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Biology subjects

Silvan, A.

Publications and source records attributed to Silvan, A..

3 recordsLinked to original sources

Sex differences in pediatric EEG inter-subject correlation during naturalistic movie watching: a large-scale characterization of the Healthy Brain Network EEG dataset

Inter-subject correlation (ISC) of EEG during naturalistic viewing is increasingly used as a candidate pediatric biomarker, yet its behavior at cohort scale remains uncharacterized. We characterize ISC in 1143 children and adolescents (765 male, 378 female, ages 5 to 21) from the Healthy Brain Network EEG dataset viewing four naturalistic film clips. Three results follow. First, ISC separates narrative from abstract stimuli by 1.7 to 2.4 fold and exceeds a resting-state baseline by 42 to 99 fold, and the previously reported developmental decline replicates throughout. Second, ISC is sex-dependent: males exceed females in every movie (t = 9.7 to 13.5; Cohen's d = 0.61 to 0.85), concentrated in delta and theta, peaking at a frontocentral cluster and at ages 11 to 14. The direction matches an earlier report where the effect was marginal, and we resolve structure that sample could not. It survives three demographic and clinical robustness analyses, same-sex and size-matched templates, ocular component removal, and exclusion of the releases overlapping that report, and is attenuated, by approximately 10 percent adjusting for interpolation count and by 23 percent in recordings free of cluster interpolation, but not explained by differential channel interpolation. Evoked response magnitude, previously proposed as an alternative account, does differ by sex here (d = 0.55 to 0.60) but mediates only 8.6 to 18.1 percent of the effect. Third, ISC predicts none of four CBCL bifactor dimensions after FDR correction at any feature resolution: zero of 16 grand-mean, 2064 per-channel and 16 cluster tests, with confidence intervals excluding standardized associations beyond |{beta}| = 0.106.

neuroscience↗

Brain-body dynamics is asymmetric and stable across cognitive states

The human body displays slow, spontaneous fluctuations in brain activity, autonomic physiology, and small incidental movements. It is unknown whether these co-fluctuations reflect a stable endogenous brain-body dynamic, or whether this dynamic varies with cognitive state. We addressed this question using a dynamical systems approach to analyze simultaneously recorded neural activity (EEG), autonomic physiology, and behavior while participants listened to spoken narratives or were at rest. We found that cognitive state did not substantially alter the endogenous dynamic. Acoustic and linguistic features predicted neural activity, which in turn affected physiological responses. Only low-level sound fluctuations exerted direct effects on autonomic signals. Peripheral physiology and behavior exerted stronger influences on EEG than the reverse. These findings suggest that slow co-fluctuations are the result of a stablebrain-body dynamic with strong bottom-up feedback, and that the narrative entrains this dynamic by engaging cognition.

neuroscience↗

Overlap and Differences of Autism and ADHD: Digital Phenotyping of Movement and Communication During Development

Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) frequently co-occur with overlapping traits. We investigated whether automated behavioral analysis during a clinician-child interview can identify distinct, objective features of the two conditions. Analyzing audio-video recordings of 2,529 youths (ages 5-22) in a broad community sample, multivariate models revealed that language difficulties often attributed to ADHD are primarily explained by age, cognitive ability, or co-occurring ASD. Increased motor activity specifically marked hyperactive-impulsive ADHD, but not ASD or inattentive ADHD. ASD was uniquely characterized by divergent narrative production and idiosyncratic responses, alongside a distinct vocal profile of higher pitch and dysphonia, despite structurally intact language. While these digital behavioral measures correlate with most diagnostic categories and age, the joint analysis effectively separates the effects of ASD from ADHD. These findings show that scalable digital assessment from recorded clinical interviews can disentangle overlapping ASD and ADHD diagnoses into domain-specific behavioral signatures.

bioengineering↗