Childhood and adolescence are critical stages of the human lifespan, in which the brain undergoes various and complex micro- and macroscopic changes (Giedd et al., 1999; Lebel et al., 2008). The typical emergence of mental illnesses during childhood and adolescence (Kessler et al., 2005) further indicates fundamental maturational reorganization. It is therefore particularly important to understand these maturational changes in brain structure and function, which are accompanied by neurophysiological changes. A substantial body of literature has focused on investigating these neurophysiological changes with electroencephalography (EEG) (for reviews, see Anderson and Perone, 2018; Segalowitz et al., 2010). Cognitive functions such as attention and memory, which undergo critical changes during maturation, have frequently been associated with EEG alpha activity (Foxe and Snyder, 2011; Klimesch, 1997; Klimesch, 2012; Niedermeyer, 1999). This lead to the notion that developmental changes in alpha activity reveals mechanisms of cortical manifestations of cognitive function. Indeed, numerous studies have investigated developmental changes in this alpha oscillation and reported evidence of an increase in the individual alpha frequency (IAF) at around 7–14 years of age (Cragg et al., 2011; Díaz de León et al., 1988; Klimesch, 1999; Lindsley, 1939; Marcuse et al., 2008; Niedermeyer, 1999; Somsen et al., 1997). However, the evidence is less clear about the amplitude of this alpha oscillation, termed alpha power. Absolute power was found to decrease with increasing age in some studies (Díaz de León et al., 1988; Gasser et al., 1988; Harmony et al., 1995; Lindsley, 1939; Whitford et al., 2007) but not in others (Clarke et al., 2001; Somsen et al., 1997). A potential confound is the utilization of fixed-frequency boundaries (e.g. 8–13 Hz), which neglects the slowing of the IAF during development. For instance, peak frequency in childhood is around 6 Hz but increases to 10 Hz in adolescents. Hence, age-related power decreases are underestimated when the slower alpha oscillation of younger children is not properly captured by predefined frequency limits, which leads to lower power values. Consequently, individualized alpha frequency bands need to be extracted, which are centered on the individual IAF of each subject (see also Donoghue et al., 2021 for simulations visualizing confounding effects of the peak frequency on band power). In addition, thickening of the skull and other maturational processes that are unrelated to changes in neural activity manifest as changes in overall neural power. Thus, they pose a crucial confound in interpreting the relationship between alpha power and age. To overcome this latter limitation, studies have examined alpha power as relative to the overall power of the spectrum, relative alpha power, which has yielded more consistent results indicating an increase of alpha power with increasing age (Clarke et al., 2001; Cragg et al., 2011; Díaz de León et al., 1988; Harmony et al., 1995; John et al., 1980; Somsen et al., 1997). However, relative power measures of different frequency bands are by definition highly interdependent, as the frequency band of interest is normalized by the power of all the other frequency bands measured (Gómez et al., 2017; Somsen et al., 1997). For example, power changes in other frequency bands, such as the theta band, manifest as changes in relative alpha power, even though the true oscillatory alpha power may remain stable (see Appendix 1—figure 1A). Additionally, non-oscillatory changes in the power spectrum introduce confounds in the analysis of relative band power measures (see simulated example in Appendix 1—figure 1B). This was previously observed by simulations in previous studies showing that non-oscillatory changes affect both relative band power measures Donoghue et al., 2021 and band power ratio measures (Donoghue et al., 2020a). Therefore, the extent to which these earlier works on alpha power and brain maturation are in effect confounded by changes in other frequency bands, by non-oscillatory variations, or by a slowing of the IAF remains unclear. To address this question, true alpha oscillatory power needs to be separated from other, non-oscillatory signal components.

Recent methodological developments have provided a means by which this separation can be achieved. These new approaches decompose measured power into periodic and aperiodic signal components (see Figure 4; Donoghue et al., 2020b; Hughes et al., 2012a; Wen and Liu, 2016). The aperiodic signal (i.e. ‘1/f signal’) is characterized by its intercept and slope, as its amplitude decreases with higher frequencies f. The aperiodic signal contains important physiological information (see He, 2014 for a comprehensive review of the functional significance and potential generative mechanisms of aperiodic activity). More specifically, the aperiodic slope has been linked to the synchronicity of activity in the underlying neural population (Miller et al., 2009; Usher et al., 1995) and its balance between excitatory and inhibitory activity (Gao et al., 2017). Importantly, the aperiodic slope is also modulated by task performance and sensory stimulation (e.g. He, 2014). Conversely, the aperiodic intercept has been linked to general spiking activity (Voytek and Knight, 2015). Overall, the aperiodic signal needs to be considered during the analysis of spectral power rather than measuring power relative to the absolute zero (e.g. Donoghue et al., 2020b). Applying this new approach of spectral decomposition allows to extract an aperiodic-adjusted measure of alpha power, which is independent of oscillatory activity in other frequency bands and changes in overall power and aperiodic activity.

Recent studies adopted this methodology and found age-related changes in the aperiodic signal (i.e., decreased intercept and flattened slope) during childhood and adolescence (Cellier et al., 2021; Hill et al., 2022) and from childhood to middle age (Donoghue et al., 2020a; He et al., 2019). These results further pointed out the importance of considering the aperiodic signal in the investigation of alpha power during brain maturation. However, it remains largely unknown how aperiodic-adjusted alpha power evolves during this critical phase of life. The few studies performed so far have not found any significant association between aperiodic-adjusted alpha power during childhood and adolescence (Cellier et al., 2021; Hill et al., 2022) and from childhood to middle age (He et al., 2019). Due to comparatively small sample sizes in these studies, it remains unclear whether the aperiodic-adjusted alpha power truly remains stable in this period of life or whether too little statistical power was provided to detect changes in this newly emerging measure of alpha power. Furthermore, conventional measures of total and relative alpha power were either not reported (Cellier et al., 2021; Hill et al., 2022), or did not show any relation to age (He et al., 2019). Hence, comparisons and integration of these results with the large body of literature investigating maturational changes in total and relative alpha power remain limited.

To overcome limitations of previous studies, we analyzed the currently largest openly available pediatric EEG data set (N=2529), comprising children adolescents and young adults aged between 5 and 22 years (Alexander et al., 2017) and validated the results in a second, preregistered analysis (https://osf.io/7uwy2) of a dataset consisting of 369 children, adolescents, and young adults aged between 6 and 22 years.

Based on animal studies investigating cortical and subcortical neural generators of alpha activity in adult animals (Bishop, 1936; Lopes da Silva, 1991; Steriade et al., 1990), it is generally assumed that the thalamus and thalamocortical interactions strongly modulate cortical alpha activity. It can thus be hypothesized that changes in the alpha oscillation in the maturing brain are driven by structural changes of the thalamus and thalamocortical connectivity. However, to the best of our knowledge, no study investigated the relationship between anatomical maturation of the thalamus and its connectivity and alpha oscillatory power during childhood and adolescence. To close this gap, we further examined how thalamic structural changes relate to the observed changes in the different measures of alpha power. We employed magnet resonance images (MRI) to operationalize changes in the thalamus in terms of thalamic volume, and diffusion tensor imaging (DTI) to estimate thalamocortical connectivity by white matter integrity of the thalamic radiation. This was done in a subsample of the larger main dataset, for which magnet resonance images (MRI) and diffusion tensor imaging (DTI) data were additionally available.

Taken together, the present study aims to delineate the role of alpha oscillations during brain maturation by investigating conventional and newly emerging alpha oscillatory parameters, aperiodic signal components (see Table 7) and its underlying anatomical basis in a large sample of children, adolescents and young adults. Our first goal is to replicate previous literature that reported an age-related increase of the IAF, increased relative alpha power and decreased total alpha power during brain maturation. However, we hypothesize that the relation of alpha band power and brain maturation is no longer present when adjusting alpha power for the aperiodic signal component and for the age-related increase of the IAF. Additionally, based on previous literature we expect a decrease in the aperiodic intercept and a flattening of the aperiodic slope during brain maturation. Finally, as the aperiodic-adjusted individualized alpha power is unbiased by changes in other frequency bands and the aperiodic signal component, and therefore is likely to provide the most accurate reflection of the true oscillatory activity, we hypothesize that this measure shows a significant association with thalamic anatomical measures.

