Abstract

We introduce a novel connectomics method, MFCSC, that integrates information on structural connectivity (SC) from diffusion MRI tractography and functional connectivity (FC) from functional MRI, at individual subject level. The MFCSC method is based on the fact that SC only broadly predicts FC, and for each connection in the brain, the method calculates a value that quantifies the mismatch that often still exists between the two modalities. To capture underlying physiological properties, MFCSC minimises biases in SC and addresses challenges with the multimodal analysis, including by using a data-driven normalisation approach. We ran MFCSC on data from the Human Connectome Project and used the output to detect pairs of left and right unilateral connections that have distinct relationship between structure and function in each hemisphere; we suggest that this reflects cases of hemispheric functional specialisation. In conclusion, the MFCSC method provides new information on brain organisation that may not be inferred from an analysis that considers SC and FC separately.

Details

Title
MFCSC: Novel method to calculate mismatch between functional and structural brain connectomes, and its application for detecting hemispheric functional specialisations
Author
Civier, Oren 1 ; Sourty, Marion 2 ; Calamante, Fernando 3 

 The University of Sydney, School of Biomedical Engineering, Sydney, Australia (GRID:grid.1013.3) (ISNI:0000 0004 1936 834X); Swinburne University of Technology, Swinburne Neuroimaging, Melbourne, Australia (GRID:grid.1027.4) (ISNI:0000 0004 0409 2862) 
 The University of Sydney, School of Biomedical Engineering, Sydney, Australia (GRID:grid.1013.3) (ISNI:0000 0004 1936 834X) 
 The University of Sydney, School of Biomedical Engineering, Sydney, Australia (GRID:grid.1013.3) (ISNI:0000 0004 1936 834X); The University of Sydney, Sydney Imaging, Sydney, Australia (GRID:grid.1013.3) (ISNI:0000 0004 1936 834X) 
Pages
3485
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2784144150
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.