{"doi":"10.1038/s41591-024-03209-x","title":"Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations","abstract":"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.","journal":"Nature Medicine","year":2024,"id":416585,"datarank":2.9310694856245862,"base_score":4.787491742782046,"endowment":4.787491742782046,"self_citation_contribution":0.7181237614173069,"citation_network_contribution":2.2129457242072794,"self_endowment_contribution":0.7181237614173069,"citer_contribution":2.2129457242072794,"corpus_percentile":92.52726850777442,"corpus_rank":967,"citation_count":119,"citer_count":80,"citers_with_citation_signal":45,"citers_with_endowment":45,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.6146,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":58.3333,"fair_percentile":72.8829104249465,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":233799,"name":"Sandra Báez","orcid":"0000-0003-0875-1709","position":1,"is_corresponding":false},{"id":990928,"name":"Hernán Hernandez","orcid":"0000-0002-0597-6059","position":2,"is_corresponding":false},{"id":670770,"name":"Joaquín Migeot","orcid":"0000-0003-0647-5632","position":3,"is_corresponding":false},{"id":368128,"name":"Agustina Legaz","orcid":null,"position":4,"is_corresponding":false},{"id":791982,"name":"Raúl González-Gómez","orcid":"0000-0003-2341-011X","position":5,"is_corresponding":false},{"id":899370,"name":"Francesca R Farina","orcid":"0000-0002-4410-4368","position":6,"is_corresponding":false},{"id":683731,"name":"Pavel Prado","orcid":"0000-0002-1324-6400","position":7,"is_corresponding":false},{"id":993502,"name":"Jhosmary Cuadros","orcid":"0000-0001-7665-0171","position":8,"is_corresponding":false},{"id":315384,"name":"Enzo Tagliazucchi","orcid":"0000-0003-0421-9993","position":9,"is_corresponding":false},{"id":1164102,"name":"Florencia Altschuler","orcid":"0000-0001-6362-963X","position":10,"is_corresponding":false},{"id":856835,"name":"Marcelo Adrián Maito","orcid":null,"position":11,"is_corresponding":false},{"id":856836,"name":"Maria Eugenia Godoy","orcid":null,"position":12,"is_corresponding":false},{"id":804547,"name":"Josephine Cruzat","orcid":"0000-0002-3252-8657","position":13,"is_corresponding":false},{"id":308413,"name":"Pedro A. 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Cite the neuroimaging repository accession (e.g. from OpenNeuro or NeuroVault) in the reference list.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"All preprocessed data are openly available at: https://osf.io/8zjf4/.","why":"The dataset identifier (URL) appears only in the body text, not in the reference list.","gain":4.17,"priority":"important","scored":true},{"key":"r_versioning","dimension":"R","label":"Snapshot identified","action":"Version the deposit and cite the exact version analysed (a version-specific DOI, or an accession with its version suffix). A reader reproducing your work against 'the current release' is reproducing it against a different dataset.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No version token or date is given for the data snapshot.","gain":4.17,"priority":"useful","scored":true},{"key":"f_discovery_metadata","dimension":"F","label":"Description of the dataset as an object","action":"Add a 'Data Records' section: itemise every file in the deposit and every variable or sample it holds, with counts and units. Describe the dataset as an object in its own right, not as a by-product of the findings — this is what makes it discoverable to someone who is not looking for your paper.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"The fMRI dataset involved 2,953 participants from both non-LAC (USA, China, Japan) and LAC (Argentina, Chile, Colombia, Mexico, Peru), including 1,444 healthy controls.","why":"The dataset description is in running prose, not an itemised inventory.","gain":0.0,"priority":"essential","scored":false},{"key":"r_documentation_codebook","dimension":"R","label":"Documentation / codebook","action":"Ship a README and a data dictionary IN the deposit — every file, every variable, its units, its allowed values, its missing-value codes. It is the cheapest single thing that makes a dataset usable by someone who was not in the lab, and a table buried in the article does not travel with the data.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No documentation object (README, codebook) is named, and no variable-definition table exists in the article. [majority verdict 'no' (4/5 passes agreed)]","gain":0.0,"priority":"important","scored":false},{"key":"a_controlled_access_for_sensitive","dimension":"A","label":"Gatekeeper for sensitive data","action":"Route sensitive data through an institutional gatekeeper — deposit in a controlled- access repository (dbGaP, EGA) with a Data Access Committee and a published DUA — rather than through the corresponding author's inbox. An author-gated dataset dies with the author's email address, and 'on reasonable request' has been shown repeatedly not to yield data.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"All preprocessed data are openly available at: https://osf.io/8zjf4/.","why":"The data are human-subject but stated to be openly available with no gatekeeper mentioned.","gain":0.0,"priority":"useful","scored":false},{"key":"i_qualified_references","dimension":"I","label":"Identifiers for the resources the data depend on","action":"Cite by identifier every resource the data depend on — the source datasets' accessions, the reference build (GRCh38 / GCA_000001405.28), the cohort application number, the code DOI — and register those relations on the dataset record (IsDerivedFrom, IsSupplementTo). A name is not a link: it cannot be resolved, versioned, or followed by a machine.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"Alzheimer’s Disease Neuroimaging Initiative (ADNI) (USA) (https://ida.loni.usc.edu/collaboration/access/appLicense.jsp)","why":"The paper gives URLs for external datasets used, which are identifiers for those resources. [downgraded to 'no' — no verifiable quote from the paper]","gain":0.0,"priority":"useful","scored":false},{"key":"a_timeline_retention","dimension":"A","label":"Availability timing & retention","action":"State when the data become available AND how long they will be retained — cite the repository's preservation policy. NIH DMS Element 4 asks for both; most papers give neither.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"All preprocessed data are openly available at: https://osf.io/8zjf4/.","why":"The statement gives availability timing (presently available) but no persistence commitment. [majority verdict 'partial' (3/5 passes agreed)]","gain":0.0,"priority":"useful","scored":false}],"suggestions":["Attach a standard, machine-readable open licence to the deposit — CC0 or CC BY, which is what Horizon Europe and most funders expect — and print the licence identifier in the paper. 'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","Mint or cite a persistent identifier for the dataset — a repository DOI or an accession from a registered repository — and print it in the paper. A bare URL is not persistent: it is the single most common cause of a dead data link five years after publication. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","Release the data in an open, community-standard format (CSV/TSV, JSON, HDF5, NetCDF, FASTQ, VCF, NIfTI…) instead of — or alongside — any proprietary or instrument-native format, and name the format in the paper. A dataset that needs a €2,000 licence to open is not reusable. Prefer open neuroimaging formats such as NIfTI or BIDS.","Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the neuroimaging repository accession (e.g. from OpenNeuro or NeuroVault) in the reference list.","Version the deposit and cite the exact version analysed (a version-specific DOI, or an accession with its version suffix). A reader reproducing your work against 'the current release' is reproducing it against a different dataset."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"unpaywall_pdf"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"unpaywall_pdf","fair_has_llm":true,"fair_computed_at":"2026-07-20T11:01:44.777856Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}