TY - JOUR
T1 - Brain health in diverse settings
T2 - how age, demographics and cognition shape brain function
AU - Hernandez, Hernan
AU - Baez, Sandra
AU - Medel, Vicente
AU - Moguilner, Sebastian
AU - Cuadros, Jhosmary
AU - Santamaria-Garcia , Hernando
AU - Tagliazucchi, Enzo
AU - Valdes-Sosa, Pedro A.
AU - Lopera, Francisco
AU - OchoaGómez, John Fredy
AU - González Hernández, Alfredis
AU - Bonilla-Santos, Jasmin
AU - González-Montealegre, Rodrigo A.
AU - Aktürk, Tuba
AU - Yıldırım, Ebru
AU - Anghinah, Renato
AU - Legaz, Agustina
AU - Fittipaldi, Sol
AU - Yener, Görsev G.
AU - Escudero, Javier
AU - Babiloni, Claudio
AU - Lopez, Susanna
AU - Whelan, Robert
AU - Fernández Lucas, Alberto A.
AU - García, Adolfo M.
AU - Huepe, David
AU - Di Caterina, Gaetano
AU - Soto-Añari, Marcio
AU - Birba, Agustina
AU - Sainz-Ballesteros, Agustín
AU - Coronel, Carlos
AU - Herrera, Eduar
AU - Abasolo, Daniel
AU - Kilborn, Kerry
AU - Rubido, Nicolás
AU - Clark, Ruaridh
AU - Herzog, Ruben
AU - Yerlikaya, Deniz
AU - Güntekin, Bahar
AU - Parra, Mario A.
AU - Prado, Pavel
AU - Ibanez, Agustin
PY - 2024/7/15
Y1 - 2024/7/15
N2 - 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.
AB - 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.
KW - age
KW - cognition
KW - education
KW - individual differences
KW - sex
KW - brain dynamics
UR - https://www.sciencedirect.com/journal/neuroimage
U2 - 10.1016/j.neuroimage.2024.120636
DO - 10.1016/j.neuroimage.2024.120636
M3 - Article
SN - 1053-8119
VL - 295
JO - NeuroImage
JF - NeuroImage
M1 - 120636
ER -