RESUMEN
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.
Asunto(s)
Encéfalo , Cognición , Electroencefalografía , Humanos , Masculino , Femenino , Adulto , Cognición/fisiología , Persona de Mediana Edad , Encéfalo/fisiología , Anciano , Adulto Joven , Individualidad , Adolescente , Factores de Edad , Envejecimiento/fisiologíaRESUMEN
Back pain lifetime incidence is 60%-70%, while 12%-20% of older women have vertebral fractures (VFs), often with back pain. We aimed to provide objective evidence, currently lacking, regarding whether back pain and VFs affect physical activity (PA). We recruited 69 women with recent back pain (age 74.5 ± 5.4 years). Low- (0.5 < g < 1.0), medium- (1.0 ≤ g < 1.5), and high-impact (g ≥ 1.5) PA and walking time were measured (100 Hz for 7 days, hip-worn accelerometer). Linear mixed-effects models assessed associations between self-reported pain and PA, and group differences (VFs from spine radiographs/no-VF) in PA. Higher daily pain was associated with reduced low (ß = -0.12, 95% confidence interval, [-0.22, -0.03], p = .013) and medium-impact PA (ß = -0.11, 95% confidence interval, [-0.21, -0.01], p = .041), but not high-impact PA or walking time (p > .11). VFs were not associated with PA (all p > .2). Higher daily pain levels but not VFs were associated with reduced low- and medium-impact PA, which could increase sarcopenia and falls risk in older women with back pain.
Asunto(s)
Dolor de Espalda , Ejercicio Físico , Posmenopausia , Fracturas de la Columna Vertebral , Humanos , Femenino , Anciano , Fracturas de la Columna Vertebral/fisiopatología , Dolor de Espalda/fisiopatología , Dolor de Espalda/etiología , Ejercicio Físico/fisiología , Posmenopausia/fisiología , Acelerometría , Dimensión del Dolor , Caminata/fisiología , Anciano de 80 o más AñosRESUMEN
Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on the brain age gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American countries -LAC, 8 non-LAC). Based on higher-order interactions in brain signals, we developed a BAG deep learning architecture for functional magnetic resonance imaging (fMRI=2,953) and electroencephalography (EEG=2,353). The datasets comprised healthy controls, and individuals with mild cognitive impairment, Alzheimer's disease, and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated with frontoposterior networks. Structural socioeconomic inequality and other disparity-related factors (pollution, health disparities) were influential predictors of increased brain age gaps, especially in LAC (R2=0.37, F2=0.59, RMSE=6.9). A gradient of increasing BAG from controls to mild cognitive impairment to Alzheimer's disease was found. In LAC, we observed larger BAGs in females in control and Alzheimer's disease groups compared to respective males. Results were not explained by variations in signal quality, demographics, or acquisition methods. Findings provide a quantitative framework capturing the multimodal diversity of accelerated brain aging.