Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Functional corticostriatal connection topographies predict goal-directed behaviour in humans

Abstract

Anatomical tracing studies in non-human primates have suggested that corticostriatal connectivity is topographically organized: nearby locations in striatum are connected with nearby locations in cortex. The topographic organization of corticostriatal connectivity is thought to underpin many goal-directed behaviours, but these topographies have not been completely characterized in humans and their relationship to uniquely human behaviours remains to be fully determined. Instead, the dominant approach employs parcellations that cannot model the continuous nature of the topography, nor accommodate overlapping cortical projections in the striatum. Here we employ a different approach to studying human corticostriatal circuitry: we estimate smoothly varying and spatially overlapping ‘connection topographies’ from resting-state functional magnetic resonance imaging. These correspond exceptionally well with and extend the topographies predicted from primate tracing studies. We show that striatal topography is preserved in regions not previously known to have topographic connections with the striatum and that many goal-directed behaviours can be mapped precisely onto individual variations in the spatial layout of striatal connectivity.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Summary of the analysis pipeline.
Figure 2: Correspondence with previous studies in animal models.
Figure 3: Relative importance of different variables in driving the multivariate correspondence between the topography from the left striatum and the behavioural set (derived from all 466 subjects used in the analysis).
Figure 4: Relative importance of different variables in driving the multivariate correspondence between the topography from the right striatum and the behavioural set (derived from all 466 subjects used in the analysis).

References

  1. 1

    Haber, S. N., Fudge, J. L. & McFarland, N. R. Striatonigrostriatal pathways in primates form an ascending spiral from the shell to the dorsolateral striatum. J. Neurosci. 20, 2369–2382 (2000).

    CAS  Article  Google Scholar 

  2. 2

    Haber, S. N. The primate basal ganglia: parallel and integrative networks. J. Chem. Neuroanat. 26, 317–330 (2003).

    Article  Google Scholar 

  3. 3

    Alexander, G. E., DeLong, M. R. & Strick, P. L. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu. Rev. Neurosci. 9, 357–381 (1986).

    CAS  Article  Google Scholar 

  4. 4

    Kemp, J. M. & Powell, T. P. S. Connexions of striatum and globus pallidus: synthesis and speculation. Phil. Trans. R. Soc. Lond. B 262, 441– 457 (1971).

    Article  Google Scholar 

  5. 5

    Haber, S. N. & Knutson, B. The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology 35, 4–26 (2010).

    Article  Google Scholar 

  6. 6

    Samejima, K., Ueda, Y., Doya, K. & Kimura, M. Representation of action-specific reward values in the striatum. Science 310, 1337–1340 (2005).

    CAS  Article  Google Scholar 

  7. 7

    Balleine, B. W., Delgado, M. R. & Hikosaka, O. The role of the dorsal striatum in reward and decision-making. J. Neurosci. 27, 8161–8165 (2007).

    CAS  Article  Google Scholar 

  8. 8

    Maclean, P. D. Cerebral evolution and emotional processes: new findings on striatal complex. Ann. NY Acad. Sci. 193, 137– 149 (1972).

    Article  Google Scholar 

  9. 9

    Takikawa, Y., Kawagoe, R. & Hikosaka, O. Reward-dependent spatial selectivity of anticipatory activity in monkey caudate neurons. J. Neurophysiol. 87, 508–515 (2002).

    Article  Google Scholar 

  10. 10

    Haber, S. N., Kim, K.-S., Mailly, P. & Calzavara, R. Reward-related cortical inputs define a large striatal region in primates that interface with associative cortical connections, providing a substrate for incentive-based learning. J. Neurosci. 26, 8368–8376 (2006).

    CAS  Article  Google Scholar 

  11. 11

    Haber, S. N. Corticostriatal circuitry. Dialogues Clin. Neurosci. 18, 7–21 (2016).

    PubMed  PubMed Central  Google Scholar 

  12. 12

    Faraone, S. et al. Attention deficit/hyperactivity disorder. Nat. Rev. Dis. Primers 1, 15020 (2015).

    Article  Google Scholar 

  13. 13

    Goodman, W. K. et al. Deep brain stimulation for intractable obsessive compulsive disorder: pilot study using a blinded, staggered-onset design. Biol. Psychiatry 67, 535–542 (2010).

    Article  Google Scholar 

  14. 14

    Cilia, R. et al. Pathological gambling in patients with Parkinson’s disease is associated with fronto-striatal disconnection: a path modeling analysis. Mov. Disord. 26, 225–233 (2011).

