Graph Neural Networks Elucidating Mesial Temporal Lobe Epilepsy Through Simultaneously Acquired PET and DTI Data – A Pilot Study

Motivation: There is an urgent need to improve diagnostic accuracy and surgical outcomes for Mesial Temporal Lobe Epilepsy (MTLE) patients, particularly those with drug-resistant forms and unclear epileptogenic zones Goal(s): To explore the relation between structural connectivity and FDG PET uptake by using Graph Neural Network. Approach: Graphs were constructed based on diffusion images of the patients. A graph network was trained to predict FDG PET uptake in selected regions. Results: The graph network was able to predict FDG uptake in several regions such as thalamus, middle temporal, and entorhinal cortex. Whereas the network failed to predict uptake in some other regions. Impact: The study advances understanding of the underlying mechanisms in MTLE by illuminating the relationship between white matter structural connectivity and regional metabolic activity, which could lead to enhanced diagnostic approaches and targeted therapies.