with Ag/AgCl coated, carbon filled electrodes. The four electrooculogram channels were removed from the net for infant comfort. The net was connected to a NetAmps 300 amplifier (Electrical Geodesic Inc.) and referenced online to a single vertex electrode (Cz). Data were sampled at 500 Hz. Channel impedances were kept at or below 100 kΩ, which is within recommended guidelines given the high‐input impedance capabilities of the system's amplifier. At infancy and 3 years, participants watched a computer‐generated video of slow‐moving infant toys in a dim room, while baseline EEG (2 min) was recorded. Participants sat on their caregiver's lap for the infancy visit. At 5 and 7 years, participants watched a computer‐generated screen‐saver style video of slow‐moving lights while baseline EEG was recorded (4 min).</p> <hd id="AN0187573602-12">Preprocessing</hd> <p>Raw Net Station (Electrical Geodesics Inc) EEG files were exported into the MATLAB MAT‐file format for preprocessing in MATLAB (version R2023a), using the Batch Automated Processing Platform (BEAPP; Levin et al. [<reflink idref="bib20" id="ref45">20</reflink>]), with integrated Harvard Automated Preprocessing Pipeline for EEG (HAPPE; Gabard‐Durnam et al. [<reflink idref="bib12" id="ref46">12</reflink>]). Data were high‐pass filtered at a 1 Hz and low‐pass filtered at 100 Hz. Data were resampled to 250 Hz and then preprocessed using the HAPPE module, which includes line noise removal, bad channel rejection, and artifact removal using combined wavelet‐enhanced independent component analysis (ICA) and Multiple Artifact Rejection Algorithm (MARA; Winkler et al. [<reflink idref="bib40" id="ref47">40</reflink>]). The following channels were used in addition to the 10–20 electrodes for MARA: 28, 19, 4, 117, 13, 112, 41, 47, 37, 55, 87, 103, 98, 65, 67, 77, 90, 75. After artifact removal, bad channels were interpolated. Data were referenced to the average reference, detrended to the signal mean, and segmented into 2‐s epochs. HAPPE's amplitude and joint probability criteria were used to reject epochs contaminated with artifact. Recordings with fewer than 20 segments (40 s of total EEG), percent good channels < 80%, percent independent components rejected > 80%, mean artifact probability of components kept > 0.3, or percent variance retained < 25% were rejected. Across the full sample, there were <emph>n</emph> = 100 (12%) data points rejected. Across each time point, mean epochs retained were: 45.27 (SD = 8.67) at infancy, 49.92 (SD = 6.60) at 3 years, 103.11 (SD = 9.54) at 5 years, and 102.98 (SD = 12.54) at 7 years.</p> <hd id="AN0187573602-13">Power Spectral Density</hd> <p>Power spectral density was calculated in the BEAPP Power Spectral Density (PSD) module using a multitaper spectral analysis with three orthogonal tapers and a frequency step of 0.5 Hz (0.1 Hz after linear interpolation). For each electrode, the PSD was averaged across segments and then further averaged across all channels to derive the whole scalp average. PSD was further averaged in electrodes across frontal, central, temporal, and posterior ROIs (Figure 1).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01sep25/cdev14261-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14261-fig-0001.jpg" title="1 Electrode layout for whole scalp and regions of interest. Regions of interest are color‐coded as frontal (light blue), central (yellow), temporal (green), and posterior (orange). Whole scalp channels are derived from a combination of the electrodes used for each region and the electrodes marked in dark blue." /> </p> <p></p> <hd id="AN0187573602-15">Spectral Parameterization</hd> <p>The PSD was parameterized using SpecParam (Donoghue et al. [<reflink idref="bib11" id="ref48">11</reflink>]) and exponent (slope) and offset values extracted for the main analyses. The exponent <emph>x</emph> from the 1/f<sups><emph>x</emph></sups> distribution is a positive value, with a larger exponent indicating a steeper slope. The adapted version of SpecParam originally described by Wilkinson et al. ([<reflink idref="bib39" id="ref49">39</reflink>]) was adopted to improve model fit in the infant EEG. In this version, the robust_ap_fit function is modified so that the initial estimate of the flattened power spectra (flatspec) has a baseline elevated such that the lowest point is ≥ 0. Offset was calculated from aperiodic power at 2.5 Hz, given SpecParam extrapolates offset to the estimated aperiodic power at 0 Hz, and there is elevated error near frequency boundaries. Further details are available in the papers by Donoghue et al. ([<reflink idref="bib11" id="ref50">11</reflink>]) and Wilkinson et al. ([<reflink idref="bib39" id="ref51">39</reflink>]). The modification had limited impact on slope and offset at later ages. Pearson's correlation values between whole scalp slope calculated using the original and modified scripts were 0.97, 0.98, and 0.99 at 3, 5, and 7 years, respectively. Correlation values between whole sc offset calculated using the original and modified scripts were 0.96, 0.97, and 0.98 at 3, 5, and 7 years, respectively. The SpecParam model was used across a 2–55 Hz frequency range in the fixed mode (no spectral knee) with peak_width_limits set to [0.5, 18.0], max_n_peaks = 7, and peak_threshold = 2. Mean <emph>R</emph><sups>2</sups> ranged from 0.994–0.998 across each age group and region, SD ranged from 0.002–0.030. Mean estimated error for the sample at across each age group and region ranged from 0.013–0.020, SD ranged from 0.006–0.016. Mean <emph>R</emph><sups>2</sups> and mean estimated error in each age group and region are in Table S23.