This study investigated the role of alpha oscillations during brain maturation. Our results replicated the well-documented finding of an increasing IAF in this developmental phase of life from childhood to adolescence. The main aim of the study was to delineate the developmental trajectory of the power of the alpha oscillation by considering changes in the individual alpha frequency and aperiodic signal components in a large sample size. A significant decrease of total individualized alpha power was observed with increasing age across samples. However, when correcting for the aperiodic signal, the results changed considerably. The aperiodic-adjusted individualized alpha power increased significantly from childhood to adolescence, which is consistent with the results obtained from relative alpha power in the present study and in previous literature (Clarke et al., 2001; Cragg et al., 2011; Díaz de León et al., 1988; Harmony et al., 1995; John et al., 1980; Somsen et al., 1997). The aperiodic signal showed a decreased intercept during brain maturation and a flattened slope. Results were largely consistent across the subsample of the HBN dataset without any given diagnosis, the full HBN dataset, and the validation analyses. However, in the validation dataset and the HBN subsample without any given diagnoses, aperiodic-adjusted but not relative alpha power showed significant age-related increases, indicating a risk of false-negative results when investigating relative alpha power in brain maturation. The covariates of psychiatric diagnoses did not show any significant influences on oscillatory alpha or aperiodic signal parameters in the two datasets. Only in the Bayesian sequential updating analysis, combining both datasets, ADHD was associated with decreased aperiodic-adjusted alpha power. Future research is needed to investigate possible relations between psychiatric diseases and periodic and aperiodic EEG signal components in more detail, which is beyond the scope of the present report. Gender effects on alpha power and aperiodic signal components were found in the HBN dataset; however, these effects did not generalize on the validation sample. Importantly, when relating alpha power measures to anatomical measures derived from DTI, only aperiodic-adjusted and relative alpha power showed a significant relation to the white matter integrity of the thalamic radiations, but total alpha power did not.

All analysis code described below is available at https://osf.io/4nzyk/. This repository further contains all extracted EEG features and demographics which were used in the statistical models.

Main study

Experimental setup and procedure

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The participants of the HBN sample were comfortably seated in a chair in a sound‐shielded room at a distance of 70 cm from a 17-inch CRT monitor (SONY Trinitron Multiscan G220, display dimensions 330×240 mm, resolution 800×600 pixels, vertical refresh rate of 100 Hz). The room was equipped with a chinrest to minimize head movements. Subjects were informed that EEG would be recorded while they rested with their eyes alternately open or closed. Instructions for the tasks were presented on the computer screen, and a research assistant answered questions from the participant from the adjacent control room through an intercom. Compliance with the task instructions was confirmed through a live video-feed to the control room. The task procedure was that participants rested with their eyes open for 20s (a total of 1min 40s), followed by 40s (a total of 3min 20s) with their eyes closed, repeated five times. Prerecorded verbal instructions automatically informed the participants when to open or close their eyes via loudspeakers. Participants were asked to maintain a fixed gaze on the fixation cross throughout EO blocks. The total duration of the EEG recording was 5min. The alternating order of EO and EC was designed to avoid fatigue and maintain vigilance. The duration of EC blocks was twice as long as EO blocks because eyes-closed data is more robust and contains fewer artifacts. This protocol has been used in various previous studies (Langer et al., 2012; Langer et al., 2013).

Electroencephalography recording and preprocessing

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The EEG was recorded at a sampling rate of 500 Hz using a high‐density 128‐channel EEG Geodesic Netamps system (Electrical Geodesics, Eugene, Oregon). The recording reference was at Cz, the vertex of the head, and impedances were kept below 40 kΩ.

All analyses were performed using MATLAB 2018b (The MathWorks, Inc, Natick, Massachusetts, United States). EEG data was automatically preprocessed using the current version (2.4.3) of the MATLAB toolbox Automagic (Pedroni et al., 2019). Our pipeline consisted of the following steps. First, bad channels were detected by the algorithms implemented in the clean_rawdata eeglab plugin (http://sccn.ucsd.edu/wiki/Plugin_list_process). A channel was defined as a bad electrode when data recorded by that electrode was correlated at less than 0.85 with an estimate based on other channels. Furthermore, a channel was defined as bad if it had more line noise relative to its signal than all other channels (four standard deviations). Finally, a channel was considered bad if it had a longer flat-line than 5 s. These bad channels were automatically removed and later interpolated using a spherical spline interpolation (EEGLAB function eeg_interp.m). The interpolation was later performed as a final step before the automatic quality assessment of the EEG files (see below). Next, data was filtered using a high-pass filter (–6 dB cut off: 0.5 Hz). Line noise artifacts were removed by applying Zapline (Cheveigné, 2020), removing seven power line components. Subsequently, independent component analysis (ICA) was performed. Components reflecting artifactual activity were classified by the pretrained classifier ICLabel (Pion-Tonachini et al., 2019). Components which were classified as any class of artifacts, including line noise, channel noise, muscle activity, eye activity, and heart artifacts, with a probability higher than 0.8 were removed from the data. Subsequently, residual bad channels were excluded if their standard deviation exceeded a threshold of 25 μV. Very high transient artifacts (>±100 μV) were excluded from the calculation of the standard deviation of each channel. However, if this resulted in a significant loss of channel data (>25%), the channel was removed from the data. After this, the pipeline automatically assessed the quality of the resulting EEG files based on four criteria: A data file was marked as bad-quality EEG and not included in the analysis if, first, the proportion of high-amplitude data points in the signals (>30 μV) was larger than 0.20; second, more than 20% of time points showed a variance larger than 15 microvolt across channels; third, 30% of the channels showed high variance (>15 μV); and fourth, the ratio of bad channels was higher than 0.3. Finally, 13 of 128 electrodes in the outermost circumference, attached to chin and neck, were excluded from further processing as they capture little brain activity and mainly record muscular activity. Additionally, 10 EOG electrodes were separated from the data and not used for further analysis, yielding a total of 105 EEG electrodes. Data was then referenced to the common average reference.

Spectral analysis

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Spectral analysis was performed on data from the concatenated five blocks of the eyes-closed condition. Only data from the eyes-closed condition was analyzed, because this data contains fewer artifacts and generally shows the strongest alpha oscillatory activity. The first and the last second of each eyes-closed block was discarded to exclude motor activity related to opening and closing the eyes and auditory activity due to the prompt from the speakers. The remaining data was concatenated, resulting in a total of 190s of continuous EEG data. This data was again segmented into 2s epochs, and each epoch containing large amplitude artifacts (>+90 μV, < –90 μV) was excluded from further processing. In 37subjects, more than 50% of trials exceeded this threshold; thus, these subjects were not included in subsequent analyses (see Appendix 2—figure 1). For the remaining data, on average, 2.95% of trials were excluded by this criterion. Power spectral densities (PSDs) were then calculated using Welch’s Method Welch, 1967 implemented in the EEGLab toolbox (Delorme and Makeig, 2004). Zero padding was applied to provide a frequency resolution of 0.25Hz in the 2s sliding time windows within Welch’s algorithm. Averaging the individual PSDs of each window resulted in a smoothed power spectrum that complies with the requirements of the specparam algorithm (Donoghue et al., 2020b) used subsequently (see Specparam algorithm and aperiodic-adjusted alpha power). Additionally, PSDs were transformed to log scale to scale results equal to outputs from the specparam algorithm, which only operates in log power space. In the following, we describe the two approaches to extracting total alpha power and aperiodic-adjusted alpha power together with the aperiodic signal. See Table 7 for an overview of all extracted parameters.

Table 7

Parameter	Description
Individual alpha frequency (IAF)	Frequency at maximum power in search window 7–14 Hz
Total canonical alpha power	Averaged log power in the fixed-frequency window [8Hz–13Hz], extracted from the total power spectrum
Total individualized alpha power	Averaged log power in window [- 4 Hz to +2 Hz] relative to IAF, extracted from the total power spectrum
Relative canonical alpha power	Averaged power in the fixed-frequency window [8Hz–13Hz], divided by the average power of the full power spectrum (2–40 Hz), extracted from the total power spectrum
Relative individualized alpha power	Averaged power in window [- 4 Hz to +2 Hz] relative to IAF, divided by the average power of the full power spectrum (2–40 Hz), extracted from the total power spectrum
Aperiodic-adjusted canonical alpha power	Canonical alpha power, extracted from the aperiodic-adjusted log power spectrum
Aperiodic-adjusted individual alpha power	Individualized alpha power, extracted from the aperiodic-adjusted log power spectrum
Aperiodic intercept	Intercept parameter of the aperiodic signal extracted by specparam
Aperiodic exponent	Exponent parameter (i.e. negative slope) of the aperiodic signal extracted by specparam

1. Note: The term total power spectrum refers to the power spectrum as extracted from the data using Welch’s algorithm. The aperiodic-adjusted power spectrum results from a subtraction of the aperiodic signal from the total power spectrum.

