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Efficient, Graph-based White Matter Connectivity from Orientation Distribution Functions via Multi-directional Graph Propagation

1Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
2Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA.
3Department of Psychiatry, Emory University School of Medicine, Atlanta, GA,USA.
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Proceedings of SPIE 2011Feb; 7962(79620S):1-8.
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Appears in Collections:
P01 DA022446/DA/NIDA NIH HHS/United States
P30 HD003110/HD/NICHD NIH HHS/United States
R01 MH091645/MH/NIMH NIH HHS/United States
R41 NS059095/NS/NINDS NIH HHS/United States
RC1 AA019211/AA/NIAAA NIH HHS/United States
U24 AA020022/AA/NIAAA NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Boucharin A., Oguz I., Vachet C., Shi Y., Sanchez M., Styner M. Efficient, Graph-based White Matter Connectivity from Orientation Distribution Functions via Multi-directional Graph Propagation. Proceedings of SPIE 2011Feb; 7962(79620S):1-8. PMID: 23066452. PMCID: PMC3468156.
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The use of regional connectivity measurements derived from diffusion imaging datasets has become of considerable interest in the neuroimaging community in order to better understand cortical and subcortical white matter connectivity. Current connectivity assessment methods are based on streamline fiber tractography, usually applied in a Monte-Carlo fashion. In this work we present a novel, graph-based method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. The computation is based on a multi-directional graph propagation method applied to sampled orientation distribution function (ODF), which can be computed directly from the original diffusion imaging data. We show early results of our method on synthetic and real datasets. The results illustrate the potential of our method towards subject- specific connectivity measurements that are performed in an efficient, stable and reproducible manner. Such individual connectivity measurements would be well suited for application in population studies of neuropathology, such as Autism, Huntington’s Disease, Multiple Sclerosis or leukodystrophies. The proposed method is generic and could easily be applied to non-diffusion data as long as local directional data can be derived.

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