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@INPROCEEDINGS{MendlHeinisch:1048937,
      author       = {Mendl-Heinisch, Camilla and Bittner, Nora and Miller,
                      Tatiana and Dellani, Paulo R. and Caspers, Svenja and
                      Jockwitz, Christiane},
      title        = {{P}rediction of individual cognitive test performance based
                      on imaging and non-imaging data in older adults},
      reportid     = {FZJ-2025-05034},
      year         = {2025},
      abstract     = {Early detection of cognitive decline gains relevance in
                      normal aging given its impact on the quality of life of
                      older adults. While using brain imaging data alone can be
                      challenging, there is an opportunity to use health-related
                      and demographic data as biomarker as these are easily
                      accessible and have already been shown to be associated with
                      cognitive dysfunction.Thus, using machine learning (ML) we
                      examined the practicality of 1) imaging, 2) health related
                      and 3) demographic data, in the prediction of cognitive
                      functioning (16 cognitive test scores) in 494 older adults
                      (67 +/- 7 years) from 1000BRAINS. Prediction performance was
                      obtained for each modality and its combinations using
                      cross-validation and four algorithms.Predictability
                      differences emerged across modalities and cognitive
                      functions. In terms of individual tests, vocabulary,
                      executive and episodic memory functions were moderately
                      predicted from demographic and partially from brain data;
                      working memory showed low predictability across
                      modalities.Overall, health-related data showed limited
                      predictability across cognitive functions despite known
                      associations between cardiovascular health and cognitive
                      decline. Strikingly, demographic variables outperformed
                      health and imaging data highlighting their impact on
                      predictions of cognition. Finally, we observed higher
                      predictability of executive and episodic memory functions,
                      which are important for the prognosis of neurodegenerative
                      diseases.},
      month         = {May},
      date          = {2025-05-07},
      organization  = {Aging and Cognition Conference, Pavia
                       (Germany), 7 May 2025 - 10 May 2025},
      subtyp        = {After Call},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1048937},
}