Reduced Modular Segregation of White Matter Brain Networks in Attention Deficit Hyperactivity Disorder

Abstract

Objective: Despite studies reporting alterations in the brain networks of patients with ADHD, alterations in the modularity of white matter (WM) networks are still unclear. Method: Based on the results of module division by generalized Louvain algorithm, the modularity of ADHD was evaluated. The correlation between the modular changes of ADHD and its clinical characteristics was analyzed. Results: The participation coefficient and the connectivity between modules of ADHD increased, and the modularity coefficient decreased. Provincial hubs of ADHD did not change, and the number of connector hubs increased. All results showed that the modular segregation of WM networks of ADHD decreased. Modules with reduced modular segregation are mainly responsible for language and motor functions. Moreover, modularity showed evident correlation with the symptoms of ADHD. Conclusion: The modularity changes in WM network provided a novel insight into the understanding of brain cognitive alterations in ADHD.