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ConTrack: finding the most likely pathways between brain regions using diffusion tractography.

Magnetic resonance diffusion-weighted imaging coupled with fiber tractography (DFT) is the only non-invasive method for measuring white matter pathways in the living human brain. DFT is often used to discover new pathways. But there are also many applications, particularly in visual neuroscience, in which we are confident that two brain regions are connected, and we wish to find the most likely pathway forming the connection. In several cases, current DFT algorithms fail to find these candidate pathways. To overcome this limitation, we have developed a probabilistic DFT algorithm (ConTrack) that identifies the most likely pathways between two regions. We introduce the algorithm in three parts: a sampler to generate a large set of potential pathways, a scoring algorithm that measures the likelihood of a pathway, and an inferential step to identify the most likely pathways connecting two regions. In a series of experiments using human data, we show that ConTrack estimates known pathways at positions that are consistent with those found using a high quality deterministic algorithm. Further we show that separating sampling and scoring enables ConTrack to identify valid pathways, known to exist, that are missed by other deterministic and probabilistic DFT algorithms.

Pubmed ID: 18831651

Authors

  • Sherbondy AJ
  • Dougherty RF
  • Ben-Shachar M
  • Napel S
  • Wandell BA

Journal

Journal of vision

Publication Data

October 3, 2008

Associated Grants

  • Agency: NEI NIH HHS, Id: EY015000
  • Agency: NEI NIH HHS, Id: R01 EY015000
  • Agency: NEI NIH HHS, Id: R01 EY015000-01
  • Agency: NIGMS NIH HHS, Id: U54 GM072970
  • Agency: NIGMS NIH HHS, Id: U54 GM072970

Mesh Terms

  • Algorithms
  • Brain Mapping
  • Child
  • Corpus Callosum
  • Diffusion Magnetic Resonance Imaging
  • Female
  • Humans
  • Male
  • Models, Neurological
  • Nerve Fibers
  • Neuroanatomy
  • Neurosurgery
  • Occipital Lobe
  • Preoperative Care
  • Visual Pathways