Structural inequality and temporal brain dynamics across diverse samples

Baez, Sandra and Hernandez, Hernan and Moguilner, Sebastian and Cuadros, Jhosmary and Santamaria-Garcia, Hernando and Medel, Vicente and Migeot, Joaquín and Cruzat, Josephine and Valdes-Sosa, Pedro A. and Lopera, Francisco and González- Hernández, Alfredis and Bonilla-Santos, Jasmin and Gonzalez-Montealegre, Rodrigo A. and Aktürk, Tuba and Legaz, Agustina and Altschuler, Florencia and Fittipaldi, Sol and Yener, Görsev G. and Escudero, Javier and Babiloni, Claudio and Lopez, Susanna and Whelan, Robert and Fernández Lucas, Alberto A. and Huepe, David and Soto-Añari, Marcio and Coronel-Oliveros, Carlos and Herrera, Eduar and Abasolo, Daniel and Clark, Ruaridh A. and Güntekin, Bahar and Duran-Aniotz, Claudia and Parra, Mario A. and Lawlor, Brian and Tagliazucchi, Enzo and Prado, Pavel and Ibanez, Agustin (2024) Structural inequality and temporal brain dynamics across diverse samples. Clinical and Translational Medicine. ISSN 2001-1326 (In Press)

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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.

ORCID iDs

Baez, Sandra, Hernandez, Hernan, Moguilner, Sebastian, Cuadros, Jhosmary, Santamaria-Garcia, Hernando, Medel, Vicente, Migeot, Joaquín, Cruzat, Josephine, Valdes-Sosa, Pedro A., Lopera, Francisco, González- Hernández, Alfredis, Bonilla-Santos, Jasmin, Gonzalez-Montealegre, Rodrigo A., Aktürk, Tuba, Legaz, Agustina, Altschuler, Florencia, Fittipaldi, Sol, Yener, Görsev G., Escudero, Javier, Babiloni, Claudio, Lopez, Susanna, Whelan, Robert, Fernández Lucas, Alberto A., Huepe, David, Soto-Añari, Marcio, Coronel-Oliveros, Carlos, Herrera, Eduar, Abasolo, Daniel, Clark, Ruaridh A. ORCID logoORCID: https://orcid.org/0000-0003-4601-2085, Güntekin, Bahar, Duran-Aniotz, Claudia, Parra, Mario A. ORCID logoORCID: https://orcid.org/0000-0002-2412-648X, Lawlor, Brian, Tagliazucchi, Enzo, Prado, Pavel and Ibanez, Agustin;
CORE (COnnecting REpositories)