From Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder
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Golland_From connectivity.pdf
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Author(s)
Venkataraman, Archana
Kubicki, Marek
Golland, Polina
Date Issued
July 2013
Journal
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Venkataraman, Archana, Marek Kubicki, and Polina Golland. “From Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder.” IEEE Trans. Med. Imaging 32, no. 11 (November 2013): 2078–2098.
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Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/tmi.2013.2272976
