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

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dc.contributor.authorMoguilner, Sebastian
dc.contributor.authorBaez, Sandra
dc.contributor.authorHernandez, Hernan
dc.contributor.authorMigeot, Joaquín
dc.contributor.authorLegaz, Agustina
dc.contributor.authorGonzalez, Raul
dc.contributor.authorFarina, Francesca
dc.contributor.authorPrado, Pavel
dc.contributor.authorCuadros, Jhosmary
dc.contributor.authorTagliazucchi, Enzo
dc.contributor.authorAltschuler, Florencia
dc.contributor.authorMaito, Marcelo
dc.contributor.authorGodoy, María
dc.contributor.authorCruzat, Josefina
dc.contributor.authorValdes, Pedro
dc.contributor.authorLopera, Francisco
dc.contributor.authorOchoa, John
dc.contributor.authorGonzález, Alfredis
dc.contributor.authorBonilla, Jazmín
dc.contributor.authorGonzalez, Rodrigo
dc.contributor.authorAnghinah, Renato
dc.contributor.authord'Almeida, Luis
dc.contributor.authorFittipaldi, Sol
dc.contributor.authorMedel, Vicente
dc.contributor.authorOlivares, Daniela
dc.contributor.authorYener, Görsev
dc.contributor.authorEscudero, Javier
dc.contributor.authorBabiloni, Claudio
dc.contributor.authorWhelan, Robert
dc.contributor.authorGuntekin, Bahar
dc.contributor.authorYırıkoğulları, Harun
dc.contributor.authorSantamaria, Hernando
dc.contributor.authorFernández, Alberto
dc.contributor.authorHuepe, David
dc.contributor.authorDi Caterina, Gaetano
dc.contributor.authorSoto, Marcio
dc.contributor.authorBirba, Agustina
dc.contributor.authorSainz, Agustin
dc.contributor.authorCoronel, Carlos
dc.contributor.authorYigezu, Amanuel
dc.contributor.authorBehrens, Maria Isabel
dc.date.accessioned2024-12-18T15:31:06Z
dc.date.available2024-12-18T15:31:06Z
dc.date.issued2024
dc.descriptionHerrera, Eduar; Yerlikaya, Deniz; Hu, Kun; Parra, Mario; Reyes, Pablo; García, Adolfo; Custodio, Nilton; Cardona, Juan; Barttfeld, Pablo; Brusco, Ignacio; Bruno, Martín; Ana, Sosa; Pina, Stefanie; Takada; Leonel; Resende, Elisa; Possin, Katherine; de Oliveira, Maira; Lopez, Alejandro; Lawlor, Brian; Robertson, Ian; Kosik, Kenneth; Duran, Claudia; Valcour, Victor; Yokoyama, Jennifer; Miller, Bruce; Ibanez, Agustin.
dc.description.abstractBrain 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.
dc.description.versionVersión Aceptada
dc.identifier.citationMoguilner S, Baez S, Hernandez H, Migeot J, Legaz A, Gonzalez-Gomez R, Farina FR, Prado P, Cuadros J, Tagliazucchi E, Altschuler F, Maito MA, Godoy ME, Cruzat J, Valdes-Sosa PA, Lopera F, Ochoa-Gómez JF, Hernandez AG, Bonilla-Santos J, Gonzalez-Montealegre RA, Anghinah R, d'Almeida Manfrinati LE, Fittipaldi S, Medel V, Olivares D, Yener GG, Escudero J, Babiloni C, Whelan R, Güntekin B, Yırıkoğulları H, Santamaria-Garcia H, Lucas AF, Huepe D, Di Caterina G, Soto-Añari M, Birba A, Sainz-Ballesteros A, Coronel-Oliveros C, Yigezu A, Herrera E, Abasolo D, Kilborn K, Rubido N, Clark RA, Herzog R, Yerlikaya D, Hu K, Parra MA, Reyes P, García AM, Matallana DL, Avila-Funes JA, Slachevsky A, Behrens MI, Custodio N, Cardona JF, Barttfeld P, Brusco IL, Bruno MA, Sosa Ortiz AL, Pina-Escudero SD, Takada LT, Resende E, Possin KL, de Oliveira MO, Lopez-Valdes A, Lawlor B, Robertson IH, Kosik KS, Duran-Aniotz C, Valcour V, Yokoyama JS, Miller B, Ibanez A. Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations. Nat Med. 2024 Dec;30(12):3646-3657. doi: 10.1038/s41591-024-03209-x. Epub 2024 Aug 26. Erratum in: Nat Med. 2024 Dec;30(12):3779. doi: 10.1038/s41591-024-03294-y.
dc.identifier.doihttps://doi.org/10.1038/s41591-024-03294-y
dc.identifier.urihttps://hdl.handle.net/11447/9509
dc.language.isoen
dc.subjectAged
dc.subject80 and over
dc.subjectAging
dc.subjectAlzheimer Disease / diagnostic imaging
dc.subjectAlzheimer Disease / epidemiology Alzheimer Disease / physiopathology
dc.subjectBrain / diagnostic imaging
dc.subjectBrain / physiopathology
dc.subjectCognitive Dysfunction /
dc.subjectepidemiology Cognitive Dysfunction / physiopathology
dc.subjectDementia / epidemiology
dc.subjectDementia / physiopathology
dc.subjectElectroencephalography Female
dc.subjectHealth Status Disparities
dc.subjectHumans
dc.subjectMagnetic Resonance Imaging
dc.subjectMale
dc.subjectMiddle Aged
dc.subjectSocioeconomic Factors
dc.titleBrain clocks capture diversity and disparities in aging and dementia across geographically diverse populations
dc.typeArticle
dcterms.accessRightsAcceso Abierto
dcterms.sourceNature Medicine
dspace.entity.typePublication
relation.isAuthorOfPublicationb0cb6d05-60d8-4c6f-96ed-e0bd1460aa3c
relation.isAuthorOfPublication.latestForDiscoveryb0cb6d05-60d8-4c6f-96ed-e0bd1460aa3c

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