Computation of individual alpha peak frequency

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The IAF was found by determining the frequency of maximum power between a lower and upper frequency limit. Following previous work, these frequencies limits were set to 7 and 14Hz (Posthuma et al., 2001; Smit et al., 2006). If the peak was located at the border of the search range, no alpha peak was extracted for that subject, and the corresponding data was excluded from further analysis (67subjects, see Appendix 2—figure 1).

Extraction of total and relative alpha power

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To replicate the results of previous published findings, this standard analysis approach included no adjustment for the aperiodic background signal. If an alpha peak was identified (see Computation of individual alpha peak frequency), individualized total alpha power was extracted by averaging log power in the defined window [–4Hz to+2Hz] relative to the IAF (Klimesch, 1999). This individualized alpha power measure was chosen over a canonically defined alpha range, 8–13Hz (Babiloni et al., 2020), as the shift of the IAF during maturation might introduce a bias when power is averaged within a fixed-frequency window. Canonical alpha band power was also extracted for supplementary analysis.

Relative individualized and relative canonical alpha power were calculated by dividing the corresponding total alpha power values by the average power of the full spectrum (2–40 Hz).

Specparam algorithm and aperiodic-adjusted alpha power

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The specparam algorithm (Donoghue et al., 2020b) parameterizes the neural power spectrum to separate neural oscillations from the aperiodic background signal. The algorithm estimates oscillatory peaks that are superimposed on the aperiodic background signal (see Figure 4) and are therefore measured relative to this rather than to the absolute zero. Consequently, the specparam algorithm parametrizes the PSD by iteratively fitting the aperiodic background curve (L) to the observed smoothed spectral signal, resulting in two parameters: the aperiodic intercept b and the aperiodic exponent χ (i.e. slope, the smaller χ, the flatter the spectrum).

(1) $L=b-\log \left(k+F^{\chi }\right)$

Figure 4

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In equation 1, F represents the vector of input frequencies and k the knee parameter, which is not further discussed here, as it is set to 0 in the proposed analysis: no bend of the aperiodic component is additionally modeled in the data, which is the default state of the specparam algorithm.

To extract oscillatory components, this aperiodic signal is subtracted from the power spectrum. Gaussians are fitted iteratively to the remaining signal and subsequently subtracted whenever data points exceed two standard deviations of the data. The Gaussians represent the true oscillatory components in the data; if data points are below the specified threshold, they are considered as noise. This results in a data-driven number of Gaussians, each parameterized by the frequency center, power relative to the aperiodic signal and the frequency bandwidth. The power spectrum is therefore modeled as defined by equation 2,

(2) $P=L+\sum _{n=0}^N{G}_n+\epsilon$

where G_n represents the n^th Gaussian and ε the noise not captured by the model. Note that this description of the algorithm is simplified; for a more detailed definition, see Donoghue et al., 2020b.

In the current study, the frequency range of 2–40 Hz of the power spectrum was passed to the algorithm because very low frequencies may lead to overfitting of noise as small bandwidth peaks. The current release (1.0.0) of the specparam toolbox from the github repository (https://github.com/fooof-tools/fooof; Donoghue, 2022) was used. The algorithm was used with these settings: peak width limits: [0.5, 12]; max number of peaks: infinite; minimum peak height: 0; peak threshold: 2 sd above mean; and aperiodic mode: fixed. The resulting periodic signal P (“Oscillatory Signal” in Figure 4) was used to extract aperiodic-adjusted canonical alpha power (average across 8–13 Hz) and aperiodic-adjusted individualized alpha power (average across a [–4 Hz to +2 Hz] window centered on the IAF).

Data was only used for further analysis when the model fit of the specParam model was above a threshold of R² >0.90.

In the analysis of the main HBN sample, an overall high specParam model fit was observed across the sample (mean R²=0.9943, sd = 0.0098). Similar model fits were observed in the validation dataset (mean R²=0.9941, sd = 0.0120). Model fit was assessed for each of the five occipital electrodes separately (see 4.2.7). Across all subjects, only 11 out of 9,705 model fits were below the cut-off of R² <0.90 in the HBN sample, and 6 out of 1,675 in the validation sample. Consequently, in 99.94% of subjects, all five electrodes could be used to estimate the average occipital periodic and aperiodic parameters. For those subjects with insufficient model fits for specific occipital electrodes, the average occipital periodic and aperiodic parameters were calculated from the average of the remaining electrodes with adequate model fit. In the HBN sample, the numbers of occipital electrodes available in these subjects were: Two (N=1 subject), three (N=1 subject), and four (N=6 subjects). In the validation sample, these numbers of available electrodes were similar: Three (N=2 subject), four (N=2 subjects). No subject was excluded based on the specParam model fit.

A series of control analyses were conducted: The first control analyses indicated a small but significant relation between age and gender with the specParam model fit (see Appendix 4). Controlling for this in the statistical model by adding the specParam model fit as an additional predictor did not change any of the main results (see Appendix 4—table 1). Additional control analyses were subsequently performed to investigate whether possible overfitting of the specParam models (mean R²=0.9943, see above) confounded the results. Following the guidelines of Ostlund et al.’s (2022), specParam model fitting parameters and data exclusion criteria were adapted to minimize both overfitting and underfitting of the model (for the details about this approach see Appendix 5). Results were highly consistent with the main results reported in Table 1 and did not indicate any changes to the main results (see Appendix 5—table 1). Finally, a control analysis using the periodic alpha peak power from the specParam algorithm instead of average individualized band power showed highly consistent results and did not change any conclusions (see Appendix 6, Appendix 6—table 1).

Electrode cluster analysis

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To test the hypothesis derived from literature review, an electrode-cluster-based analysis was performed. This cluster was based on data from the parietal and occipital electrodes, here referred to as the parieto-occipital cluster (see Figure 1). These electrodes were chosen because of the strong prominence of Oz and Pz electrodes in research on EEG alpha oscillations (Klimesch, 1999) and previous findings for age effects on alpha band power in these electrodes (Clarke et al., 2001; Cragg et al., 2011; Gasser et al., 1988). To account for individual anatomical differences, the following electrodes were selected to create a more robust cluster: E72 (POz), E75 (Oz), E62 (Pz), E67 (PO3), E77 (PO4). All parameters described above were extracted for each electrode and subsequently averaged within this cluster. Prior to statistical analyses, data was excluded from further processing if any extracted parameter exceeded a threshold of three standard deviations above or below the mean of the sample (excluded N=30, mean age = 12.58, sd = 3.92). See Appendix 2—figure 1 for a detailed flow chart of all exclusion criteria and the resulting sample size.

Statistical analysis

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Bayesian generalized linear mixed models were formulated using the brms R package (Bürkner, 2017). Statistical models were separately fitted to the full sample and the subsampled dataset of subjects without any given diagnosis. For both samples, the predictor variable was age, and the covariates gender, EHI, and site were added. In the full sample, a categorical variable for diagnosis was added (no diagnosis, ADHD diagnosis, other diagnosis) to account for the high prevalence of ADHD in this dataset (N_{no diagnosis} = 190, N_{ADHD diagnosis} = 1038, N_{other diagnosis} = 542). However, the focus of this paper is on brain maturation; thus, this information is only included to control for possible confounding effects of psychiatric disorders.

To determine the best model for each analysis, multiple models were fitted with varying degrees of interaction terms and compared using the Watanabe Akaike Information Criteria (WAIC, Wantanabe, 2013). Additionally, expected log pointwise predictive density (ELDP, i.e. the expected predictive accuracy of the model) was calculated using the R function ‘loo_cmpare’ (Vehtari et al., 2017), yielding consistent results. For an overview of all models tested and the resulting WAIC and ELDP, see Supplementary file 1.

The final models were these:

For the subsample of subjects without any given diagnosis:

(3) $\left[d{v}^{\prime }s\right]\sim age+gender+EHI+site$

For the full sample:

(4) $\left[d{v}^{\prime }s\right]\sim age+gender+diagnosis+EHI+site$

The multivariate models defined above were fitted each to a set of dependent variables: Total individualized alpha power, aperiodic-adjusted individualized alpha power, relative individualized alpha power, aperiodic intercept, aperiodic slope and IAF. Additionally, for supplementary analyses, model two was also fitted to measures of canonical alpha power (i.e. total canonical alpha power, relative canonical alpha power and aperiodic-adjusted canonical alpha power).

