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Decoupling Function and Anatomy in Atlases of Functional Connectivity patterns: Language Mapping in Tumor Patients

Institution:
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: langs@csail.mit.edu.
2Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publication Date:
Dec-2014
Journal:
Neuroimage
Volume Number:
103
Pages:
462-75
Citation:
Neuroimage. 2014 Dec;103:462-75.
PubMed ID:
25172207
PMCID:
PMC4401430
Appears in Collections:
NAC, NA-MIC, NCIGT
Sponsors:
P01 CA067165/CA/NCI NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
R01 HD067312/HD/NICHD NIH HHS/United States
U41 RR019703/RR/NCRR NIH HHS/United States
P41 EB015898/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Langs G., Sweet A., Lashkari D., Tie Y., Rigolo L., Golby A.J., Golland P. Decoupling Function and Anatomy in Atlases of Functional Connectivity patterns: Language Mapping in Tumor Patients. Neuroimage. 2014 Dec;103:462-75. PMID: 25172207. PMCID: PMC4401430.
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In this paper we construct an atlas that summarizes functional connectivity characteristics of a cognitive process from a population of individuals. The atlas encodes functional connectivity structure in a low-dimensional embedding space that is derived from a diffusion process on a graph that represents correlations of fMRI time courses. The functional atlas is decoupled from the anatomical space, and thus can represent functional networks with variable spatial distribution in a population. In practice the atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. We derive an algorithm for fitting this generative model to the observed data in a population. Our results in a language fMRI study demonstrate that the method identifies coherent and functionally equivalent regions across subjects. The method also successfully maps functional networks from a healthy population used as a training set to individuals whose language networks are affected by tumors.

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