Journal article

Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography?

Tabinda Sarwar, Kotagiri Ramamohanarao, Andrew Zalesky



PURPOSE: Human connectomics necessitates high-throughput, whole-brain reconstruction of multiple white matter fiber bundles. Scaling up tractography to meet these high-throughput demands yields new fiber tracking challenges, such as minimizing spurious connections and controlling for gyral biases. The aim of this study is to determine which of the two broadest classes of tractography algorithms-deterministic or probabilistic-is most suited to mapping connectomes. METHODS: This study develops numerical connectome phantoms that feature realistic network topologies and that are matched to the fiber complexity of in vivo diffusion MRI (dMRI) data. The phantoms are utilized to evaluate the perfor..

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University of Melbourne Researchers


Awarded by 16 NIH Institutes and Centers

Awarded by Australian National Health and Medical Research Council (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 Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. A. Z. was supported by the Australian National Health and Medical Research Council (NHMRC) Senior Research Fellowship B (ID: 1136649).