3D Convolutional Neural Networks for Classification of Functional Connectomes
Resting-state functional MRI (rs-fMRI) scans hold the potential to serve as a diagnostic or prognostic tool for a wide variety of conditions, such as autism, Alzheimer’s disease, and stroke. While a growing number of studies have demonstrated the promise of machine learning algorithms for rs-fMRI based clinical or behavioral prediction, most prior models have been limited in their capacity to exploit the richness of the data. For example, classification techniques applied to rs-fMRI often rely on region-based summary statistics and/or linear models. In this work, we propose a novel volumetric Convolutional Neural Network (CNN) framework that takes advantage of the full-resolution 3D spatial structure of rs-fMRI data and fits non-linear predictive models. We showcase our approach on a challenging large-scale dataset (ABIDE, with \(N>2,000\)) and report state-of-the-art accuracy results on rs-fMRI-based discrimination of autism patients and healthy controls.
KeywordsFunctional connectivity fMRI Convolutional neural networks Autism ABIDE
This work was supported by NIH grants R01LM012719 & R01AG053949 (MS), R21NS10463401 & R01NS10264601A1 (AK), NSF NeuroNex grant 1707312 (MS) & Anna-Maria & Stephen Kellen Foundation Junior Faculty Fellowship (AK).
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