A deep learning model for data-driven discovery of functional connectivity
This work addresses the challenge of automating and improving functional connectivity analysis for brain disorder diagnosis, though it is incremental as it builds on existing graphical neural network methods.
The authors tackled the problem of functional connectivity analysis in fMRI data by proposing BrainGNN, a deep learning model that learns connectivity structures and classifies subjects, achieving state-of-the-art performance on a schizophrenia dataset with results consistent with literature.
Functional connectivity (FC) studies have demonstrated the overarching value of studying the brain and its disorders through the undirected weighted graph of fMRI correlation matrix. Most of the work with the FC, however, depends on the way the connectivity is computed, and further depends on the manual post-hoc analysis of the FC matrices. In this work we propose a deep learning architecture BrainGNN that learns the connectivity structure as part of learning to classify subjects. It simultaneously applies a graphical neural network to this learned graph and learns to select a sparse subset of brain regions important to the prediction task. We demonstrate the model's state-of-the-art classification performance on a schizophrenia fMRI dataset and demonstrate how introspection leads to disorder relevant findings. The graphs learned by the model exhibit strong class discrimination and the sparse subset of relevant regions are consistent with the schizophrenia literature.