Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

Surgical Planning Laboratory

The Publication Database hosted by SPL

All Publications | Upload | Advanced Search | Gallery View | Download Statistics | Help | Import | Log in

From Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder

Institution:
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
2Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
IEEE Engineering in Medicine and Biology Society
Publication Date:
Nov-2013
Journal:
IEEE Trans Med Imaging
Volume Number:
32
Issue Number:
11
Pages:
2078-98
Citation:
IEEE Trans Med Imaging. 2013 Nov;32(11):2078-98.
PubMed ID:
23864168
PMCID:
PMC4278385
Keywords:
Brain Connectivity, Diffusion Weighted Imaging (DWI), Functional Magnetic Resonance Imaging (fMRI), Population Analysis
Appears in Collections:
NAC, NA-MIC
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
R01 MH074794/MH/NIMH NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Venkataraman A., Kubicki M., Golland P. From Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder. IEEE Trans Med Imaging. 2013 Nov;32(11):2078-98. PMID: 23864168. PMCID: PMC4278385.
Downloaded: 736 times. [view map]
Paper: Download, View online
Export citation:
Google Scholar: link

We propose a novel approach to identify the foci of a neurological disorder based on anatomical and functional connectivity information. Specifically, we formulate a generative model that characterizes the network of abnormal functional connectivity emanating from the affected foci. This allows us to aggregate pairwise connectivity changes into a region-based representation of the disease. We employ the variational expectation-maximization algorithm to fit the model and subsequently identify both the afflicted regions and the differences in connectivity induced by the disorder. We demonstrate our method on a population study of schizophrenia.

Additional Material
1 File (154.592kB)
Venkataraman-IEEE-TMI2013-fig5.jpg (154.592kB)