Computing the probability of local brain connectivity using diffusion tensor imaging

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The diffusion tensor model has been used to analyze magnetic resonance diffusion data and has been successful in both neuroscientific and clinical applications. We propose an enhancement of this model with a local connectivity parameter that better accords with the known structure of white-matter. In addition to providing diffusion tensor parameter estimation the calculation provides the probability that a given pixel is connected to one of its nearest neighbors. These probabilities can be used in further calculations to determine the probability of connectivity between different brain regions. Implementation of the algorithm is discussed in addition to its usage in simulated and in-vivo magnetic resonance diffusion tensor data.

Original languageEnglish
Title of host publicationBayesian Inference and Maximum Entropy Methods in Science and Engineering - 27th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2007
Pages346-353
Number of pages8
DOIs
StatePublished - 2007
Event27th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2007 - Saratoga Springs, NY, United States
Duration: Jul 8 2007Jul 13 2007

Publication series

NameAIP Conference Proceedings
Volume954
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference27th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2007
CountryUnited States
CitySaratoga Springs, NY
Period07/8/0707/13/07

Keywords

  • Brain connectivity
  • DTI
  • Diffusion tensor imaging
  • MRI

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