</p> <hd id="AN0187573602-16">Statistical Analysis</hd> <p>Statistical analyses were conducted using the Statsmodels (Seabold and Perktold [<reflink idref="bib34" id="ref52">34</reflink>]) package in Python 3.10.10. Descriptive statistics for the sample's sociodemographic characteristics and main study variables were calculated. Slope and offset trajectory analyses were conducted for the whole scalp, as well as for frontal, central, temporal, and posterior regions.</p> <p>The developmental trajectory for each slope measure was plotted and analyzed quantitatively using individual growth curve (IGC) models. Separate models were fit with slope or offset as the dependent variable (whole scalp, frontal, central, temporal, posterior) and time point as the fixed effect. All models were fit with a random intercept for each participant (the grouping factor) and random slopes specified for time point, to account for individual variation in baseline levels and deviation from the main effect across time points. Pairwise contrasts were calculated to further quantify change between each time point. To investigate potential sex differences in developmental trajectories of slope, IGC models were re‐fit with sex added as a factorial fixed effect, nested by time. If significant, sex was added as a covariate in subsequent models. In supplementary analyses, the main trajectory models were re‐fit with SpecParam model fit (<emph>R</emph><sups>2</sups>), % segments retained (segments post rejection/segments pre rejection × 100), and gestational age added as covariates. Supplementary Generalized Additive Mixed Models (GAMMs) with age in months at time of EEG assessment as the fixed effect were fit using the mgcv package (Wood, [<reflink idref="bib41" id="ref53">41</reflink>]) in R 4.3.2 to further verify the main results.</p> <p>To investigate the potential associations between maternal anxiety symptoms and whole scalp slope and offset over time, linear mixed models (LMMs) were used. The maternal anxiety variables were transformed using the Box–Cox method to account for positive (right) skew in the data, and subsequently standardized to facilitate interpretability. The models were specified with maternal anxiety symptoms, nested by time as the fixed effect, whole scalp slope or offset as the outcome, and a random intercept specified for each participant. Parental education and household income were considered as potential covariates for these analyses; if significantly associated with slope or offset as predictor variables, they were included as covariates in the corresponding models. We focused on whole scalp slope and offset for these analyses as they were exploratory and to limit the likelihood of encountering Type 1 errors when running a large number of comparisons, or Type 2 errors (if over‐correcting for too many comparisons), and as prior research has primarily focused on whole scalp slope.</p> <hd id="AN0187573602-17">Results</hd> <p></p> <hd id="AN0187573602-18">Sample Characteristics</hd> <p>Sociodemographic characteristics for the sample are presented in Table 1. Children in the sample were predominantly non‐Hispanic White. Parental education levels and annual household income indicated that most families were middle‐to‐high socioeconomic status (Table 2).</p> <p>1 TABLE Parent and child demographic information collected at infancy (N  = 391).</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Variables</th><th align="center"><italic>n</italic></th><th align="center">%</th></tr></thead><tbody valign="top"><tr><td align="left">Child sex</td></tr><tr><td align="left">Male</td><td align="center">209</td><td align="center">53.5</td></tr><tr><td align="left">Female</td><td align="center">182</td><td align="center">46.5</td></tr><tr><td align="left">Child race</td></tr><tr><td align="left">White</td><td align="center">309</td><td align="center">79</td></tr><tr><td align="left">Black/African American</td><td align="center">11</td><td align="center">2.8</td></tr><tr><td align="left">Asian American</td><td align="center">18</td><td align="center">4.4</td></tr><tr><td align="left">More than one race</td><td align="center">49</td><td align="center">12.5</td></tr><tr><td align="left">Not reported</td><td align="center">4</td><td align="center">1</td></tr><tr><td align="left">Child ethnicity</td></tr><tr><td align="left">Non‐Latino/a, Spanish, or Hispanic</td><td align="center">347</td><td align="center">88.7</td></tr><tr><td align="left">Latino/a, Spanish, or Hispanic</td><td align="center">40</td><td align="center">10.2</td></tr><tr><td align="left">Not reported</td><td align="center">4</td><td align="center">1.0</td></tr><tr><td align="left">Maternal educational attainment</td></tr><tr><td