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001048935 005__ 20251211202155.0
001048935 037__ $$aFZJ-2025-05032
001048935 1001_ $$0P:(DE-Juel1)145386$$aJockwitz, Christiane$$b0$$ufzj
001048935 1112_ $$aAging and Cognition Conference$$cPavia$$d2025-05-07 - 2025-05-10$$wItaly
001048935 245__ $$aPrediction of individual cognitive test scores from brain and non-brain data across the adult lifespan
001048935 260__ $$c2025
001048935 3367_ $$033$$2EndNote$$aConference Paper
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001048935 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1765445673_13421$$xAfter Call
001048935 520__ $$aPredicting cognitive decline in aging remains a challenging but important topic. Existing results are heterogeneous, potentially due to the non-linear nature of both, cognitive decline and the factors that influence it. We here aimed to systematically examine the predictability of cognitive abilities based on brain and non-brain data across five decades of the adult lifespan in the large German National Cohort (NAKO; N = 23,863; 25 to 75 years). Brain summary statistics (e.g. total grey matter), health (e.g. body-mass-index) and demographic (i.e. age, sex, education) data were used to predict four cognitive scores using a machine learning (ML; repeated nested cross-validation; four regression algorithms) approach.Current results emphasize that demographics tend to outperform brain and health factors in predicting cognitive abilities in a large sample spanning the whole adulthood, with better predictability for episodic memory and interference compared to verbal fluency and working memory. Contrary to the hypothesis of a worse prediction at older ages, prediction appeared to be similarly low in each decade. Hence, sample size seems to matter even more than sample homogeneity. Including a wide age range for reaching large sample sizes, though, could come at the cost of predicting a hidden age effect.
001048935 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001048935 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
001048935 7001_ $$0P:(DE-Juel1)180200$$aMendl-Heinisch, Camilla$$b1$$ufzj
001048935 7001_ $$0P:(DE-Juel1)181023$$aMiller, Tatiana$$b2$$ufzj
001048935 7001_ $$0P:(DE-Juel1)180197$$aDellani, Paulo R.$$b3$$ufzj
001048935 7001_ $$0P:(DE-Juel1)131675$$aCaspers, Svenja$$b4$$eCorresponding author$$ufzj
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001048935 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145386$$aForschungszentrum Jülich$$b0$$kFZJ
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001048935 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)181023$$aForschungszentrum Jülich$$b2$$kFZJ
001048935 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180197$$aForschungszentrum Jülich$$b3$$kFZJ
001048935 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131675$$aForschungszentrum Jülich$$b4$$kFZJ
001048935 9131_ $$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
001048935 9141_ $$y2025
001048935 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001048935 980__ $$aconf
001048935 980__ $$aVDB
001048935 980__ $$aI:(DE-Juel1)INM-1-20090406
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