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From Brain Connectivity Models to Identifying Foci of a Neurological Disorder

Institution:
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
2Psychiatry Neuroimaging Laboratory, Harvard Medical School, Boston, MA, USA.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2012
Publication Date:
Oct-2012
Journal:
Med Image Comput Comput Assist Interv
Volume Number:
15
Issue Number:
Pt 1
Pages:
715-22
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2012 Oct;15(Pt 1):715-22.
PubMed ID:
23285615
PMCID:
PMC3539203
Keywords:
Projects:GenerativeBrainConnectivity
Appears in Collections:
NAC, NA-MIC
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Venkataraman A., Kubicki M., Golland P. From Brain Connectivity Models to Identifying Foci of a Neurological Disorder. Int Conf Med Image Comput Comput Assist Interv. 2012 Oct;15(Pt 1):715-22. PMID: 23285615. PMCID: PMC3539203.
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We propose a novel approach to identify the foci of a neuro-logical 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. We employ the variational EM algorithm to fit the model and to identify both the afflicted regions and the differences in connectivity induced by the disorder. We demonstrate our method on a population study of schizophrenia.

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Venkataraman-MICCAI2012-fig3.jpg (184.786kB)