Compared to adult EEG, EEG signals recorded from pediatric populations have shorter recording periods and contain more artifact contamination. Therefore, pediatric EEG data necessitate specific preprocessing approaches in order to remove environmental noise and physiological artifacts without losing large amounts of data. However, there is presently a scarcity of standard automated preprocessing pipelines suitable for pediatric EEG.In an effort to achieve greater standardization of EEG preprocessing, and in particular for the analysis of pediatric data, we developed the Maryland Analysis of Developmental EEG (MADE) pipeline as an automated preprocessing pipeline compatible with EEG data recorded with different hardware systems, different populations, levels of artifact contamination, and length of recordings. MADE uses EEGLAB and functions from some EEGLAB plugins, and includes additional customizable features particularly useful for EEG data collected from pediatric populations.MADE processes event-related and resting state EEG from raw data files through a series of preprocessing steps and outputs processed clean data ready to be analyzed in time, frequency, or time-frequency domain. MADE provides a report file at the end of the preprocessing that describes a variety of features of the processed data to facilitate the assessment of the quality of processed data. In this paper we discuss some practical issues, which are specifically relevant to pediatric EEG preprocessing. We also provide custom-written scripts to address these practical issues.

A major challenge for electroencephalograph (EEG) studies on pediatric populations is that large amounts of data are lost due to artifacts (e.g., movement and blinks). Independent component analysis (ICA) can separate artifactual and neural activity, allowing researchers to remove such artifactual activity and retain a greater percentage of EEG data for analyses. However, manual identification of artifactual components is time‐consuming and requires subjective judgment. Automated algorithms, like ADJUST and ICLabel, have been validated on adults, but to our knowledge, no such algorithms have been optimized for pediatric data. Therefore, in an attempt to automate artifact selection for pediatric data collected with geodesic nets, we modified ADJUST's algorithm. Our “adjusted‐ADJUST” algorithm was compared to the “original‐ADJUST” algorithm and ICLabel in adults, children, and infants on three different performance measures: respective classification agreement with expert coders, the number of trials retained following artifact removal, and the reliability of the EEG signal after preprocessing with each algorithm. Overall, the adjusted‐ADJUST algorithm performed better than the original‐ADJUST algorithm and no ICA correction with adult and pediatric data. Moreover, in some measures, it performed better than ICLabel for pediatric data. These results indicate that optimizing existing algorithms improves artifact classification and retains more trials, potentially facilitating EEG studies with pediatric populations. Adjusted‐ADJUST is freely available under the terms of the GNU General Public License at: https://github.com/ChildDevLab/MADE-EEG-preprocessing-pipeline/tree/master/adjusted_adjust_scripts.

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Processing feedback from the environment is an essential function during development to adapt behavior in advantageous ways. One measure of feedback processing, the feedback negativity (FN), is an ERP observed following the presentation of feedback. Findings detailing developmental changes in the FN have been mixed, possibly due to limitations in traditional ERP measurement methods. Recent work shows that both theta and delta frequency activity contribute to the FN; utilizing time-frequency methods to measure change in power and phase in these frequency bands may provide more accurate representation of feedback processing development in childhood and adolescence. We employ time-frequency power and intertrial phase synchrony measures, in addition to conventional time-domain ERP methods, to examine the development of feedback processing in the theta (4-7 Hz) and delta (.1-3 Hz) bands throughout adolescence. A sample of 54 female participants (8-17 years old) completed a gambling task while EEG was recorded. As expected, time-domain ERP amplitudes showed no association with age. In contrast, significant effects were observed for the time-frequency measures, with theta power decreasing with age and delta power increasing with age. For intertrial phase synchrony, delta synchrony increased with age, while age-related changes in theta synchrony differed for gains and losses. Collectively, these findings highlight the importance of considering time-frequency dynamics when exploring how the processing of feedback develops through late childhood and adolescence. In particular, the role of delta band activity and theta synchrony appear central to understanding age-related changes in the neural response to feedback.

