Produção Nacional
Revisado por pares
Brain health in diverse settings: How age, demographics and cognition shape brain function
2024; Elsevier BV; Volume: 295; Linguagem: Inglês
10.1016/j.neuroimage.2024.120636
ISSN1095-9572
AutoresHernán Hernandez, Sandra Báez, Vicente Medel, Sebastián Moguilner, Jhosmary Cuadros, Hernando Santamaría‐García, Enzo Tagliazucchi, Pedro A. Valdés‐Sosa, Francisco Lopera, John Fredy Ochoa Gómez, Alfredis González‐Hernández, Jasmín Bonilla-Santos, Rodrigo A. Gonzalez‐Montealegre, Tuba Aktürk, Ebru Yıldırım, Renato Anghinah, Agustina Legaz, Sol Fittipaldi, Görsev Yener, Javier Escudero, Claudio Babiloni, Susanna Lopez, Robert Whelan, Alberto A Fernández Lucas, Adolfo M. García, David Huepe, Gaetano Di Caterina, Marcio Soto‐Añari, Agustina Birba, Agustín Sainz‐Ballesteros, Carlos Coronel, Eduar Herrera, Daniel Abásolo, Kerry Kilborn, Nicolás Rubido, Ruaridh Clark, Rubén Herzog, Deniz Yerlikaya, Bahar Güntekin, Mario A. Parra, Pavel Prado, Agustín Ibáñez,
Tópico(s)Dementia and Cognitive Impairment Research
ResumoDiversity 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.
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