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Thalamic regulation of switching between cortical representations enables cognitive flexibility

Nature Neuroscience volume 21, pages 1753–1763 (2018)Cite this article

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Abstract

Interactions between the prefrontal cortex (PFC) and mediodorsal thalamus are critical for cognitive flexibility, yet the underlying computations are unknown. To investigate frontothalamic substrates of cognitive flexibility, we developed a behavioral task in which mice switched between different sets of learned cues that guided attention toward either visual or auditory targets. We found that PFC responses reflected both the individual cues and their meaning as task rules, indicating a hierarchical cue-to-rule transformation. Conversely, mediodorsal thalamus responses reflected the statistical regularity of cue presentation and were required for switching between such experimentally specified cueing contexts. A subset of these thalamic responses sustained context-relevant PFC representations, while another suppressed the context-irrelevant ones. Through modeling and experimental validation, we find that thalamic-mediated suppression may not only reduce PFC representational interference but could also preserve unused cortical traces for future use. Overall, our study provides a computational foundation for thalamic engagement in cognitive flexibility.

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Fig. 1: Prefrontal neurons display selectivity indicative of a hierarchical cue to rule transformation during attentional switching.
Fig. 2: MD responses reflect the cueing context.
Fig. 3: Flexible switching between contexts is associated with MD-dependent changes in PFC activity.
Fig. 4: Distinct MD neurons augment and suppress context-relevant PFC representations.
Fig. 5: Benefit of PFC–MD over PFC-only architecture on switching contexts.

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All data are available from the corresponding author upon reasonable request.

References

  1. Richter, F. R. & Yeung, N. Memory and cognitive control in task switching. Psychol. Sci. 23, 1256–1263 (2012).

    Article  Google Scholar 

  2. Hanks, T. D. & Summerfield, C. Perceptual decision making in rodents, monkeys, and humans. Neuron 93, 15–31 (2017).

    Article  CAS  Google Scholar 

  3. Stokes, M. G. et al. Dynamic coding for cognitive control in prefrontal cortex. Neuron 78, 364–375 (2013).

    Article  CAS  Google Scholar 

  4. Dias, R., Robbins, T. W. & Roberts, A. C. Dissociation in prefrontal cortex of affective and attentional shifts. Nature 380, 69–72 (1996).

    Article  CAS  Google Scholar 

  5. Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).

    Article  CAS  Google Scholar 

  6. Inagaki, H. K., Inagaki, M., Romani, S. & Svoboda, K. Low-dimensional and monotonic preparatory activity in mouse anterior lateral motor cortex. J. Neurosci. 38, 4163–4185 (2018).

    Article  CAS  Google Scholar 

  7. Noonan, M. P., Crittenden, B. M., Jensen, O. & Stokes, M. G. Selective inhibition of distracting input. Behav. Brain Res. 355, 36–47 (2018).

    Article  Google Scholar 

  8. Weinberger, D. R. & Berman, K. F. Prefrontal function in schizophrenia: confounds and controversies. Phil. Trans. R. Soc. Lond. B 351, 1495–1503 (1996).

    Article  CAS  Google Scholar 

  9. Woodward, N. D., Karbasforoushan, H. & Heckers, S. Thalamocortical dysconnectivity in schizophrenia. Am. J. Psychiatry 169, 1092–1099 (2012).

    Article  Google Scholar 

  10. Kirkpatrick, J. et al. Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. USA 114, 3521–3526 (2017).

    Article  CAS  Google Scholar 

  11. Hassabis, D., Kumaran, D., Summerfield, C. & Botvinick, M. Neuroscience-inspired artificial intelligence. Neuron 95, 245–258 (2017).

    Article  CAS  Google Scholar 

  12. Sakai, K. & Passingham, R. E. Prefrontal interactions reflect future task operations. Nat. Neurosci. 6, 75–81 (2003).

