Journal article

Structure-function coupling in the human connectome: A machine learning approach

T Sarwar, Y Tian, BTT Yeo, K Ramamohanarao, A Zalesky

NEUROIMAGE | ACADEMIC PRESS INC ELSEVIER SCIENCE | Published : 2021

Abstract

While the function of most biological systems is tightly constrained by their structure, current evidence suggests that coupling between the structure and function of brain networks is relatively modest. We aimed to investigate whether the modest coupling between connectome structure and function is a fundamental property of nervous systems or a limitation of current brain network models. We developed a new deep learning framework to predict an individual's brain function from their structural connectome, achieving prediction accuracies that substantially exceeded state-of-the-art biophysical models (group: R=0.9±0.1, individual: R=0.55±0.1). Crucially, brain function predicted from an indiv..

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Grants

Awarded by NIH Institutes and Centres


Awarded by LIEF Grant


Awarded by National University of Singapore Yong Loo Lin School of Medicine


Awarded by NHMRC


Funding Acknowledgements

Data were provided (in part) by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centres that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Centre for Systems Neuroscience at Washington University. This research was undertaken using the LIEF HPC-GPGPU Facility hosted at the University of Melbourne. This Facility was established with the assistance of LIEF Grant LE170100200. We thank The University of Melbourne's School of Engineering-PhD Write Up Award for supporting this research.BTTY is supported by the Singapore National Research Foundation (NRF) Fellowship (Class of 2017) and the National University of Singapore Yong Loo Lin School of Medicine (NUHSRO/2020/124/TMR/LOA. AZ is supported by an NHMRC Senior Research Fellowship (ID: APP1118153).