Altered visual network modularity and communication in ADHD subtypes: Classification via source-localized EEG modules

Abstract

The neurobiological basis of ADHD subtypes remains unclear. This study investigates how ADHD subtypes affect modularity of functional brain networks across different oscillatory bands. We analyzed EEG data from across three groups: normally developing, ADHD-Inattentive, and ADHD-Combined. EEG source-localized current densities were estimated and lagged coherence between the ROIs was calculated across seven frequency bands. We evaluated the modularity of five functional brain networks (default mode, central control, salience, visual, and sensorimotor) and assessed edge betweenness centrality to examine network communications. Nonparametric tests revealed significantly lower visual network modularity in ADHD groups in the alpha1 band (8–10 Hz). Communication between the visual and other networks (excluding the salience) was also significantly reduced in ADHD groups. No significant modularity or inter-network communication differences were observed between the ADHD subtypes. A supervised classification algorithm using subnetwork modularity as input achieved high accuracy (88.9 %) in classifying normally developing and ADHD groups based on alpha1 band data. The modularity of the sensorimotor, visual, and default mode networks emerged as key predictors. However, classification accuracy declined when distinguishing between the two ADHD subtypes. Results suggest impairment in early sensory (visual) processing in ADHD. Additionally, the combined modularity of the sensorimotor, visual, and default mode networks may serve as a potential biomarker for ADHD. Our results support a shared neural basis for ADHD subtypes, reinforcing the view that they are likely subtypes of the same disorder rather than distinct conditions.