{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T06:28:53Z","timestamp":1781072933812,"version":"3.54.1"},"reference-count":37,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2024,6,2]],"date-time":"2024-06-02T00:00:00Z","timestamp":1717286400000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["103703"],"award-info":[{"award-number":["103703"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["106469"],"award-info":[{"award-number":["106469"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["142255"],"award-info":[{"award-number":["142255"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Human Brain Mapping"],"published-print":{"date-parts":[[2024,6]]},"abstract":"Abstract<\/jats:title>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\u2010BD working group, we investigated T1\u2010weighted 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\u2010to\u2010use and interpret method to study multivariate associations between brain structure and system\u2010level variables.<\/jats:p><\/jats:sec>Practitioner Points<\/jats:title>\nIn 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.<\/jats:p><\/jats:list-item>\nSignificant associations of many different system\u2010level variables with the same brain network suggest a lack of one\u2010to\u2010one mapping of individual clinical and demographic factors to specific patterns of brain changes.<\/jats:p><\/jats:list-item>\nPCA 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\u2010level variables.<\/jats:p><\/jats:list-item>\n<\/jats:list><\/jats:p><\/jats:sec>","DOI":"10.1002\/hbm.26682","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T06:34:16Z","timestamp":1717396456000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders\u2014ENIGMA<\/scp> study in people with bipolar disorders and obesity"],"prefix":"10.1002","volume":"45","author":[{"given":"Sean R.","family":"McWhinney","sequence":"first","affiliation":[{"name":"Department of Psychiatry Dalhousie University Halifax Nova Scotia Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1402-1470","authenticated-orcid":false,"given":"Jaroslav","family":"Hlinka","sequence":"additional","affiliation":[{"name":"Department of Complex Systems Institute of Computer Science, Czech Academy of Sciences Prague Czech Republic"},{"name":"National Institute of Mental Health Klecany Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eduard","family":"Bakstein","sequence":"additional","affiliation":[{"name":"National Institute of Mental Health Klecany Czech Republic"},{"name":"Department of Cybernetics Czech Technical University Prague Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorielle M. F.","family":"Dietze","sequence":"additional","affiliation":[{"name":"Department of Psychiatry Dalhousie University Halifax Nova Scotia Canada"},{"name":"Department of Medical Neuroscience Dalhousie University Halifax Nova Scotia Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily L. 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