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Structural Connectivity Analysis Using Finsler Geometry

Year: 2019
Type: article
Abstract: In this work we demonstrate how Finsler geometry—and specifically the related geodesic tractography— can be levied to analyze structural connections between different brain regions. We present new theoretical developments which support the definition of a novel Finsler metric and associated con-nectivity measures, based on closely related works on the Riemannian framework for diffusion MRI. Using data from the Human Connectome Project, as well as population data from an autism spectrum disorder study, we demonstrate that this new Finsler metric, together with the new connectivity measures, results in connectivity maps that are much closer to known tract anatomy compared to previous geodesic connectivity methods. Our implementation can be used to compute geodesic distance and connectivity maps for segmented areas, and is publicly available.
DOI: 10.1137/18m1209428
Authors: Tom Dela Haije, Peter Savadjiev, Andrea Fuster, Robert T. Schultz, Ragini Verma, Luc Florack, Carl-Fredrik Westin
Institutions: University of Copenhagen, Eindhoven University of Technology, McGill University Health Centre, McGill University, University of Pennsylvania, Brigham and Women's Hospital, Harvard University
Language: English
Field: Medicine
Open Access status: green
Funders: Brigham and Women's Hospital, Villum Fonden, National Alliance for Research on Schizophrenia and Depression, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, National Institutes of Health, McDonnell Center for Systems Neuroscience, National Institute of Mental Health, National Institute of Biomedical Imaging and Bioengineering
Awards: Neuroimaging Analysis Center (NAC), G5689921624, Novel computational methods for higher order diffusion MRI in autism, Novel Diffusion MRI in Early Psychosis, G8299918676, G8768894928, G9000089182, G9000093201
OpenAlex ID: W2894081547
FWCI: 0.487
Cites: 54
Cited by: 5
Related to: 10