Journal Article

FZJ-2024-07122

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png How measurement noise limits the accuracy of brain-behaviour predictions

Gell, M. (Corresponding author)FZJ* ; Eickhoff, S. B.FZJ* ; Omidvarnia, A.FZJ* ; Küppers, V.FZJ* ; Patil, K. R.FZJ* ; Satterthwaite, T. D. ; Müller, V. I.FZJ* ; Langner, R. (Corresponding author)FZJ*

2024
Nature Publishing Group UK [London]

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Please use a persistent id in citations: doi:10.1038/s41467-024-54022-6  doi:10.34734/FZJ-2024-07122

Abstract: 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.

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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)
2. Grundlagen der Elektrochemie (IET-1)

Research Program(s):

1. 5254 - Neuroscientific Data Analytics and AI (POF4-525) (POF4-525)

Appears in the scientific report 2024

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