  Brain gender differences have been known for a long time and are the possible
reason for many psychological, psychiatric and behavioral differences between
males and females. Predicting genders from brain functional connectivity (FC)
can build the relationship between brain activities and gender, and extracting
important gender related FC features from the prediction model offers a way to
investigate the brain gender difference. Current predictive models applied to
gender prediction demonstrate good accuracies, but usually extract individual
functional connections instead of connectivity patterns in the whole
connectivity matrix as features. In addition, current models often omit the
effect of the input brain FC scale on prediction and cannot give any model
uncertainty information. Hence, in this study we propose to predict gender from
multiple scales of brain FC with deep learning, which can extract full FC
patterns as features. We further develop the understanding of the feature
extraction mechanism in deep neural network (DNN) and propose a DNN feature
ranking method to extract the highly important features based on their
contributions to the prediction. Moreover, we apply Bayesian deep learning to
the brain FC gender prediction, which as a probabilistic model can not only
make accurate predictions but also generate model uncertainty for each
prediction. Experiments were done on the high-quality Human Connectome Project
S1200 release dataset comprising the resting state functional MRI data of 1003
healthy adults. First, DNN reaches 83.0%, 87.6%, 92.0%, 93.5% and 94.1%
accuracies respectively with the FC input derived from 25, 50, 100, 200, 300
independent component analysis (ICA) components. DNN outperforms the
conventional machine learning methods on the 25-ICA-component scale FC, but the
linear machine learning method catches up as the number of ICA components
increases...
