Electrophysiological Analysis of Attention Deficit Hyperactivity Disorder (ADHD) Subtypes: A Subnetwork Modularity Approach

Poster Session F - Tuesday, April 16, 2024, 8:00 – 10:00 am EDT, Sheraton Hall ABC

Leila Rafiei¹ (leylarafiei943@gmail.com), Amirhossein Ghaderi²; ¹Isfahan University of Medical Sciences, ²York University

The neurobiological basis of Attention Deficit Hyperactivity Disorder (ADHD) and its subtypes is not well understood, with some studies suggesting that ADHD-I and ADHD-C may have different neural foundations. This study uses a subnetwork modularity approach based on graph theoretical analysis of EEG data to investigate the neural basis of ADHD and its subtypes. The LORETA algorithm was used to estimate current densities in 84 regions of interest (ROIs) in the cortex and calculate functional connectivity between these ROIs in different EEG frequency bands. The modularity of five functional brain networks (default mode, central control, salience, visual, and sensorimotor) was evaluated using the Newman modularity algorithm. Edge betweenness centrality was also used to assess communication between these functional brain networks. The study found that different brain networks have modularity in certain frequency bands, and ADHD groups showed reduced modularity of the visual network compared to normal groups in the alpha1 band (8-10 Hz). The communication between the visual network and other brain networks, except the salience network, was also reduced in ADHD groups (in the alpha1 band). However, there were no significant differences in the modularity of brain networks and communication among them between two ADHD subtypes. The results suggest a novel mechanism for ADHD involving lower intrinsic modularity in the visual network, disturbed communication between the visual network and other networks, and potential impact on the function of control and sensorimotor networks.

Topic Area: ATTENTION: Other