TY  - JOUR
AU  - Gell, Martin
AU  - Eickhoff, Simon B.
AU  - Omidvarnia, Amir
AU  - Küppers, Vincent
AU  - Patil, Kaustubh R.
AU  - Satterthwaite, Theodore D.
AU  - Müller, Veronika I.
AU  - Langner, Robert
TI  - How measurement noise limits the accuracy of brain-behaviour predictions
JO  - Nature Communications
VL  - 15
IS  - 1
SN  - 2041-1723
CY  - [London]
PB  - Nature Publishing Group UK
M1  - FZJ-2024-07122
SP  - 10678
PY  - 2024
AB  - Major efforts in human neuroimaging strive to understand individual differences and find biomarkers for clinical applications by predicting behavioural phenotypes from brain imaging data. To identify generalisable and replicable brain-behaviour prediction models, sufficient measurement reliability is essential. However, the selection of prediction targets is predominantly guided by scientific interest or data availability rather than psychometric considerations. Here, we demonstrate the impact of low reliability in behavioural phenotypes on out-of-sample prediction performance. Using simulated and empirical data from four large-scale datasets, we find that reliability levels common across many phenotypes can markedly limit the ability to link brain and behaviour. Next, using 5000 participants from the UK Biobank, we show that only highly reliable data can fully benefit from increasing sample sizes from hundreds to thousands of participants. Our findings highlight the importance of measurement reliability for identifying meaningful brain–behaviour associations from individual differences and underscore the need for greater emphasis on psychometrics in future research.
LB  - PUB:(DE-HGF)16
C6  - 39668158
UR  - <Go to ISI:>//WOS:001377360000005
DO  - DOI:10.1038/s41467-024-54022-6
UR  - https://juser.fz-juelich.de/record/1034337
ER  -