Prediction of Neuropsychological Scores from Functional Connectivity Matrices Using Deep Autoencoders

Abstract

Deep learning models are being increasingly used in precision medicine thanks to their ability to provide accurate predictions of clinical outcome from large-scale datasets of patient’s records. However, in many cases data scarcity has forced the adoption of simpler (linear) feature extraction methods, which are less prone to overfitting. In this work, we exploit data augmentation and transfer learning techniques to show that deep, non-linear autoencoders can in fact extract relevant features from resting state functional connectivity matrices of stroke patients, even when the available data is modest. The latent representations extracted by the autoencoders can then be given as input to regularized regression methods to predict neurophsychological scores, significantly outperforming recently proposed methods based on linear feature extraction.