Biophysical models applied to dementia patients reveal links between geographical origin, gender, disease duration, and loss of neural inhibition

Moguilner, Sebastian; Herzog, Rubén; Perl, Yonatan Sanz; Medel, Vicente; Cruzat, Josefina; Coronel, Carlos; Kringelbach, Morten; Deco, Gustavo; Ibáñez, Agustín; Tagliazucchi, Enzo

doi:10.1186/s13195-024-01449-0

Cited by 4 publications

(1 citation statement)

References 105 publications

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“…Our results suggest that being from LAC is associated with accelerated aging. The better fit of the non-LAC compared to the LAC models supports the notion that universal models of brain phenotypes do not generalize well to underrepresented populations 24,29,40 . Diversity-related factors associated with different exposome and disease disparities 4,10,24,41 may influence the BAGs in LAC and non-LAC.…”

Section: Discussionsupporting

confidence: 57%

Brain clocks capture diversity and disparity in aging and dementia

Ibanez,

Moguilner,

Baez

et al. 2024

Preprint

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Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on the brain age gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American countries -LAC, 8 non-LAC). Based on higher-order interactions in brain signals, we developed a BAG deep learning architecture for functional magnetic resonance imaging (fMRI=2,953) and electroencephalography (EEG=2,353). The datasets comprised healthy controls, and individuals with mild cognitive impairment, Alzheimer’s disease, and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated with frontoposterior networks. Structural socioeconomic inequality and other disparity-related factors (pollution, health disparities) were influential predictors of increased brain age gaps, especially in LAC (R²=0.37, F²=0.59, RMSE=6.9). A gradient of increasing BAG from controls to mild cognitive impairment to Alzheimer’s disease was found. In LAC, we observed larger BAGs in females in control and Alzheimer’s disease groups compared to respective males. Results were not explained by variations in signal quality, demographics, or acquisition methods. Findings provide a quantitative framework capturing the multimodal diversity of accelerated brain aging.

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Section: Discussionsupporting

confidence: 57%

Brain clocks capture diversity and disparity in aging and dementia

Ibanez,

Moguilner,

Baez

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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Biomarkers of neurodegeneration across the Global South

McGlinchey,

Duran-Aniotz,

Akinyemi

et al. 2024

The Lancet Healthy Longevity

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

Moguilner,

Baez,

Hernandez

et al. 2024

Nat Med

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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.

show abstract

Biophysical models applied to dementia patients reveal links between geographical origin, gender, disease duration, and loss of neural inhibition

Cited by 4 publications

References 105 publications

Brain clocks capture diversity and disparity in aging and dementia

Brain clocks capture diversity and disparity in aging and dementia

Biomarkers of neurodegeneration across the Global South

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

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