Devin Schoen

UC San Francisco

“T1-Weighted Magnetic Resonance Imaging and Hypergraph Neural Networks Used to Predict Deep Brain Stimulation Outcome Variance in Parkinson’s Disease”

My research investigates leveraging T1-weighted magnetic resonance images as a consistent pre-operative source to uncover biomarkers associated with Deep Brain Stimulation (DBS) outcomes in Parkinson’s disease patients. Utilizing fractal dimension analysis and a Hypergraph Neural Network (HGNN) for classification, the study identifies significant correlations between structural changes in specific brain regions and changes in Levodopa Equivalent Daily Dose (ΔLEDD). The integration of multimodal data, alongside a nuanced understanding of patient-specific responses, lays the groundwork for advancing personalized treatment approaches and enhancing our comprehension of Parkinson’s disease and DBS mechanisms.

ABSTRACT

My work explores using T1-weighted MRI to uncover biomarkers influencing Deep Brain Stimulation outcomes in Parkinson’s disease. Fractal dimension analysis and a Hypergraph Neural Network reveal correlations between structural changes and medication reductions, advancing personalized treatment insights for Parkinson’s patients.
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