@inproceedings{420940be712d47348420f5385322180f,

title = "Computing the probability of local brain connectivity using diffusion tensor imaging",

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.",

keywords = "Brain connectivity, DTI, Diffusion tensor imaging, MRI",

author = "Shimony, {Joshua S.} and Epstein, {Adrian A.} and Bretthorst, {G. Larry}",

note = "Copyright: Copyright 2009 Elsevier B.V., All rights reserved.; null ; Conference date: 08-07-2007 Through 13-07-2007",

year = "2007",

doi = "10.1063/1.2821281",

language = "English",

isbn = "9780735404687",

series = "AIP Conference Proceedings",

pages = "346--353",

booktitle = "Bayesian 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",

}