A depression network of functionally connected regions discovered via multi-attribute canonical correlation graphs

• Published in: NeuroImage (Orlando, Fla.) Vol. 141; pp. 431 - 441
• Main Authors: Kang, Jian, Bowman, F. DuBois, Mayberg, Helen, Liu, Han
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2016; Elsevier Limited
• Subjects: Adult; Algorithms; Brain; Brain - diagnostic imaging; Brain - physiopathology; Brain mapping; Chinese languages; Connectome - methods; Data Interpretation, Statistical; Depressive Disorder, Major - diagnostic imaging; Depressive Disorder, Major - physiopathology; Female; Functional magnetic resonance imaging; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; Mental depression; Middle Aged; Nerve Net - diagnostic imaging; Nerve Net - physiopathology; Neural networks; Neuroimaging; Remission; Reproducibility of Results; Sensitivity and Specificity; Statistics; Statistics as Topic
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2016.06.042

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 simulationstudies. •A region-level functional connectivity network that uses all voxel level information•Perform whole brain analysis and requires only moderate computing costs•Both single subject connectivity analysis and also group level analysis•Do not require strong assumptions about the probability distributions generating the data•Network based classifier can make prediction on the disease risk