Analysis of Dynamic Network Reconfiguration in Adults with Attention-Deficit/Hyperactivity Disorder Based Multilayer Network. (21st May 2021)

• Record Type: Journal Article
• Authors: Cui, Xiaohong; Ding, Congli; Wei, Jing; Xue, Jiayue; Wang, Xiaoyue; Wang, Bin; Xiang, Jie
• Is Part Of: Cerebral cortex. Volume 31:Number 11(2021)
• Page Start: 4945
• Page End: 4957
• Publication Date: 2021-05-21
• Subjects: ADHD -- dynamic analysis -- multilayer network -- network reconfiguration -- rs-fMRI; Cerebral cortex -- Periodicals; Brain -- Periodicals; 612.825
• DOI: 10.1093/cercor/bhab133
• Languages: English
• ISSNs: 1047-3211

Abstract

Abstract: Attention-deficit/hyperactivity disorder (ADHD) has been reported exist abnormal topology structure in the brain network. However, these studies often treated the brain as a static monolithic structure, and dynamic characteristics were ignored. Here, we investigated how the dynamic network reconfiguration in ADHD patients differs from that in healthy people. Specifically, we acquired resting-state functional magnetic resonance imaging data from a public dataset including 40 ADHD patients and 50 healthy people. A novel model of a "time-varying multilayer network" and metrics of recruitment and integration were applied to describe group differences. The results showed that the integration scores of ADHD patients were significantly lower than those of controls at every level. The recruitment scores were lower than healthy people except for the whole-brain level. It is worth noting that the subcortical network and the thalamus in ADHD patients exhibited reduced alliance preference both within and between functional networks. In addition, we also found that recruitment and integration coefficients showed a significant correlation with symptom severity in some regions. Our results demonstrate that the capability to communicate within or between some functional networks is impaired in ADHD patients. These evidences provide a new opportunity for studying the characteristics of ADHD brain networks.