Combining phenotypic and resting-state fMRI data for autism classification with recurrent neural networks

Abstract

Accurate identification of autism spectrum disorder (ASD) from resting-state functional magnetic resonance imaging (rsfMRI) is a challenging task due in large part to the heterogeneity of ASD. Recent work has shown better classification accuracy using a recurrent neural network with rsfMRI time-series as inputs. However, phenotypic features, which are often available and likely carry predictive information, are excluded from the model, and combining such data with rsfMRI into the recurrent neural network is not a straightforward task. In this paper, we present several methodologies for incorporating phenotypic data with rsfMRI into a single deep learning framework for classifying ASD. We test the proposed architectures using a cross-validation framework on the large, heterogeneous first cohort from the Autism Brain Imaging Data Exchange. Our best model achieved an accuracy of 70.1%, outperforming prior work.