Subtyping Schizophrenia Patients Based on Patterns of Structural Brain Alterations
Year: 2021
Type: article
Study design: Observational Study
Abstract: Schizophrenia is a complex and heterogeneous syndrome. Whether quantitative imaging biomarkers can identify discrete subgroups of patients as might be used to foster personalized medicine approaches for patient care remains unclear. Cross-sectional structural MR images of 163 never-treated first-episode schizophrenia patients (FES) and 133 chronically ill patients with midcourse schizophrenia from the Bipolar and Schizophrenia Network for Intermediate Phenotypes (B-SNIP) consortium and a total of 403 healthy controls were recruited. Morphometric measures (cortical thickness, surface area, and subcortical structures) were extracted for each subject and then the optimized subtyping results were obtained with nonsupervised cluster analysis. Three subgroups of patients defined by distinct patterns of regional cortical and subcortical morphometric features were identified in FES. A similar three subgroup pattern was identified in the independent dataset of patients from the multi-site B-SNIP consortium. Similarities of classification patterns across these two patient cohorts suggest that the 3-group typology is relatively stable over the course of illness. Cognitive functions were worse in subgroup 1 with midcourse schizophrenia than those in subgroup 3. These findings provide novel insight into distinct subgroups of patients with schizophrenia based on structural brain features. Findings of different cognitive functions among the subgroups support clinical differences in the MRI-defined illness subtypes. Regardless of clinical presentation and stage of illness, anatomic MR subgrouping biomarkers can separate neurobiologically distinct subgroups of schizophrenia patients, which represent an important and meaningful step forward in differentiating subtypes of patients for studies of illness neurobiology and potentially for clinical trials.
Source: Schizophrenia Bulletin
Institutions: University of Münster, West China Hospital of Sichuan University, University of Electronic Science and Technology of China, The University of Texas Southwestern Medical Center, Beth Israel Deaconess Medical Center, Harvard University, Yale University, University of Georgia, University of Chicago, New Jersey Institute of Technology, King's College London, University of Cincinnati
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: Alexander von Humboldt-Stiftung, National Natural Science Foundation of China, China Postdoctoral Science Foundation, Sichuan University, National Institutes of Health, West China Hospital, Sichuan University, National Institute of Mental Health, National Center for Advancing Translational Sciences
Awards: G1044130170, G1280963080, G1323888141, G1544504024, 5/5 BIPOLAR-SCHIZOPHRENIA NETWORK FOR INTERMEDIATE PHENOTYPES (B-SNIP) - Resubmission - 1, G208937423, G2241124166, G2300285399, 1/5 Bipolar-Schizophrenia Network for Intermediate Phenotypes 2 (B-SNIP 2) - Resu, G2458313802, G388085881, G3966096240, G4113121903, G4136061041, G4541319330, G4863277230, G4991243177, G568434048, G6027800162, G6301362210, G6528626969, G661573911, 4/4-Psychosis and Affective Research Domains and Intermediate Phenotypes (PARDIP), 3/5 Bipolar-Schizophrenia Network for Intermediate Phenotypes 2 (BSNIP-2), Yale Clinical and Translational Science Award, G900877862
OpenAlex ID: W3196616636