Structural inequality and temporal brain dynamics across diverse samples

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 BabiloniSusanna 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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

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.
Original languageEnglish
Article numbere70032
JournalClinical and Translational Medicine
Volume14
Issue number10
DOIs
Publication statusPublished - 3 Oct 2024

Keywords

  • brain dynamics
  • cognition
  • demographics
  • EEG
  • individual differences
  • structural inequality

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