Sangam: A Confluence of Knowledge Streams

Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy

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dc.contributor Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.contributor Fischl, Bruce
dc.creator Yendiki, Anastasia
dc.creator Panneck, Patricia
dc.creator Srinivasan, Priti
dc.creator Stevens, Allison
dc.creator Zollei, Lilla
dc.creator Augustinack, Jean
dc.creator Wang, Ruopeng
dc.creator Salat, David
dc.creator Ehrlich, Stefan
dc.creator Behrens, Tim
dc.creator Jbabdi, Saad
dc.creator Gollub, Randy
dc.creator Fischl, Bruce
dc.date 2017-03-16T16:17:03Z
dc.date 2017-03-16T16:17:03Z
dc.date 2011-10
dc.date 2011-03
dc.date.accessioned 2023-03-01T18:12:24Z
dc.date.available 2023-03-01T18:12:24Z
dc.identifier 1662-5196
dc.identifier http://hdl.handle.net/1721.1/107436
dc.identifier Yendiki, Anastasia. “Automated Probabilistic Reconstruction of White-Matter Pathways in Health and Disease Using an Atlas of the Underlying Anatomy.” Frontiers in Neuroinformatics 5 (2011): n. pag. © 2011 Frontiers Media
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/279152
dc.description We have developed a method for automated probabilistic reconstruction of a set of major white-matter pathways from diffusion-weighted MR images. Our method is called TRACULA (TRActs Constrained by UnderLying Anatomy) and utilizes prior information on the anatomy of the pathways from a set of training subjects. By incorporating this prior knowledge in the reconstruction procedure, our method obviates the need for manual interaction with the tract solutions at a later stage and thus facilitates the application of tractography to large studies. In this paper we illustrate the application of the method on data from a schizophrenia study and investigate whether the inclusion of both patients and healthy subjects in the training set affects our ability to reconstruct the pathways reliably. We show that, since our method does not constrain the exact spatial location or shape of the pathways but only their trajectory relative to the surrounding anatomical structures, a set a of healthy training subjects can be used to reconstruct the pathways accurately in patients as well as in controls.
dc.description National Institute for Biomedical Imaging and Bioengineering (U.S.) (Pathway to Independence Award EB008129)
dc.description National Institutes of Health (U.S.). Blueprint for Neuroscience Research (U01-MH093765)
dc.description National Center for Research Resources (U.S.) (P41-RR14075 and U24-RR021382)
dc.description National Institute for Biomedical Imaging and Bioengineering (U.S.) (R01-EB006758)
dc.description National Institute on Aging (R01-AG022381)
dc.description National Center for Complementary and Alternative Medicine (U.S.) (RC1-AT005728)
dc.description National Institute of Neurological Disorders and Stroke (U.S.) (R01-NS052585, R21-NS072652, and R01-NS070963)
dc.description Ellison Medical Foundation. Autism & Dyslexia Project
dc.format application/pdf
dc.language en_US
dc.publisher Frontiers Research Foundation
dc.relation http://dx.doi.org/10.3389/fninf.2011.00023
dc.relation Frontiers in Neuroinformatics
dc.rights Creative Commons Attribution 4.0 International License
dc.rights http://creativecommons.org/licenses/by/4.0/
dc.source Frontiers
dc.title Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy
dc.type Article
dc.type http://purl.org/eprint/type/JournalArticle


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