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| Home > Publications database > Leveraging lifestyle clusters and multimodal brain features to enhance cognitive prediction models in healthy older adults > print |
| 001 | 1048931 | ||
| 005 | 20251211202155.0 | ||
| 037 | _ | _ | |a FZJ-2025-05028 |
| 100 | 1 | _ | |a Zhang, Mingxian |0 P:(DE-Juel1)196013 |b 0 |u fzj |
| 111 | 2 | _ | |a Aging and Cognition Conference |c Pavia |d 2025-05-07 - 2025-05-10 |w Italy |
| 245 | _ | _ | |a Leveraging lifestyle clusters and multimodal brain features to enhance cognitive prediction models in healthy older adults |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Abstract |b abstract |m abstract |0 PUB:(DE-HGF)1 |s 1765445645_14051 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Abstract |2 DataCite |
| 336 | 7 | _ | |a OTHER |2 ORCID |
| 520 | _ | _ | |a Developing 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. |
| 536 | _ | _ | |a 5251 - Multilevel Brain Organization and Variability (POF4-525) |0 G:(DE-HGF)POF4-5251 |c POF4-525 |f POF IV |x 0 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 1 |
| 700 | 1 | _ | |a Bittner, Nora |0 P:(DE-Juel1)166110 |b 1 |e Corresponding author |u fzj |
| 700 | 1 | _ | |a Mendl-Heinisch, Camilla |0 P:(DE-Juel1)180200 |b 2 |u fzj |
| 700 | 1 | _ | |a Miller, Tatiana |0 P:(DE-Juel1)181023 |b 3 |u fzj |
| 700 | 1 | _ | |a Moebus, Susanne |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Dragano, Nico |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Caspers, Svenja |0 P:(DE-Juel1)131675 |b 6 |u fzj |
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| 910 | 1 | _ | |a Institute for Urban Public Health, University of Duisburg-Essen, Essen, Germany |0 I:(DE-HGF)0 |b 4 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Institute of Medical Sociology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany |0 I:(DE-HGF)0 |b 5 |6 P:(DE-HGF)0 |
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| 914 | 1 | _ | |y 2025 |
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| 920 | 1 | _ | |0 I:(DE-Juel1)INM-1-20090406 |k INM-1 |l Strukturelle und funktionelle Organisation des Gehirns |x 0 |
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