    Article  CAS  Google Scholar 

  13. Miller, E. K. & Buschman, T. J. Cortical circuits for the control of attention. Curr. Opin. Neurobiol. 23, 216–222 (2013).

    Article  CAS  Google Scholar 

  14. Buschman, T. J. & Miller, E. K. Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science 315, 1860–1862 (2007).

    Article  CAS  Google Scholar 

  15. Buschman, T. J. & Miller, E. K. Goal-direction and top-down control. Phil. Trans. R. Soc. Lond. B 369, 20130471 (2014).

    Article  Google Scholar 

  16. Spaak, E., Watanabe, K., Funahashi, S. & Stokes, M. G. Stable and dynamic coding for working memory in primate prefrontal cortex. J. Neurosci. 37, 6503–6516 (2017).

    Article  CAS  Google Scholar 

  17. Schmitt, L. I. et al. Thalamic amplification of cortical connectivity sustains attentional control. Nature 545, 219–223 (2017).

    Article  CAS  Google Scholar 

  18. Bolkan, S. S. et al. Thalamic projections sustain prefrontal activity during working memory maintenance. Nat. Neurosci. 20, 987–996 (2017).

    Article  CAS  Google Scholar 

  19. Parnaudeau, S. et al. Mediodorsal thalamus hypofunction impairs flexible goal-directed behavior. Biol. Psychiatry 77, 445–453 (2015).

    Article  Google Scholar 

  20. Rikhye, R. V., Wimmer, R. D. & Halassa, M. M. Toward an integrative theory of thalamic function. Annu. Rev. Neurosci. 41, 163–183 (2018).

    Article  CAS  Google Scholar 

  21. Mitchell, A. S. & Chakraborty, S. What does the mediodorsal thalamus do? Front. Syst. Neurosci. 7, 37 (2013).

    Article  Google Scholar 

  22. Marton, T., Seifikar, H., Luongo, F.J., Lee, A.T. & Sohal, V.S. Roles of prefrontal cortex and mediodorsal thalamus in task engagement and behavioral flexibility. J. Neurosci. 1728-17 (2018).

  23. Wimmer, R. D. et al. Thalamic control of sensory selection in divided attention. Nature 526, 705–709 (2015).

    Article  CAS  Google Scholar 

  24. Braver, T. S., Reynolds, J. R. & Donaldson, D. I. Neural mechanisms of transient and sustained cognitive control during task switching. Neuron 39, 713–726 (2003).

    Article  CAS  Google Scholar 

  25. Shipp, S. The brain circuitry of attention. Trends Cogn. Sci. 8, 223–230 (2004).

    Article  Google Scholar 

  26. Bruno, R. M. & Simons, D. J. Feedforward mechanisms of excitatory and inhibitory cortical receptive fields. J. Neurosci. 22, 10966–10975 (2002).

    Article  CAS  Google Scholar 

  27. Diester, I. & Nieder, A. Complementary contributions of prefrontal neuron classes in abstract numerical categorization. J. Neurosci. 28, 7737–7747 (2008).

    Article  CAS  Google Scholar 

  28. Quirk, M. C., Sosulski, D. L., Feierstein, C. E., Uchida, N. & Mainen, Z. F. A defined network of fast-spiking interneurons in orbitofrontal cortex: responses to behavioral contingencies and ketamine administration. Front. Syst. Neurosci. 3, 13 (2009).

    Article  Google Scholar 

  29. Wallis, J. D., Anderson, K. C. & Miller, E. K. Single neurons in prefrontal cortex encode abstract rules. Nature 411, 953–956 (2001).

    Article  CAS  Google Scholar 

  30. Miller, E. K., Freedman, D. J. & Wallis, J. D. The prefrontal cortex: categories, concepts and cognition. Phil. Trans. R. Soc. Lond. B 357, 1123–1136 (2002).

