Brain health in diverse settings : how age, demographics and cognition shape brain function

Hernandez, Hernan and Baez, Sandra and Medel, Vicente and Moguilner, Sebastian and Cuadros, Jhosmary and Santamaria-Garcia, Hernando and Tagliazucchi, Enzo and Valdes-Sosa, Pedro A. and Lopera, Francisco and OchoaGómez, John Fredy and González Hernández, Alfredis and Bonilla-Santos, Jasmin and González-Montealegre, Rodrigo A. and Aktürk, Tuba and Yıldırım, Ebru and Anghinah, Renato and Legaz, Agustina and Fittipaldi, Sol and Yener, Görsev G. and Escudero, Javier and Babiloni, Claudio and Lopez, Susanna and Whelan, Robert and Fernández Lucas, Alberto A. and García, Adolfo M. and Huepe, David and Di Caterina, Gaetano and Soto-Añari, Marcio and Birba, Agustina and Sainz-Ballesteros, Agustín and Coronel, Carlos and Herrera, Eduar and Abasolo, Daniel and Kilborn, Kerry and Rubido, Nicolás and Clark, Ruaridh and Herzog, Ruben and Yerlikaya, Deniz and Güntekin, Bahar and Parra, Mario A. and Prado, Pavel and Ibanez, Agustin (2024) Brain health in diverse settings : how age, demographics and cognition shape brain function. NeuroImage, 295. 120636. ISSN 1053-8119 (

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Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1,298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.