Connectivity matrix from a fODF weighted graph: An alternative to probabilistic 
tractography

Paquette, Michael; Eichner, Cornelius; Anwander, Alfred

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Connectivity matrix from a fODF weighted graph: An alternative to probabilistic tractography

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Paquette, Michael
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Eichner, Cornelius
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Anwander, Alfred
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Paquette, M., Eichner, C., & Anwander, A. (2022). Connectivity matrix from a fODF weighted graph: An alternative to probabilistic tractography. In Proceedings of the 31st Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM).

Cite as: https://hdl.handle.net/21.11116/0000-000B-40AD-A

Abstract

We introduce a computationally efficient fODF-weighted graph structure where shortest-paths through white matter compute the probability of connection while naturally limiting the angle of propagation between steps. Connectivity matrices obtained from this structure maintain many properties of probabilistic streamline count connectomes while avoiding the sampling bias of tractography.