Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.
Journal: Human brain mapping
Volume: 45
Issue: 8
Year of Publication: 2024
Affiliated Institutions:
Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada.
Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic.
National Institute of Mental Health, Klecany, Czech Republic.
Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany.
Vita-Salute San Raffaele University, Milan, Italy.
Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Deakin University, Geelong, Victoria, Australia.
Unit for Psychosomatics/CL Outpatient Clinic for Adults, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
Institute for Translational Psychiatry, University of Münster, Münster, Germany.
Unit for Psychosomatics and C-L Psychiatry for Adults, Oslo University Hospital, Oslo, Norway.
FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain.
Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, Galway, Ireland.
Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA.
Research Group in Psychiatry GIPSI, Department of Psychiatry, Faculty of Medicine, Universidad de Antioquia, Medellin, Colombia.
Department of Behavioural Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
Department of Psychiatry, University of California San Diego, La Jolla, California, USA.
Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Instituto de Salud Carlos III, University of Barcelona, Barcelona, Spain.
Neuroscience Research Australia, Randwick, New South Wales, Australia.
Department of Psychology, Stanford University, Stanford, California, USA.
Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Neuroscience Institute, University of Cape Town, Cape Town, South Africa.
Institute of Neuroscience and Physiology, Sahlgrenska Academy at Gothenburg University, Gothenburg, Sweden.
Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.
Department of Psychiatry, University of Vermont College of Medicine, Burlington, Vermont, USA.
Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine & Health, University of New South Wales, Sydney, New South Wales, Australia.
Institute for Translational Neuroscience, University of Münster, Münster, Germany.
UCLA Center for Neurobehavioral Genetics, Los Angeles, California, USA.
Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
Research Group, Instituto de Alta Tecnología Médica, Ayudas diagnósticas SURA, Medellin, Colombia.
Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA.
West Region, Institute of Mental Health, Singapore, Singapore.
Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia.
Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa.
Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA.
Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Rotterdam, The Netherlands.
Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Instituto de Salud Carlos III, Institute of Neuroscience, University of Barcelona, Hospital Clínic, Barcelona, Spain.
University of British Columbia, Vancouver, British Columbia, Canada.
Institute of Clinical Medicine, Norwegian Centre for Mental Disorders Research (NORMENT), University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
Abstract summary
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.
Authors & Co-authors:
McWhinney Sean R SR
Hlinka Jaroslav J
Bakstein Eduard E
Dietze Lorielle M F LMF
Corkum Emily L V ELV
Abé Christoph C
Alda Martin M
Alexander Nina N
Benedetti Francesco F
Berk Michael M
Bøen Erlend E
Bonnekoh Linda M LM
Boye Birgitte B
Brosch Katharina K
Canales-Rodríguez Erick J EJ
Cannon Dara M DM
Dannlowski Udo U
Demro Caroline C
Diaz-Zuluaga Ana A
Elvsåshagen Torbjørn T
Eyler Lisa T LT
Fortea Lydia L
Fullerton Janice M JM
Goltermann Janik J
Gotlib Ian H IH
Grotegerd Dominik D
Haarman Bartholomeus B
Hahn Tim T
Howells Fleur M FM
Jamalabadi Hamidreza H
Jansen Andreas A
Kircher Tilo T
Klahn Anna Luisa AL
Kuplicki Rayus R
Lahud Elijah E
Landén Mikael M
Leehr Elisabeth J EJ
Lopez-Jaramillo Carlos C
Mackey Scott S
Malt Ulrik U
Martyn Fiona F
Mazza Elena E
McDonald Colm C
McPhilemy Genevieve G
Meier Sandra S
Meinert Susanne S
Melloni Elisa E
Mitchell Philip B PB
Nabulsi Leila L
Nenadić Igor I
Nitsch Robert R
Opel Nils N
Ophoff Roel A RA
Ortuño Maria M
Overs Bronwyn J BJ
Pineda-Zapata Julian J
Pomarol-Clotet Edith E
Radua Joaquim J
Repple Jonathan J
Roberts Gloria G
Rodriguez-Cano Elena E
Sacchet Matthew D MD
Salvador Raymond R
Savitz Jonathan J
Scheffler Freda F
Schofield Peter R PR
Schürmeyer Navid N
Shen Chen C
Sim Kang K
Sponheim Scott R SR
Stein Dan J DJ
Stein Frederike F
Straube Benjamin B
Suo Chao C
Temmingh Henk H
Teutenberg Lea L
Thomas-Odenthal Florian F
Thomopoulos Sophia I SI
Urosevic Snezana S
Usemann Paula P
van Haren Neeltje E M NEM
Vargas Cristian C
Vieta Eduard E
Vilajosana Enric E
Vreeker Annabel A
Winter Nils R NR
Yatham Lakshmi N LN
Thompson Paul M PM
Andreassen Ole A OA
Ching Christopher R K CRK
Hajek Tomas T
Study Outcome
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