  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.
