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

ABSTRACT: The specificity and sensitivity of resting state functional MRI (rs-fMRI) measurements depend on preprocessing 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 an intriguing 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) a

SUBMITTER: Khosla M 

PROVIDER: S-EPMC6777738 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature