Browsing by Author "Migeot, Joaquin"

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    Article
    Citation - WoS: 69
    Citation - Scopus: 66
    Brain Clocks Capture Diversity and Disparities in Aging and Dementia Across Geographically Diverse Populations
    (NATURE PORTFOLIO, 2024-08-26) Moguilner, Sebastian; Baez, Sandra; Hernandez, Hernan; Migeot, Joaquin; Legaz, Agustina; Gonzalez-Gomez, Raul; Farina, Francesca R.; Ibanez, Agustin
    Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries). Based on higher-order interactions, we developed a brain-age gap deep learning architecture for functional magnetic resonance imaging (2,953) and electroencephalography (2,353). The datasets comprised healthy controls and individuals with mild cognitive impairment, Alzheimer disease and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (functional magnetic resonance imaging: mean directional error = 5.60, root mean square error (r.m.s.e.) = 11.91; electroencephalography: mean directional error = 5.34, r.m.s.e. = 9.82) associated with frontoposterior networks compared with non-LAC models. Structural socioeconomic inequality, pollution and health disparities were influential predictors of increased brain-age gaps, especially in LAC (R-2 = 0.37, F-2 = 0.59, r.m.s.e. = 6.9). An ascending brain-age gap from healthy controls to mild cognitive impairment to Alzheimer disease was found. In LAC, we observed larger brain-age gaps in females in control and Alzheimer disease groups compared with the respective males. The results were not explained by variations in signal quality, demographics or acquisition methods. These findings provide a quantitative framework capturing the diversity of accelerated brain aging. Analyses of neuroimaging datasets from 5,306 participants across 15 countries found generally larger brain-age gaps in Latin American compared with non-Latin American populations, which were influenced by disparities in socioeconomic and health-related factors.
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    Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Creative Experiences and Brain Clocks
    (Nature Portfolio, 2025-10-03) Coronel-Oliveros, Carlos; Migeot, Joaquin; Lehue, Fernando; Amoruso, Lucia; Kowalczyk-Grebska, Natalia; Jakubowska, Natalia; Ibanez, Agustin
    Creative experiences may enhance brain health, yet metrics and mechanisms remain elusive. We characterized brain health using brain clocks, which capture deviations from chronological age (i.e., accelerated or delayed brain aging). We combined M/EEG functional connectivity (N = 1,240) with machine learning support vector machines, whole-brain modeling, and Neurosynth metanalyses. From this framework, we reanalyzed previously published datasets of expert and matched non-expert participants in dance, music, visual arts, and video games, along with a pre/post-learning study (N = 232). We found delayed brain age across all domains and scalable effects (expertise>learning). The higher the level of expertise and performance, the greater the delay in brain age. Age-vulnerable brain hubs showed increased connectivity linked to creativity, particularly in areas related to expertise and creative experiences. Neurosynth analysis and computational modeling revealed plasticity-driven increases in brain efficiency and biophysical coupling, in creativity-specific delayed brain aging. Findings indicate a domain-independent link between creativity and brain health.
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    Article
    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