Person: Behrens, Maria Isabel
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Behrens
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Maria Isabel
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Publication Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations(2024) Moguilner, Sebastian; Baez, Sandra; Hernandez, Hernan; Migeot, Joaquín; Legaz, Agustina; Gonzalez, Raul; Farina, Francesca; Prado, Pavel; Cuadros, Jhosmary; Tagliazucchi, Enzo; Altschuler, Florencia; Maito, Marcelo; Godoy, María; Cruzat, Josefina; Valdes, Pedro; Lopera, Francisco; Ochoa, John; González, Alfredis; Bonilla, Jazmín; Gonzalez, Rodrigo; Anghinah, Renato; d'Almeida, Luis; Fittipaldi, Sol; Medel, Vicente; Olivares, Daniela; Yener, Görsev; Escudero, Javier; Babiloni, Claudio; Whelan, Robert; Guntekin, Bahar; Yırıkoğulları, Harun; Santamaria, Hernando; Fernández, Alberto; Huepe, David; Di Caterina, Gaetano; Soto, Marcio; Birba, Agustina; Sainz, Agustin; Coronel, Carlos; Yigezu, Amanuel; Behrens, Maria IsabelBrain 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. Los relojes cerebrales, que cuantifican las discrepancias entre la edad cerebral y la edad cronológica, son prometedores para comprender la salud y la enfermedad cerebral. Sin embargo, se desconoce el impacto de la diversidad (incluida la geográfica, socioeconómica, sociodemográfica, sexual y neurodegenerativa) en la brecha de edad cerebral. Analizamos conjuntos de datos de 5306 participantes en 15 países (7 países de América Latina y el Caribe (ALC) y 8 países no pertenecientes a ALC). Con base en interacciones de orden superior, desarrollamos una arquitectura de aprendizaje profundo de brecha de edad cerebral para imágenes de resonancia magnética funcional (2953) y electroencefalografía (2353). Los conjuntos de datos comprendían controles sanos e individuos con deterioro cognitivo leve, enfermedad de Alzheimer y demencia frontotemporal variante conductual. Los modelos LAC evidenciaron edades cerebrales más avanzadas (imágenes por resonancia magnética funcional: error direccional medio = 5,60, error cuadrático medio (rmse) = 11,91; electroencefalografía: error direccional medio = 5,34, rmse = 9,82) asociadas con redes frontoposteriores en comparación con los modelos no LAC. La desigualdad socioeconómica estructural, la contaminación y las disparidades en la salud fueron predictores influyentes de mayores brechas de edad cerebral, especialmente en LAC (R² = 0,37, F² = 0,59, rmse = 6,9). Se encontró una brecha ascendente de edad cerebral desde controles sanos hasta deterioro cognitivo leve y enfermedad de Alzheimer. En LAC, observamos brechas de edad cerebral más grandes en mujeres en los grupos de control y enfermedad de Alzheimer en comparación con los respectivos hombres. Los resultados no se explicaron por variaciones en la calidad de la señal, la demografía o los métodos de adquisición. Estos hallazgos proporcionan un marco cuantitativo que captura la diversidad del envejecimiento cerebral acelerado.Publication Author Correction: Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations(2024) Moguilner, Sebastian; Baez, Sandra; Hernandez, Hernan; Migeot, Joaquín; Legaz, Agustina; Gonzalez, Raul; Farina, Francesca; Prado, Pavel; Cuadros, Jhosmary; Tagliazucchi, Enzo; Altschuler, Florencia; Maito, Marcelo; Godoy, María; Cruzat, Josefina; Valdes, Pedro; Lopera, Francisco; Ochoa, John; Gonzalez, Alfredis; Bonilla, Jasmin; Gonzalez, Rodrigo; Anghinah, Renato; d'Almeida, Luís; Fittipaldi, Sol; Medel, Vicente; Olivares, Daniela; Yener, Görsev; Escudero, Javier; Babiloni, Claudio; Whelan, Robert; Güntekin, Bahar; Yırıkoğulları, Harun; Santamaria, Hernando; Fernández, Alberto; Huepe, David; Di Caterina, Gaetano; Soto, Marcio; Birba, Agustina; Sainz, Agustin; Coronel, Carlos; Yigezu, Amanuel; Behrens, Maria IsabelLos relojes cerebrales capturan la diversidad y las disparidades en el envejecimiento y la demencia en poblaciones geográficamente diversas. 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.