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Neuroimage. 2014 Dec;103:462-475. doi: 10.1016/j.neuroimage.2014.08.029. Epub 2014 Aug 27.

Decoupling function and anatomy in atlases of functional connectivity patterns: language mapping in tumor patients.

Author information

1
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA; Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria. Electronic address: langs@csail.mit.edu.
2
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: sweet@csail.mit.edu.
3
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: danial@csail.mit.edu.
4
Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: ytie@bwh.harvard.edu.
5
Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: lrigolo@bwh.harvard.edu.
6
Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: agolby@bwh.harvard.edu.
7
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: polina@csail.mit.edu.

Abstract

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.

PMID:
25172207
PMCID:
PMC4401430
DOI:
10.1016/j.neuroimage.2014.08.029
[Indexed for MEDLINE]
Free PMC Article

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