M168. CLINICAL-ANATOMICAL PHENOTYPES OF SCHIZOPHRENIA. (18th May 2020)

• Record Type: Journal Article
• Authors: Kirschner, Matthias; Shafiei, Golia; Markello, Ross D; Markowsky, Carolina; Talpalaru, Alexandra; Hodzic-Santor, Benazir; Devenyi, Gabriel A; Lepage, Martin; Chakravarty, M Mallar; Dagher, Alain; Misic, Bratislav
• Is Part Of: Schizophrenia bulletin. Volume 46(2020)Supplement 1
• Page Start: S200
• Page End: S201
• Publication Date: 2020-05-18
• Subjects: Schizophrenia -- Periodicals; Schizophrenia -- Research -- Periodicals; 616.898005
• DOI: 10.1093/schbul/sbaa030.480
• Languages: English
• ISSNs: 0586-7614

Abstract

Abstract: Background: Although widespread structural brain abnormalities have been consistently reported in schizophrenia, their relation to the heterogeneous clinical manifestations is not well understood. Multivariate methods are needed to uncover covariance patterns between multiple symptom dimensions and system-wide brain imaging data. Methods: This cross-sectional study used structural magnetic resonance imaging and neuropsychological data from 133 patients with chronic schizophrenia (48 female, 34.8±13.2 years) from the Northwestern University Schizophrenia Data and Software Tool (NUSDAST). We estimate disease-related voxel-wise tissue volume loss using deformation-based morphometry (DBM) of T1 weighted images. In patients with schizophrenia, multiple clinical dimensions including positive/negative symptoms and cognitive deficits, demographic data as well as individual tissue volume loss (DBM) were included in the multivariate model. Clinical-anatomical phenotypes were identified using partial least squares analysis. Results: Multivariate analysis revealed three distinct clinical-anatomical phenotypes accounting for 27.5%, 15%, and 13% of the shared covariance between clinical-behavioural data and tissue volume loss (total of 55.5%). The first clinical-anatomical phenotype encompassed cognitive impairments, severity of negative symptoms and tissue volume loss within the default mode network and visual network. The second clinical-anatomical phenotype was associated with additional cognitive impairments and tissue volume loss within the frontoparietal and ventral attention network, while the third clinical-anatomical phenotype encompassed a mixed positive and negative symptoms phenotype and tissue volume loss within the dorsal attention network. Critically, the pattern of volume loss within the first most prevalent clinical-anatomical phenotype mediated (a*b) the effect of socioeconomic status on clinical outcome (cognitive performance and negative symptoms) (a*b=-0.033(0.008); P<1.0×〖10〗^(-4); 95% CI [-0.049, -0.018]). Finally, we partly replicated the first clinical-anatomical phenotype in an independent sample of patients with schizophrenia (n=108). Discussion: The heterogeneous clinical manifestation of schizophrenia can be significantly explained by three clinical-anatomical phenotypes. Despite their distributed topography, each phenotype is centered on a specific, well-defined set of intrinsic networks.