iGENMap: Individualized Generative Mapping of Cortical Networks Using Functional Eigenmodes

Abstract

Accepted as a Late-Breaking Abstract at the Society for Neuroscience Annual Meeting 2026. We show that individual functional eigenmodes closely follow each subject’s fine-scale cortical network topography, and that this correspondence can be learned across subjects to map individual networks from far less fMRI data than current state-of-the-art approaches require. More details, slides, and the poster will be shared closer to the meeting.

Penghui Du

MSc Neuro-X, EPFL · Visiting Graduate Student, Buckner Lab, Harvard

Neuro-X master’s student at EPFL and visiting graduate student in the Buckner Lab at Harvard, working on precision functional mapping and multimodal neuroimaging.