    Article  Google Scholar 

  15. 15

    Selemon, L. D. & Goldmanrakic, P. S. Longitudinal topography and interdigitation of corticostriatal projections in the rhesus monkey. J. Neurosci. 5, 776–794 (1985).

    CAS  Article  Google Scholar 

  16. 16

    Jarbo, K. & Verstynen, T. D. Converging structural and functional connectivity of orbitofrontal, dorsolateral prefrontal, and posterior parietal cortex in the human striatum. J. Neurosci. 35, 3865–3878 (2015).

    CAS  Article  Google Scholar 

  17. 17

    Fudge, J. L. & Haber, S. N. Defining the caudal ventral striatum in primates: cellular and histochemical features. J. Neurosci. 22, 10078–10082 (2002).

    CAS  Article  Google Scholar 

  18. 18

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

    CAS  Article  Google Scholar 

  19. 19

    Yeterian, E. H. & Van Hoesen, G. W. Cortico-striate projections in rhesus monkey: organization of certain cortico-caudate connections. Brain Res. 139, 43–63 (1978).

    CAS  Article  Google Scholar 

  20. 20

    Averbeck, B. B., Lehman, J., Jacobson, M. & Haber, S. N. Estimates of projection overlap and zones of convergence within frontal-striatal circuits. J. Neurosci. 34, 9497–9505 (2014).

    CAS  Article  Google Scholar 

  21. 21

    Thivierge, J.-P. & Marcus, G. F. The topographic brain: from neural connectivity to cognition. Trends Neurosci. 30, 251–259 (2007).

    CAS  Article  Google Scholar 

  22. 22

    Jbabdi, S., Sotiropoulos, S. N. & Behrens, T. E. The topographic connectome. Curr. Opin. Neurobiol. 23, 207–215 (2013).

    CAS  Article  Google Scholar 

  23. 23

    Leh, S. E., Ptito, A., Chakravarty, M. M. & Strafella, A. P. Fronto-striatal connections in the human brain: a probabilistic diffusion tractography study. Neurosci. Lett. 419, 113–118 (2007).

    CAS  Article  Google Scholar 

  24. 24

    Cohen, M. X., Schoene-Bake, J.-C., Elger, C. E. & Weber, B. Connectivity-based segregation of the human striatum predicts personality characteristics. Nat. Neurosci. 12, 32–34 (2009).

    CAS  Article  Google Scholar 

  25. 25

    Verstynen, T. D., Badre, D., Jarbo, K. & Schneider, W. Microstructural organizational patterns in the human corticostriatal system. J. Neurophysiol. 107, 2984–2995 (2012).

    Article  Google Scholar 

  26. 26

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

    CAS  Article  Google Scholar 

  27. 27

    Choi, E. Y., Yeo, B. T. T. & Buckner, R. L. The organization of the human striatum estimated by intrinsic functional connectivity. J. Neurophysiol. 108, 2242–2263 (2012).

    Article  Google Scholar 

  28. 28

    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  Article  Google Scholar 

  29. 29

    Haak, K., Marquand, A. & Beckmann, C. Connectopic mapping with resting-state fMRI. Neuroimage https://doi.org/10.1016/j.neuroimage.2017.06.075 (2017).

  30. 30

    Navarro Schröder, T., Haak, K. V., Zaragoza Jimenez, N. I., Beckmann, C. F. & Doeller, C. F. Functional topography of the human entorhinal cortex. eLife 4, e06738 (2015).

    Article  Google Scholar 

  31. 31

    Van Essen, D. C. et al. The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013).

    Article  Google Scholar 

  32. 32

    Belkin, M. & Niyogi, P. Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv. Neural. Inf. Process. Syst. 14, 664–668 (2002).

    Google Scholar 

  33. 33

    Gelfand, A ., Diggle, P., Fuentes, M & Guttorp, P. Handbook of Spatial Statistics (CRC, 2010).

    Book  Google Scholar 

  34. 34

    Barch, D. M. et al. Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013).

    Article  Google Scholar 

  35. 35

    Smith, R., Keramatian, K. & Christoff, K. Localizing the rostrolateral prefrontal cortex at the individual level. Neuroimage 36, 1387–1396 (2007).

    Article  Google Scholar 

  36. 36

    Craddock, R. C., James, G. A., Holtzheimer, P. E. III, Hu, X. P. & Mayberg, H. S. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33, 1914–1928 (2012).

    Article  Google Scholar 

  37. 37

    Kelly, C. et al. l-Dopa modulates functional connectivity in striatal cognitive and motor networks: a double-blind placebo-controlled study. J. Neurosci. 29, 7364–7378 (2009).

