001048931 001__ 1048931
001048931 005__ 20251211202155.0
001048931 037__ $$aFZJ-2025-05028
001048931 1001_ $$0P:(DE-Juel1)196013$$aZhang, Mingxian$$b0$$ufzj
001048931 1112_ $$aAging and Cognition Conference$$cPavia$$d2025-05-07 - 2025-05-10$$wItaly
001048931 245__ $$aLeveraging lifestyle clusters and multimodal brain features to enhance cognitive prediction models in healthy older adults
001048931 260__ $$c2025
001048931 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1765445645_14051
001048931 3367_ $$033$$2EndNote$$aConference Paper
001048931 3367_ $$2BibTeX$$aINPROCEEDINGS
001048931 3367_ $$2DRIVER$$aconferenceObject
001048931 3367_ $$2DataCite$$aOutput Types/Conference Abstract
001048931 3367_ $$2ORCID$$aOTHER
001048931 520__ $$aDeveloping neuroimaging markers for normal cognitive aging is challenging due to variability in the brain and behavior among older adults, complicating the identification of predictors. However, modifiable lifestyle factors may help link underlying group differences in brain structure and function. Examining brain differences across distinct lifestyle groups may better identify informative features for predicting cognitive performance rather than relying solely on data-driven methods. This study explored whether lifestyle-related brain features could predict cognitive function in healthy older adults at baseline and after ~4 years, using multimodal MRI data from 563 participants of the 1000BRAINS cohort. We performed KModes clustering analysis on eight lifestyle factors to identify four distinct lifestyle groups and conducted univariate analyses to find significant between-group brain differences. These differences were used in a lifestyle-related model for machine learning, compared to data-driven models for predicting 13 cognitive tests. The lifestyle-related brain model better predicted visual and episodic memory than data-driven models but showed limited generalization. Correlations between predicted and actual cognitive scores were significant at both baseline and follow-up. This study highlights the potential for integrating lifestyle information as a form of feature selection to help improve predictive models of cognitive performance during aging, pending further external validation.
001048931 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001048931 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
001048931 7001_ $$0P:(DE-Juel1)166110$$aBittner, Nora$$b1$$eCorresponding author$$ufzj
001048931 7001_ $$0P:(DE-Juel1)180200$$aMendl-Heinisch, Camilla$$b2$$ufzj
001048931 7001_ $$0P:(DE-Juel1)181023$$aMiller, Tatiana$$b3$$ufzj
001048931 7001_ $$0P:(DE-HGF)0$$aMoebus, Susanne$$b4
001048931 7001_ $$0P:(DE-HGF)0$$aDragano, Nico$$b5
001048931 7001_ $$0P:(DE-Juel1)131675$$aCaspers, Svenja$$b6$$ufzj
001048931 909CO $$ooai:juser.fz-juelich.de:1048931$$popenaire$$pVDB$$pec_fundedresources
001048931 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)196013$$aForschungszentrum Jülich$$b0$$kFZJ
001048931 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)166110$$aForschungszentrum Jülich$$b1$$kFZJ
001048931 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180200$$aForschungszentrum Jülich$$b2$$kFZJ
001048931 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)181023$$aForschungszentrum Jülich$$b3$$kFZJ
001048931 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Institute for Urban Public Health, University of Duisburg-Essen, Essen, Germany$$b4
001048931 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Institute of Medical Sociology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany$$b5
001048931 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131675$$aForschungszentrum Jülich$$b6$$kFZJ
001048931 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001048931 9141_ $$y2025
001048931 920__ $$lno
001048931 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001048931 980__ $$aabstract
001048931 980__ $$aVDB
001048931 980__ $$aI:(DE-Juel1)INM-1-20090406
001048931 980__ $$aUNRESTRICTED