Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks
This work addresses the need for more accurate diagnostic tools for schizophrenia patients using brain imaging data, though it appears incremental as it builds on existing neural network approaches.
The authors tackled the problem of diagnosing schizophrenia by developing a recurrent-convolutional neural network to automatically learn representations from 4-D fMRI data, achieving improved accuracy by exploiting spatial and temporal information at the whole-brain voxel level.
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.