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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.

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.

References

  1. Saarinen, J. & Levi, D. M. Perceptual learning in vernier acuity: what is learned? Vision Res. 35, 519–527 (1995).

    CAS  PubMed  Google Scholar 

  2. Christian, J. et al. Socio-cognitive profiles for visual learning in young and older adults. Front. Aging Neurosci. 7, 1–11 (2015).

    Google Scholar 

  3. Siegelman, N., Bogaerts, L., Christiansen, M. H. & Frost, R. Towards a theory of individual differences in statistical learning. Philos. Trans. R. Soc. B 372, 20160059 (2017).

    Google Scholar 

  4. Aslin, R. N. & Newport, E. L. Statistical learning: from acquiring specific items to forming general rules. Curr. Dir. Psychol. Sci. 21, 170–176 (2012).

    PubMed  PubMed Central  Google Scholar 

  5. Acerbi, L., Vijayakumar, S. & Wolpert, D. M. On the origins of suboptimality in human probabilistic inference. PLoS Comput. Biol. 10, e1003661 (2014).

    PubMed  PubMed Central  Google Scholar 

  6. Eckstein, M. P. et al. Rethinking human visual attention: spatial cueing effects and optimality of decisions by honeybees, monkeys and humans. Vision Res. 85, 5–9 (2013).

    PubMed  PubMed Central  Google Scholar 

  7. Murray, R. F., Patel, K. & Yee, A. Posterior probability matching and human perceptual decision making. PLoS Comput. Biol. 11, e1004342 (2015).

    PubMed  PubMed Central  Google Scholar 

  8. Erev, I. & Barron, G. On adaptation, maximization, and reinforcement learning among cognitive strategies. Psychol. Rev. 112, 912–931 (2005).

    PubMed  Google Scholar 

  9. Shanks, D. R., Tunney, R. J. & McCarthy, J. D. A re-examination of probability matching and rational choice. J. Behav. Decis. Mak. 15, 233–250 (2002).

    Google Scholar 

  10. Wang, R., Shen, Y., Tino, P., Welchman, A. E. & Kourtzi, Z. Learning predictive statistics from temporal sequences: dynamics and strategies. J. Vis. 17, 1 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Schulze, C., van Ravenzwaaij, D. & Newell, B. R. Of matchers and maximizers: how competition shapes choice under risk and uncertainty. Cogn. Psychol. 78, 78–98 (2015).

    PubMed  Google Scholar 

  12. Herrnstein, R. J. Relative and absolute strength of response as a function of frequency of reinforcement. J. Exp. Anal. Behav. 4, 267–272 (1961).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Wang, R., Shen, Y., Tino, P., Welchman, A. & Kourtzi, Z. Learning predictive statistics: strategies and brain mechanisms. J. Neurosci. 37, 8412–8427 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Gheysen, F., Van Opstal, F., Roggeman, C., Van Waelvelde, H. & Fias, W. The neural basis of implicit perceptual sequence learning. Front. Hum. Neurosci. 5, 137 (2011).

    PubMed  PubMed Central  Google Scholar 

  15. Stillman, C. M. et al. Caudate resting connectivity predicts implicit probabilistic sequence learning. Brain Connect. 3, 601–610 (2013).

    PubMed  PubMed Central  Google Scholar 

  16. Turk-Browne, N. B., Scholl, B. J., Chun, M. M. & Johnson, M. K. Neural evidence of statistical learning: efficient detection of visual regularities without awareness. J. Cogn. Neurosci. 21, 1934–1945 (2009).

    PubMed  PubMed Central  Google Scholar 

  17. Fox, M. D. & Raichle, M. E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007).

    CAS  PubMed  Google Scholar 

  18. Deco, G. & Corbetta, M. The dynamical balance of the brain at rest. Neurosci. 17, 107–123 (2011).

    Google Scholar 

  19. Kelly, C. & Castellanos, F. X. Strengthening connections: functional connectivity and brain plasticity. Neuropsychol. Rev. 24, 63–76 (2014).

