Functional-Positional Context Embedding in Graph Neural Networks for Improved Parkinson's Disease Diagnosis

Graph neural networks for fMRI data often emphasize region-to-region similarities but neglect important factors like hemispheric asymmetry and functional grouping. We introduce a novel framework integrating Functional and Positional Context Embedding with Hemispheric Asymmetry Features to enhance early-stage Parkinson's Disease classification. Our approach refines graph representations of functional connectivity, yielding improved detection of subtle pathological patterns. These findings underscore the value of neuroscience-informed design in brain connectivity analysis.