Journal Article

FZJ-2025-04364

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Pitfalls in using ML to predict cognitive function performance

Kuhles, G. (Corresponding author)FZJ* ; Hamdan, S. ; Heim, S.FZJ* ; Eickhoff, S. B.FZJ* ; Patil, K. R.FZJ* ; Camilleri, J. A. (Last author)FZJ* ; Weis, S. (Last author)FZJ*

2025
Springer Nature [London]

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Please use a persistent id in citations: doi:10.1038/s41598-025-24325-9  doi:10.34734/FZJ-2025-04364

Abstract: Machine learning analyses are widely used for predicting cognitive abilities, yet there are pitfalls that need to be considered during their implementation and interpretation of the results. Hence, the present study aimed at drawing attention to the risks of erroneous conclusions incurred by confounding variables illustrated by a case example predicting executive function (EF) performance by prosodic features. Healthy participants (n = 231) performed speech tasks and EF tests. From 264 prosodic features, we predicted EF performance using 66 variables, controlling for confounding effects of age, sex, and education. A reasonable prediction performance was apparently achieved for EF variables of the Trail Making Test. However, in-depth analyses revealed indications of confound leakage, leading to inflated prediction accuracies, due to a strong relationship between confounds and targets. These findings highlight the need to control confounding variables in ML pipelines and caution against potential pitfalls in ML predictions.

Keyword(s): Information and Communication (1st) ; Medicine (2nd)

Note: This study was supported by the Deutsche Forschungsgemeinschaft (DFG, GE 2835/2–1, EI 816/16 − 1 and EI 816/21 − 1), the National Institute of Mental Health (R01-MH074457), the Helmholtz Portfolio Theme “Supercomputing and Modeling for the Human Brain”, the Virtual Brain Cloud (EU H2020, no. 826421) & the National Institute on Aging (R01AG067103).

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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)
2. Strukturelle und funktionelle Organisation des Gehirns (INM-1)

Research Program(s):

1. 5251 - Multilevel Brain Organization and Variability (POF4-525) (POF4-525)
2. 5252 - Brain Dysfunction and Plasticity (POF4-525) (POF4-525)

Appears in the scientific report 2025

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