Structural inequality and temporal brain dynamics across diverse samples
| dc.contributor.author | Baez, Sandra | |
| dc.contributor.author | Hernandez, Hernan | |
| dc.contributor.author | Moguilner, Sebastian | |
| dc.contributor.author | Cuadros, Jhosmary | |
| dc.contributor.author | Aktürk, Tuba | |
| dc.contributor.author | Güntekin, Bahar | |
| dc.contributor.author | Ibanez, Agustin | |
| dc.date.accessioned | 2025-09-29T08:14:31Z | |
| dc.date.available | 2025-09-29T08:14:31Z | |
| dc.date.issued | 2024 | |
| dc.department | İstanbul Medipol Üniversitesi, Tıp Fakültesi, Temel Tıp Bilimleri Bölümü, Biyofizik Ana Bilim Dalı | |
| dc.department | İstanbul Medipol Üniversitesi, Rektörlük, Sağlık Bilim ve Teknolojileri Araştırma Enstitüsü | |
| dc.description.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. | |
| dc.description.sponsorship | Fogarty International Center (FIC) ; National Institutes of Health, National Institutes of Aging ; Alzheimer's Association ; Rainwater Charitable Foundation ; FONDEF | |
| dc.identifier.citation | Baez, S., Hernandez, H., Moguilner, S., Cuadros, J., Aktürk, T., Güntekin, B. ... Ibanez, A. (2024). Structural inequality and temporal brain dynamics across diverse samples. Clinical and Translational Medicine, 14(10). http://dx.doi.org/10.1002/ctm2.70032 | |
| dc.identifier.doi | 10.1002/ctm2.70032 | |
| dc.identifier.issn | 2001-1326 | |
| dc.identifier.issue | 10 | |
| dc.identifier.pmid | 39360669 | |
| dc.identifier.uri | http://dx.doi.org/10.1002/ctm2.70032 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12511/13051 | |
| dc.identifier.volume | 14 | |
| dc.identifier.wos | WOS:001324725200001 | |
| dc.identifier.wosquality | Q1 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | PubMed | |
| dc.institutionauthor | Aktürk, Tuba | |
| dc.institutionauthor | Güntekin, Bahar | |
| dc.institutionauthorid | 0000-0002-7555-3801 | |
| dc.institutionauthorid | 0000-0002-0860-0524 | |
| dc.language.iso | en | |
| dc.relation.ec | info:eu-repo/grantAgreement/EC/FP7/R01AG057234 | |
| dc.relation.ec | info:eu-repo/grantAgreement/EC/FP7/SG-20-725707 | |
| dc.relation.ec | info:eu-repo/grantAgreement/EC/FP7/1210195 | |
| dc.relation.ec | info:eu-repo/grantAgreement/EC/FP7/ANID/FONDAP/15150012 | |
| dc.relation.ec | info:eu-repo/grantAgreement/EC/FP7/BL-SRGP2020-02 | |
| dc.relation.ispartof | Clinical and Translational Medicine | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Brain Dynamics | |
| dc.subject | Cognition | |
| dc.subject | Demographics | |
| dc.subject | EEG | |
| dc.subject | Individual Differencess | |
| dc.subject | Tructural Income Inequality | |
| dc.title | Structural inequality and temporal brain dynamics across diverse samples | |
| dc.type | Article |
