Detection of Pathological Functional Connectome in Brains with Low-grade Gliomas Using Graph Convolutional Network

Motivation: Alterations in the functional connectome may serve as new biomarkers to infer the disease profile of glioma. Goal(s): To detect the pathological functional connectome (Patho-FCN) that characterizes the functional plasticity due to low grade glioma. Approach: Dynamic functional connectivity-based graph convolutional network (dFC-GCN) models were constructed to distinguish patients from healthy controls. Class activation mapping was utilized to identify the top 5% salient nodes constituting the Patho-FCN, where the information flow was assessed using the time-delay and probabilistic flow estimation. Results: The dFC-GCN model identified a contralesional Patho-FCN with altered information propagation patterns, and achieved an averaged classification accuracy of 96.1%. Impact: The pathological functional connectome detected with the proposed methodology in this study provides a novel biomarker to characterize cerebral glioma. Theranostic scheme targeting pathological connectome may innovate the management of glioma.