align="left">Less than high school</td><td align="center">2</td><td align="center">0.6</td></tr><tr><td align="left">High school/GED</td><td align="center">18</td><td align="center">4.6</td></tr><tr><td align="left">Associate degree</td><td align="center">7</td><td align="center">1.8</td></tr><tr><td align="left">Bachelor's degree</td><td align="center">115</td><td align="center">29.4</td></tr><tr><td align="left">Master's degree</td><td align="center">167</td><td align="center">42.7</td></tr><tr><td align="left">M.D., Ph.D., J.D., or equivalent</td><td align="center">80</td><td align="center">20.5</td></tr><tr><td align="left">Not reported</td><td align="center">2</td><td align="center">0.5</td></tr><tr><td align="left">Paternal educational attainment</td></tr><tr><td align="left">Less than high school</td><td align="center">1</td><td align="center">0.3</td></tr><tr><td align="left">High school/GED</td><td align="center">33</td><td align="center">8.4</td></tr><tr><td align="left">Associate degree</td><td align="center">19</td><td align="center">4.9</td></tr><tr><td align="left">Bachelor's degree</td><td align="center">116</td><td align="center">29.7</td></tr><tr><td align="left">Master's degree</td><td align="center">121</td><td align="center">30.9</td></tr><tr><td align="left">M.D., Ph.D., J.D., or equivalent</td><td align="center">94</td><td align="center">24.0</td></tr><tr><td align="left">Not reported</td><td align="center">7</td><td align="center">1.8</td></tr><tr><td align="left">Annual family household income</td></tr><tr><td align="left">Less than $35,000</td><td align="center">10</td><td align="center">2.8</td></tr><tr><td align="left">$35,000–$49,000</td><td align="center">15</td><td align="center">3.8</td></tr><tr><td align="left">$50,000–$74,999</td><td align="center">40</td><td align="center">10.2</td></tr><tr><td align="left">$75,000–$99,000</td><td align="center">57</td><td align="center">14.6</td></tr><tr><td align="left">$100,000 or more</td><td align="center">241</td><td align="center">61.6</td></tr><tr><td align="left">Not reported</td><td align="center">28</td><td align="center">7.2</td></tr></tbody></table> </ephtml> </p> <p>2 TABLE Descriptive statistics for maternal anxiety.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="center">Mean</th><th align="center">SD</th><th align="center">Min</th><th align="center">Max</th></tr></thead><tbody valign="top"><tr><td align="left">Maternal anxiety symptoms (infancy)</td><td align="center">33.45</td><td align="center">7.17</td><td align="center">20</td><td align="center">56</td></tr><tr><td align="left">Maternal anxiety symptoms (3 years)</td><td align="center">33.34</td><td align="center">7.0</td><td align="center">21</td><td align="center">57</td></tr><tr><td align="left">Maternal anxiety symptoms (5 years)</td><td align="center">33.09</td><td align="center">7.95</td><td align="center">20</td><td align="center">58</td></tr><tr><td align="left">Maternal anxiety symptoms (7 years)</td><td align="center">34.5</td><td align="center">8.76</td><td align="center">21</td><td align="center">68</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note:</emph> Maternal anxiety symptoms were calculated from the trait anxiety form of the Spielberger State–Trait Anxiety Inventory (STAI‐T).</p> <hd id="AN0187573602-19">Developmental Trajectories</hd> <p>The aperiodic fit and developmental trajectories of slope and offset for the whole scalp at each time point are depicted in Figure 2. Longitudinal developmental trajectories for each of the aperiodic slope and offset variables were quantified using individual growth curve models, with pairwise contrasts between each age. Across the whole scalp, slope increased (steepened) significantly from infancy to 5 years (0.05, <emph>p</emph> < 0.001). There was a significant decrease (flattening) from 5 to 7 years (−0.03, <emph>p</emph> = 0.039). Offset significantly increased from infancy to 3 years (0.10, <emph>p</emph> < 0.001), and then significantly decreased from 3 years to 7 years (−0.27, <emph>p</emph> < 0.001). Full model parameters and pairwise comparisons are provided in Tables S1 and S2.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01sep25/cdev14261-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14261-fig-0002.jpg" title="2 Aperiodic fit and slope and offset at each time point across whole scalp." /> </p> <p></p> <hd id="AN0187573602-21">Regional Differences</hd> <p>Changes in slope across each time point by region are depicted in Figure 3; aperiodic fit for each region is provided in Figure S1. The pattern of developmental trajectories differed regionally. Across regions, significant increases (steepening) in slope were seen from infancy to 3 years (<emph>ps</emph> < 0.001). However, increases in frontal regions were decelerated through 5 years, whereas in temporal regions, they occurred at an accelerated rate. In central and posterior regions, significant decreases (flattening) were observed from ages 3 years to 7 years (<emph>ps</emph> < 0.001). There were nonsignificant mean decreases in the frontal and temporal regions from 5 years to 7 years. Overall, slope began decreasing (flattening) at