Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity
- PMID: 38825977
- PMCID: PMC11144951
- DOI: 10.1002/hbm.26682
Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity
Abstract
Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.
Keywords: MRI; bipolar disorder; body mass index; obesity; principal component analysis; psychiatry.
© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
Conflict of interest statement
PMT & CRKC received a grant from Biogen, Inc., for research unrelated to this manuscript. DJS has received research grants and/or consultancy honoraria from Lundbeck and Sun. LNY has received speaking/consulting fees and/or research grants from Abbvie, Alkermes, Allergan, AstraZeneca, CANMAT, CIHR, Dainippon Sumitomo Pharma, Janssen, Lundbeck, Otsuka, Sunovion, and Teva. TE received speaker's honoraria from Lundbeck and Janssen Cilag and is a consultant to Sumitomo Pharma America. Thanks also for the support of the European Union Horizon 2020 research and innovation program (EU.3.1.1. Understanding health, wellbeing, and disease: Grant No 754907 and EU.3.1.3. Treating and managing disease: Grant No 945151). EV has received grants and served as consultant, advisor, or CME speaker for the following entities (unrelated to the present work): AB‐Biotics, Abbott, Allergan, Angelini, Dainippon Sumitomo Pharma, Ferrer, Gedeon Richter, Janssen, Lundbeck, Otsuka, Sage, Sanofi‐Aventis, and Takeda. PMT and CRKC have received partial research support from Biogen, Inc. (Boston, USA) for work unrelated to the topic of this manuscript. EV has received grants and served as consultant, advisor, or CME speaker for the following entities: AB‐Biotics, AbbVie, Adamed, Angelini, Biogen, Biohaven, Boehringer‐Ingelheim, Celon Pharma, Compass, Dainippon Sumitomo Pharma, Ethypharm, Ferrer, Gedeon Richter, GH Research, Glaxo‐Smith Kline, HMNC, Idorsia, Johnson & Johnson, Lundbeck, Medincell, Merck, Novartis, Orion Corporation, Organon, Otsuka, Roche, Rovi, Sage, Sanofi‐Aventis, Sunovion, Takeda, and Viatris, outside the submitted work. Yatham reports grants from Abbvie and Dainippon Sumitomo, and served as an advisor or consultant or speaker to JAMA Pharma, Intracellular Therapies, Merck, Allergan, GSK, Gedeon Richter, Sanofi, Sunovion, and Alkermes, outside the submitted work.
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