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A Riemannian approach to predicting brain function from the structural connectome

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Title
A Riemannian approach to predicting brain function from the structural connectome
Author(s)
Benkarim, Oualid; Paquola, Casey; Bo-yong Park; Royer, Jessica; Rodríguez-Cruces, Raúl; Vos de Wael, Reinder; Misic, Bratislav; Piella, Gemma; Bernhardt, Boris C.
Publication Date
2022-08
Journal
NeuroImage, v.257
Publisher
Academic Press Inc.
Abstract
© 2022Ongoing brain function is largely determined by the underlying wiring of the brain, but the specific rules governing this relationship remain unknown. Emerging literature has suggested that functional interactions between brain regions emerge from the structural connections through mono- as well as polysynaptic mechanisms. Here, we propose a novel approach based on diffusion maps and Riemannian optimization to emulate this dynamic mechanism in the form of random walks on the structural connectome and predict functional interactions as a weighted combination of these random walks. Our proposed approach was evaluated in two different cohorts of healthy adults (Human Connectome Project, HCP; Microstructure-Informed Connectomics, MICs). Our approach outperformed existing approaches and showed that performance plateaus approximately around the third random walk. At macroscale, we found that the largest number of walks was required in nodes of the default mode and frontoparietal networks, underscoring an increasing relevance of polysynaptic communication mechanisms in transmodal cortical networks compared to primary and unimodal systems.
URI
https://pr.ibs.re.kr/handle/8788114/11998
DOI
10.1016/j.neuroimage.2022.119299
ISSN
1053-8119
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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