ABSTRACT
OBJECTIVES: To explore the alterations in the white matter (WM) structural connectome in children with drug-naïve attention-deficit/hyperactivity disorder (ADHD). METHODS: Forty-nine pediatric ADHD and 51 age- and gender-matched typically developing (TD) children aged 6-14 years old were enrolled. This cross-sectional study applied graph theoretical analysis to assess the white matter organization based on deterministic diffusion tensor imaging (DTI). WM structural connectivity was constructed in 90 cortical and subcor-tical regions, and topological parameters of the resulting graphs were calculated. Networks were compared between two groups. The digit cancellation test (DCT) was taken to evaluate clinical symptom severity in pediatric ADHD, using the concentration index and the total cancellation test scores. Then, a partial correlation analysis was performed to explore the re-lationship between significant topologic metrics and clinical symptom severity. RESULTS: Compared to TDs, ADHD showed an increase in the characteristic path length (Lp), normalized clustering coefficient (γ), small-worldness (σ), and a decrease in the global effi-ciency (Eglob) (all P <0.05). Furthermore, ADHD showed reduced nodal centralities mainly in the regions of default mode (DMN), central executive network (CEN), basal ganglia, and bilateral thalamus (all P <0.05). After performing Benjamini-Hochberg's procedure, only left orbital part of superior frontal gyrus (ORBsup.L) and left caudate (CAU) 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 (ORBmid.L) (r = -0.302, P = 0.042). CONCLUSIONS: 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.
