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

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).

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Item Permalink: https://hdl.handle.net/21.11116/0000-000B-40AD-A Version Permalink: https://hdl.handle.net/21.11116/0000-000B-9ACD-1
Genre: Conference Paper

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https://index.mirasmart.com/ISMRM2022/PDFfiles/0431.html (Any fulltext)
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 Creators:
Paquette, Michael1, Author                 
Eichner, Cornelius1, Author                 
Anwander, Alfred1, Author                 
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1Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634551              

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 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.

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Language(s): eng - English
 Dates: 2022-05-07
 Publication Status: Published online
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Title: Proceedings of the 31st Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM)
Source Genre: Proceedings
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Pages: - Volume / Issue: 31 Sequence Number: 0431 Start / End Page: - Identifier: -