001     1048937
005     20251211202155.0
037 _ _ |a FZJ-2025-05034
100 1 _ |a Mendl-Heinisch, Camilla
|0 P:(DE-Juel1)180200
|b 0
|u fzj
111 2 _ |a Aging and Cognition Conference
|c Pavia
|d 2025-05-07 - 2025-05-10
|w Germany
245 _ _ |a Prediction of individual cognitive test performance based on imaging and non-imaging data in older adults
260 _ _ |c 2025
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a Other
|2 DataCite
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a LECTURE_SPEECH
|2 ORCID
336 7 _ |a Conference Presentation
|b conf
|m conf
|0 PUB:(DE-HGF)6
|s 1765445686_13421
|2 PUB:(DE-HGF)
|x After Call
520 _ _ |a 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.
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
|u fzj
700 1 _ |a Miller, Tatiana
|0 P:(DE-Juel1)181023
|b 2
|u fzj
700 1 _ |a Dellani, Paulo R.
|0 P:(DE-Juel1)180197
|b 3
|u fzj
700 1 _ |a Caspers, Svenja
|0 P:(DE-Juel1)131675
|b 4
|u fzj
700 1 _ |a Jockwitz, Christiane
|0 P:(DE-Juel1)145386
|b 5
|u fzj
909 C O |o oai:juser.fz-juelich.de:1048937
|p openaire
|p VDB
|p ec_fundedresources
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)180200
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)166110
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)181023
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)180197
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)131675
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 5
|6 P:(DE-Juel1)145386
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5251
|x 0
914 1 _ |y 2025
920 1 _ |0 I:(DE-Juel1)INM-1-20090406
|k INM-1
|l Strukturelle und funktionelle Organisation des Gehirns
|x 0
980 _ _ |a conf
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-1-20090406
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21