PuSH - Publikationsserver des Helmholtz Zentrums München

Mendl-Heinisch, C.* ; Bittner, N.* ; Miller, T.* ; Dellani, P.* ; Bamberg, F.* ; Berger, K.* ; Bohmann, P.* ; Decker, J.A.* ; Flöel, A.* ; Greiser, K.H.* ; Harries, M.* ; Kapar, J.* ; Keil, T.* ; Klett-Tammen, C.J.* ; Krist, L.* ; Kröncke, T.* ; Leitzmann, M.* ; Niendorf, T.* ; Peters, A. ; Pischon, T.* ; Riedel, O.* ; Ringhof, S.* ; Schlett, C.L.* ; Schulze, M.B.* ; Wielpütz, M.O.* ; Wirkner, K.* ; Caspers, S.* ; Jockwitz, C.*

Prediction of cognitive test scores: A comparison of brain structure, health, demographic, and cognitive data across adulthood.

GeroScience, DOI: 10.1007/s11357-026-02232-9 (2026)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Cognitive performance prediction may help identify early cognitive decline. However, the heterogeneity of research findings impedes the identification of key predictors. This study used 21,877 participants (25-74 years) from the German National Cohort (NAKO Gesundheitsstudie, NAKO) to systematically predict cognitive test scores based on brain structure, demographic, health-related, and cognitive data. Importantly, validation analyses were performed across study sites and external samples (1000BRAINS). Higher predictability was observed in the total sample compared to age-specific subgroups (10% difference in explained variance). Demographic (e.g. age) and cognitive data (e.g. memory) outperformed brain structure (e.g. grey matter volume) and health-related data (e.g. hypertension). Cognitive tests were differentially predictable, most evident between episodic memory and motor speed (R2 ≤ 0.32 versus R2 ≤ 0.18). Differences in predictability between age groups finally highlight the importance of comparing prediction outcomes between adult lifespan and age-specific groups to elucidate general and age-sensitive predictors of cognitive test scores.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Age Decades ; Brain Structure ; Cognitive Functions ; Demographic ; Health-related ; Machine Learning Analyses ; Prediction; Working-memory; Segmentation; Design; Risk
ISSN (print) / ISBN 2509-2715
e-ISSN 2509-2723
Zeitschrift GeroScience
Verlag Springer
Verlagsort Dordrecht, Netherlands
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology (EPI)
Helmholtz AI - FZJ (HAI - FZJ)
Förderungen Universittsklinikum Dsseldorf. Anstalt ffentlichen Rechts (8911)
Federal Ministry of Research, Technology and Space (BMFTR)
European Union's Horizon Europe Research and Innovation Programme