age 3 years in posterior and central regions, but not until age 5 years in frontal and temporal regions. Offset increased from infancy to 3 years in frontal (<emph>p</emph> < 0.001), central (<emph>p</emph> = 0.004), and temporal (<emph>p</emph> < 0.001) regions, and then decreased to age 7 years (<emph>ps</emph> < 0.001). The greatest increase from infancy to 3 years was observed in the frontal region. Offset was stable from infancy to 3 years in the posterior region and then decreased to 7 years (<emph>p</emph> < 0.001). Full models and pairwise comparisons are provided in Tables S3 to S13. The pattern and significance of results for slope and offset remained consistent when modeled as a smooth function of age using GAMMs (Table S11; Figure S14) and with the additional covariates added (Tables S12 to S21).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01sep25/cdev14261-fig-0003.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14261-fig-0003.jpg" title="3 Developmental trajectories of slope and offset in frontal, central, temporal, and posterior ROIs." /> </p> <p></p> <hd id="AN0187573602-23">Sex Differences</hd> <p>Child sex, nested by time, was added to the models to investigate potential sex differences. There were significant sex differences for slope across the whole scalp. Female participants had larger slope values (steeper slopes) at 3 years (<emph>b</emph> = −0.04, <emph>p</emph> < 0.001) and 5 years (<emph>b</emph> = −0.07, <emph>p</emph> < 0.001). For offset across the whole scalp, there was a significant sex difference, whereby offset was greater in males at 3 years (<emph>b</emph> = −0.38, <emph>p</emph> < 0.035). Trajectories for whole scalp slope and offset stratified by sex are shown in Figure 4. There also were sex differences in slope in frontal and temporal regions and in offset in central and temporal regions. Slope was larger (steeper) in female participants in the frontal region at age 5 years (<emph>b</emph> = −0.06, <emph>p</emph> = 0.004), and in the temporal region at ages 3 years (<emph>b</emph> = −0.07, <emph>p</emph> < 0.001) and 5 years (<emph>b</emph> = −0.06, <emph>p</emph> = 0.001) (Figure S2). Offset was greater in female participants in the central region at age 3 years (<emph>b</emph> = −0.54, <emph>p</emph> = 0.020), and in the temporal region at ages 3 years (<emph>b</emph> = −0.8, <emph>p</emph> < 0.001), 5 years (<emph>b</emph> = −0.11, <emph>p</emph> = 0.001), and 7 years (<emph>b</emph> = −0.8, <emph>p</emph> = 0.013) (Figure S3).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01sep25/cdev14261-fig-0004.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14261-fig-0004.jpg" title="4 Developmental trajectories for aperiodic slope and offset stratified by sex in whole scalp ROI." /> </p> <p></p> <hd id="AN0187573602-25">Whole Scalp Slope and Maternal Anxiety Symptoms</hd> <p>We investigated how maternal anxiety may be associated with slope and offset at each age using mixed effects models, with anxiety nested in each time point as the predictor and slope or offset as the outcome variable. Household income, maternal education, and paternal education were considered as potential covariates, but there were no significant associations with slope or offset. Therefore, these variables were not considered further in analyses. The direction of the observed associations between maternal anxiety and slope varied by time point. There was a significant negative association between maternal anxiety symptoms and child slope (i.e., greater maternal anxiety symptoms and flatter slope) at Age 3, and a significant positive association between maternal anxiety symptoms and child slope (i.e., greater maternal anxiety symptoms and steeper slope), at Age 7 (Table 3; Figure 5). There were no significant associations between maternal anxiety symptoms and offset (Table 4).</p> <p>3 TABLE Mixed effects model for the associations between maternal anxiety symptoms, nested within time point, and EEG slope across the whole scalp.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="center">Estimate</th><th align="center">SE</th><th align="center"><italic>t</italic></th><th align="center">Sig.</th><th align="center">95% Confidence interval</th></tr><tr><th align="center">Lower bound</th><th align="center">Upper bound</th></tr></thead><tbody valign="top"><tr><td align="left">Maternal anxiety symptoms (infancy)</td><td align="center">0.02</td><td align="center">0.06</td><td align="center">0.36</td><td align="center">0.716</td><td align="center">−0.01</td><td align="center">0.22</td></tr><tr><td align="left">Maternal anxiety symptoms (3 years)</td><td align="center">−0.16</td><td align="center">0.08</td><td align="center">−2.08</td><td align="center">0.038</td><td align="center">−0.31</td><td align="center">−0.01</td></tr><tr><td align="left">Maternal anxiety symptoms (5 years)</td><td align="center">0.01</td><td align="center">0.09</td><td align="center">0.10</td><td align="center">0.920</td><td align="center">−0.16</td><td align="center">0.18</td></tr><tr><td align="left">Maternal anxiety symptoms (7 years)</td><td align="center">0.19</td><td align="center">0.09</td><td align="center">2.06</td><td align="center">0.040</td><td align="center">0.01</td><td align="center">0.36</td></tr><tr><td align="left">Sex (Male)</td><td align="center">−0.24</td><td align="center">0.09</td><td align="center">−2.87</td><td align="center">0.004</td><td align="center">−0.40</td><td align="center">−0.08</td></tr><tr><td align="left">Participant Var</td><td align="center">0.15</td><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center" /></tr></tbody></table> </ephtml> </p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01sep25/cdev14261-fig-0005.