A sex-dependent computer-aided diagnosis system for autism spectrum disorder using connectivity of resting-state fMRI. (1st October 2022)

• Record Type: Journal Article
• Authors: Haghighat, Hossein; Mirzarezaee, Mitra; Araabi, Babak Nadjar; Khadem, Ali
• Is Part Of: Journal of neural engineering. Volume 19:Number 5(2022)
• Publication Date: 2022-10-01
• Subjects: autism spectrum disorder (ASD) -- sex -- computer aided diagnosis system (CADS) -- resting-state functional magnetic resonance imaging (rs-fMRI) -- functional connectivity (FC) -- effective connectivity (EC) -- group independent component analysis (GICA); Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ac86a4
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective. Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with the main symptoms of social communication disabilities. ASD is more than four times more common among males than females. The diagnosis of ASD is currently a subjective process by experts the same for males and females. Various studies have suggested the use of brain connectivity features for the diagnosis of ASD. Also, sex-related biological factors have been shown to play a role in ASD etiology and influence the brain connectivity. Therefore, proposing an accurate computer-aided diagnosis system (CADS) for ASD which considers the sex of subjects seems necessary. In this study, we present a sex-dependent connectivity-based CADS for ASD using resting-state functional magnetic resonance imaging. The proposed CADS classifies ASD males from normal males, and ASD females from normal females. Approach. After data preprocessing, group independent component analysis (GICA) was applied to obtain the resting-state networks (RSNs) followed by applying dual-regression to obtain the time course of each RSN for each subject. Afterwards, functional connectivity measures of full correlation and partial correlation and the effective connectivity measure of bivariate Granger causality were computed between time series of RSNs. To consider the role of sex differences in the classification process, male, female, and mixed groups were taken into account, and feature selection and classification were designed for each sex group separately. At the end, the classification accuracy was computed for each sex group. Main results. In the female group, a classification accuracy of 93.3% was obtained using full correlation while in the male group, a classification accuracy of 86.7% was achieved using both full correlation and bivariate Granger causality. Also, in the mixed group, a classification accuracy of 83.3% was obtained using full correlation. Significance. This supports the importance of considering sex in diagnosing ASD patients from normal controls.