    PubMed  PubMed Central  Google Scholar 

  20. Sampaio-Baptista, C. & Johansen-Berg, H. White matter plasticity in the adult brain. Neuron 96, 1239–1251 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Behrens, T. E. J. et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 6, 750–757 (2003).

    CAS  PubMed  Google Scholar 

  22. Román, F. J. et al. Enhanced structural connectivity within a brain sub-network supporting working memory and engagement processes after cognitive training. Neurobiol. Learn. Mem. 141, 33–43 (2017).

    PubMed  Google Scholar 

  23. Heitger, M. H. et al. Motor learning-induced changes in functional brain connectivity as revealed by means of graph-theoretical network analysis. Neuroimage 61, 633–650 (2012).

    CAS  PubMed  Google Scholar 

  24. Farrar, D. & Glauber, R. Multicollinearity in regression analysis: the problem revisited. Rev. Econ. Stat. 49, 92–107 (1967).

    Google Scholar 

  25. Seger, C. A. in The Basal Ganglia IX (eds Groenewegen, H., Voorn, P., Berendse, H., Mulder, A. & Cools, A.) 25–39 (Springer, New York, 2009)..

  26. Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15, 273–289 (2002).

    CAS  PubMed  Google Scholar 

  27. Smith, S. M. et al. Correspondence of the brain’s functional architecture during activation and rest. Proc. Natl Acad. Sci. USA 106, 13040–13045 (2009).

    CAS  PubMed  Google Scholar 

  28. Van Dijk, K. R. A. et al. Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J. Neurophysiol. 103, 297–321 (2010).

    PubMed  Google Scholar 

  29. Van den Heuvel, M. P. & Hulshoff Pol, H. E. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 20, 519–534 (2010).

    CAS  PubMed  Google Scholar 

  30. Di Martino, A. et al. Functional connectivity of human striatum: a resting state fMRI study. Cereb. Cortex 18, 2735–2747 (2008).

    CAS  PubMed  Google Scholar 

  31. Pauli, W. M., O’Reilly, R. C., Yarkoni, T. & Wager, T. D. Regional specialization within the human striatum for diverse psychological functions. Proc. Natl Acad. Sci. USA 113, 1907–1912 (2016).

    CAS  PubMed  Google Scholar 

  32. Lehéricy, S. et al. Diffusion tensor fiber tracking shows distinct corticostriatal circuits in humans. Ann. Neurol. 55, 522–529 (2004).

    PubMed  Google Scholar 

  33. Draganski, B. et al. Evidence for segregated and integrative connectivity patterns in the human basal ganglia. J. Neurosci. 28, 7143–7152 (2008).

    CAS  PubMed  Google Scholar 

  34. Balleine, B. W. & O’Doherty, J. P. Human and rodent homologies in action control: corticostriatal determinants of goal-directed and habitual action. Neuropsychopharmacology 35, 48–69 (2010).

    PubMed  Google Scholar 

  35. Piray, P., Toni, I. & Cools, R. Human choice strategy varies with anatomical projections from ventromedial prefrontal cortex to medial striatum. J. Neurosci. 36, 2857–2867 (2016).

    CAS  PubMed  Google Scholar 

  36. McNamee, D., Liljeholm, M., Zika, O. & O’Doherty, J. P. Characterizing the associative content of brain structures involved in habitual and goal-directed actions in humans: a multivariate fMRI study. J. Neurosci. 35, 3764–3771 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Heekeren, H. R., Marrett, S. & Ungerleider, L. G. The neural systems that mediate human perceptual decision making. Nat. Rev. Neurosci. 9, 467–479 (2008).

    CAS  PubMed  Google Scholar 

  38. Ahissar, M. & Hochstein, S. The reverse hierarchy theory of visual perceptual learning. Trends Cogn. Sci. 8, 457–464 (2004).

    PubMed  Google Scholar 

  39. van den Heuvel, M. P. & Sporns, O. Network hubs in the human brain. Trends Cogn. Sci. 17, 683–696 (2013).

