A research of ADHD resting-state brain functional networks based on minimum spanning tree and hierarchical graph clustering

Attention Deficit Hyperactivity Disorder (ADHD) is a common psychiatric disorder in childhood, and its pathogenesis may be related to abnormalities in brain network connectivity.In this study, we employ minimum spanning tree and hierarchical clustering algorithms to analyze the differences in brain functional network structures between ADHD patients and normal individuals.Initially, we compute Pearson correlation coefficients to construct functional connectivity matrices, delving into the mean matrices for both groups to pinpoint regions marked by significant discrepancies.Subsequently, we construct the minimum spanning trees for each group, assessing their average leaf scores and examining the variations in network topology.In the final phase, we apply hierarchical clustering to the minimum spanning trees of both cohorts, with an analysis of the community structure conducted through homogeneous and heterogeneous slicing.The results show significant differences in functional connectivity networks in the ADHD group, revealing unique features in the neural mechanisms.