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The challenges and prospects of brain-based prediction of behaviour

Nature Human Behaviour volume 7, pages 1255–1264 (2023)Cite this article

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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.

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Fig. 1: Model development and validation for neuroimaging-based psychometric predictions.
Fig. 2: Prediction accuracies measured by Pearson’s correlation.
Fig. 3: Visualizations of model interpretations.

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Acknowledgements

This work was supported by the Deutsche Forschungsgemeinschaft (GE 2835/2–1, EI 816/ 4–1), the Helmholtz Portfolio Theme ‘Supercomputing and Modelling for the Human Brain’ and the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 720270 (HBP SGA1) and grant agreement no. 785907 (HBP SGA2).

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Authors and Affiliations

  1. Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany

    Jianxiao Wu, Jingwei Li, Simon B. Eickhoff & Sarah Genon

  2. Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany

    Jianxiao Wu, Jingwei Li, Simon B. Eickhoff & Sarah Genon

  3. Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA

    Dustin Scheinost

  4. Department of Statistics and Data Science, Yale University, New Haven, CT, USA

    Dustin Scheinost

  5. Child Study Center, Yale School of Medicine, New Haven, CT, USA

    Dustin Scheinost

  6. Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA

    Dustin Scheinost

  7. Department of Biomedical Engineering, Yale School of Engineering and Applied Sciences, New Haven, CT, USA

    Dustin Scheinost

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Wu, J., Li, J., Eickhoff, S.B. et al. The challenges and prospects of brain-based prediction of behaviour. Nat Hum Behav 7, 1255–1264 (2023). https://doi.org/10.1038/s41562-023-01670-1

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