Background: Children with the temperament of behavioral inhibition (BI) face increased risk for social anxiety. However, not all children with BI develop anxiety symptoms. Inhibitory control (IC) has been suggested as a moderator of the pathway between BI and social anxiety. This study uses longitudinal data to characterize development of IC and tests the hypothesis that IC moderates associations between early BI and later social anxiety symptoms. Methods: Children completed a Go/Nogo task at ages 5, 7, and 10 years as part of a longitudinal study of BI (measured at 2-3 years) and social anxiety symptoms (measured at 12 years). To assess IC development, response strategy (criterion) and inhibitory performance (d 0 ) were characterized using signal detection theory. Latent growth models were used to characterize the development of IC and examine relations among BI, IC parameters, and social anxiety symptoms. Results: IC response strategy did not change between 5 and 10 years of age, whereas IC performance improved over time. BI scores in toddlerhood predicted neither initial levels (intercept) nor changes (slope) in IC response strategy or IC performance. However, between ages 5 and 10, rate of change in IC performance, but not response strategy, moderated relations between BI and later parent-reported social anxiety symptoms. Specifically, greater age-related improvements in IC performance predicted higher levels of social anxiety in high BI children. Conclusions: IC development in childhood occurs independent of BI levels. However, rapid increases in IC performance moderate risk for social anxiety symptoms in children with BI. Implications for theory and practice are discussed.

Background: Children with Behavioral Inhibition (BI) temperament face increased social anxiety risk. However, not all children with BI develop anxiety symptoms. Inhibitory control (IC) has been suggested as a moderator of the pathway between BI and social anxiety. The current study uses longitudinal data to characterize development of IC and tests the hypothesis that IC moderates associations between early BI and later social anxiety symptoms. Methods: Children completed a Go/Nogo task at ages 5, 7, and 9 years as part of a longitudinal study of BI (measured at 2-3 years) and social anxiety symptoms (measured at 12 years). To assess IC development, response strategy (criterion) and inhibitory performance (d’) were characterized using signal detection theory. Latent growth models were used to characterize the development of IC and examine relations among BI, IC parameters, and social anxiety symptoms. Results: IC response strategy did not change between 5 and 9 years of age, whereas IC performance improved over time. BI scores in toddlerhood predicted neither initial levels (intercept) nor changes (slope) in IC response strategy or IC performance. However, between ages 5 and 9, rate of change in IC performance, but not response strategy, moderated relations between BI and later parent-reported social anxiety symptoms. Specifically, greater age-related improvements in IC performance predicted higher levels of social anxiety in high BI children.Conclusions: IC development in childhood occurs independent of BI levels. However, rapid IC development moderates risk for social anxiety symptoms in children with BI. Implications for theory and practice are discussed.

EEG provides a rich measure of brain activity that can be characterized as neuronal oscillations. However, most developmental EEG work to date has focused on analyzing EEG data as Event-Related Potentials (ERPs) or power based on the Fourier transform. While these measures have been productive, they do not leverage all the information contained within the EEG signal. Namely, ERP analyses ignore non-phase-locked signals and Fourier-based power analyses ignore temporal information. Time-frequency analyses can better characterize the oscillations contained in the EEG data. By separating power and phase information across different frequencies, time-frequency measures provide a closer interpretation of the neurophysiological mechanisms, facilitate translation across neurophysiology disciplines, and capture processes not observed by ERP or Fourier-based analyses (e.g., connectivity). Despite their unique contributions, a literature review of this journal reveals that time-frequency analyses of EEG are yet to be embraced by the developmental cognitive neuroscience field. This manuscript presents a conceptual introduction to time-frequency analyses for developmental researchers. To facilitate the use of time-frequency analyses, we include a tutorial of accessible scripts, based on Cohen (2014), to calculate time-frequency power (signal strength), inter-trial phase synchrony (signal consistency), and two types of phase-based connectivity (inter-channel phase synchrony and weighted phase lag index).

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