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Front Psychiatry. 2017 Sep 26;8:179. doi: 10.3389/fpsyt.2017.00179. eCollection 2017.

Biclustered Independent Component Analysis for Complex Biomarker and Subtype Identification from Structural Magnetic Resonance Images in Schizophrenia.

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The Mind Research Network, Albuquerque, NM, United States.
Department of Biosciences and Bioengineering, Indian Institute of Technology, Guwahati, India.
Computational Biology Center, IBM Thomas J. Watson Research, Yorktown Heights, NY, United States.
Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, Irvine, CA, United States.
Department of Psychiatry, School of Medicine, University of California, San Francisco, San Francisco, CA, United States.
Divisions of Electronics and Information Engineering, Chonbuk National University, Jeonju, South Korea.
Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States.
MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States.
NORMENT, KG Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.
Department of Research, Diakonhjemmet Hospital, Oslo, Norway.
Department of Neurosurgery, University of New Mexico Health Sciences Center, Albuquerque, NM, United States.
Department of Psychiatry, University of New Mexico, Albuquerque, NM, United States.
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States.
Department of Psychology, Neuroscience Institute, Georgia State University, Atlanta, GA, United States.


Clinical and cognitive symptoms domain-based subtyping in schizophrenia (Sz) has been critiqued due to the lack of neurobiological correlates and heterogeneity in symptom scores. We, therefore, present a novel data-driven framework using biclustered independent component analysis to detect subtypes from the reliable and stable gray matter concentration (GMC) of patients with Sz. The developed methodology consists of the following steps: source-based morphometry (SBM) decomposition, selection and sorting of two component loadings, subtype component reconstruction using group information-guided ICA (GIG-ICA). This framework was applied to the top two group discriminative components namely the insula/superior temporal gyrus/inferior frontal gyrus (I-STG-IFG component) and the superior frontal gyrus/middle frontal gyrus/medial frontal gyrus (SFG-MiFG-MFG component) from our previous SBM study, which showed diagnostic group difference and had the highest effect sizes. The aggregated multisite dataset consisted of 382 patients with Sz regressed of age, gender, and site voxelwise. We observed two subtypes (i.e., two different subsets of subjects) each heavily weighted on these two components, respectively. These subsets of subjects were characterized by significant differences in positive and negative syndrome scale (PANSS) positive clinical symptoms (p = 0.005). We also observed an overlapping subtype weighing heavily on both of these components. The PANSS general clinical symptom of this subtype was trend level correlated with the loading coefficients of the SFG-MiFG-MFG component (r = 0.25; p = 0.07). The reconstructed subtype-specific component using GIG-ICA showed variations in voxel regions, when compared to the group component. We observed deviations from mean GMC along with conjunction of features from two components characterizing each deciphered subtype. These inherent variations in GMC among patients with Sz could possibly indicate the need for personalized treatment and targeted drug development.


biclustering; gray matter concentration; group information-guided independent component analysis; independent component analysis; positive and negative syndrome scale symptoms; subtypes

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