Browsing by Author "Medel, Vicente"

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Citation - WoS: 24
Citation - Scopus: 27
Brain Health in Diverse Settings: How Age, Demographics and Cognition Shape Brain Function
(Academic Press Inc., 2024-07) Hernandez H.; Baez S.; Medel V.; Moguilner S.; Cuadros J.; Santamaria-Garcia H.; Tagliazucchi E.; Moguilner, Sebastian; Cuadros, Jhosmary; Hernandez, Hernan; Medel, Vicente; Ibanez, Agustin; Baez, Sandra; Santamaria-Garcia, Hernando
Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function. © 2024 The Author(s)
Citation - WoS: 1
Citation - Scopus: 1
Diversity-Sensitive Brain Clocks Linked to Biophysical Mechanisms in Aging and Dementia
(SpringerNature, 2025-09-18) Coronel-Oliveros, Carlos; Moguilner, Sebastian; Hernandez, Hernan; Cruzat, Josephine; Baez, Sandra; Medel, Vicente; Ibanez, Agustin
Brain clocks track the deviations between predicted brain age and chronological age (brain age gaps, BAGs). These BAGs can be used to measure accelerated aging, monitoring deviations from the healthy brain trajectories associated with brain diseases and different cumulative burdens. However, the underlying biophysical mechanisms associated with BAGs in aging and dementia remain unclear. Here we combine source space connectivity (via electroencephalography) with generative brain modeling in healthy controls from the global south and north, alongside patients with Alzheimer's disease and behavioral variant frontotemporal dementia (bvFTD) (N = 1,399). BAGs in aging were influenced by geography (south > north), income (low > high), sex (female > male) and education (low > high), with larger BAGs in patients, especially females, with Alzheimer's disease. Biophysical modeling revealed BAGs related to hyperexcitability and structural disintegration in aging, while hypoexcitability and severe disintegration were linked to dementia. Our work sheds light on the biophysical mechanisms of accelerated aging and dementia in diverse populations.
Citation - WoS: 14
Structural Inequality and Temporal Brain Dynamics Across Diverse Samples
(John Wiley & Sons Ltd, 2024-10) Baez, Sandra; Hernandez, Hernan; Moguilner, Sebastian; Cuadros, Jhosmary; Santamaria-Garcia, Hernando; Medel, Vicente; Migeot, Joaquin; Ibanez, Agustin
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