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Conference Paper

Quantifying brain connectivity: A comparative tractography study

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Yo,  Ting-Shuo
Methods and Development Unit Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Anwander,  Alfred
Methods and Development Unit Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Knösche,  Thomas R.
Methods and Development Unit Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Yo, T.-S., Anwander, A., Descoteaux, M., Fillard, P., Poupon, C., & Knösche, T. R. (2009). Quantifying brain connectivity: A comparative tractography study. In G.-Z. Yang, D. Hawkes, D. Rueckert, A. Noble, & C. Taylor (Eds.), 12th International Conference of Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009, Part I (pp. 886-893). Heidelberg: Springer.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0012-080B-D
Abstract
In this paper, we compare a representative selection of current state-of-the-art algorithms in diffusion-weighted magnetic resonance imaging (dwMRI) tractography, and propose a novel way to quantitatively define the connectivity between brain regions. As criterion for the comparison, we quantify the connectivity computed with the different methods. We provide initial results using diffusion tensor, spherical deconvolution, ball-and-stick model, and persistent angular structure (PAS) along with deterministic and probabilistic tractography algorithms on a human DWI dataset. The connectivity is presented for a representative selection of regions in the brain in matrices and connectograms.Our results show that fiber crossing models are able to reveal connections between more brain areas than the simple tensor model. Probabilistic approaches show in average more connected regions but lower connectivity values than deterministic methods.