biostudies-literatureMillar PRNCATS NIH HHSNIA NIH HHSNCRR NIH HHSMedical Research CouncilBrightFocus FoundationNINDS NIH HHSNational Institutes of HealthAlzheimer&apos;s AssociationAlzheimer's AssociationNIH HHSe81869https://www.ebi.ac.uk/biostudies/studies/S-EPMC9988262biostudies-literatureUnknown12<h4>Background</h4>Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored.<h4>Methods</h4>We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationshieLifeMultimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study.PMC9988262R01 AG052550S10 RR022984UL1 TR002345K01 AG053474P01-AG03991UKDRI-1001R01 NS092865P30-AG066444U19 AG0324385-R01-AG057680P30 AG066444R01 AG057680SG-20-690363-DIAN5-R01-AG052550U19-AG032438P01 AG026276P01-AG026276R01 AG067505A2022014FP01 AG003991Kensaku KCortaiga HAki ARandall BStephan KChrismary DJohn MSnezana ISusan BJohn RAylin DJames NJames MRichard PSochenda SNeal S MWendy SElke KBJinbin XDavid HDeb KEric MJane DDominantly Inherited Alzheimer NetworkAllegri RFJohannes LKevin TDarcy DErik JGordon BALuckett PHCruchaga CBecca FLaura HSBrigitte NMiguel GVirginia BPatricio C MOscar LMDMathias JHiroyuki SGina JCourtney BAlyssa GHelena CBrian GPerrin RJColleen FTamara DJasmeer CChristoph LKathleen CBernardino GAkemi NRiddhi PNoelia EErin FArlene MUlricke OWilliam KShaney FCeleste KAustin MYakushev IHamid STakeshi IJulia GAlison GMichio SHassenstab JJMillar PRYoshiki NDuc DFarlow MRNelly JMClifford JKatie NDay GSFagan AMBateman RJSamantha GStephen SXiong XNubling GJacob MLaura CNeill GRChristian HJacob BEmily GLisa CHisako FEstrella MRChengjie XJasmin CJennifer SColin MNeelesh NCharlie CLisa HRobert KSarah TJill BPeter SRicardo AAnna HJoanne NAnne FJill GAnna DKwasi MSarah KRalph MAnces BMLori SSarah GGregory S DSchindler SEKenji ISarah BJonathan VSarah AMori HJason HQing WElise WNicolas BAlan RYan LKristine SMorgan GCarlos CBianca EAlex GMarty FMorris JCBeth SRyoko IAntoinette OLingyan PJared BElizabeth HNick FNicholas T SAllan LTammie BBenzinger TLSSusanne GSPeter WCath MOliver PWilliam Bill BRuss HfalseMultimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study.<h4>Background</h4>Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored.<h4>Methods</h4>We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationshi2023-01-01T00:00:00Z2023 Jan2026-03-27T16:47:07.506Z2025-04-05T17:45:04.801ZS-EPMC99882623660733510.7554/eLife.81869