Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations
Authors: Moguilner Sebastian , Baez Sandra , Hernandez Hernan , Migeot Joaquín , Legaz Agustina , Gonzalez-Gomez Raul , Farina Francesca R. , Prado Pavel , Cuadros Jhosmary , Tagliazucchi Enzo , Altschuler Florencia , Maito Marcelo Adrián , Godoy María E. , Cruzat Josephine , Valdes-Sosa Pedro A. , Lopera Francisco , Ochoa-Gómez John Fredy , Hernandez Alfredis Gonzalez , Bonilla-Santos Jasmin , Gonzalez-Montealegre Rodrigo A. , Anghinah Renato , d’Almeida Manfrinati Luís E. , Fittipaldi Sol , Medel Vicente , Olivares Daniela , Yener Görsev G. , Escudero Javier , Babiloni Claudio , Whelan Robert , Güntekin Bahar , Yırıkoğulları Harun , Santamaria-Garcia Hernando , Lucas Alberto Fernández , Huepe David , Di Caterina Gaetano , Soto-Añari Marcio , Birba Agustina , Sainz-Ballesteros Agustin , Coronel-Oliveros Carlos , Yigezu Amanuel , Herrera Eduar , Abasolo Daniel , Kilborn Kerry , Rubido Nicolás , Clark Ruaridh A. , Herzog Ruben , Yerlikaya Deniz , Hu Kun , Parra Mario A. , Reyes Pablo , García Adolfo M. , Matallana Diana L. , Avila-Funes José Alberto , Slachevsky Andrea , Behrens María I. , Custodio Nilton , Cardona Juan F. , Barttfeld Pablo , Brusco Ignacio L. , Bruno Martín A. , Sosa Ortiz Ana L. , Pina-Escudero Stefanie D. , Takada Leonel T. , Resende Elisa , Possin Katherine L. , de Oliveira Maira Okada , Lopez-Valdes Alejandro , Lawlor Brian , Robertson Ian H. , Kosik Kenneth S. , Duran-Aniotz Claudia , Valcour Victor , Yokoyama Jennifer S. , Miller Bruce , Ibanez Agustin 
Published: August 2024

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.