Assisted diagnosis of neuropsychiatric disorders based on functional connectivity: A survey on application and performance evaluation of graph neural network

The functional connectivity network which provides a perspective of interactions among all brain regions and reveals the functional organizational structure of the brain has gained considerable traction in the domain of diagnosis of neuropsychiatric disorders. To further extract lesion attributes within brain networks , Graph Neural Networks (GNNs) can achieve interpretable feature extraction and compression by simulating the structure and signal transmission in real brains. This has led to the prevalence of GNNs in utilizing non-invasive resting-state functional magnetic resonance imaging (rs-fMRI) data for brain graph construction and disease diagnosis. However, this computer-aided diagnostic (CAD) approach still needs to be further optimized in terms of data collection and model construction, and its diagnostic accuracy and clinical value are expected to be further improved. Therefore, this review conducts an in-depth and extensive investigation and analysis of GNNs in the diagnosis of neuro-psychiatric disorders. It presents the overall process of disease diagnosis, offering novel insights and methodologies to researchers in the field, thereby facilitating further improvement and optimization of related technologies.