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. 2024 Jun 1;45(8):e26682.
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

Sean R McWhinney  1 Jaroslav Hlinka  2   3 Eduard Bakstein  3   4 Lorielle M F Dietze  1   5 Emily L V Corkum  1 Christoph Abé  6 Martin Alda  1   3 Nina Alexander  7 Francesco Benedetti  8   9 Michael Berk  10 Erlend Bøen  11 Linda M Bonnekoh  12   13 Birgitte Boye  14   15 Katharina Brosch  7   16 Erick J Canales-Rodríguez  17   18 Dara M Cannon  19 Udo Dannlowski  12 Caroline Demro  20 Ana Diaz-Zuluaga  21 Torbjørn Elvsåshagen  15   22   23 Lisa T Eyler  24   25 Lydia Fortea  26 Janice M Fullerton  27   28 Janik Goltermann  12 Ian H Gotlib  29 Dominik Grotegerd  12 Bartholomeus Haarman  30 Tim Hahn  12 Fleur M Howells  31   32 Hamidreza Jamalabadi  7 Andreas Jansen  7   33 Tilo Kircher  7 Anna Luisa Klahn  34 Rayus Kuplicki  35 Elijah Lahud  20 Mikael Landén  34   36 Elisabeth J Leehr  12 Carlos Lopez-Jaramillo  21 Scott Mackey  37 Ulrik Malt  11   38 Fiona Martyn  19 Elena Mazza  8   9 Colm McDonald  19 Genevieve McPhilemy  19 Sandra Meier  1 Susanne Meinert  12   39 Elisa Melloni  8   9 Philip B Mitchell  40 Leila Nabulsi  19 Igor Nenadić  7 Robert Nitsch  39 Nils Opel  12   41   42 Roel A Ophoff  43 Maria Ortuño  44 Bronwyn J Overs  27 Julian Pineda-Zapata  45 Edith Pomarol-Clotet  17   18 Joaquim Radua  26 Jonathan Repple  12   46 Gloria Roberts  40 Elena Rodriguez-Cano  17   18 Matthew D Sacchet  47 Raymond Salvador  17   18 Jonathan Savitz  35   48 Freda Scheffler  31   32 Peter R Schofield  27   28 Navid Schürmeyer  12 Chen Shen  49 Kang Sim  50   51 Scott R Sponheim  20   52 Dan J Stein  31   32   53 Frederike Stein  7 Benjamin Straube  7 Chao Suo  54 Henk Temmingh  32 Lea Teutenberg  7 Florian Thomas-Odenthal  7 Sophia I Thomopoulos  55 Snezana Urosevic  20   52 Paula Usemann  7 Neeltje E M van Haren  56   57 Cristian Vargas  21 Eduard Vieta  58 Enric Vilajosana  44 Annabel Vreeker  56   59 Nils R Winter  12 Lakshmi N Yatham  60 Paul M Thompson  55 Ole A Andreassen  22 Christopher R K Ching  55 Tomas Hajek  1   3
Affiliations

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity

Sean R McWhinney et al. Hum Brain Mapp. .

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.

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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.

Figures

FIGURE 1
FIGURE 1
PCA results with variance explained by each component (left) and distribution of the first two components for cortical thickness and surface area (right). Cortical thickness distributions are broken out by research site.
FIGURE 2
FIGURE 2
Factor loadings of the first principal component for cortical thickness (top) and surface area (bottom).

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