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Delay activity of specific prefrontal interneuron subtypes modulates memory-guided behavior

Nature Neuroscience volume 20, pages 854–863 (2017)Cite this article

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Abstract

Memory-guided behavior requires maintenance of task-relevant information without sensory input, but the underlying circuit mechanism remains unclear. Calcium imaging in mice performing a delayed Go or No-Go task revealed robust delay activity in dorsomedial prefrontal cortex, with different pyramidal neurons signaling Go and No-Go action plans. Inhibiting pyramidal neurons by optogenetically activating somatostatin- or parvalbumin-positive interneurons, even transiently during the delay, impaired task performance, primarily by increasing inappropriate Go responses. In contrast, activating vasoactive intestinal peptide (VIP)-positive interneurons enhanced behavioral performance and neuronal action plan representation. Furthermore, while endogenous activity of somatostatin and parvalbumin neurons was strongly biased toward Go trials, VIP neurons were similarly active in Go and No-Go trials. Somatostatin or VIP neuron activation also impaired or enhanced performance, respectively, in a delayed two-alternative forced-choice task. Thus, dorsomedial prefrontal cortex is a crucial component of the short-term memory network, and activation of its VIP neurons improves memory retention.

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Figure 1: A delayed Go vs. No-Go auditory task and behavioral performance.
Figure 2: Pyramidal neuron activity during the delayed Go vs. No-Go task.
Figure 3: Optogenetic activation of dmPFC SST and PV neurons impaired behavioral performance.
Figure 4: VIP neuron activation improved behavioral performance.
Figure 5: Behavioral effects of optogenetic silencing of VIP, SST or PV neurons in the dmPFC.
Figure 6: VIP neuron activation improved dmPFC coding of action plan.
Figure 7: Calcium imaging from each interneuron subtype.
Figure 8: Optogenetic activation of SST and VIP neurons during a delayed 2-AFC task.

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  • 04 May 2017

    In the version of this article initially published online, a duplicate of the panel title "Cell 2" was overlaid across the image in Figure 6a. The error has been corrected in the print, PDF and HTML versions of this article.

References

  1. Baddeley, A.D. Working Memory (Clarendon Press; Oxford University Press, 1986).

  2. Fuster, J.M. The Prefrontal Cortex (Academic Press/Elsevier, 2008).

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

    CAS  PubMed  Google Scholar 

  4. Bauer, R.H. & Fuster, J.M. Delayed-matching and delayed-response deficit from cooling dorsolateral prefrontal cortex in monkeys. J. Comp. Physiol. Psychol. 90, 293–302 (1976).

    CAS  PubMed  Google Scholar 

  5. Buckley, M.J. et al. Dissociable components of rule-guided behavior depend on distinct medial and prefrontal regions. Science 325, 52–58 (2009).

    CAS  PubMed  Google Scholar 

  6. Funahashi, S., Bruce, C.J. & Goldman-Rakic, P.S. Dorsolateral prefrontal lesions and oculomotor delayed-response performance: evidence for mnemonic “scotomas”. J. Neurosci. 13, 1479–1497 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Erlich, J.C., Bialek, M. & Brody, C.D. A cortical substrate for memory-guided orienting in the rat. Neuron 72, 330–343 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Kopec, C.D., Erlich, J.C., Brunton, B.W., Deisseroth, K. & Brody, C.D. Cortical and subcortical contributions to short-term memory for orienting movements. Neuron 88, 367–377 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Guo, Z.V. et al. Flow of cortical activity underlying a tactile decision in mice. Neuron 81, 179–194 (2014).

    CAS  PubMed  Google Scholar 

  10. Li, N., Chen, T.W., Guo, Z.V., Gerfen, C.R. & Svoboda, K. A motor cortex circuit for motor planning and movement. Nature 519, 51–56 (2015).

    CAS  PubMed  Google Scholar 

  11. Li, N., Daie, K., Svoboda, K. & Druckmann, S. Robust neuronal dynamics in premotor cortex during motor planning. Nature 532, 459–464 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Kim, D. et al. Distinct roles of parvalbumin- and somatostatin-expressing interneurons in working memory. Neuron 92, 902–915 (2016).

    CAS  PubMed  Google Scholar 

  13. Fuster, J.M. & Alexander, G.E. Neuron activity related to short-term memory. Science 173, 652–654 (1971).

    CAS  PubMed  Google Scholar 

  14. Funahashi, S., Chafee, M.V. & Goldman-Rakic, P.S. Prefrontal neuronal activity in rhesus monkeys performing a delayed anti-saccade task. Nature 365, 753–756 (1993).

