  The specificty and sensitivity of resting state functional MRI (rs-fMRI)
measurements depend on pre-processing choices, such as the parcellation scheme
used to define regions of interest (ROIs). In this study, we critically
evaluate the effect of brain parcellations on machine learning models applied
to rs-fMRI data. Our experiments reveal a remarkable trend: On average, models
with stochastic parcellations consistently perform as well as models with
widely used atlases at the same spatial scale. We thus propose an ensemble
learning strategy to combine the predictions from models trained on
connectivity data extracted using different (e.g., stochastic) parcellations.
We further present an implementation of our ensemble learning strategy with a
novel 3D Convolutional Neural Network (CNN) approach. The proposed CNN approach
takes advantage of the full-resolution 3D spatial structure of rs-fMRI data and
fits non-linear predictive models. Our ensemble CNN framework overcomes the
limitations of traditional machine learning models for connectomes that often
rely on region-based summary statistics and/or linear models. We showcase our
approach on a classification (autism patients versus healthy controls) and a
regression problem (prediction of subject's age), and report promising results.