    Article  Google Scholar 

  31. Yates, J. L., Park, I. M., Katz, L. N., Pillow, J. W. & Huk, A. C. Functional dissection of signal and noise in MT and LIP during decision-making. Nat. Neurosci. 20, 1285–1292 (2017).

    Article  CAS  Google Scholar 

  32. Park, I. M., Meister, M. L. R., Huk, A. C. & Pillow, J. W. Encoding and decoding in parietal cortex during sensorimotor decision-making. Nat. Neurosci. 17, 1395–1403 (2014).

    Article  CAS  Google Scholar 

  33. Parnaudeau, S., Bolkan, S. S. & Kellendonk, C. The mediodorsal thalamus: an essential partner of the prefrontal cortex for cognition. Biol. Psychiatry 83, 648–656 (2018).

    Article  Google Scholar 

  34. Ferguson, B. R. & Gao, W.-J. Thalamic control of cognition and social behavior via regulation of gamma-aminobutyric acidergic signaling and excitation/inhibition balance in the medial prefrontal cortex. Biol. Psychiatry 83, 657–669 (2018).

    Article  CAS  Google Scholar 

  35. Delevich, K., Tucciarone, J., Huang, Z. J. & Li, B. The mediodorsal thalamus drives feedforward inhibition in the anterior cingulate cortex via parvalbumin interneurons. J. Neurosci. 35, 5743–5753 (2015).

    Article  CAS  Google Scholar 

  36. Kim, H. R., Hong, S. Z. & Fiorillo, C. D. T-type calcium channels cause bursts of spikes in motor but not sensory thalamic neurons during mimicry of natural patterns of synaptic input. Front. Cell. Neurosci. 9, 428 (2015).

    PubMed  PubMed Central  Google Scholar 

  37. Masse, N. Y., Grant, G. D. & Freedman, D. J. Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization.” Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1803839115 (2018).

    Article  CAS  Google Scholar 

  38. Enel, P., Procyk, E., Quilodran, R. & Dominey, P. F. Reservoir computing properties of neural dynamics in prefrontal cortex. PLoS Comput. Biol. 12, e1004967 (2016).

    Article  Google Scholar 

  39. Maass, W., Natschläger, T. & Markram, H. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14, 2531–2560 (2002).

    Article  Google Scholar 

  40. Haykin, S. Neural Networks and Learning Machines. (Pearson, London, UK, 2008).

    Google Scholar 

  41. Minsky, M. & Papert, S.A. Perceptrons: an Introduction to Computational Geometry. (MIT Press, Boston, MA, USA, 2017).

  42. Movshon, J. A., Thompson, I. D. & Tolhurst, D. J. Spatial summation in the receptive fields of simple cells in the cat’s striate cortex. J. Physiol. (Lond.) 283, 53–77 (1978).

    Article  CAS  Google Scholar 

  43. Muhammad, R., Wallis, J. D. & Miller, E. K. A comparison of abstract rules in the prefrontal cortex, premotor cortex, inferior temporal cortex, and striatum. J. Cogn. Neurosci. 18, 974–989 (2006).

    Article  Google Scholar 

  44. Guillery, R. W. & Sherman, S. M. Thalamic relay functions and their role in corticocortical communication: generalizations from the visual system. Neuron 33, 163–175 (2002).

    Article  CAS  Google Scholar 

  45. Yang, G. R., Murray, J. D. & Wang, X.-J. A dendritic disinhibitory circuit mechanism for pathway-specific gating. Nat. Commun. 7, 12815 (2016).

    Article  CAS  Google Scholar 

  46. Tremblay, R., Lee, S. & Rudy, B. GABAergic interneurons in the neocortex: from cellular properties to circuits. Neuron 91, 260–292 (2016).

    Article  CAS  Google Scholar 

  47. Groh, A. et al. Convergence of cortical and sensory driver inputs on single thalamocortical cells. Cereb. Cortex 24, 3167–3179 (2014).

