On Reconstruction of Cortical Functional Maps Using Subject-Specific Geometric and Connectome Eigenmodes

• Published in: Proceedings (International Symposium on Biomedical Imaging) pp. 1 - 5
• Main Authors: Olsen, Anders S., L., Sina Mansour, Pang, James C., Zalesky, Andrew, Van De Ville, Dimitri, Behjat, Hamid
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.04.2025
• Subjects: Accuracy; Biomedical imaging; Brain modeling; brain structure-function interplay; cortical geometry; functional MRI; Geometry; Image reconstruction; Magnetic resonance imaging; Signal reconstruction; Smoothing methods; structural connectivity; Surface reconstruction; Technological innovation
• ISSN: 1945-8452
• DOI: 10.1109/ISBI60581.2025.10980669

Understanding the interplay between human brain structure and function is crucial to discern neural dynamics. This study explores the relation between brain structure and macroscale functional activity using subject-specific structural connectome eigenmodes, complementing prior work that focused on group-level models and geometry. Leveraging data from the Human Connectome Project, we assess accuracy in reconstructing various functional MRI-based cortical maps using individualised eigenmodes, specifically, across a range of connectome construction parameters. Our results show only minor differences in performance between surface geometric eigen-modes, a local neighborhood graph, a highly smoothed null model, and individual and group-level connectomes at modest smoothing and density levels. Furthermore, our results suggest that spatially smooth eigenmodes best explain functional data. The absence of improvement of individual connectomes and surface geometry over smoothed null models calls for further methodological innovation to better quantify and understand the degree to which brain structure constrains brain function.