Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks

TL;DR

This paper introduces a recurrent-convolutional neural network approach to analyze 4-D fMRI data, capturing spatial and temporal brain activity patterns to improve schizophrenia diagnosis accuracy.

Contribution

It presents a novel deep learning method that automatically learns from full brain imaging data, surpassing traditional feature-based techniques.

Findings

Effective identification of schizophrenia patients from fMRI data.

Preservation of spatial and temporal information improves diagnostic accuracy.

Demonstrates the potential of deep learning in neuropsychiatric disorder diagnosis.

Abstract

Smart systems that can accurately diagnose patients with mental disorders and identify effective treatments based on brain functional imaging data are of great applicability and are gaining much attention. Most previous machine learning studies use hand-designed features, such as functional connectivity, which does not maintain the potential useful information in the spatial relationship between brain regions and the temporal profile of the signal in each region. Here we propose a new method based on recurrent-convolutional neural networks to automatically learn useful representations from segments of 4-D fMRI recordings. Our goal is to exploit both spatial and temporal information in the functional MRI movie (at the whole-brain voxel level) for identifying patients with schizophrenia.