Linked Life Data

21396459

Source:http://linkedlifedata.com/resource/pubmed/id/21396459

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2011-5-2
pubmed:abstractText
The amygdala plays an important role in emotional and social functions, and amygdala dysfunction has been associated with multiple neuropsychiatric disorders, including autism, anxiety, and depression. Although the amygdala is composed of multiple anatomically and functionally distinct nuclei, typical structural magnetic resonance imaging (MRI) sequences are unable to discern them. Thus, functional MRI (fMRI) studies typically average the BOLD response over the entire structure, which reveals some aspects of amygdala function as a whole but does not distinguish the separate roles of specific nuclei in humans. We developed a method to segment the human amygdala into its four major nuclei using only diffusion-weighted imaging and connectivity patterns derived mainly from animal studies. We refer to this new method as Tractography-based Segmentation, or TractSeg. The segmentations derived from TractSeg were topographically similar to their corresponding amygdaloid nuclei, and were validated against a high-resolution scan in which the nucleic boundaries were visible. In addition, nuclei topography was consistent across subjects. TractSeg relies on short scan acquisitions and widely accessible software packages, making it attractive for use in healthy populations to explore normal amygdala nucleus function, as well as in clinical and pediatric populations. Finally, it paves the way for implementing this method in other anatomical regions which are also composed of functional subunits that are difficult to distinguish with standard structural MRI.
pubmed:grant
http://linkedlifedata.com/resource/pubmed/grant/1R21NS072652-01, http://linkedlifedata.com/resource/pubmed/grant/1S10RR019, http://linkedlifedata.com/resource/pubmed/grant/1S10RR023043, http://linkedlifedata.com/resource/pubmed/grant/1S10RR023401, http://linkedlifedata.com/resource/pubmed/grant/AG022381, http://linkedlifedata.com/resource/pubmed/grant/DA023427, http://linkedlifedata.com/resource/pubmed/grant/P41-RR14075, http://linkedlifedata.com/resource/pubmed/grant/R01 EB006758-03, http://linkedlifedata.com/resource/pubmed/grant/R01 NS052585-01, http://linkedlifedata.com/resource/pubmed/grant/R01 NS052585-01A1, http://linkedlifedata.com/resource/pubmed/grant/R01EB006758, http://linkedlifedata.com/resource/pubmed/grant/R21 NS072652-01, http://linkedlifedata.com/resource/pubmed/grant/S10 RR019307-01, http://linkedlifedata.com/resource/pubmed/grant/S10 RR023401-01A2, http://linkedlifedata.com/resource/pubmed/grant/U24 RR021382
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1095-9572
pubmed:author
pubmed:copyrightInfo
Copyright © 2011 Elsevier Inc. All rights reserved.
pubmed:issnType
Electronic
pubmed:day
1
pubmed:volume
56
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1353-61
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Connectivity-based segmentation of human amygdala nuclei using probabilistic tractography.
pubmed:affiliation
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. zsaygin@mit.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural