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Structural inequality and temporal brain dynamics across diverse samples

Authors :
Sandra Baez
Hernan Hernandez
Sebastian Moguilner
Jhosmary Cuadros
Hernando Santamaria‐Garcia
Vicente Medel
Joaquín Migeot
Josephine Cruzat
Pedro A. Valdes‐Sosa
Francisco Lopera
Alfredis González‐Hernández
Jasmin Bonilla‐Santos
Rodrigo A. Gonzalez‐Montealegre
Tuba Aktürk
Agustina Legaz
Florencia Altschuler
Sol Fittipaldi
Görsev G. Yener
Javier Escudero
Claudio Babiloni
Susanna Lopez
Robert Whelan
Alberto A Fernández Lucas
David Huepe
Marcio Soto‐Añari
Carlos Coronel‐Oliveros
Eduar Herrera
Daniel Abasolo
Ruaridh A. Clark
Bahar Güntekin
Claudia Duran‐Aniotz
Mario A. Parra
Brian Lawlor
Enzo Tagliazucchi
Pavel Prado
Agustin Ibanez
Source :
Clinical and Translational Medicine, Vol 14, Iss 10, Pp n/a-n/a (2024)
Publication Year :
2024
Publisher :
Wiley, 2024.

Abstract

Abstract Background 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. Methods 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 = 1394 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 analysed. Findings 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 characterised by less complex signals, increased random asynchronous neural activity, and reduced alpha and beta power, particularly over temporoposterior regions. Conclusion These findings might challenge conventional neuroscience approaches that tend to overemphasise 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.

Details

Language :
English
ISSN :
20011326
Volume :
14
Issue :
10
Database :
Directory of Open Access Journals
Journal :
Clinical and Translational Medicine
Publication Type :
Academic Journal
Accession number :
edsdoj.8efa1adac3a48f192b3880edf541244
Document Type :
article
Full Text :
https://doi.org/10.1002/ctm2.70032