Stochastic encoding of graphs in deep learning allows for complex analysis of gender classification in resting-state and task functional brain networks from the UK Biobank

TL;DR

This paper introduces a stochastic encoding ensemble of CNNs for classifying gender from brain connectivity MRI data, improving interpretability and accuracy, and revealing key brain networks involved in resting and task states.

Contribution

The study presents a novel stochastic encoding method combined with ensemble CNNs and a multivariate balancing algorithm for better interpretability and accuracy in brain network classification.

Findings

Resting-state data yields higher classification accuracy than task data.

Inner salience network is most influential in resting-state classification.

Connections to the central executive network are key in task data classification.

Abstract

Classification of whole-brain functional connectivity MRI data with convolutional neural networks (CNNs) has shown promise, but the complexity of these models impedes understanding of which aspects of brain activity contribute to classification. While visualization techniques have been developed to interpret CNNs, bias inherent in the method of encoding abstract input data, as well as the natural variance of deep learning models, detract from the accuracy of these techniques. We introduce a stochastic encoding method in an ensemble of CNNs to classify functional connectomes by gender. We applied our method to resting-state and task data from the UK BioBank, using two visualization techniques to measure the salience of three brain networks involved in task- and resting-states, and their interaction. To regress confounding factors such as head motion, age, and intracranial volume, we introduced a multivariate balancing algorithm to ensure equal distributions of such covariates between classes in our data. We achieved a final AUROC of 0.8459. We found that resting-state data classifies more accurately than task data, with the inner salience network playing the most important role of the three networks overall in classification of resting-state data and connections to the central executive network in task data.