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

Hernan Hernandez, Sandra Baez, Vicente Medel, Sebastian Moguilner, Jhosmary Cuadros, Hernando Santamaria-Garcia, Enzo Tagliazucchi, Pedro A. Valdes-Sosa, Francisco Lopera, John Fredy OchoaGómez, Alfredis González-Hernández, Jasmin Bonilla-Santos, Rodrigo A. Gonzalez-Montealegre, Tuba Aktürk, Ebru Yıldırım, Renato Anghinah, Agustina Legaz, Sol Fittipaldi, Görsev G. Yener, Javier EscuderoClaudio Babiloni, Susanna Lopez, Robert Whelan, Alberto A.Fernández Lucas, Adolfo M. García, David Huepe, Gaetano Di Caterina, Marcio Soto-Añari, Agustina Birba, Agustin Sainz-Ballesteros, Carlos Coronel, Eduar Herrera, Daniel Abasolo, Kerry Kilborn, Nicolás Rubido, Ruaridh Clark, Ruben Herzog, Deniz Yerlikaya, Bahar Güntekin, Mario A. Parra, Pavel Prado, Agustin Ibanez

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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 = 1298) 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.

Original languageEnglish
Article number120636
JournalNeuroImage
Volume295
DOIs
StatePublished - 15 Jul 2024
Externally publishedYes

Keywords

  • Age
  • Brain dynamics
  • Cognition
  • Education
  • Individual differences
  • Sex

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