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 ) and report state-of-the-art accuracy results on rs-fMRI-based discrimination of autism patients and healthy controls.

In MICCAI DLMIA workshop
Meenakshi Khosla
Meenakshi Khosla
Assistant Professor of Cognitive Science

My research interests include computational neuroscience, artificial intelligence and large-scale data analysis.