Abnormal brain regions in two-group cross-location dynamics model of autism

Resting-state fMRI studies have suggested that autism spectrum disorder (ASD) is associated with aberrant dynamic changes. However, existing research either has difficulty showing the brain's dynamic characteristics or cannot obtain stable results. We examined the 'two-group cross-location hidden Markov model' of each region of interest (ROI) to identify possible pathogenic features of ASD. Specifically, we selected resting-state fMRI data with complete scales and good quality from Autism Brain Imaging Data Exchange (ABIDEI). Eligible data included 145 ASD and 157 control (CON). Two groups of subjects were separated to train Hidden Markov models representing respective populations. Then, we used each model to estimate the likelihood values of all participants. Using the likelihood value as features, we tested the significant differences of 200 ROIs and finally identified ROIs with common significant differences in the two types of models. Additionally, we investigated the relationship between likelihood values of significantly different ROIs and clinical scales. some ROIs were negatively correlated with the Autism Diagnostic Observation Schedule and positively correlated with full IQ. Finally, we constructed a support vector machine to classify ASD and CON. Overall, our findings suggested that the abnormal areas in the frontopolar area, orbitofrontal area, inferior temporal gyrus, middle temporal gyrus and fusiform gyrus are prominent features of ASD and are closely related to clinical functional decline. The average accuracy rate reached 74.9% after ten cross-validations. This 'two-group cross-localized Hidden Markov Model' provides a robust and powerful framework for understanding the dysfunctional brain architecture of ASD.