Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/5569
Title: Structural inequality and temporal brain dynamics across diverse samples
Authors: Baez, Sandra
Hernandez, Hernan
Moguilner, Sebastian
Cuadros, Jhosmary
Santamaria-Garcia, Hernando
Medel, Vicente
Migeot, Joaquin
Keywords: brain dynamics
cognition
demographics
EEG
individual differences
structural income inequality
Age-Related-Changes
Income Inequality
Alzheimers-Disease
Electromagnetic Tomography
Complexity Analysis
Cognitive Decline
Small-Worldness
Power Spectra
Eeg
Gender
Publisher: John Wiley & Sons Ltd
Abstract: BackgroundStructural 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.MethodsHere, 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.FindingsDespite 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.ConclusionThese 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. We analysed EEG data from 1394 participants across 10 countries, using the Gini coefficient and sociodemographic variables to predict EEG metrics.Four categories of EEG metrics were computed: complexity, aperiodic spectral components, power spectrum, and connectivity.ROC curves, feature importance rankings, and topographical brain region information were reported.Structural income inequality consistently predicts EEG metrics, surpassing individual demographic factors. image
URI: https://doi.org/10.1002/ctm2.70032
https://hdl.handle.net/20.500.14365/5569
ISSN: 2001-1326
Appears in Collections:PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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