Using regional homogeneity from functional MRI for diagnosis of ASD among males

This paper presents an approach for automatic diagnosis of Autism Spectrum Disorder (ASD) among males using functional Magnetic Resonance Imaging (fMRI). fMRI has the capability to identify any abnormal neural interactions that may be responsible for behavioral symptoms observed in ASD patients. In this paper, the regional homogeneity of the voxels in the 116 regions of the automated anatomical labeling (AAL) atlas of the brain are used as features which result in a large set of 54837 features. Chi-square feature selection method is then used to identify the most significant features and only these features are then used for classification with a metacognitive radial basis function classifier. Since genetic studies have indicated that ASD manifests differently in males and females, a large scale study specific to males is highlighted here using the publicly available preprocessed fMRI dataset from the Autism Brain Imaging Data Exchange (ABIDE), unlike existing studies which are either smaller in scale or consider both males and females together. Among the males, it is shown here that the classification performance can be improved (by up to 10%) by considering adults and adolescents separately. By using Chi-square algorithm the number of features was reduced drastically to lower than 200 in contrast to the thousands of features that have been used in recent studies.