    CAS  Article  Google Scholar 

  38. 38

    Hariri, A. R. et al. Preference for immediate over delayed rewards is associated with magnitude of ventral striatal activity. J. Neurosci. 26, 13213–13217 (2006).

    CAS  Article  Google Scholar 

  39. 39

    Partiot, A. et al. Delayed response tasks in basal ganglia lesions in man: further evidence for a striato-frontal cooperation in behavioural adaptation. Neuropsychologia 34, 709–721 (1996).

    CAS  Article  Google Scholar 

  40. 40

    Buckner, R. L., Andrews-Hanna, J. R. & Schacter, D. L. The brain’s default network: anatomy, function, and relevance to disease. Ann. NY Acad. Sci. 1124, 1–38 (2008).

    Article  Google Scholar 

  41. 41

    Tomasi, D. et al. Dopamine transporters in striatum correlate with deactivation in the default mode network during visuospatial attention. PLoS ONE 4, e6102 (2009).

    Article  Google Scholar 

  42. 42

    Weissman, D. H., Roberts, K. C., Visscher, K. M. & Woldorff, M. G. The neural bases of momentary lapses in attention. Nat. Neurosci. 9, 971–978 (2006).

    CAS  Article  Google Scholar 

  43. 43

    Van Hoesen, G. W., Yeterian, E. H. & Lavizzo-Mourey, R. Widespread corticostriate projections from temporal cortex of the rhesus monkey. J. Comp. Neurol. 199, 205–219 (1981).

    CAS  Article  Google Scholar 

  44. 44

    Yeterian, E. H. & Pandya, D. N. Corticostriatal connections of the superior temporal region in rhesus monkeys. J. Comp. Neurol. 399, 384–402 (1998).

    CAS  Article  Google Scholar 

  45. 45

    Badgaiyan, R. D. Dopamine is released in the striatum during human emotional processing. Neuroreport 21, 1172–1176 (2010).

    CAS  Article  Google Scholar 

  46. 46

    Smith, S. M. et al. Network modelling methods for fMRI. Neuroimage 54, 875–891 (2011).

    Article  Google Scholar 

  47. 47

    Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013).

    Article  Google Scholar 

  48. 48

    Smith, S. M. et al. A positive–negative mode of population covariation links brain connectivity, demographics and behavior. Nat. Neurosci. 18, 1565–1567 (2015).

    CAS  Article  Google Scholar 

  49. 49

    Salimi-Khorshidi, G. et al. Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. Neuroimage 90, 449–468 (2014).

    Article  Google Scholar 

  50. 50

    Bishop, C. Pattern Recognition and Machine Learning (Springer, 2006).

    Google Scholar 

  51. 51

    Kraha, A., Turner, H., Nimon, K., Zientek, L. R. & Henson, R. K. Tools to support interpreting multiple regression in the face of multicollinearity. Front. Psychol. 3, 44 (2012).

    Article  Google Scholar 

  52. 52

    Haufe, S. et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87, 96–110 (2014).

    Article  Google Scholar 

  53. 53

    Winkler, A. M., Webster, M. A., Vidaurre, D., Nichols, T. E. & Smith, S. M. Multi-level block permutation. Neuroimage 123, 253–268 (2015).

    Article  Google Scholar 

  54. 54

    Efron, B., Tibshirani, R., Storey, J. D. & Tusher, V. Empirical Bayes analysis of a microarray experiment. J. Am. Stat. Assoc. 96, 1151–1160 (2001).

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge support from The Netherlands Organization for Scientific Research (NWO) by VIDI grants to A.F.M. (grant no. 016.156.415) and C.F.B. (864.12.003), a VENI grant to K.V.H. (016.171.068) and under the Gravitation Programme (024.001.006 supporting A.F.M.). We also acknowledge funding from the Wellcome Trust UK Strategic Award (098369/Z/12/Z). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Affiliations

Authors

Contributions

A.F.M., K.V.H. and C.F.B. devised the experiments, A.F.M. and K.V.H. analysed the data and all authors wrote the manuscript.

Corresponding author

Correspondence to Andre F. Marquand.

Ethics declarations

Competing interests

C.F.B. is director of and shareholder in SBGNeuro Ltd. The other authors declare no competing interests.

Supplementary information

Supplementary Materials

Supplementary Methods, Supplementary Figures 1–5, Supplementary References

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Marquand, A., Haak, K. & Beckmann, C. Functional corticostriatal connection topographies predict goal-directed behaviour in humans. Nat Hum Behav 1, 0146 (2017). https://doi.org/10.1038/s41562-017-0146

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing