Abstract
Connectomics is a popular approach for understanding the brain with neuroimaging data. Yet, a connectome generated from one atlas is different in size, topology, and scale compared to a connectome generated from another atlas. These differences hinder interpreting, generalizing, and combining connectomes and downstream results from different atlases. Recently, it was proposed that a mapping between atlases can be estimated such that connectomes from one atlas (i.e., source atlas) can be reconstructed into a connectome from a different atlas (i.e., target atlas) without re-processing the data. This approach used optimal transport to estimate the mapping between one source atlas and one target atlas. Yet, restricting the optimal transport problem to only a single source atlases ignores additional information when multiple source atlases are available, which is likely. Here, we propose a novel optimal transport based solution to combine information from multiple source atlases to better estimate connectomes for the target atlas. Reconstructed connectomes based on multiple source atlases are more similar to their “gold-standard” counterparts and better at predicting IQ than reconstructed connectomes based on a single source mapping. Importantly, these results hold for a wide-range of different atlases. Overall, our approach promises to increase the generalization of connectome-based results across different atlases.
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Acknowledgements
Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; U54 MH091657) and 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. Amin Karbasi is partially supported by NSF (IIS-1845032), ONR (N00014-19-1-2406), and Tata.
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This research study was conducted retrospectively using human subject data made available in open access by the Human Connectome Project. Approval was granted by local IRB. Yale Human Research Protection Program (HIC #2000023326) on May 3, 2018.
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Dadashkarimi, J., Karbasi, A., Scheinost, D. (2022). Combining Multiple Atlases to Estimate Data-Driven Mappings Between Functional Connectomes Using Optimal Transport. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_37
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