ASD detection in males using MRI- an age-group based study

Abstract

This paper presents an automatic, non-invasive method for detecting Autism Spectrum Disorders (ASD) among males using structural Magnetic Resonance Imaging. Whole brain Voxel Based Morphometry (VBM) analysis is first used to identify the brain regions that are affected for ASD patients and gray matter probability in these regions are used as features for classification. In contrast to existing studies which are small in scale, this paper presents a large scale study using the publicly available dataset from Autism Brain Imaging Data Exchange. Taking a cue from genetic studies which indicate that ASD manifests differently among males and females, this paper considers males only. Even among males, this study shows that better classification accuracy can be achieved by considering adult and adolescent males separately. By using a Metacognitive Radial basis Function Network classifier, classification accuracy of 59.73%, 61.49% and 70.41% is achieved when considering all males, adolescent males and adult males respectively. This is about (5 to 10%) higher than Support Vector Machine classifier which is commonly used in literature for this problem and about (6 to 15%) higher than Naive Bayes classifier. It is also found that all three classifiers perform better when considering adult and adolescent males separately instead of considering all males together underscoring the need to consider different age-groups separately for ASD detection. VBM analysis indicates that the precentral gyrus, motor cortex, medial frontal gyrus and the paracentral lobule areas are possibly affected for adolescent males with ASD while the superior frontal gyrus and the frontal eye fields areas are possibly affected for adult males with ASD.