To correct for multiple comparisons, the significance level was adjusted. We assumed a high correlation between the total of 9 outcome variables (including the canonical alpha power measures, see Supplementary file 2C), as many of the dependent variables represent various characteristics of the individual alpha oscillations. To account for this, we first calculated the effective number of tests of all dependent variables using Nyholt’s approach (Nyholt, 2004). Following this approach, the significance level (0.05) was then adjusted using the Šidák correction (Nyholt, 2004). Subsequently, the credible intervals (CIs) of the posterior distributions were calculated from the newly estimated levels of significance. The resulting significance level was 0.0083, yielding 99.17% credible intervals. We refrained from calculating Bayes factors for point estimates as evidence of the effect being zero or unequal to zero, as these Bayes factors, which are based on the Savage–Dickey ratio, depend strongly on the arbitrary choice of the prior distribution of each effect. Instead, we considered a model parameter significant if its 99.17% CI did not include zero. In line with Gelman’s recommendations (Gelman et al., 2007), predictors and outcome variables of the Bayesian regression model were scaled as follows: Each numeric parameter (age, EHI, IAF, total canonical alpha power, total individualized alpha power, relative canonical alpha power, relative individualized alpha power, aperiodic-adjusted canonical alpha power, aperiodic-adjusted individualized alpha power, aperiodic intercept, and aperiodic slope) was scaled to provide a mean of 0 and standard deviation 0.5. Uninformative Cauchy priors were used (mean = 0, sd = 2.5), as proposed by Gelman (Gelman et al., 2007).

Appendix 2—figure 1 visualizes the detailed exclusion criteria and their effect on the sample size of the main HBN dataset.

Appendix 2—figure 1

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All data generated or analyzed during this study are included in the manuscript and supporting file; Source Data files have been provided for all figures. All data analysis code is additionally provided in: https://osf.io/4nzyk/. This repository further contains all extracted EEG features and demographics which were used in the statistical models. The raw data for the HBN sample (N = 2529) is available here: (http://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/sharing_neuro.html). The raw data from the validation data set (N = 369) presented in this article are not readily available for the public research community, because we do not have permission from the participants to share the raw data. We can only share derivatives of the validation data. Requests to access the raw validation datasets should be directed to NL, n.langer@psychologie.uzh.ch.

1. 1. Alexander L

   (2017) fcon

   ID cmi_healthy_brain_network. Neuroimaging Data Access.

   http://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/sharing_neuro.html

1. 1. Alcauter S
   2. Lin W
   3. Smith JK
   4. Short SJ
   5. Goldman BD
   6. Reznick JS
   7. Gilmore JH
   8. Gao W

   (2014) Development of thalamocortical connectivity during infancy and its cognitive correlations

   The Journal of Neuroscience 34:9067–9075.

   https://doi.org/10.1523/JNEUROSCI.0796-14.2014

   • PubMed
   • Google Scholar
2. 1. Alexander LM
   2. Escalera J
   3. Ai L
   4. Andreotti C
   5. Febre K
   6. Mangone A
   7. Vega-Potler N
   8. Langer N
   9. Alexander A
   10. Kovacs M
   11. Litke S
   12. O’Hagan B
   13. Andersen J
   14. Bronstein B
   15. Bui A
   16. Bushey M
   17. Butler H
   18. Castagna V
   19. Camacho N
   20. Chan E
   21. Citera D
   22. Clucas J
   23. Cohen S
   24. Dufek S
   25. Eaves M
   26. Fradera B
   27. Gardner J
   28. Grant-Villegas N
   29. Green G
   30. Gregory C
   31. Hart E
   32. Harris S
   33. Horton M
   34. Kahn D
   35. Kabotyanski K
   36. Karmel B
   37. Kelly SP
   38. Kleinman K
   39. Koo B
   40. Kramer E
   41. Lennon E
   42. Lord C
   43. Mantello G
   44. Margolis A
   45. Merikangas KR
   46. Milham J
   47. Minniti G
   48. Neuhaus R
   49. Levine A
   50. Osman Y
   51. Parra LC
   52. Pugh KR
   53. Racanello A
   54. Restrepo A
   55. Saltzman T
   56. Septimus B
   57. Tobe R
   58. Waltz R
   59. Williams A
   60. Yeo A
   61. Castellanos FX
   62. Klein A
   63. Paus T
   64. Leventhal BL
   65. Craddock RC
   66. Koplewicz HS
   67. Milham MP

   (2017) An open resource for transdiagnostic research in pediatric mental health and learning disorders

   Scientific Data 4:170181.

   https://doi.org/10.1038/sdata.2017.181

   • PubMed
   • Google Scholar
3. 1. Anderson AJ
   2. Perone S

   (2018) Developmental change in the resting state electroencephalogram: Insights into cognition and the brain

   Brain and Cognition 126:40–52.

   https://doi.org/10.1016/j.bandc.2018.08.001

   • PubMed
   • Google Scholar
4. 1. Andersson JLR
   2. Graham MS
   3. Zsoldos E
   4. Sotiropoulos SN

   (2016) Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images

   NeuroImage 141:556–572.

   https://doi.org/10.1016/j.neuroimage.2016.06.058

   • PubMed
   • Google Scholar
5. 1. Andersson JLR
   2. Sotiropoulos SN

   (2016) An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging

   NeuroImage 125:1063–1078.

   https://doi.org/10.1016/j.neuroimage.2015.10.019

   • PubMed
   • Google Scholar
6. 1. Andersson JLR
   2. Graham MS
   3. Drobnjak I
   4. Zhang H
   5. Filippini N
   6. Bastiani M

   (2017) Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement

   NeuroImage 152:450–466.

   https://doi.org/10.1016/j.neuroimage.2017.02.085

   • PubMed
   • Google Scholar
7. 1. Babiloni C
   2. Barry RJ
   3. Başar E
   4. Blinowska KJ
   5. Cichocki A
   6. Drinkenburg W
   7. Klimesch W
   8. Knight RT
   9. Lopes da Silva F
   10. Nunez P
   11. Oostenveld R
   12. Jeong J
   13. Pascual-Marqui R
   14. Valdes-Sosa P
   15. Hallett M

   (2020) International Federation of Clinical Neurophysiology (IFCN) - EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms, Part 1: Applications in clinical research studies

   Clinical Neurophysiology 131:285–307.

   https://doi.org/10.1016/j.clinph.2019.06.234

   • PubMed
   • Google Scholar
8. 1. Ball G
   2. Pazderova L
   3. Chew A
   4. Tusor N
   5. Merchant N
   6. Arichi T
   7. Allsop JM
   8. Cowan FM
   9. Edwards AD
   10. Counsell SJ

   (2015) Thalamocortical connectivity predicts cognition in children born preterm

   Cerebral Cortex 25:4310–4318.

   https://doi.org/10.1093/cercor/bhu331

   • PubMed
   • Google Scholar
9. 1. Basser PJ
   2. Pajevic S
   3. Pierpaoli C
   4. Duda J
   5. Aldroubi A

   (2000) In vivo fiber tractography using DT-MRI data

   Magnetic Resonance in Medicine 44:625–632.

   https://doi.org/10.1002/1522-2594(200010)44:4<625::aid-mrm17>3.0.co;2-o

   • PubMed
   • Google Scholar
10. 1. Bazanova OM
    2. Vernon D

    (2014) Interpreting EEG alpha activity

    Neuroscience and Biobehavioral Reviews 44:94–110.

    https://doi.org/10.1016/j.neubiorev.2013.05.007

    • PubMed
    • Google Scholar
11. 1. Benninger C
    2. Matthis P
    3. Scheffner D

    (1984) EEG development of healthy boys and girls: Results of a longitudinal study

    Electroencephalography and Clinical Neurophysiology 57:1–12.

    https://doi.org/10.1016/0013-4694(84)90002-6

    • PubMed
    • Google Scholar
12. 1. Bishop GH

    (1936) The interpretation of cortical potentials

    Cold Spring Harbor Symposia on Quantitative Biology 4:305–319.

    https://doi.org/10.1101/SQB.1936.004.01.032

    • Google Scholar
13. Preprint

    1. Bürkner PC

    (2017) Advanced bayesian multilevel modeling with the R Package brms

    arXiv.

    https://arxiv.org/pdf/1705.11123v2.pdf

    • Google Scholar
14. Software

    1. Cardinal D

    (2022) Vistasoft, version swh:1:rev:4216bcedef88ed34f8857178791e9f01459b5305

    GitHub.

    https://github.com/vistalab/vistasoft
15. 1. Carlson HL
    2. Laliberté C
    3. Brooks BL
    4. Hodge J
    5. Kirton A
    6. Bello-Espinosa L
    7. Hader W
    8. Sherman EMS

