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
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Parra, Brian Lawlor, Enzo Tagliazucchi, Pavel Prado, Agustin Ibanez","doi":"10.1002/ctm2.70032","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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/<i>f</i> slope, knee, offset), as well as graph-theoretic measures were analysed.</p>\n </section>\n \n <section>\n \n <h3> Findings</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"14 10","pages":""},"PeriodicalIF":7.9000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11447638/pdf/","citationCount":"0","resultStr":"{\"title\":\"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\",\"doi\":\"10.1002/ctm2.70032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Structural income inequality – the uneven income distribution across regions or countries – could affect brain structure and function, beyond individual differences. 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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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>These findings might challenge conventional neuroscience approaches that tend to overemphasise the influence of individual-level factors, while neglecting structural factors. 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Structural inequality and temporal brain dynamics across diverse samples
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.
期刊介绍：
Clinical and Translational Medicine (CTM) is an international, peer-reviewed, open-access journal dedicated to accelerating the translation of preclinical research into clinical applications and fostering communication between basic and clinical scientists. It highlights the clinical potential and application of various fields including biotechnologies, biomaterials, bioengineering, biomarkers, molecular medicine, omics science, bioinformatics, immunology, molecular imaging, drug discovery, regulation, and health policy. With a focus on the bench-to-bedside approach, CTM prioritizes studies and clinical observations that generate hypotheses relevant to patients and diseases, guiding investigations in cellular and molecular medicine. The journal encourages submissions from clinicians, researchers, policymakers, and industry professionals.
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