DeepAI AI Chat
Log In Sign Up

A deep learning model for data-driven discovery of functional connectivity

by   Usman Mahmood, et al.
Georgia State University

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.


page 9

page 10


Functional connectivity patterns of autism spectrum disorder identified by deep feature learning

Autism spectrum disorder (ASD) is regarded as a brain disease with globa...

Brain Age Prediction Based on Resting-State Functional Connectivity Patterns Using Convolutional Neural Networks

Brain age prediction based on neuroimaging data could help characterize ...

Improving the Level of Autism Discrimination through GraphRNN Link Prediction

Dataset is the key of deep learning in Autism disease research. However,...