Disrupted Small-World Networks in Children with Drug-Naïve Attention-Deficit/Hyperactivity Disorder: A DTI-Based Network Analysis

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, while the potential neurological mechanisms are poorly understood. To explore the alterations in the white matter (WM) structural connectome in children with drug-naïve ADHD, forty-nine ADHD and 51 age- and gender-matched typically developing (TD) children aged 6–14 years were enrolled. WM structural connectivity based on deterministic diffusion tensor imaging (DTI) was constructed in 90 cortical and subcortical regions, and topological parameters of the resulting graphs were calculated. Network metrics were compared between two groups. The concentration index and the total cancellation test scores of digit cancellation test were used to evaluate clinical symptom severity in ADHD. Then, a partial correlation analysis was performed to explore the relationship between significant topologic metrics and clinical symptom severity. Compared to TD group, ADHD showed an increase in the characteristic path length (Lp), normalized clustering coefficient (γ), small worldness (σ), and a decrease in the global efficiency (Eglob) (all p < 0.05). Furthermore, ADHD showed reduced nodal centralities mainly in the regions of default mode network (DMN), central executive network (CEN), basal ganglia, and bilateral thalamus (all p < 0.05). After performing Benjamini-Hochberg’s procedure, only the left orbital part of superior frontal gyrus and the left caudate were statistically significant (p < 0.05, FDR-corrected). In addition, the concentration index of ADHD was negatively correlated with the nodal betweenness of the left orbital part of the middle frontal gyrus (r = −0.302, p = 0.042). Our findings revealed an ADHD-related shift of WM network topology toward “regularization” pattern, characterized by decreased global network integration, which is also reflected by changed nodal centralities involving DMN, CEN, basal ganglia, and bilateral thalamus. ADHD could be understood by examining the dysfunction of large-scale spatially distributed neural networks.