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14261-fig-0005.jpg" title="5 Scatterplots showing the associations of maternal anxiety with whole scalp slope at each age. Associations are significant at Age 3 and Age 7." /> </p> <p></p> <p>4 TABLE Mixed effects model for the associations between maternal anxiety symptoms, nested within time point, and EEG offset across the whole scalp.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="center">Estimate</th><th align="center">SE</th><th align="center"><italic>t</italic></th><th align="center">Sig.</th><th align="center">95% Confidence interval</th></tr><tr><th align="center">Lower bound</th><th align="center">Upper bound</th></tr></thead><tbody valign="top"><tr><td align="left">Maternal anxiety symptoms (infancy)</td><td align="center">0.06</td><td align="center">0.06</td><td align="center">1.00</td><td align="center">0.315</td><td align="center">−0.06</td><td align="center">0.17</td></tr><tr><td align="left">Maternal anxiety symptoms (3 years)</td><td align="center">−0.05</td><td align="center">0.08</td><td align="center">−0.60</td><td align="center">0.548</td><td align="center">−0.20</td><td align="center">0.11</td></tr><tr><td align="left">Maternal anxiety symptoms (5 years)</td><td align="center">0.07</td><td align="center">0.08</td><td align="center">0.87</td><td align="center">0.385</td><td align="center">−0.09</td><td align="center">0.22</td></tr><tr><td align="left">Maternal anxiety symptoms (7 years)</td><td align="center">−0.13</td><td align="center">0.09</td><td align="center">−1.28</td><td align="center">0.201</td><td align="center">0.30</td><td align="center">0.06</td></tr><tr><td align="left">Sex (Male)</td><td align="center">0.04</td><td align="center">0.08</td><td align="center">0.51</td><td align="center">0.610</td><td align="center">−0.12</td><td align="center">0.20</td></tr><tr><td align="left">Participant Var</td><td align="center">0.129</td><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center" /></tr></tbody></table> </ephtml> </p> <hd id="AN0187573602-27">Discussion</hd> <p>In the current study, we aimed to characterize the developmental trajectories of EEG aperiodic activity (slope and offset) from early to middle childhood in a longitudinal sample assessed from infancy (5–12 months) through 7 years of age. We investigated potential regional and sex differences. Furthermore, we examined whether the nature of the association between child aperiodic activity and maternal anxiety symptoms varied by age, in line with changes in developmental trajectories. We observed nonlinear trajectories of the slope and offset, with a pattern of increasing (steepening) slope during early childhood and decreasing (flattening) slope in middle childhood. These patterns of trajectories demonstrated differences by brain region and by sex. Additionally, the direction of associations between slope and maternal anxiety varied by age of assessment, in line with observed changes in the direction of the developmental trajectory.</p> <hd id="AN0187573602-28">Developmental Trajectories</hd> <p>Longitudinally, there were distinct nonlinear developmental trajectories in slope and offset, which varied across brain regions. Across the whole scalp, slope increased from infancy to 5 years, with the greatest change from infancy to 3 years. There was a significant decrease from 5 to 7 years. Offset increased until 3 years, then decreased until 7 years. These results are consistent with those of McSweeney et al. ([<reflink idref="bib26" id="ref54">26</reflink>]), who reported a nonlinear developmental trend in aperiodic activity (suggesting a quadratic model was a better fit compared to linear) in their cross‐sectional sample of 4‐ to 11‐year‐old children. The observed trajectories also align with the complex systems framework, which posits that biological systems are characterized by nonlinear dynamics (Cohen et al. [<reflink idref="bib10" id="ref55">10</reflink>]).</p> <p>Similar nonlinear trajectories have been observed in studies of developmental trajectories of cortical structure (Shaw et al. [<reflink idref="bib35" id="ref56">35</reflink>]). Consistent with the hypothesis that offset reflects broadband neuronal firing, the observed shift in trajectory may mark the broad transition from rapid neurodevelopmental growth that occurs in early childhood to synaptic pruning and ongoing systems optimization. Aperiodic slope is hypothesized to index E‐I balance. Early childhood is characterized by unique maturational processes in inhibitory networks, including ongoing migration, maturation, and integration of GABAergic inhibitory neurons into the cortices that likely contribute to the early reduction in E‐I ratio (Paredes et al. [<reflink idref="bib29" id="ref57">29</reflink>]; Wilkinson et al. [<reflink idref="bib39" id="ref58">39</reflink>]). As children continue to mature, ongoing network optimization may modulate the GABAergic and glutamatergic "push‐pull" mechanism, resulting in a shift toward increased excitatory tone (Hill et al. [<reflink idref="bib17" id="ref59">17</reflink>]; McKeon et al. [<reflink idref="bib24" id="ref60">24</reflink>]).