Deep Learning Approach for Classification and Interpretation of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder predominantly found in children. The current behavior-based diagnosis of ASD is arduous and requires expertise. Therefore, it is appealing to develop an accurate computer-aided tool for diagnosing ASD. Although resting-state functional magnetic resonance imaging (rsfMRI) has proven to be successful in capturing the neural organization of the brain, automated detection of ASD using rsfMRI scans is a challenging task due to heterogeneity in the dataset and limited sample size. This paper proposes a Multilayer Perceptron (MLP) based classification model with auto encoder pretraining for classifying ASD from Typically Developing (TD) using rsfMRI scans obtained from the ABIDE-1 dataset. Our model achieves new state-of-the-art performance on the ABIDE-1 dataset with a 10-fold cross-validation accuracy of 74.82%. Further, we use the Integrated Gradients (IG) and DeepLIFT techniques to identify the correlations between brain regions that contribute most to the classification task. Our analysis identifies the following regions, Left Lingual Gyrus, Right Insula Lobe, Right Cuneus, Right Middle Frontal Gyrus, Left Superior Temporal Gyrus to be associated with ASD. Interestingly, these regions in the brain are primarily responsible for social cognition, language, attention, decision making and visual processing, which are known to be altered in ASD.