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  Quantifying brain connectivity: A comparative tractography study

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.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0012-080B-D Version Permalink: https://hdl.handle.net/21.11116/0000-0001-EA2F-4
Genre: Conference Paper

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Yo_MICCAI_preprint_2009.pdf (Preprint), 2MB
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 Creators:
Yo, Ting-Shuo1, Author           
Anwander, Alfred1, Author           
Descoteaux, Maxime2, Author
Fillard, Pierre2, Author
Poupon, Cyril2, Author
Knösche, Thomas R.1, Author           
Affiliations:
1Methods and Development Unit Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634557              
2External Organizations, ou_persistent22              

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Free keywords: Fractional Anisotropy; Local Model; Representative Selection; Anatomical Connectivity; Dynamic Causal Modelling
 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.

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Language(s): eng - English
 Dates: 2009-09-20
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-642-04268-3_109
 Degree: -

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Title: 12th International Conference of Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009, Part I
Source Genre: Proceedings
 Creator(s):
Yang, G.-Z.1, Editor
Hawkes, D.1, Editor
Rueckert, D.1, Editor
Noble, A.1, Editor
Taylor, C.1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: Heidelberg : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 886 - 893 Identifier: -