    PubMed  Google Scholar 

  40. Blondel, V. D., Guillaume, J. L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, P10008 (2008).

    Google Scholar 

  41. Watts, D. J. & Strogatz, S. H. Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998).

    CAS  PubMed  Google Scholar 

  42. McIntosh, A. R. & Lobaugh, N. J. Partial least squares analysis of neuroimaging data: applications and advances. NeuroImage 23, 250–263 (2004).

    Google Scholar 

  43. Whitaker, K. J. et al. Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome. Proc. Natl Acad. Sci. USA 113, 201601745 (2016).

    Google Scholar 

  44. Vértes, P. E. et al. Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks. Philos. Trans. R. Soc. Lond. B 371, 735–769 (2016).

    Google Scholar 

  45. Levy, D. J. & Glimcher, P. W. The root of all value: a neural common currency for choice. Curr. Opin. Neurobiol. 22, 1027–1038 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Ridderinkhof, K. R., van den Wildenberg, W. P., Segalowitz, S. J. & Carter, C. S. Neurocognitive mechanisms of cognitive control: the role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning. Brain Cogn. 56, 129–140 (2004).

    PubMed  Google Scholar 

  47. D’Ardenne, K. et al. Role of prefrontal cortex and the midbrain dopamine system in working memory updating. Proc. Natl Acad. Sci. USA 109, 19900–19909 (2012).

    PubMed  Google Scholar 

  48. Lewis, C. M., Baldassarre, A., Committeri, G., Romani, G. L. & Corbetta, M. Learning sculpts the spontaneous activity of the resting human brain. Proc. Natl Acad. Sci. USA 106, 17558–17563 (2009).

    CAS  PubMed  Google Scholar 

  49. Ventura-Campos, N. et al. Spontaneous brain activity predicts learning ability of foreign sounds. J. Neurosci. 33, 9295–9305 (2013).

    CAS  PubMed  Google Scholar 

  50. Ma, L., Narayana, S., Robin, D. A., Fox, P. T. & Xiong, J. Changes occur in resting state network of motor system during 4 weeks of motor skill learning. NeuroImage 58, 226–233 (2011).

    PubMed  PubMed Central  Google Scholar 

  51. Albert, N. B., Robertson, E. M. & Miall, R. C. The resting human brain and motor learning. Curr. Biol. 19, 1023–1027 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Robbins, T. Shifting and stopping: fronto-striatal substrates, neurochemical modulation and clinical implications. Philos. Trans. R. Soc. B 362, 917–932 (2007).

    CAS  Google Scholar 

  53. Sami, S. & Miall, R. C. Graph network analysis of immediate motor-learning induced changes in resting state BOLD. Front. Hum. Neurosci. 7, 1–14 (2013).

    Google Scholar 

  54. Campbell, K. L. et al. Robust resilience of the frontotemporal syntax system to aging. J. Neurosci. 36, 5214–5227 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Pernet, C. R., Wilcox, R. & Rousselet, G. A. Robust correlation analyses: false positive and power validation using a new open source MATLAB toolbox. Front. Psychol. 3, 606 (2013).

    PubMed  PubMed Central  Google Scholar 

  56. Benjamini, Y. & Yekutieli, D. False discovery rate-adjusted multiple confidence intervals for selected parameters. J. Am. Stat. Assoc. 100, 71–93 (2005).

    CAS  Google Scholar 

  57. Krishnan, A., Williams, L. J., McIntosh, A. R. & Abdi, H. Partial least squares (PLS) methods for neuroimaging: a tutorial and review. NeuroImage 56, 455–475 (2011).

    PubMed  Google Scholar 

  58. Sporns, O. Network attributes for segregation and integration in the human brain. Curr. Opin. Neurobiol. 23, 162–171 (2013).

    CAS  PubMed  Google Scholar 

  59. Milan, L. & Whittaker, J. Application of the parametric bootstrap to models that incorporate a singular value decomposition. Appl. Stat. 44, 31–49 (1995).

    Google Scholar 

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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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