TY - JOUR
T1 - Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations
AU - Moguilner, Sebastian
AU - Baez, Sandra
AU - Hernandez, Hernan
AU - Migeot, Joaquín
AU - Legaz, Agustina
AU - Gonzalez-Gomez, Raul
AU - Farina, Francesca R.
AU - Prado, Pavel
AU - Cuadros, Jhosmary
AU - Tagliazucchi, Enzo
AU - Altschuler, Florencia
AU - Maito, Marcelo Adrián
AU - Godoy, María E.
AU - Cruzat, Josephine
AU - Valdes-Sosa, Pedro A.
AU - Lopera, Francisco
AU - Ochoa-Gómez, John Fredy
AU - Hernandez, Alfredis Gonzalez
AU - Bonilla-Santos, Jasmin
AU - Gonzalez-Montealegre, Rodrigo A.
AU - Anghinah, Renato
AU - d’Almeida Manfrinati, Luís E.
AU - Fittipaldi, Sol
AU - Medel, Vicente
AU - Olivares, Daniela
AU - Yener, Görsev G.
AU - Escudero, Javier
AU - Babiloni, Claudio
AU - Whelan, Robert
AU - Güntekin, Bahar
AU - Yırıkoğulları, Harun
AU - Santamaria-Garcia, Hernando
AU - Lucas, Alberto Fernández
AU - Huepe, David
AU - Di Caterina, Gaetano
AU - Soto-Añari, Marcio
AU - Birba, Agustina
AU - Sainz-Ballesteros, Agustin
AU - Coronel-Oliveros, Carlos
AU - Yigezu, Amanuel
AU - Herrera, Eduar
AU - Abasolo, Daniel
AU - Kilborn, Kerry
AU - Rubido, Nicolás
AU - Clark, Ruaridh A.
AU - Herzog, Ruben
AU - Yerlikaya, Deniz
AU - Hu, Kun
AU - Parra, Mario A.
AU - Reyes, Pablo
AU - García, Adolfo M.
AU - Matallana, Diana L.
AU - Avila-Funes, José Alberto
AU - Slachevsky, Andrea
AU - Behrens, María I.
AU - Custodio, Nilton
AU - Cardona, Juan F.
AU - Barttfeld, Pablo
AU - Brusco, Ignacio L.
AU - Bruno, Martín A.
AU - Sosa Ortiz, Ana L.
AU - Pina-Escudero, Stefanie D.
AU - Takada, Leonel T.
AU - Resende, Elisa
AU - Possin, Katherine L.
AU - de Oliveira, Maira Okada
AU - Lopez-Valdes, Alejandro
AU - Lawlor, Brian
AU - Robertson, Ian H.
AU - Kosik, Kenneth S.
AU - Duran-Aniotz, Claudia
AU - Valcour, Victor
AU - Yokoyama, Jennifer S.
AU - Miller, Bruce
AU - Ibanez, Agustin
PY - 2024/8/26
Y1 - 2024/8/26
N2 - 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.
AB - 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.
KW - brain clocks
KW - brain health
KW - aging
KW - dementia
KW - Alzheimer's disease
UR - http://www.scopus.com/inward/record.url?scp=85202073322&partnerID=8YFLogxK
U2 - 10.1038/s41591-024-03209-x
DO - 10.1038/s41591-024-03209-x
M3 - Article
AN - SCOPUS:85202073322
SN - 1078-8956
JO - Nature Medicine
JF - Nature Medicine
ER -