ENHANCING THE CLASSIFICATION OF AUTISM SPECTRUM DISORDER FROM RS-FMRI FUNCTIONAL CONNECTIVITY DATA USING TEMPORAL INFORMATION

Abstract

Autism Spectrum Disorder (ASD) affects the patient’s cognitive development which leads to difficulties in social functioning, daily tasks, and independent living. This necessitates intervention at an early age to take preventive measures and provide vital care. Manual diagnosis methods like Autism Diagnostic Observation Schedule (ADOS) assessment adopts symptom-based criteria which typically manifest at a later age. To automate this process, correlations computed from BOLD (Blood Oxygen-level dependent) signals obtained through resting state functional magnetic resonance imaging (rs-fMRI) data of patients across sparse brain regions has been used recently as a measure of functional connectivity. The goal of this study is to investigate the effect of temporal patterns in rs-fMRI time-series data through functional connectivity for automated identification of ASD using a worldwide multisite dataset called Autism Brain Imaging Data Exchange (ABIDE). Our suggested 2-stage network consisting of i) Ensemble Convolutional Neural Network (CNN) for feature extraction from correlation matrices of multiple shorter windows of rs-fMRI time-series and ii) Temporal Convolutional Network (TCN) for classification on the same data after integrating the temporal dimension, has shown improvements in identification of ASD versus typical controls. Examining the rs-fMRI time-series functional connectivity in segments has shown higher gain in classification suggesting presence of relevant features in smaller segments. Due to the limited availability of functional neuro-imaging data for examination using a deep learning architecture, our study also demonstrates tackling the overfitting problem using noise injection and data augmentation.