Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity
Authors: McWhinney Sean R. , Hlinka Jaroslav , Bakstein Eduard , Dietze Lorielle M. F. , Corkum Emily L. V. , Abé Christoph , Alda Martin , Alexander Nina , Benedetti Francesco , Berk Michael , Bøen Erlend , Bonnekoh Linda M. , Boye Birgitte , Brosch Katharina , Canales‐Rodríguez Erick J. , Cannon Dara M. , Dannlowski Udo , Demro Caroline , Diaz‐Zuluaga Ana , Elvsåshagen Torbjørn , Eyler Lisa T. , Fortea Lydia , Fullerton Janice M. , Goltermann Janik , Gotlib Ian H. , Grotegerd Dominik , Haarman Bartholomeus , Hahn Tim , Howells Fleur M. , Jamalabadi Hamidreza , Jansen Andreas , Kircher Tilo , Klahn Anna Luisa , Kuplicki Rayus , Lahud Elijah , Landén Mikael , Leehr Elisabeth J. , Lopez‐Jaramillo Carlos , Mackey Scott , Malt Ulrik , Martyn Fiona , Mazza Elena , McDonald Colm , McPhilemy Genevieve , Meier Sandra , Meinert Susanne , Melloni Elisa , Mitchell Philip B. , Nabulsi Leila , Nenadić Igor , Nitsch Robert , Opel Nils , Ophoff Roel A. , Ortuño Maria , Overs Bronwyn J. , Pineda‐Zapata Julian , Pomarol‐Clotet Edith , Radua Joaquim , Repple Jonathan , Roberts Gloria , Rodriguez‐Cano Elena , Sacchet Matthew D. , Salvador Raymond , Savitz Jonathan , Scheffler Freda , Schofield Peter R. , Schürmeyer Navid , Shen Chen , Sim Kang , Sponheim Scott R. , Stein Dan J. , Stein Frederike , Straube Benjamin , Suo Chao , Temmingh Henk , Teutenberg Lea , Thomas‐Odenthal Florian , Thomopoulos Sophia I. , Urosevic Snezana , Usemann Paula , van Haren Neeltje E. M. , Vargas Cristian , Vieta Eduard , Vilajosana Enric , Vreeker Annabel , Winter Nils R. , Yatham Lakshmi N. , Thompson Paul M. , Andreassen Ole A. , Ching Christopher R. K. , Hajek Tomas 
Published: June 2024

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.