</p> <p>In contrast with the results of Wilkinson et al. ([<reflink idref="bib39" id="ref61">39</reflink>]) and the present study, earlier studies by Schaworonkow and Voytek ([<reflink idref="bib33" id="ref62">33</reflink>]) and Rico‐Picó et al. ([<reflink idref="bib30" id="ref63">30</reflink>]) reported a trend of flattening slope between 1–7 months and 6–18 months, respectively. However, these studies parameterized the EEG across much narrower frequency ranges (1–10 Hz by Schaworonkow & Voytek, and 1–20 Hz by Rico‐Picó et al.), citing methodological limitations related to high levels of muscle noise. In their systematic review, Stanyard et al. ([<reflink idref="bib37" id="ref64">37</reflink>]) highlighted variation in the frequency range for parameterization across studies, which likely impacts slope estimation, in conjunction with other potentially influential methodological variations in preprocessing and power estimation. Here we opted to use the method first reported by Wilkinson et al. ([<reflink idref="bib39" id="ref65">39</reflink>]) given the extensive model optimization for early childhood, and because higher frequency activity is believed to be particularly relevant for investigating E‐I balance (e.g., Gao et al. [<reflink idref="bib13" id="ref66">13</reflink>]). However, further research is required to investigate the impact of variation in frequency range on SpecParam estimation; to date we are only aware of one relevant study, a preprint by Boncompte et al. ([<reflink idref="bib5" id="ref67">5</reflink>]) that aims to investigate the impact of frequency range on aperiodic slope estimates in intracortical recordings of 62 epilepsy patients.</p> <p>In analyses that examined trajectories by region, slope increased significantly from infancy through age 5 years, before stabilizing from 5 to 7 years of age, in the frontal and temporal regions. In the central and posterior regions, we found significant decreases in slope earlier, from 3 to 7 years. While offset increased from infancy to 3 years and then decreased to 7 years in frontal, central, and temporal regions, the greatest increase from infancy to 3 years was seen in the frontal region; offset remained stable in the posterior region from infancy to 3 years. These results are consistent with those of Rico‐Picó et al. ([<reflink idref="bib30" id="ref68">30</reflink>]), who reported greater offsets and steeper slopes in posterior compared to anterior regions in early childhood. Cellier et al. ([<reflink idref="bib7" id="ref69">7</reflink>]) investigated age‐related trends in aperiodic activity using linear regression modeling in two independent samples between 3 and 24 years of age. They observed a significant negative association between age and both anterior and posterior offset starting from 3 years, but only posterior for slope. In the present study, there were decreases from 3 years in posterior slope, posterior offset, and anterior offset, whereas frontal slope continued to increase up to 5 years, with relative stabilization until 7 years. These results likely correspond with the null results in anterior slope observed by Cellier et al. ([<reflink idref="bib7" id="ref70">7</reflink>]) when adopting a linear approach.</p> <p>Our findings highlight differential regional development in aperiodic activity, which may reflect the caudal–rostral pattern of development observed in the cortex (e.g., Shaw et al. ([<reflink idref="bib35" id="ref71">35</reflink>])). Cortical development follows a tightly regulated genetic "blueprint" (Silbereis et al. [<reflink idref="bib36" id="ref72">36</reflink>]). Accelerated development in more posterior regions may culminate in an earlier shift from increasing to decreasing aperiodic activity. In their recent review, Stanyard et al. ([<reflink idref="bib37" id="ref73">37</reflink>]) reported a posterior–anterior shift in the aperiodic slope during aging across studies, consistent with our results. Importantly, slope and offset are inherently correlated; prior studies with cross‐sectional, linear designs have reported similar trends (i.e., a linear decrease) for both during aging. The unique trajectories of slope and offset in this study provide further support for the notion that they index unique neurological processes (i.e., E‐I balance vs. overall neuronal population spiking, respectively). The relative "lag" observed in frontal slope may reflect extended maturation of E‐I related processes in the frontal cortex, which is necessary for the development of higher‐order cognitive functions.