    (2014) Reliability and variability of diffusion tensor imaging (DTI) tractography in pediatric epilepsy

    Epilepsy & Behavior 37:116–122.

    https://doi.org/10.1016/j.yebeh.2014.06.020

    • PubMed
    • Google Scholar
16. 1. Cellier D
    2. Riddle J
    3. Petersen I
    4. Hwang K

    (2021) The development of theta and alpha neural oscillations from ages 3 to 24 years

    Developmental Cognitive Neuroscience 50:100969.

    https://doi.org/10.1016/j.dcn.2021.100969

    • PubMed
    • Google Scholar
17. 1. Charlton RA
    2. Barrick TR
    3. Lawes INC
    4. Markus HS
    5. Morris RG

    (2010) White matter pathways associated with working memory in normal aging

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 46:474–489.

    https://doi.org/10.1016/j.cortex.2009.07.005

    • PubMed
    • Google Scholar
18. 1. Cheveigné Ade

    (2020) ZapLine: A simple and effective method to remove power line artifacts

    NeuroImage 207:116356.

    https://doi.org/10.1016/j.neuroimage.2019.116356

    • PubMed
    • Google Scholar
19. 1. Clarke AR
    2. Barry RJ
    3. McCarthy R
    4. Selikowitz M

    (2001) Age and sex effects in the EEG: development of the normal child

    Clinical Neurophysiology 112:806–814.

    https://doi.org/10.1016/s1388-2457(01)00488-6

    • PubMed
    • Google Scholar
20. 1. Collier Q
    2. Veraart J
    3. Jeurissen B
    4. den Dekker AJ
    5. Sijbers J

    (2015) Iterative reweighted linear least squares for accurate, fast, and robust estimation of diffusion magnetic resonance parameters

    Magnetic Resonance in Medicine 73:2174–2184.

    https://doi.org/10.1002/mrm.25351

    • PubMed
    • Google Scholar
21. 1. Cragg L
    2. Kovacevic N
    3. McIntosh AR
    4. Poulsen C
    5. Martinu K
    6. Leonard G
    7. Paus T

    (2011) Maturation of EEG power spectra in early adolescence: a longitudinal study

    Developmental Science 14:935–943.

    https://doi.org/10.1111/j.1467-7687.2010.01031.x

    • PubMed
    • Google Scholar
22. 1. Delignette-Muller ML
    2. Dutang C

    (2015)

    Itdistrplus: an R package for fitting distributions

    Journal of Statistical Software 64:1–22.

    • Google Scholar
23. 1. Delorme A
    2. Makeig S

    (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

    Journal of Neuroscience Methods 134:9–21.

    https://doi.org/10.1016/j.jneumeth.2003.10.009

    • PubMed
    • Google Scholar
24. 1. Díaz de León AE
    2. Harmony T
    3. Marosi E
    4. Becker J
    5. Alvarez A

    (1988) Effect of different factors on EEG spectral parameters

    The International Journal of Neuroscience 43:123–131.

    https://doi.org/10.3109/00207458808985789

    • PubMed
    • Google Scholar
25. 1. Donoghue T
    2. Dominguez J
    3. Voytek B

    (2020a) Electrophysiological frequency band ratio measures conflate periodic and aperiodic neural activity

    ENeuro 7:ENEURO.0192-20.2020.

    https://doi.org/10.1523/ENEURO.0192-20.2020

    • PubMed
    • Google Scholar
26. 1. Donoghue T
    2. Haller M
    3. Peterson EJ
    4. Varma P
    5. Sebastian P
    6. Gao R
    7. Noto T
    8. Lara AH
    9. Wallis JD
    10. Knight RT
    11. Shestyuk A
    12. Voytek B

    (2020b) Parameterizing neural power spectra into periodic and aperiodic components

    Nature Neuroscience 23:1655–1665.

    https://doi.org/10.1038/s41593-020-00744-x

    • PubMed
    • Google Scholar
27. 1. Donoghue T
    2. Schaworonkow N
    3. Voytek B

    (2021) Methodological considerations for studying neural oscillations

    The European Journal of Neuroscience 55:3502–3527.

    https://doi.org/10.1111/ejn.15361

    • PubMed
    • Google Scholar
28. Software

    1. Donoghue T

    (2022) Fooof, version swh:1:rev:b376e009eb5b2ad2809660e7568203dc109db50a

    GitHub.

    https://github.com/fooof-tools/fooof
29. 1. Dustman RE
    2. Shearer DE
    3. Emmerson RY

    (1999) Life-span changes in EEG spectral amplitude, amplitude variability and mean frequency

    Clinical Neurophysiology 110:1399–1409.

    https://doi.org/10.1016/s1388-2457(99)00102-9

    • PubMed
    • Google Scholar
30. Software

    1. Dziemian S

    (2021) DTIPreprocesingPipeline, version swh:1:rev:e4b48053d150976e1036a46b25870d7973965efc

    GitHub.

    https://github.com/sdziem/DTIPreprocesingPipeline
31. 1. Fieremans E
    2. Jensen JH
    3. Helpern JA

    (2011) White matter characterization with diffusional kurtosis imaging

    NeuroImage 58:177–188.

    https://doi.org/10.1016/j.neuroimage.2011.06.006

    • PubMed
    • Google Scholar
32. 1. Fischl B
    2. Salat DH
    3. Busa E
    4. Albert M
    5. Dieterich M
    6. Haselgrove C
    7. van der Kouwe A
    8. Killiany R
    9. Kennedy D
    10. Klaveness S
    11. Montillo A
    12. Makris N
    13. Rosen B
    14. Dale AM

    (2002) Whole Brain Segmentation

    Neuron 33:341–355.

    https://doi.org/10.1016/S0896-6273(02)00569-X

    • PubMed
    • Google Scholar
33. 1. Fischl B
    2. Salat DH
    3. van der Kouwe AJW
    4. Makris N
    5. Ségonne F
    6. Quinn BT
    7. Dale AM

    (2004) Sequence-independent segmentation of magnetic resonance images

    NeuroImage 23 Suppl 1:S69–S84.

    https://doi.org/10.1016/j.neuroimage.2004.07.016

    • PubMed
    • Google Scholar
34. Book

    1. Forman AK
    2. Poswanger K
    3. Waldherr K

    (2006)

    Grundintelligenztest Skala 2 (CFT 20-R) Mit Wortschatztest (WS) Und Zahlenfolgentest (ZF)

    Hogrefe, Göttingen.

    • Google Scholar
35. 1. Foxe JJ
    2. Snyder AC

    (2011) The role of alpha-band brain oscillations as a sensory suppression mechanism during selective attention

    Frontiers in Psychology 2:154.

    https://doi.org/10.3389/fpsyg.2011.00154

    • PubMed
    • Google Scholar
36. 1. Gao R
    2. Peterson EJ
    3. Voytek B

    (2017) Inferring synaptic excitation/inhibition balance from field potentials

    NeuroImage 158:70–78.

    https://doi.org/10.1016/j.neuroimage.2017.06.078

    • PubMed
    • Google Scholar
37. 1. Gasser T
    2. Verleger R
    3. Bächer P
    4. Sroka L

    (1988) Development of the EEG of school-age children and adolescents. I. Analysis of band power

    Electroencephalography and Clinical Neurophysiology 69:91–99.

    https://doi.org/10.1016/0013-4694(88)90204-0

    • PubMed
    • Google Scholar
38. 1. Gelman A
    2. Jakulin A
    3. Su YS
    4. Pittau MG

    (2007) A default prior distribution for logistic and other regression models

    SSRN Electronic Journal 1:22.

    https://doi.org/10.2139/ssrn.1010421

    • Google Scholar
39. 1. Gershon RC
    2. Wagster MV
    3. Hendrie HC
    4. Fox NA
    5. Cook KF
    6. Nowinski CJ

    (2013) NIH toolbox for assessment of neurological and behavioral function

    Neurology 80:S2–S6.

    https://doi.org/10.1212/WNL.0b013e3182872e5f

    • PubMed
    • Google Scholar
40. 1. Giedd JN
    2. Blumenthal J
    3. Jeffries NO
    4. Castellanos FX
    5. Liu H
    6. Zijdenbos A
    7. Paus T
    8. Evans AC
    9. Rapoport JL

    (1999) Brain development during childhood and adolescence: a longitudinal MRI study

    Nature Neuroscience 2:861–863.

