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001048937 005__ 20251211202155.0
001048937 037__ $$aFZJ-2025-05034
001048937 1001_ $$0P:(DE-Juel1)180200$$aMendl-Heinisch, Camilla$$b0$$ufzj
001048937 1112_ $$aAging and Cognition Conference$$cPavia$$d2025-05-07 - 2025-05-10$$wGermany
001048937 245__ $$aPrediction of individual cognitive test performance  based on imaging and non-imaging data in older adults
001048937 260__ $$c2025
001048937 3367_ $$033$$2EndNote$$aConference Paper
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001048937 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1765445686_13421$$xAfter Call
001048937 520__ $$aEarly 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.
001048937 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001048937 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
001048937 7001_ $$0P:(DE-Juel1)166110$$aBittner, Nora$$b1$$ufzj
001048937 7001_ $$0P:(DE-Juel1)181023$$aMiller, Tatiana$$b2$$ufzj
001048937 7001_ $$0P:(DE-Juel1)180197$$aDellani, Paulo R.$$b3$$ufzj
001048937 7001_ $$0P:(DE-Juel1)131675$$aCaspers, Svenja$$b4$$ufzj
001048937 7001_ $$0P:(DE-Juel1)145386$$aJockwitz, Christiane$$b5$$ufzj
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001048937 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180200$$aForschungszentrum Jülich$$b0$$kFZJ
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001048937 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180197$$aForschungszentrum Jülich$$b3$$kFZJ
001048937 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131675$$aForschungszentrum Jülich$$b4$$kFZJ
001048937 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145386$$aForschungszentrum Jülich$$b5$$kFZJ
001048937 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$$x0
001048937 9141_ $$y2025
001048937 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
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001048937 980__ $$aI:(DE-Juel1)INM-1-20090406
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