Understanding Autism Using Machine Learning: A Structural MRI Study

In this study, we propose a Computer-Aided Diagnostic (CAD) system to diagnose and understand autism spectrum disorder (ASD) using structural MRI (sMRI). Starting with identifying morphological anomalies within the cortical regions of ASD subjects. Every cortical feature receives a score corresponding to their contribution in diagnosing a subject to be ASD or typically developed (TD). Scores are determined by hyper-optimized machine learning (ML) classifiers. An early personalized diagnosis of ASD becomes possible by the proposed CAD system. The proposed framework implements multiple stages including cerebral cortex extraction from structure MRI (sMRI). Moreover, the proposed framework identify the altered brain regions. We can summarize this framework in the following procedures: i) Cerebral cortex segmentation, ii) Parcellation of the cortex to Desikan-Killiany (DK) atlas; iii) Annotating brain regions which are associated with ASD; iv) Blocking for the confounding effect of both age and sex; v) Tailoring ASD neuro-atlases; vi) Classifying ASD using neural networks (NN). We uti-lized Autism Brain Imaging Data Exchange (ABIDE I) dataset to test the proposed framework. The proposed achieved a balanced accuracy score of 97% ± 2%. In this study, we demonstrate the ability to describe specific developmental patterns of the brain in autism using tailored neuro-atlases, as well as, developing an objective CAD system using morphological features extracted from sMRI scans.