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.
| Original language | English |
|---|---|
| Article number | 3485 |
| Journal | Scientific Reports |
| Volume | 13 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2023 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2023, The Author(s).
Funding
We are grateful for the support of the National Health and Medical Research Council of Australia (grant numbers APP1091593 and APP1117724); the Australian Research Council (grant number DP170101815); the Victorian Government’s Operational Infrastructure Support; and of Melbourne Bioinformatics at the University of Melbourne (grant number UOM0048). OC is supported by fellowship funding from the National Imaging Facility (NIF), a National Collaborative Research Infrastructure Strategy (NCRIS) capability at Swinburne Neuroimaging, Swinburne University of Technology. We also acknowledge the Sydney Informatics Hub and the University of Sydney’s high performance computing cluster Artemis for providing some of the high-performance computing resources that have contributed to the research results reported within this paper. Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University, St. Louis, MO. Lastly, we would like to thank Xiaoyun Liang for his assistance and suggestions, and the two anonymous reviewers for their constructive comments. We are grateful for the support of the National Health and Medical Research Council of Australia (grant numbers APP1091593 and APP1117724); the Australian Research Council (grant number DP170101815); the Victorian Government’s Operational Infrastructure Support; and of Melbourne Bioinformatics at the University of Melbourne (grant number UOM0048). OC is supported by fellowship funding from the National Imaging Facility (NIF), a National Collaborative Research Infrastructure Strategy (NCRIS) capability at Swinburne Neuroimaging, Swinburne University of Technology. We also acknowledge the Sydney Informatics Hub and the University of Sydney’s high performance computing cluster Artemis for providing some of the high-performance computing resources that have contributed to the research results reported within this paper. Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University, St. Louis, MO. Lastly, we would like to thank Xiaoyun Liang for his assistance and suggestions, and the two anonymous reviewers for their constructive comments.
| Funders | Funder number |
|---|---|
| University of Sydney’s high performance computing cluster Artemis | |
| National Institutes of Health | |
| NIH Blueprint for Neuroscience Research | |
| McDonnell Center for Systems Neuroscience | |
| National Imaging Facility | |
| Australian Research Council | DP170101815 |
| National Health and Medical Research Council | APP1117724, APP1091593 |
| Swinburne University of Technology | 1U54MH091657 |
| University of Melbourne | UOM0048 |
| State Government of Victoria |