Biclustered Independent Component Analysis for Complex Biomarker and Subtype Identification from Structural Magnetic Resonance Images in Schizophrenia - Gupta, Cota Navin; Castro, Eduardo; Rachkonda, Srinivas; van Erp, Theo G. M.; Potkin, Steven; Ford, Judith M.; Mathalon, Daniel; Lee, Hyo Jong; Mueller, Bryon A.; Greve, Douglas N.; Andreassen, Ole A.; Agartz, Ingrid; et al. (Frontiers in Psychiatry, 2017)
Clinical and cognitive symptoms domain based subtyping in schizophrenia 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 (B-ICA) to detect subtypes from the reliable and stable gray matter concentration (GMC) of patients with schizophrenia (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 schizophrenia 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 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 schizophrenia could possibly indicate the need for personalized treatment and targeted drug development.