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A Depression Network of Functionally Connected Regions Discovered via Multi-Attribute Canonical Correlation Graphs

Author(s): Kang, Jian; Bowman, F. DuBois; Mayberg, Helen; Liu, Han

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Abstract: To establish brain network properties associated with major depressive disorder (MDD) using resting-state functional magnetic resonance imaging (Rs-fMRI) data, we develop a multi-attribute graph model to construct a region-level functional connectivity network that uses all voxel level information. For each region pair, we define the strength of the connectivity as the kernel canonical correlation coefficient between voxels in the two regions; and we develop a permutation test to assess the statistical significance. We also construct a network based classifier for making predictions on the risk of MDD. We apply our method to Rs-fMRI data from 20 MDD patients and 20 healthy control subjects in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study. Using this method, MDD patients can be distinguished from healthy control subjects based on significant differences in the strength of regional connectivity. We also demonstrate the performance of the proposed method using simulation studies.
Publication Date: Nov-2016
Citation: Kang, Jian, Bowman, F DuBois, Mayberg, Helen, Liu, Han. (2016). A depression network of functionally connected regions discovered via multi-attribute canonical correlation graphs. NeuroImage, 141 (431 - 441). doi:10.1016/j.neuroimage.2016.06.042
DOI: doi:10.1016/j.neuroimage.2016.06.042
ISSN: 1053-8119
Pages: 431 - 441
Type of Material: Journal Article
Journal/Proceeding Title: NeuroImage
Version: Author's manuscript

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