A Novel Framework for Grading Autism Severity Using Task-Based FMRI

Autism is a developmental disorder associated with difficulties in communication and social interaction. Currently, the gold standard in autism diagnosis is the autism diagnostic observation schedule (ADOS) interviews that assign a score indicating the level of severity for each individual. However, current researchers investigate developing objective technologies to diagnose autism employing brain image modalities. One of such image modalities is task-based functional MRI which exhibits alterations in functional activity that is believed to be important in explaining autism causative factors. Although autism is defined over a wide spectrum, previous diagnosis approaches only divide subjects into normal or autistic. In this paper, a novel framework for grading the severity level of autistic subjects using task-based fMRI data is presented. A speech experiment is used to obtain local features related to the functional activity of the brain. According to ADOS reports, the adopted dataset of 39 subjects is classified to three groups (13 subjects per group): mild, moderate and severe. Individual analysis with the general linear model (GLM) is used for feature extraction for each 246 brain areas according to the Brainnetome atlas (BNT). Our classification results are obtained by random forest classifier after recursive feature elimination (RFE) with 72% accuracy. Finally, we validate our selected features by applying higher level group analysis to prove how informative they are and to infer the significant statistical differences between groups