Structural networks in children with autism spectrum disorder with regression: A graph theory study

Background: Regression is frequently described in Autism spectrum disorder (ASD). Limited comprehensive studies have been conducted in patients with ASD with regression. Purpose: To explore the network topological properties in ASD children with (ASD-R) and without (ASD-NR) regression. Methods: In this study, 29 ASD-R, 68 ASD-NR, and 40 children with developmental delay (DD) were recruited. We utilized graph theory to characterize the white matter structure networks by using diffusion tensor imaging and T1-weighted imaging on a 3-T magnetic resonance system. Statistical analyses were performed using IBM SPSS (version 23). Results: ANCOVA showed significant differences in global efficiency, characteristic path length and sigma among the ASD-R, ASD-NR and DD groups, but the difference was not significant between the ASD-R and ASD-NR groups. There were 10 common hubs based on regional degree and regional efficiency in all groups. The hubness of the left superior frontal gyrus-dorsolateral, left middle occipital gyrus and right precuneus were enhanced (by regional degree) and that of the right thalamus was reduced (by regional efficiency) in the ASD-R relative to the ASD-NR group. After controlling for the course of regression, the CARS scores were significantly correlated with the regional efficiency of the right precuneus in the ASD-R group. Conclusions: The ASD-R children were different from the ASD-NR children in the distribution of hub regions, although there were no global network property differences between them. In ASD-R children, the right precuneus (PCUN.R) might play an important role and relate to autism symptom severity.