    https://doi.org/10.1038/13158

    • PubMed
    • Google Scholar
41. 1. Gómez CM
    2. Rodríguez-Martínez EI
    3. Fernández A
    4. Maestú F
    5. Poza J
    6. Gómez C

    (2017) Absolute power spectral density changes in the magnetoencephalographic activity during the transition from childhood to adulthood

    Brain Topography 30:87–97.

    https://doi.org/10.1007/s10548-016-0532-0

    • PubMed
    • Google Scholar
42. 1. Han X
    2. Jovicich J
    3. Salat D
    4. van der Kouwe A
    5. Quinn B
    6. Czanner S
    7. Busa E
    8. Pacheco J
    9. Albert M
    10. Killiany R
    11. Maguire P
    12. Rosas D
    13. Makris N
    14. Dale A
    15. Dickerson B
    16. Fischl B

    (2006) Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer

    NeuroImage 32:180–194.

    https://doi.org/10.1016/j.neuroimage.2006.02.051

    • PubMed
    • Google Scholar
43. 1. Harmony T
    2. Marosi E
    3. Becker J
    4. Rodríguez M
    5. Reyes A
    6. Fernández T
    7. Silva J
    8. Bernal J

    (1995) Longitudinal quantitative EEG study of children with different performances on a reading-writing test

    Electroencephalography and Clinical Neurophysiology 95:426–433.

    https://doi.org/10.1016/0013-4694(95)00135-2

    • PubMed
    • Google Scholar
44. 1. He BJ

    (2014) Scale-free brain activity: past, present, and future

    Trends in Cognitive Sciences 18:480–487.

    https://doi.org/10.1016/j.tics.2014.04.003

    • PubMed
    • Google Scholar
45. Preprint

    1. He W
    2. Donoghue T
    3. Sowman PF
    4. Seymour RA
    5. Brock J
    6. Crain S
    7. Voytek B
    8. Hillebrand A

    (2019) Co-Increasing Neuronal Noise and Beta Power in the Developing Brain

    bioRxiv.

    https://doi.org/10.1101/839258

    • Google Scholar
46. 1. Hill AT
    2. Clark GM
    3. Bigelow FJ
    4. Lum JAG
    5. Enticott PG

    (2022) Periodic and aperiodic neural activity displays age-dependent changes across early-to-middle childhood

    Developmental Cognitive Neuroscience 54:101076.

    https://doi.org/10.1016/j.dcn.2022.101076

    • PubMed
    • Google Scholar
47. 1. Hua K
    2. Zhang J
    3. Wakana S
    4. Jiang H
    5. Li X
    6. Reich DS
    7. Calabresi PA
    8. Pekar JJ
    9. van Zijl PCM
    10. Mori S

    (2008) Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification

    NeuroImage 39:336–347.

    https://doi.org/10.1016/j.neuroimage.2007.07.053

    • PubMed
    • Google Scholar
48. 1. Hughes AM
    2. Whitten TA
    3. Caplan JB
    4. Dickson CT

    (2012a) BOSC: a better oscillation detection method, extracts both sustained and transient rhythms from rat hippocampal recordings

    Hippocampus 22:1417–1428.

    https://doi.org/10.1002/hipo.20979

    • PubMed
    • Google Scholar
49. 1. Hughes EJ
    2. Bond J
    3. Svrckova P
    4. Makropoulos A
    5. Ball G
    6. Sharp DJ
    7. Edwards AD
    8. Hajnal JV
    9. Counsell SJ

    (2012b) Regional changes in thalamic shape and volume with increasing age

    NeuroImage 63:1134–1142.

    https://doi.org/10.1016/j.neuroimage.2012.07.043

    • PubMed
    • Google Scholar
50. 1. Huttenlocher PR
    2. de Courten C

    (1987)

    The development of synapses in striate cortex of man

    Human Neurobiology 6:1–9.

    • PubMed
    • Google Scholar
51. 1. Jenkinson M
    2. Beckmann CF
    3. Behrens TEJ
    4. Woolrich MW
    5. Smith SM

    (2012) FSL

    NeuroImage 62:782–790.

    https://doi.org/10.1016/j.neuroimage.2011.09.015

    • PubMed
    • Google Scholar
52. 1. Jin Y
    2. O’halloran JP
    3. Plon L
    4. Sandman CA
    5. Potkin SG

    (2006) Alpha EEG predicts visual reaction time

    International Journal of Neuroscience 116:1035–1044.

    https://doi.org/10.1080/00207450600553232

    • PubMed
    • Google Scholar
53. 1. John ER
    2. Ahn H
    3. Prichep L
    4. Trepetin M
    5. Brown D
    6. Kaye H

    (1980) Developmental equations for the electroencephalogram

    Science 210:1255–1258.

    https://doi.org/10.1126/science.7434026

    • PubMed
    • Google Scholar
54. 1. Kail R

    (2000) Speed of Information Processing

    Journal of School Psychology 38:51–61.

    https://doi.org/10.1016/S0022-4405(99)00036-9

    • Google Scholar
55. 1. Kaufman J
    2. Birmaher B
    3. Brent D
    4. Rao U
    5. Flynn C
    6. Moreci P
    7. Williamson D
    8. Ryan N

    (1997) Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data

    Journal of the American Academy of Child and Adolescent Psychiatry 36:980–988.

    https://doi.org/10.1097/00004583-199707000-00021

    • PubMed
    • Google Scholar
56. 1. Keller SS
    2. Gerdes JS
    3. Mohammadi S
    4. Kellinghaus C
    5. Kugel H
    6. Deppe K
    7. Ringelstein EB
    8. Evers S
    9. Schwindt W
    10. Deppe M

    (2012) Volume estimation of the thalamus using freesurfer and stereology: consistency between methods

    Neuroinformatics 10:341–350.

    https://doi.org/10.1007/s12021-012-9147-0

    • PubMed
    • Google Scholar
57. 1. Kellner E
    2. Dhital B
    3. Kiselev VG
    4. Reisert M

    (2016) Gibbs-ringing artifact removal based on local subvoxel-shifts

    Magnetic Resonance in Medicine 76:1574–1581.

    https://doi.org/10.1002/mrm.26054

    • PubMed
    • Google Scholar
58. 1. Kessler RC
    2. Berglund P
    3. Demler O
    4. Jin R
    5. Merikangas KR
    6. Walters EE

    (2005) Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication

    Archives of General Psychiatry 62:593–602.

    https://doi.org/10.1001/archpsyc.62.6.593

    • PubMed
    • Google Scholar
59. 1. Klimesch W
    2. Doppelmayr M
    3. Schimke H
    4. Pachinger T

    (1996) Alpha frequency, reaction time, and the speed of processing information

    Journal of Clinical Neurophysiology 13:511–518.

    https://doi.org/10.1097/00004691-199611000-00006

    • PubMed
    • Google Scholar
60. 1. Klimesch W

    (1997) EEG-alpha rhythms and memory processes

    International Journal of Psychophysiology 26:319–340.

    https://doi.org/10.1016/s0167-8760(97)00773-3

    • PubMed
    • Google Scholar
61. 1. Klimesch W

    (1999) EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis

    Brain Research. Brain Research Reviews 29:169–195.

    https://doi.org/10.1016/s0165-0173(98)00056-3

    • PubMed
    • Google Scholar
62. 1. Klimesch W

    (2012) α-band oscillations, attention, and controlled access to stored information

    Trends in Cognitive Sciences 16:606–617.

    https://doi.org/10.1016/j.tics.2012.10.007

    • PubMed
    • Google Scholar
63. 1. Langer N.
    2. Pedroni A
    3. Gianotti LRR
    4. Hänggi J
    5. Knoch D
    6. Jäncke L

    (2012) Functional brain network efficiency predicts intelligence

    Human Brain Mapping 33:1393–1406.

    https://doi.org/10.1002/hbm.21297

    • PubMed
    • Google Scholar
64. 1. Langer N
    2. von Bastian CC
    3. Wirz H
    4. Oberauer K
    5. Jäncke L

    (2013) The effects of working memory training on functional brain network efficiency

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 49:2424–2438.

    https://doi.org/10.1016/j.cortex.2013.01.008

    • PubMed
    • Google Scholar
65. 1. Laufs H
    2. Kleinschmidt A
    3. Beyerle A
    4. Eger E
    5. Salek-Haddadi A
    6. Preibisch C
    7. Krakow K

    (2003) EEG-correlated fMRI of human alpha activity

    NeuroImage 19:1463–1476.

    https://doi.org/10.1016/s1053-8119(03)00286-6

    • PubMed
    • Google Scholar
66. 1. Lebel C
    2. Walker L
    3. Leemans A
    4. Phillips L
    5. Beaulieu C

