Knowledge Enhanced Graph Neural Network for Advanced Classification of Depression in fMRI Imaging

fMRI data demonstrates considerable efficacy in diagnosing depression. However, it is challenged by high dimensionality and limited sample sizes, hindering the effectiveness of traditional data processing methods. In response, we introduce a data and knowledge dual-driven deep learning model capable of adeptly processing unstructured fMRI data, even in small-sample scenarios. Our work features the development of an innovative graph convolutional network that dynamically updates node and edge information while integrating prior medical statistical knowledge. This synergy of deep learning with medical insights enables a more precise differentiation between individuals with depression and healthy subjects, offering fresh perspectives in the search for depression biomarkers. This approach promises to enhance the tools and strategies available for early depression diagnosis and treatment, promoting a substantial stride in mental health research.