TY - JOUR
T1 - Structural inequality and temporal brain dynamics across diverse samples
AU - Baez, Sandra
AU - Hernandez, Hernan
AU - Moguilner, Sebastian
AU - Cuadros, Jhosmary
AU - Santamaria-Garcia, Hernando
AU - Medel, Vicente
AU - Migeot, Joaquín
AU - Cruzat, Josephine
AU - Valdes-Sosa, Pedro A.
AU - Lopera, Francisco
AU - González- Hernández, Alfredis
AU - Bonilla-Santos, Jasmin
AU - Gonzalez-Montealegre, Rodrigo A.
AU - Aktürk, Tuba
AU - Legaz, Agustina
AU - Altschuler, Florencia
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 - Huepe, David
AU - Soto-Añari, Marcio
AU - Coronel-Oliveros, Carlos
AU - Herrera, Eduar
AU - Abasolo, Daniel
AU - Clark, Ruaridh A.
AU - Güntekin, Bahar
AU - Duran-Aniotz, Claudia
AU - Parra, Mario A.
AU - Lawlor, Brian
AU - Tagliazucchi, Enzo
AU - Prado, Pavel
AU - Ibanez, Agustin
PY - 2024/10/3
Y1 - 2024/10/3
N2 - Structural income inequality — the uneven income distribution across regions or countries — could affect brain structure and function, beyond individual differences. However, the impact of structural income inequality on the brain dynamics and the roles of demographics and cognition in these associations remains unexplored. Here, we assessed the impact of structural income inequality, as measured by the Gini coefficient on multiple EEG metrics, while considering the subject-level effects of demographic (age, sex, education) and cognitive factors. Resting-state EEG signals were collected from a diverse sample (countries=10; healthy individuals=1,394 from Argentina, Brazil, Colombia, Chile, Cuba, Greece, Ireland, Italy, Turkey, and United Kingdom). Complexity (fractal dimension, permutation entropy, Wiener entropy, spectral structure variability), power spectral and aperiodic components (1/f slope, knee, offset), as well as graph-theoretic measures were analyzed. Despite variability in samples, data collection methods, and EEG acquisition parameters, structural inequality systematically predicted electrophysiological brain dynamics, proving to be a more crucial determinant of brain dynamics than individual-level factors. Complexity and aperiodic activity metrics captured better the effects of structural inequality on brain function. Following inequality, age and cognition emerged as the most influential predictors. The overall results provided convergent multimodal metrics of biologic embedding of structural income inequality characterized by less complex signals, increased random asynchronous neural activity, and reduced alpha and beta power, particularly over temporo-posterior regions. These findings might challenge conventional neuroscience approaches that tend to overemphasize the influence of individual-level factors, while neglecting structural factors. Results pave the way for neuroscience-informed public policies aimed at tackling structural inequalities in diverse populations.
AB - Structural income inequality — the uneven income distribution across regions or countries — could affect brain structure and function, beyond individual differences. However, the impact of structural income inequality on the brain dynamics and the roles of demographics and cognition in these associations remains unexplored. Here, we assessed the impact of structural income inequality, as measured by the Gini coefficient on multiple EEG metrics, while considering the subject-level effects of demographic (age, sex, education) and cognitive factors. Resting-state EEG signals were collected from a diverse sample (countries=10; healthy individuals=1,394 from Argentina, Brazil, Colombia, Chile, Cuba, Greece, Ireland, Italy, Turkey, and United Kingdom). Complexity (fractal dimension, permutation entropy, Wiener entropy, spectral structure variability), power spectral and aperiodic components (1/f slope, knee, offset), as well as graph-theoretic measures were analyzed. Despite variability in samples, data collection methods, and EEG acquisition parameters, structural inequality systematically predicted electrophysiological brain dynamics, proving to be a more crucial determinant of brain dynamics than individual-level factors. Complexity and aperiodic activity metrics captured better the effects of structural inequality on brain function. Following inequality, age and cognition emerged as the most influential predictors. The overall results provided convergent multimodal metrics of biologic embedding of structural income inequality characterized by less complex signals, increased random asynchronous neural activity, and reduced alpha and beta power, particularly over temporo-posterior regions. These findings might challenge conventional neuroscience approaches that tend to overemphasize the influence of individual-level factors, while neglecting structural factors. Results pave the way for neuroscience-informed public policies aimed at tackling structural inequalities in diverse populations.
KW - brain dynamics
KW - cognition
KW - demographics
KW - EEG
KW - individual differences
KW - structural inequality
UR - https://github.com/euroladbrainlat/Structural-inequality-and-brain-dynamics-across-diverse-samples
U2 - 10.1002/ctm2.70032
DO - 10.1002/ctm2.70032
M3 - Article
SN - 2001-1326
VL - 14
JO - Clinical and Translational Medicine
JF - Clinical and Translational Medicine
IS - 10
M1 - e70032
ER -