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

Regression is frequently described in Autism spectrum disorder (ASD). Limited comprehensive studies have been conducted in patients with ASD with regression.

To explore the network topological properties in ASD children with (ASD-R) and without (ASD-NR) regression.

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).

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.

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.