{"doi":"10.7554/elife.87297.1","title":"The (Limited?) Utility of Brain Age as a Biomarker for Capturing Cognitive Decline","abstract":"Abstract For decades, neuroscientists have been on a quest to search for a biomarker that can help capture age-related cognitive decline. One well-known candidate is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI data. Here we aim to formally evaluate the utility of Brain Age as a biomarker for capturing cognitive decline. Using 504 aging participants (36-100 years old) from the Human Connectome Project in Aging, we created 26 age-prediction models for Brain Age based on different combinations of MRI modalities. We first tested how much Brain Age from these age-prediction models added to what we had already known from a person’s chronological age in capturing cognitive decline. Based on the commonality analyses, we found a large degree of overlap between Brain Age and chronological age, so much so that, at best, Brain Age could uniquely add only around 1.6% in explaining variation in cognitive decline. Next, the age-prediction models that performed better at predicting chronological age did NOT necessarily create better Brain Age for capturing cognitive decline over and above chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining cognitive decline, thus not improving the models’ ability to capture cognitive decline. Lastly, unlike Brain Age, Brain Cognition, or a predicted value based on machine-learning models built to predict cognitive abilities from brain MRI data, provided much higher unique effects. Brain Cognition added over 11% to explain variation in cognitive decline beyond chronological age, leading to around a 1/3-time improvement of the total variation explained. Accordingly, while demonstrating the limited utility of Brain Age, we provided a solution to improve our ability to use brain MRI data as a biomarker for cognitive decline.","journal":null,"year":2023,"id":399337,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9444,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":567986,"name":"Narun Pat","orcid":null,"position":1,"is_corresponding":false},{"id":868897,"name":"Alina Tetereva","orcid":"0000-0003-2077-628X","position":0,"is_corresponding":true}],"reference_count":54,"raw_metadata":null,"created_at":"2026-07-19T01:19:56.084411Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}