As we listen to speech, our ability to understand what was said requires us to retrieve and bind together individual word meanings into a coherent discourse representation. This so-called semantic unification is a fundamental cognitive skill, and its development relies on the integration of neural activity throughout widely distributed functional brain networks. In this proof-of-concept study, we examine, for the first time, how these functional brain networks develop in children. Twenty-six children (ages 4-17) listened to well-formed sentences and sentences containing a semantic violation, while EEG was recorded. Children with stronger vocabulary showed N400 effects that were more concentrated to centroparietal electrodes and greater EEG phase synchrony (phase lag index; PLI) between right centroparietal and bilateral frontocentral electrodes in the delta frequency band (1-3 Hz) 1.27-1.53 s after listening to well-formed sentences compared to sentences containing a semantic violation. These effects related specifically to individual differences in receptive vocabulary, perhaps pointing to greater recruitment of functional brain networks important for top-down semantic unification with development. Less skilled children showed greater delta phase synchrony for violation sentences 3.41-3.64 s after critical word onset. This later effect was partly driven by individual differences in nonverbal reasoning, perhaps pointing to non-verbal compensatory processing to extract meaning from speech in children with less developed vocabulary. We suggest that functional brain network communication, as measured by momentary changes in the phase synchrony of EEG oscillations, develops throughout the school years to support language comprehension in different ways depending on children's verbal and nonverbal skill levels.

The heterogeneity of attention-deficit/hyperactivity disorder (ADHD) traits (inattention vs. hyperactivity/impulsivity) complicates diagnosis and intervention. Identifying how the configuration of large-scale functional brain networks during cognitive processing correlate with this heterogeneity could help us understand the neural mechanisms altered across ADHD presentations. Here, we recorded high-density EEG while 62 non-clinical participants (ages 18-24; 32 male) underwent an inhibitory control task (Go/No-Go). Functional EEG networks were created using sensors as nodes and across-trial phase-lag index values as edges. Using cross-validated LASSO regression, we examined whether graph-theory metrics applied to both static networks (averaged across time-windows: -500 to 0ms, 0 to500ms) and dynamic networks (temporally layered with 2ms intervals), were associated with hyperactive/impulsive and inattentive traits. Network configuration during response execution/inhibition was associated with hyperactive/impulsive (mean R2 across test sets = .20, SE = .02), but not inattentive traits. Post-stimulus results at higher frequencies (Beta, 14-29Hz; Gamma, 30-90Hz) showed the strongest association with hyperactive/impulsive traits, and predominantly reflected less burst-like integration between modules in oscillatory beta networks during execution, and increased integration/small-worldness in oscillatory gamma networks during inhibition. We interpret the beta network results as reflecting weaker integration between specialized pre-frontal and motor systems during motor response preparation, and the gamma results as reflecting a compensatory mechanism used to integrate processing between less functionally specialized networks. This research demonstrates that the neural network mechanisms underlying response execution/inhibition might be associated with hyperactive/impulsive traits, and that dynamic, task-related changes in EEG functional networks may be useful in disentangling ADHD heterogeneity.

Reductions in response control (greater reaction time variability and commission error rate) are consistently observed in those diagnosed with attention-deficit/hyperactivity disorder (ADHD). Previous research suggests these reductions arise from a dysregulation of large-scale cortical networks. Here, we extended our understanding of this cortical-network/response-control pathway important to the neurobiology of ADHD. First, we assessed how dynamic changes in three resting-state EEG network properties thought to be relevant to ADHD (phase-synchronization, modularity, oscillatory power) covaried with response control during a simple perceptual decision-making task in 112 children/adolescents (aged 8-16) with and without formal ADHD diagnoses. Second, we tested whether these associations differed in males and females who were matched in age, ADHD-status and ADHD- subtype. We found that changes in oscillatory power (as opposed to phase-synchrony and modularity) are most related with response control, and that this relationship is stronger in ADHD compared to controls. Specifically, a tendency to dwell in an electrophysiological state characterized by high alpha/beta power (8-12/13-30Hz) and low delta/theta power (1-3/4-7Hz) supported response control, particularly in those with ADHD. Time in this state might reflect an increased initiation of alpha-suppression mechanisms, recruited by those with ADHD to suppress processing unfavourable to response control. We also found marginally significant evidence that this relationship is stronger in males compared to females, suggesting a distinct etiology for response control in the female presentation of ADHD.

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