3D Convolutional Neural Networks for Classification of Functional Connectomes
This work addresses the need for better diagnostic tools for conditions like autism using fMRI data, representing an incremental improvement over prior methods.
The authors tackled the problem of classifying autism from resting-state fMRI data by proposing a 3D CNN framework that leverages full-resolution spatial structure, achieving state-of-the-art accuracy on the ABIDE dataset with over 2,000 subjects.
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.