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Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning

Nature Human Behaviour volume 3, pages 297–307 (2019)Cite this article

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Abstract

Successful human behaviour depends on the brain’s ability to extract meaningful structure from information streams and make predictions about future events. Individuals can differ markedly in the decision strategies they use to learn the environment’s statistics, yet we have little idea why. Here, we investigate whether the brain networks involved in learning temporal sequences without explicit reward differ depending on the decision strategy that individuals adopt. We demonstrate that individuals alter their decision strategy in response to changes in temporal statistics and engage dissociable circuits: extracting the exact sequence statistics relates to plasticity in motor corticostriatal circuits, while selecting the most probable outcomes relates to plasticity in visual, motivational and executive corticostriatal circuits. Combining graph metrics of functional and structural connectivity, we provide evidence that learning-dependent changes in these circuits predict individual decision strategy. Our findings propose brain plasticity mechanisms that mediate individual ability for interpreting the structure of variable environments.

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Fig. 1: Trial and sequence design.
Fig. 2: Behavioural performance.
Fig. 3: Striatal segments and ICA components.
Fig. 4: Intrinsic and extrinsic connectivity analysis.
Fig. 5: rs-fMRI and DTI graphs.
Fig. 6: PLS weights for degree and clustering coefficient.
Fig. 7: PLS components predicting decision strategy.

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Code availability

Custom code used for data analyses is available upon request from the corresponding authors.

Data availability

Behavioural and imaging data in raw and pre-processed format are available upon request from the corresponding authors.

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Acknowledgements

We thank: C. di Bernardi Luft for helping with data collection; the CamGrid team; M. L. Kringelbach, H. M. Fernandes and T. J. Van Hartevelt for help with the DTI analyses; G. Deco for helpful discussions; and H. Johansen-Berg and G. Williams for help with optimizing the DTI sequences and helpful discussions. This work was supported by grants to Z.K. from the Biotechnology and Biological Sciences Research Council (H012508 and BB/P021255/1), Leverhulme Trust (RF-2011-378), Alan Turing Institute (TU/B/000095), Wellcome Trust (205067/Z/16/Z) and (European Community’s) Seventh Framework Programme (FP7/2007-2013) under agreement PITN-GA-2011-290011; A.E.W. from the Wellcome Trust (095183/Z/10/Z) and (European Community’s) Seventh Framework Programme (FP7/2007–2013) under agreement PITN-GA-2012–316746; P.T. from the Engineering and Physical Sciences Research Council (EP/L000296/1); and P.E.V. from the MRC (MR/K020706/1). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. These authors contributed equally: Vasilis M. Karlaftis, Joseph Giorgio.

Authors and Affiliations

  1. Department of Psychology, University of Cambridge, Cambridge, UK

    Vasilis M. Karlaftis, Joseph Giorgio, Andrew E. Welchman & Zoe Kourtzi

  2. Department of Psychiatry, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK

    Petra E. Vértes

  3. Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China

    Rui Wang

  4. School of Science and Technology, Nottingham Trent University, Nottingham, UK

    Yuan Shen

  5. School of Computer Science, University of Birmingham, Birmingham, UK

    Peter Tino

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Contributions

P.T., A.E.W. and Z.K. designed the research. V.M.K., J.G. and R.W. performed the research. V.M.K., J.G., P.E.V., R.W., Y.S. and P.T. contributed analytical tools. V.M.K. and J.G. analysed the data. All authors co-wrote the paper.

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Correspondence to Zoe Kourtzi.

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Supplementary information

Supplementary Information

Supplementary Methods, Supplementary References, Supplementary Tables 1–7, Supplementary Figures 1–7

Reporting Summary

Supplementary Video 1

Resting state graph 3D movie: 3D movie depicting the resting-state graph overlaid on the MNI brain template. The graph is created based on the AAL parcellation (90 areas excluding cerebellum and vermis) and displayed at 5% density for visualization purposes. The thickness of the graph edges is proportional to the average functional connectivity. The selected nodes are coloured to represent regions within known cortico-striatal circuits: caudate and putamen (magenta), right MFG and left IFG (red), postcentral gyrus (cyan), calcarine sulcus (blue) and ACC (yellow).

Supplementary Video 2

DTI graph 3D movie: 3D movie depicting the DTI graph overlaid on the MNI brain template. The graph is created based on the AAL parcellation (90 areas excluding cerebellum and vermis) and displayed at 5% density for visualization purposes. The thickness of the graph edges is proportional to the average structural connectivity. The selected nodes are coloured to represent regions within known cortico-striatal circuits: caudate and putamen (magenta), right MFG and left IFG (red), postcentral gyrus (cyan), calcarine sulcus (blue) and ACC (yellow).

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Karlaftis, V.M., Giorgio, J., Vértes, P.E. et al. Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning. Nat Hum Behav 3, 297–307 (2019). https://doi.org/10.1038/s41562-018-0503-4

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