</p> <p>We identified significant sex differences, whereby females exhibited greater increases in (steeper) slope relative to males. These differences were most pronounced at 3 years and 5 years in the whole scalp, frontal, and temporal regions. To a lesser degree, sex differences were also seen in offset in the whole scalp, central, and temporal regions. Wilkinson et al. ([<reflink idref="bib39" id="ref74">39</reflink>]) also reported significant sex effects (nonlinear age‐by‐sex interactions) in aperiodic activity in their study from infancy to 3 years. Sex differences appeared more pronounced at 3 years and 5 years in the present study. Sex‐based differences in brain development during childhood are well established. For example, sex effects have been reported in studies of EEG power, functional connectivity, and microstates (Bagdasarov et al. [<reflink idref="bib3" id="ref75">3</reflink>]; Gartstein et al. [<reflink idref="bib14" id="ref76">14</reflink>]; Kavčič et al. [<reflink idref="bib19" id="ref77">19</reflink>]). McSweeney et al. ([<reflink idref="bib25" id="ref78">25</reflink>]) observed significant sex differences in aperiodic activity in adolescence. Thus, results across these studies suggest sex effects in the EEG slope are apparent after infancy and extend through at least adolescence. Overall, childhood appears to be a dynamic period with significant development in brain function, including age‐by‐sex interactions.</p> <hd id="AN0187573602-29">Associations of Whole Scalp Aperiodic Activity With Maternal Anxiety Symptoms From Infancy to...</hd> <p>We tested longitudinal associations between whole scalp slope and offset with maternal anxiety from infancy to age 7 years. We observed significant associations between maternal anxiety symptoms and child slope at ages 3 years and 7 years. The nature of the associations differed: at 3 years, the association was negative, whereas at 7 years, the association was positive. The direction of associations corresponded with increasing slope trajectories at 3 years and an emerging pattern of decreasing slope trajectories at 7 years. Importantly, rather than demonstrating a consistent direction of association (e.g., greater maternal anxiety always associated with flatter slope), maternal anxiety appeared to be associated with dynamic changes in developmental trajectory for slope. In other words, maternal anxiety symptoms were differentially associated with slope at different ages (i.e., negative versus positive). However, given the exploratory nature of these analyses, replication will be required to determine whether the associations hold across other samples and environmental influences. To date, one other study has investigated associations between maternal characteristics (hair cortisol, a biological marker associated with stress) and slope in infancy (Brandes‐Aitken et al. [<reflink idref="bib6" id="ref79">6</reflink>]). Replication among clinical samples will be important to further elucidate associations between maternal psychopathology and child slope. The current findings suggest that even mild maternal anxiety symptoms may have implications for child neurodevelopment. Our finding of differential associations at different ages is an important consideration for future research, as it suggests that the interpretation of potential implications of exposure to maternal psychopathology on child brain development, as reflected in measures of slope, must consider the timing of assessment.</p> <hd id="AN0187573602-30">Strengths and Limitations</hd> <p>Strengths of this study include the large longitudinal sample, multimodal measures, and investigation of timing effects. The findings should be considered within the context of the study's limitations. Aperiodic EEG measures are likely susceptible to non‐neurophysiological developmental changes in anatomy that occur during early childhood and can affect skull conductivity, such as increases in skull thickness, volume of cerebrospinal fluid, and closure of the anterior fontanel, all of which are difficult to measure and therefore control for in analyses (Antonakakis et al. [<reflink idref="bib2" id="ref80">2</reflink>]; Lew et al. [<reflink idref="bib22" id="ref81">22</reflink>]). The change in stimulus presentation between the infancy and 3‐year versus the 5‐ and 7‐year time points could potentially confound the EEG trajectories. However, stimuli were selected to be age‐appropriate and neutral (i.e., slow moving, nonarousing) to minimize this risk and maximize data quality at each age. The differences in trajectories were not isolated to the change in stimulus between 3 and 5 years (i.e., there were regional differences, developmental changes across other ages); thus, it is unlikely that trajectories were driven by the change in stimulus. We focused on aperiodic EEG activity given emerging interest in this area and the need for longitudinal analyses of developmental trajectories. Thus, we did not consider periodic measures. Further research is required to determine what the slope indexes biologically, with the E‐I hypothesis based primarily on theoretical models and animal research. Additional research is also needed to orient the current findings in the context of periodic EEG. The current sample comprised families who are predominantly White and of middle to high socioeconomic status, which may limit the generalizability of the findings. Finally, although a strength of this study included its longitudinal design, the ~2‐year gaps between assessments limited our ability to identify nuanced age‐related trends and transition points in nonlinear slope and offset development.