Objective Diagnosis of Depression and Autism Spectrum Disorder Based on fMRI Time Series Statistics

A common challenge in diagnosing neuropsychiatric disorders is the lack of objective biomarkers. Current diagnostic approaches rely on the subjective interpretation of observations instead of measurements of brain activity obtained using functional magnetic resonance imaging (fMRI). We propose a method for the objective diagnosis of depression and autism spectrum disorder (ASD), marking the first known experiment that explores the diagnostic performance of only fMRI time series statistics. We researched the importance of time series statistics based on ICA and BOLD for ASD diagnosis. Besides well-known statistics, we introduce features based on the first-order derivative and the frequency-domain representation of the signals. The performance of these features is assessed using multiple machine-learning algorithms. A test accuracy of 69% is achieved on a depression dataset consisting of 72 subjects (51 depressed, 21 controls). On an autism dataset composed of 49 subjects (24 ASD, 25 controls), a test accuracy of 67% and 74% is achieved for ICA and BOLD-based methods respectively. The best results on the ASD dataset are related to the lateral sensorimotor network and the right ventral anterior region. These results demonstrate the potential of fMRI time series statistics as objective biomarkers for neuropsychiatric disorders.