    (2008) Microstructural maturation of the human brain from childhood to adulthood

    NeuroImage 40:1044–1055.

    https://doi.org/10.1016/j.neuroimage.2007.12.053

    • PubMed
    • Google Scholar
67. 1. Lindsley DB

    (1939) A longitudinal study of the occipital alpha rhythm in normal children: frequency and amplitude standards

    The Pedagogical Seminary and Journal of Genetic Psychology 55:197–213.

    https://doi.org/10.1080/08856559.1939.10533190

    • Google Scholar
68. 1. Lopes da Silva FH
    2. van Lierop TH
    3. Schrijer CF
    4. van Leeuwen WS

    (1973) Organization of thalamic and cortical alpha rhythms: spectra and coherences

    Electroencephalography and Clinical Neurophysiology 35:627–639.

    https://doi.org/10.1016/0013-4694(73)90216-2

    • PubMed
    • Google Scholar
69. 1. Lopes da Silva F

    (1991) Neural mechanisms underlying brain waves: from neural membranes to networks

    Electroencephalography and Clinical Neurophysiology 79:81–93.

    https://doi.org/10.1016/0013-4694(91)90044-5

    • PubMed
    • Google Scholar
70. 1. Luque Laguna PA
    2. Combes AJE
    3. Streffer J
    4. Einstein S
    5. Timmers M
    6. Williams SCR
    7. Dell’Acqua F

    (2020) Reproducibility, reliability and variability of FA and MD in the older healthy population: A test-retest multiparametric analysis

    NeuroImage. Clinical 26:102168.

    https://doi.org/10.1016/j.nicl.2020.102168

    • PubMed
    • Google Scholar
71. 1. Marcuse LV
    2. Schneider M
    3. Mortati KA
    4. Donnelly KM
    5. Arnedo V
    6. Grant AC

    (2008) Quantitative analysis of the EEG posterior-dominant rhythm in healthy adolescents

    Clinical Neurophysiology 119:1778–1781.

    https://doi.org/10.1016/j.clinph.2008.02.023

    • PubMed
    • Google Scholar
72. 1. Matthis P
    2. Scheffner D
    3. Benninger C
    4. Lipinski C
    5. Stolzis L

    (1980) Changes in the background activity of the electroencephalogram according to age

    Electroencephalography and Clinical Neurophysiology 49:626–635.

    https://doi.org/10.1016/0013-4694(80)90403-4

    • PubMed
    • Google Scholar
73. 1. McIntosh AR

    (2010)

    The development of a noisy brain

    Archives Italiennes de Biologie 148:223–337.

    • Google Scholar
74. 1. Mierau A
    2. Felsch M
    3. Hülsdünker T
    4. Mierau J
    5. Bullermann P
    6. Weiß B
    7. Strüder HK

    (2016) The interrelation between sensorimotor abilities, cognitive performance and individual EEG alpha peak frequency in young children

    Clinical Neurophysiology 127:270–276.

    https://doi.org/10.1016/j.clinph.2015.03.008

    • PubMed
    • Google Scholar
75. 1. Miller KJ
    2. Sorensen LB
    3. Ojemann JG
    4. den Nijs M
    5. Sporns O

    (2009) Power-law scaling in the brain surface electric potential

    PLOS Computational Biology 5:e1000609.

    https://doi.org/10.1371/journal.pcbi.1000609

    • Google Scholar
76. 1. Mori S
    2. Crain BJ
    3. Chacko VP
    4. van Zijl PC

    (1999) Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging

    Annals of Neurology 45:265–269.

    https://doi.org/10.1002/1531-8249(199902)45:2<265::aid-ana21>3.0.co;2-3

    • PubMed
    • Google Scholar
77. Website

    1. National Institutes of Health and Northwestern University

    (2022) NIH Toolbox Technical Manual - Flanker Inhibitory Control and Attention Test

    Accessed June 15, 2022.

    http://www.healthmeasures.net/2-uncategorised/209-nih-toolbox-technical-manuals-for-ac
78. 1. Newson JJ
    2. Thiagarajan TC

    (2018) EEG frequency bands in psychiatric disorders: a review of resting state studies

    Frontiers in Human Neuroscience 12:521.

    https://doi.org/10.3389/fnhum.2018.00521

    • PubMed
    • Google Scholar
79. 1. Niedermeyer E

    (1999)

    The normal EEG of the waking adult

    Electroencephalography: Basic Principles, Clinical Applications, and Related Fields 167:155–164.

    • Google Scholar
80. 1. Nyholt DR

    (2004) A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other

    American Journal of Human Genetics 74:765–769.

    https://doi.org/10.1086/383251

    • PubMed
    • Google Scholar
81. 1. Oldfield RC

    (1971) The assessment and analysis of handedness: the Edinburgh inventory

    Neuropsychologia 9:97–113.

    https://doi.org/10.1016/0028-3932(71)90067-4

    • PubMed
    • Google Scholar
82. 1. Ostlund B
    2. Donoghue T
    3. Anaya B
    4. Gunther KE
    5. Karalunas SL
    6. Voytek B
    7. Pérez-Edgar KE

    (2022) Spectral parameterization for studying neurodevelopment: How and why

    Developmental Cognitive Neuroscience 54:101073.

    https://doi.org/10.1016/j.dcn.2022.101073

    • PubMed
    • Google Scholar
83. 1. Pedroni A
    2. Bahreini A
    3. Langer N

    (2019) Automagic: Standardized preprocessing of big EEG data

    NeuroImage 200:460–473.

    https://doi.org/10.1016/j.neuroimage.2019.06.046

    • PubMed
    • Google Scholar
84. 1. Pion-Tonachini L
    2. Kreutz-Delgado K
    3. Makeig S

    (2019) ICLabel: An automated electroencephalographic independent component classifier, dataset, and website

    NeuroImage 198:181–197.

    https://doi.org/10.1016/j.neuroimage.2019.05.026

    • PubMed
    • Google Scholar
85. 1. Posthuma D
    2. Neale MC
    3. Boomsma DI
    4. de Geus EJ

    (2001) Are smarter brains running faster? Heritability of alpha peak frequency, IQ, and their interrelation

    Behavior Genetics 31:567–579.

    https://doi.org/10.1023/a:1013345411774

    • PubMed
    • Google Scholar
86. 1. Robertson MM
    2. Furlong S
    3. Voytek B
    4. Donoghue T
    5. Boettiger CA
    6. Sheridan MA

    (2019) EEG power spectral slope differs by ADHD status and stimulant medication exposure in early childhood

    Journal of Neurophysiology 122:2427–2437.

    https://doi.org/10.1152/jn.00388.2019

    • PubMed
    • Google Scholar
87. 1. Segalowitz SJ
    2. Santesso DL
    3. Jetha MK

    (2010) Electrophysiological changes during adolescence: a review

    Brain and Cognition 72:86–100.

    https://doi.org/10.1016/j.bandc.2009.10.003

    • PubMed
    • Google Scholar
88. 1. Smit CM
    2. Wright MJ
    3. Hansell NK
    4. Geffen GM
    5. Martin NG

    (2006) Genetic variation of individual alpha frequency (IAF) and alpha power in a large adolescent twin sample

    International Journal of Psychophysiology 61:235–243.

    https://doi.org/10.1016/j.ijpsycho.2005.10.004

    • PubMed
    • Google Scholar
89. 1. Smith SM

    (2002) Fast robust automated brain extraction

    Human Brain Mapping 17:143–155.

    https://doi.org/10.1002/hbm.10062

    • PubMed
    • Google Scholar
90. 1. Somsen RJM
    2. van’t Klooster BJ
    3. van der Molen MW
    4. van Leeuwen HMP
    5. Licht R

    (1997) Growth spurts in brain maturation during middle childhood as indexed by EEG power spectra

    Biological Psychology 44:187–209.

    https://doi.org/10.1016/S0301-0511(96)05218-0

    • PubMed
    • Google Scholar
91. 1. Steriade M
    2. Gloor P
    3. Llinás RR
    4. Lopes da Silva FH
    5. Mesulam MM

    (1990) Basic mechanisms of cerebral rhythmic activities

    Electroencephalography and Clinical Neurophysiology 76:481–508.

    https://doi.org/10.1016/0013-4694(90)90001-Z

    • PubMed
    • Google Scholar
92. 1. Surwillo WW

    (1961) Frequency of the ‘alpha’ rhythm, reaction time and age

    Nature 191:823–824.

    https://doi.org/10.1038/191823a0

    • Google Scholar
93. 1. Tange O

    (2011)

    Gnu parallel-the command-line power tool

    The USENIX Magazine 36:42–47.

