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

Ensemble learning with 3D convolutional neural networks for connectome-based prediction