Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer
This work addresses the challenge of understanding brain disorders like schizophrenia through EEG analysis, offering a tool for researchers, but it is incremental as it applies existing graph neural network methods to a specific neuroscience task.
The authors tackled the problem of analyzing functional brain connectivity during a dichotic listening task using EEG data from schizophrenia patients (with and without auditory verbal hallucinations) and healthy controls, achieving state-of-the-art performance in group differentiation and providing validated insights into task-dependent connectivity.
Functional connectivity plays an essential role in modern neuroscience. The modality sheds light on the brain's functional and structural aspects, including mechanisms behind multiple pathologies. One such pathology is schizophrenia which is often followed by auditory verbal hallucinations. The latter is commonly studied by observing functional connectivity during speech processing. In this work, we have made a step toward an in-depth examination of functional connectivity during a dichotic listening task via deep learning for three groups of people: schizophrenia patients with and without auditory verbal hallucinations and healthy controls. We propose a graph neural network-based framework within which we represent EEG data as signals in the graph domain. The framework allows one to 1) predict a brain mental disorder based on EEG recording, 2) differentiate the listening state from the resting state for each group and 3) recognize characteristic task-depending connectivity. Experimental results show that the proposed model can differentiate between the above groups with state-of-the-art performance. Besides, it provides a researcher with meaningful information regarding each group's functional connectivity, which we validated on the current domain knowledge.