Inter-individual heterogeneity of functional brain networks in children with autism spectrum disorder

• Published in: Molecular autism Vol. 13; no. 1; pp. 52 - 13
• Main Authors: Guo, Xiaonan, Zhai, Guangjin, Liu, Junfeng, Cao, Yabo, Zhang, Xia, Cui, Dong, Gao, Le
• Format: Journal Article
• Language: English
• Published: London BioMed Central 26.12.2022; BioMed Central Ltd; BMC
• Subjects: Algorithms; Analysis; Autism spectrum disorder; Autism Spectrum Disorder - diagnostic imaging; Autistic children; Autistic Disorder; Brain - diagnostic imaging; Brain Mapping - methods; Care and treatment; Child; Diagnosis; Functional connectivity; Functional magnetic resonance imaging; Health aspects; Human Genetics; Humans; k-means clustering; Magnetic Resonance Imaging - methods; Male; Medicine; Medicine & Public Health; Methods; Neural Pathways - diagnostic imaging; Neurology; Neuropsychology; Neurosciences; Nuclear magnetic resonance spectroscopy; Pediatrics; Pervasive developmental disorders; Psychiatry; Subtype; China; Functional connectivity; Functional magnetic resonance imaging; k-means clustering; Subtype; Autism spectrum disorder
• ISSN: 2040-2392; 2040-2392
• DOI: 10.1186/s13229-022-00535-0

Background Autism spectrum disorder (ASD) is a neurodevelopmental disorder with considerable clinical heterogeneity. This study aimed to explore the heterogeneity of ASD based on inter-individual heterogeneity of functional brain networks. Methods Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were used in this study for 105 children with ASD and 102 demographically matched typical controls (TC) children. Functional connectivity (FC) networks were first obtained for ASD and TC groups, and inter-individual deviation of functional connectivity (IDFC) from the TC group was then calculated for each individual with ASD. A k-means clustering algorithm was used to obtain ASD subtypes based on IDFC patterns. The FC patterns were further compared between ASD subtypes and the TC group from the brain region, network, and whole-brain levels. The relationship between IDFC and the severity of clinical symptoms of ASD for ASD subtypes was also analyzed using a support vector regression model. Results Two ASD subtypes were identified based on the IDFC patterns. Compared with the TC group, the ASD subtype 1 group exhibited a hypoconnectivity pattern and the ASD subtype 2 group exhibited a hyperconnectivity pattern. IDFC for ASD subtype 1 and subtype 2 was found to predict the severity of social communication impairments and the severity of restricted and repetitive behaviors in ASD, respectively. Limitations Only male children were selected for this study, which limits the ability to study the effects of gender and development on ASD heterogeneity. Conclusions These results suggest the existence of subtypes with different FC patterns in ASD and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.