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Abstract #0431

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

Michael Paquette1, Cornelius Eichner1, and Alfred Anwander1
1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany


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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