The challenges and prospects of brain-based prediction of behaviour

Jianxiao Wu (), Jingwei Li, Simon B. Eickhoff, Dustin Scheinost and Sarah Genon ()
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Jianxiao Wu: Research Center Jülich
Jingwei Li: Research Center Jülich
Simon B. Eickhoff: Research Center Jülich
Dustin Scheinost: Yale School of Medicine
Sarah Genon: Research Center Jülich

Nature Human Behaviour, 2023, vol. 7, issue 8, 1255-1264

Abstract: Abstract Relating individual brain patterns to behaviour is fundamental in system neuroscience. Recently, the predictive modelling approach has become increasingly popular, largely due to the recent availability of large open datasets and access to computational resources. This means that we can use machine learning models and interindividual differences at the brain level represented by neuroimaging features to predict interindividual differences in behavioural measures. By doing so, we could identify biomarkers and neural correlates in a data-driven fashion. Nevertheless, this budding field of neuroimaging-based predictive modelling is facing issues that may limit its potential applications. Here we review these existing challenges, as well as those that we anticipate as the field develops. We focus on the impacts of these challenges on brain-based predictions. We suggest potential solutions to address the resolvable challenges, while keeping in mind that some general and conceptual limitations may also underlie the predictive modelling approach.

Date: 2023
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DOI: 10.1038/s41562-023-01670-1

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