    • Google Scholar
94. 1. Usher M
    2. Stemmler M
    3. Olami Z

    (1995) Dynamic pattern formation leads to 1/f noise in neural populations

    Physical Review Letters 74:326–329.

    https://doi.org/10.1103/PhysRevLett.74.326

    • PubMed
    • Google Scholar
95. 1. Vehtari A
    2. Gelman A
    3. Gabry J

    (2017) Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC

    Statistics and Computing 27:1413–1432.

    https://doi.org/10.1007/s11222-016-9696-4

    • Google Scholar
96. 1. Veraart J
    2. Poot DHJ
    3. Van Hecke W
    4. Blockx I
    5. Van der Linden A
    6. Verhoye M
    7. Sijbers J

    (2011) More accurate estimation of diffusion tensor parameters using diffusion Kurtosis imaging

    Magnetic Resonance in Medicine 65:138–145.

    https://doi.org/10.1002/mrm.22603

    • PubMed
    • Google Scholar
97. 1. Veraart J
    2. Sijbers J
    3. Sunaert S
    4. Leemans A
    5. Jeurissen B

    (2013) Weighted linear least squares estimation of diffusion MRI parameters: strengths, limitations, and pitfalls

    NeuroImage 81:335–346.

    https://doi.org/10.1016/j.neuroimage.2013.05.028

    • PubMed
    • Google Scholar
98. 1. Veraart J.
    2. Fieremans E
    3. Novikov DS

    (2016) Diffusion MRI noise mapping using random matrix theory

    Magnetic Resonance in Medicine 76:1582–1593.

    https://doi.org/10.1002/mrm.26059

    • PubMed
    • Google Scholar
99. 1. Veraart J
    2. Kochunov P
    3. McGuire S
    4. Sherman P
    5. Kellner E
    6. Novikov DS
    7. Fieremans E

    (2018) Evaluation of the accuracy and precision of the diffusion parameter EStImation with Gibbs and NoisE removal pipeline

    NeuroImage 183:532–543.

    https://doi.org/10.1016/j.neuroimage.2018.07.066

    • PubMed
    • Google Scholar
100. 1. Voytek B
     2. Knight RT

     (2015) Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease

     Biological Psychiatry 77:1089–1097.

     https://doi.org/10.1016/j.biopsych.2015.04.016

     • PubMed
     • Google Scholar
101. 1. Voytek B
     2. Kramer MA
     3. Case J
     4. Lepage KQ
     5. Tempesta ZR
     6. Knight RT
     7. Gazzaley A

     (2015) Age-related changes in 1/f neural electrophysiological noise

     The Journal of Neuroscience 35:13257–13265.

     https://doi.org/10.1523/JNEUROSCI.2332-14.2015

     • PubMed
     • Google Scholar
102. 1. Wakana S
     2. Caprihan A
     3. Panzenboeck MM
     4. Fallon JH
     5. Perry M
     6. Gollub RL
     7. Hua K
     8. Zhang J
     9. Jiang H
     10. Dubey P
     11. Blitz A
     12. van Zijl P
     13. Mori S

     (2007) Reproducibility of quantitative tractography methods applied to cerebral white matter

     NeuroImage 36:630–644.

     https://doi.org/10.1016/j.neuroimage.2007.02.049

     • PubMed
     • Google Scholar
103. 1. Wantanabe S

     (2013)

     A widely applicable Bayesian information criterion

     Journal of Machine Learning Research 14:867–897.

     • Google Scholar
104. 1. Wassermann D
     2. Rathi Y
     3. Bouix S
     4. Kubicki M
     5. Kikinis R
     6. Shenton M
     7. Westin CF

     (2011) White matter bundle registration and population analysis based on Gaussian processes

     Information Processing in Medical Imaging 22:320–332.

     https://doi.org/10.1007/978-3-642-22092-0_27

     • PubMed
     • Google Scholar
105. Book

     1. Wechsler D.

     (2003)

     Wechsler intelligence scale for children--Fourth Edition (WISC-IV)

     The Psychological Corporation.

     • Google Scholar
106. Book

     1. Wechsler D.

     (2008)

     Wechsler Adult Intelligence Scale--Fourth edition (WAIS-IV), Australian and New Zealand Language Adaptation

     NCS Pearson Inc.

     • Google Scholar
107. Book

     1. Weiß RH

     (2011)

     Wiener Matrizen-Test 2: Ein Rasch-Skaldierter Sprachfreier Kurztest Zu Erfassung Der Intelligenz

     Germany: Testzentrale.

     • Google Scholar
108. 1. Welch P

     (1967) The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms

     IEEE Transactions on Audio and Electroacoustics 15:70–73.

     https://doi.org/10.1109/TAU.1967.1161901

     • Google Scholar
109. 1. Wen H
     2. Liu Z

     (2016) Separating fractal and oscillatory components in the power spectrum of neurophysiological signal

     Brain Topography 29:13–26.

     https://doi.org/10.1007/s10548-015-0448-0

     • PubMed
     • Google Scholar
110. 1. Whitford TJ
     2. Rennie CJ
     3. Grieve SM
     4. Clark CR
     5. Gordon E
     6. Williams LM

     (2007) Brain maturation in adolescence: concurrent changes in neuroanatomy and neurophysiology

     Human Brain Mapping 28:228–237.

     https://doi.org/10.1002/hbm.20273

     • PubMed
     • Google Scholar
111. 1. Yeatman JD
     2. Dougherty RF
     3. Rykhlevskaia E
     4. Sherbondy AJ
     5. Deutsch GK
     6. Wandell BA
     7. Ben-Shachar M

     (2011) Anatomical properties of the arcuate fasciculus predict phonological and reading skills in children

     Journal of Cognitive Neuroscience 23:3304–3317.

     https://doi.org/10.1162/jocn_a_00061

     • PubMed
     • Google Scholar
112. 1. Yeatman JD
     2. Dougherty RF
     3. Myall NJ
     4. Wandell BA
     5. Feldman HM

     (2012) Tract profiles of white matter properties: automating fiber-tract quantification

     PLOS ONE 7:e49790.

     https://doi.org/10.1371/journal.pone.0049790

     • PubMed
     • Google Scholar
113. 1. Ystad M
     2. Hodneland E
     3. Adolfsdottir S
     4. Haász J
     5. Lundervold AJ
     6. Eichele T
     7. Lundervold A

     (2011) Cortico-striatal connectivity and cognition in normal aging: a combined DTI and resting state fMRI study

     NeuroImage 55:24–31.

     https://doi.org/10.1016/j.neuroimage.2010.11.016

     • PubMed
     • Google Scholar
114. 1. Zelazo PD
     2. Anderson JE
     3. Richler J
     4. Wallner-Allen K
     5. Beaumont JL
     6. Conway KP
     7. Gershon R
     8. Weintraub S

     (2014) NIH Toolbox Cognition Battery (CB): validation of executive function measures in adults

     Journal of the International Neuropsychological Society 20:620–629.

     https://doi.org/10.1017/S1355617714000472

     • PubMed
     • Google Scholar

Author details

1. Marius Tröndle

   1. Department of Psychology, University of Zurich, Methods of Plasticity Research, Zurich, Switzerland
   2. University Research Priority Program (URPP) Dynamic of Healthy Aging, Zurich, Switzerland

   Contribution

   Conceptualization, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing – review and editing

   For correspondence

   m.troendle@psychologie.uzh.ch

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1285-3038

2. Tzvetan Popov

   1. Department of Psychology, University of Zurich, Methods of Plasticity Research, Zurich, Switzerland
   2. University Research Priority Program (URPP) Dynamic of Healthy Aging, Zurich, Switzerland

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5495-1376

3. Sabine Dziemian

   1. Department of Psychology, University of Zurich, Methods of Plasticity Research, Zurich, Switzerland
   2. University Research Priority Program (URPP) Dynamic of Healthy Aging, Zurich, Switzerland

   Contribution

   Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Nicolas Langer

   1. Department of Psychology, University of Zurich, Methods of Plasticity Research, Zurich, Switzerland
   2. University Research Priority Program (URPP) Dynamic of Healthy Aging, Zurich, Switzerland
   3. Neuroscience Center Zurich (ZNZ), University of Zurich & ETH Zurich, Zurich, Switzerland

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing – review and editing

   For correspondence

   n.langer@psychologie.uzh.ch

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6038-9471

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