Brain clocks capture diversity and disparities in aging and dementia

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 (R² = 0.37, F² = 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.</p

Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

Peer reviewe

Caracterización de Jales Mineros y evaluación de su peligrosidad con base en su potencial de lixiviación

Se realizó una caracterización de un jal histórico y uno reciente obtenidos en el distrito minero de Guanajuato. Los análisis por ICP y AAS mostraron cantidades importantes de metales presentes en el siguiente orden de concentración: 12,185 mg/kg de Fe, 509 mg/kg de Mn, 53 mg/kg de Zn, 20 mg/kg de Pb y 8 mg/Kg de Cr en el jal reciente y 11,676 mg/kg de Fe, 862 mg/kg de Mn, 53 mg/kg de Zn, 17 mg/kg de Pb y 12 mg/Kg de Cr en el jal histórico. Los análisis mineralógicos mostraron que los jales están constituidos mayoritariamente por cuarzo, calcita, covelita y, en menor proporción magnetita, fierro y zinc; además, muestran una ausencia total de materia orgánica y valores de pH que van de neutros a alcalinos. Las pruebas de lixiviación indican que ninguno de los metales presentes puede lixiviar en porcentajes mayores al 0.4% por lo que no representan un riesgo ambiental en base a este criterio. La estabilidad de los metales presentes en las muestra está relacionada con la naturaleza química de las muestras que les hace que sean poco lixiviables

Ciencia Odontológica

Es para los integrantes de la Red de Investigación en Estomatología (RIE) una enorme alegría presentar el primero de una serie de 5 libros sobre casos clínicos, revisiones de la literatura e investigaciones. La RIE está integrada por cuerpos académicos de la Universidad Autónoma del Estado de Hidalgo, Universidad Autónoma del Estado de México, Universidad Autónoma de Campeche y Universidad de Guadalajara

Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

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 (R² = 0.37, F² = 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