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001     1048935
005     20251211202155.0
037 _ _ |a FZJ-2025-05032
100 1 _ |a Jockwitz, Christiane
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111 2 _ |a Aging and Cognition Conference
|c Pavia
|d 2025-05-07 - 2025-05-10
|w Italy
245 _ _ |a Prediction of individual cognitive test scores from brain and non-brain data across the adult lifespan
260 _ _ |c 2025
336 7 _ |a Conference Paper
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520 _ _ |a Predicting 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.
536 _ _ |a 5251 - Multilevel Brain Organization and Variability (POF4-525)
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536 _ _ |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)
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700 1 _ |a Mendl-Heinisch, Camilla
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700 1 _ |a Miller, Tatiana
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700 1 _ |a Dellani, Paulo R.
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700 1 _ |a Caspers, Svenja
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