</p> <hd id="AN0187573602-31">Conclusions</hd> <p>Our longitudinal findings indicate nonlinear trajectories in EEG aperiodic activity during childhood, which varied by brain region and by sex. The results emphasize the dynamic nature of cortical development in childhood. Differential developmental trajectories of aperiodic activity are likely underpinned by regionally differential timing in shifts from rapid early brain growth to network optimization and synaptic pruning. The findings add to a growing body of evidence suggesting that slope and offset index unique neurological processes (i.e., E‐I balance vs. overall neuronal population spiking, respectively). The changing nature of the associations between maternal anxiety and slope across ages emphasizes the importance of considering how normative developmental changes in slope trajectories may influence associations with putative risk factors in future research. These considerations will be crucial, both when investigating the biological mechanisms that slope and offset index and when investigating associations with other important developmental factors.</p> <hd id="AN0187573602-32">Acknowledgments</hd> <p>This research was supported by grants from the National Institute of Mental Health (MH078829) to C.A.N. and M.B.E. and from the Tommy Fuss Center for Neuropsychiatric Disease Research at Boston Children's Hospital to M.B.E. Study data were collected and managed using Research Electronic Data Capture (REDCap) tools hosted at Boston Children's Hospital. We are extremely grateful for the parents and infants who participated in this study, without whom this research would not be possible.</p> <hd id="AN0187573602-33">Data Availability Statement</hd> <p>The data necessary to reproduce the analyses presented here are not publicly accessible. The analytic code necessary to reproduce the analyses presented in this paper is not publicly accessible. The materials necessary to attempt to replicate the findings presented here are not publicly accessible. The data, code, and materials are available from the corresponding author upon reasonable request. The analyses presented here were not preregistered.</p> <p>GRAPH: Data S1.</p> <ref id="AN0187573602-34"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref31" type="bt">1</bibl> <bibtext> Funding: This work was supported by the National Institute of Mental Health (MH078829) and Tommy Fuss Center for Neuropsychiatric Disease Research.</bibtext> </blist> </ref> <ref id="AN0187573602-35"> <title> References </title> <blist> <bibtext> Adamson, B., N. Letourneau, and C. Lebel. 2018. " Prenatal Maternal Anxiety and Children's Brain Structure and Function: A Systematic Review of Neuroimaging Studies." Journal of Affective Disorders 241 : 117 – 126. https://doi.org/10.1016/j.jad.2018.08.029.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref41" type="bt">2</bibl> <bibtext> Antonakakis, M., S. Schrader, Ü. Aydin, et al. 2020. " Inter‐Subject Variability of Skull Conductivity and Thickness in Calibrated Realistic Head Models." 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  Data: <searchLink fieldCode="AR" term="%22Dashiell+D%2E+Sacks%22">Dashiell D. Sacks</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2842-1301">0000-0003-2842-1301</externalLink>)<br /><searchLink fieldCode="AR" term="%22Viviane+Valdes%22">Viviane Valdes</searchLink><br /><searchLink fieldCode="AR" term="%22Carol+L%2E+Wilkinson%22">Carol L. Wilkinson</searchLink><br /><searchLink fieldCode="AR" term="%22April+R%2E+Levin%22">April R. Levin</searchLink><br /><searchLink fieldCode="AR" term="%22Charles+A%2E+Nelson%22">Charles A. Nelson</searchLink><br /><searchLink fieldCode="AR" term="%22Michelle+Bosquet+Enlow%22">Michelle Bosquet Enlow</searchLink>
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: Aperiodic electroencephalography (EEG) activity is hypothesized to index biological mechanisms that underpin brain functioning. This longitudinal study characterized the developmental trajectories of the aperiodic slope (i.e., aperiodic exponent) and offset from infancy to 7 years of age in a US community sample (N = 391, 46.5% female, predominantly White; data collection 2013-2023). The study further examined whether differential developmental trajectories resulted in differential associations between child aperiodic activity and maternal anxiety symptoms. Developmental trajectories for slope and offset were nonlinear and characterized by relative increases in early childhood and a subsequent decrease or stabilization by Age 7, with variation by brain region and sex. Maternal anxiety was negatively associated with slope at 3 years and positively associated with slope at 7 years. Implications for child brain development are discussed.
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