Changes in brain structural network connection of children with attention deficit hyperactivity disorder

Abstract

Objective To explore the changes in brain structure network connection in children with attention deficit hyperactivity disorder(ADHD), and to provide novel markers for early identification of ADHD in clinical practice. Methods Deterministic diffusion-tensor tractography and graph theory approaches were used to investigate the topologic organization of the brain structural connectome in 25 children with ADHD and 23 healthy control children from May 2017 to May 2018, at Children′s Hospital of Xuzhou Medical University.Individual white matter networks were constructed for each participant, then the global properties, nodal properties and edge-wise distributions were compared between the two groups. Results (1)The global efficiency of the ADHD group (0.30±0.13) was significantly lower than that of the healthy control group (0.38±0.11), but the clustering coefficient (0.35±0.28) and the characteristic path length (2.94±0.38) were significantly higher than those of the healthy control group (0.28±0.10, 2.65±0.37), and the differences were statistically significant (t=-2.41, 2.31, 2.62, all P<0.05). (2)In the ADHD group, the nodal efficiency of the left inferior frontal gyrus, triangular part (0.13±0.06), left supramarginal gyrus (0.30±0.10), left inferior parietal, angular gyri (0.29±0.10), left precuneus (0.26±0.12)were significantly lower than the healthy control group(0.17±0.07, 0.38±0.10, 0.40±0.12, 0.35±0.12), while the nodal efficiency of the right superior frontal gyrus, orbital part and right paracentral lobule were significantly higher than the healthy control group(0.49±0.17, 0.43±0.14), and the differences were statistically significant[t=-2.52, -2.62, -3.11, -2.77, 2.34, 2.79, all P<0.05, false discovery rate(FDR) corrected]. (3)A disrupted subnetwork was observed that consisted of left frontoparietal areas, basal ganglia, thalamus and insular network (P<0.05, FDR corrected), which has the potential to discriminate individuals with ADHD from healthy control children(area under receiver operating characteristic curve was 0.78). (4)Diminished strength of the subnet work connections was correlated with the attention defect in patients with ADHD(r=-0.607, P=0.003). Conclusions Using magnetic resonance diffusion tensor imaging, with the help of graph theory analysis technology, ADHD children can be observed changes in brain structure network at multiple levels.The distribution pattern of brain network structure connection changes is expected to become a new marker for identifying ADHD. Key words: Attention deficit hyperactivity disorder; Diffusion tensor image; Graph theory; Structural network