    Article  Google Scholar 

  48. Jaramillo, J., Mejias, J.F. & Wang, X.-J. Engagement of pulvino-cortical feedforward and feedback pathways in cognitive computations. Preprint at bioRxiv https://doi.org/10.1101/322560 (2018).

  49. Imamizu, H. et al. Explicit contextual information selectively contributes to predictive switching of internal models. Exp. Brain Res. 181, 395–408 (2007).

    Article  Google Scholar 

  50. Liang, L. et al. Scalable, lightweight, integrated and quick-to-assemble (SLIQ) hyperdrives for functional circuit dissection. Front. Neural Circuits 11, 8 (2017).

    Article  Google Scholar 

  51. Berndt, A. et al. Structural foundations of optogenetics: determinants of channelrhodopsin ion selectivity. Proc. Natl. Acad. Sci. USA 113, 822–829 (2016).

    Article  CAS  Google Scholar 

  52. Gradinaru, V., Thompson, K. R. & Deisseroth, K. eNpHR: a Natronomonas halorhodopsin enhanced for optogenetic applications. Brain Cell Biol. 36, 129–139 (2008).

    Article  Google Scholar 

  53. Akrami, A., Kopec, C. D., Diamond, M. E. & Brody, C. D. Posterior parietal cortex represents sensory history and mediates its effects on behaviour. Nature 554, 368–372 (2018).

    Article  CAS  Google Scholar 

  54. Chung, J. E. et al. A fully automated approach to spike sorting. Neuron 95, 1381–1394.e6 (2017).

    Article  CAS  Google Scholar 

  55. Bayati, H., Davoudi, H. & Fatemizadeh, E. A heuristic method for finding the optimal number of clusters with application in medical data. IEEE Eng. Med. Biol. Soc. Annu. Conf. 2008, 4684–4687 (2008).

    Google Scholar 

  56. Meyers, E. M. The neural decoding toolbox. Front. Neuroinform. 7, 8 (2013).

    Article  Google Scholar 

  57. Pillow, J. W. et al. Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature 454, 995–999 (2008).

    Article  CAS  Google Scholar 

  58. Pillow, J. W., Paninski, L., Uzzell, V. J., Simoncelli, E. P. & Chichilnisky, E. J. Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. J. Neurosci. 25, 11003–11013 (2005).

    Article  CAS  Google Scholar 

  59. Yu, B. M. et al. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. J. Neurophysiol. 102, 614–635 (2009).

    Article  Google Scholar 

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Acknowledgements

We thank R.D. Wimmer for help with experiments and members of the Halassa Lab for technical assistance and discussions. We also thank W. Gerstner, M. Fee, E. Miller, and M. Wilson for helpful discussions, and we thank J.W. Pillow and D. Zlotowski for advice on the GLM. This work was supported by grants from the National Institutes of Health and from the Brain and Behavior, Klingenstein, Pew, and Simons Foundations, as well as the Human Frontiers Science Program to M.M.H. and the German Federal Ministry of Education and Research to A.G. through a Bernstein Award to R. Memmesheimer.

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Authors and Affiliations

  1. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Rajeev V. Rikhye & Michael M. Halassa

  2. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Rajeev V. Rikhye & Michael M. Halassa

  3. Neural Network Dynamics and Computation Group, Institute for Genetics, University of Bonn, Bonn, Germany

    Aditya Gilra

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R.V.R. conceived and performed experiments, analyzed and interpreted data, and wrote the paper. A.G. developed, simulated, and analyzed the thalamocortical computational model. M.M.H. conceived and supervised experiments, analyzed and interpreted the data, and wrote the paper. M.M.H. also acquired funding.

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Correspondence to Michael M. Halassa.

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Rikhye, R.V., Gilra, A. & Halassa, M.M. Thalamic regulation of switching between cortical representations enables cognitive flexibility. Nat Neurosci 21, 1753–1763 (2018). https://doi.org/10.1038/s41593-018-0269-z

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