    CAS  PubMed  Google Scholar 

  15. Miller, E.K., Erickson, C.A. & Desimone, R. Neural mechanisms of visual working memory in prefrontal cortex of the macaque. J. Neurosci. 16, 5154–5167 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Romo, R., Brody, C.D., Hernández, A. & Lemus, L. Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 399, 470–473 (1999).

    CAS  PubMed  Google Scholar 

  17. Mante, V., Sussillo, D., Shenoy, K.V. & Newsome, W.T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Baeg, E.H. et al. Dynamics of population code for working memory in the prefrontal cortex. Neuron 40, 177–188 (2003).

    CAS  PubMed  Google Scholar 

  19. Fujisawa, S., Amarasingham, A., Harrison, M.T. & Buzsáki, G. Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex. Nat. Neurosci. 11, 823–833 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Liu, D. et al. Medial prefrontal activity during delay period contributes to learning of a working memory task. Science 346, 458–463 (2014).

    CAS  PubMed  Google Scholar 

  21. Courtin, J. et al. Prefrontal parvalbumin interneurons shape neuronal activity to drive fear expression. Nature 505, 92–96 (2014).

    PubMed  Google Scholar 

  22. Fu, Y. et al. A cortical circuit for gain control by behavioral state. Cell 156, 1139–1152 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Kim, H., Ährlund-Richter, S., Wang, X., Deisseroth, K. & Carlén, M. Prefrontal parvalbumin neurons in control of attention. Cell 164, 208–218 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Kvitsiani, D. et al. Distinct behavioural and network correlates of two interneuron types in prefrontal cortex. Nature 498, 363–366 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Lee, S., Kruglikov, I., Huang, Z.J., Fishell, G. & Rudy, B. A disinhibitory circuit mediates motor integration in the somatosensory cortex. Nat. Neurosci. 16, 1662–1670 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Pi, H.J. et al. Cortical interneurons that specialize in disinhibitory control. Nature 503, 521–524 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Pinto, L. & Dan, Y. Cell-type-specific activity in prefrontal cortex during goal-directed behavior. Neuron 87, 437–450 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Sparta, D.R. et al. Activation of prefrontal cortical parvalbumin interneurons facilitates extinction of reward-seeking behavior. J. Neurosci. 34, 3699–3705 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Lee, S.H. et al. Activation of specific interneurons improves V1 feature selectivity and visual perception. Nature 488, 379–383 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Chen, T.W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Ghosh, K.K. et al. Miniaturized integration of a fluorescence microscope. Nat. Methods 8, 871–878 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Zagha, E., Ge, X. & McCormick, D.A. Competing neural ensembles in motor cortex gate goal-directed motor output. Neuron 88, 565–577 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Meyers, E.M., Qi, X.L. & Constantinidis, C. Incorporation of new information into prefrontal cortical activity after learning working memory tasks. Proc. Natl. Acad. Sci. USA 109, 4651–4656 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Zhang, S. et al. Selective attention. Long-range and local circuits for top-down modulation of visual cortex processing. Science 345, 660–665 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Pfeffer, C.K., Xue, M., He, M., Huang, Z.J. & Scanziani, M. Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat. Neurosci. 16, 1068–1076 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Aron, A.R. & Poldrack, R.A. Cortical and subcortical contributions to Stop signal response inhibition: role of the subthalamic nucleus. J. Neurosci. 26, 2424–2433 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Wong, K.F. & Wang, X.J. A recurrent network mechanism of time integration in perceptual decisions. J. Neurosci. 26, 1314–1328 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Machens, C.K., Romo, R. & Brody, C.D. Flexible control of mutual inhibition: a neural model of two-interval discrimination. Science 307, 1121–1124 (2005).

    CAS  PubMed  Google Scholar 

  39. Wimmer, K., Nykamp, D.Q., Constantinidis, C. & Compte, A. Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory. Nat. Neurosci. 17, 431–439 (2014).

    CAS  PubMed  Google Scholar 

  40. Harvey, C.D., Coen, P. & Tank, D.W. Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature 484, 62–68 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Yamamoto, J., Suh, J., Takeuchi, D. & Tonegawa, S. Successful execution of working memory linked to synchronized high-frequency gamma oscillations. Cell 157, 845–857 (2014).

    CAS  PubMed  Google Scholar 

  42. Salazar, R.F., Dotson, N.M., Bressler, S.L. & Gray, C.M. Content-specific fronto-parietal synchronization during visual working memory. Science 338, 1097–1100 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Miyashita, Y. & Chang, H.S. Neuronal correlate of pictorial short-term memory in the primate temporal cortex. Nature 331, 68–70 (1988).

    CAS  PubMed  Google Scholar 

  44. Euston, D.R., Gruber, A.J. & McNaughton, B.L. The role of medial prefrontal cortex in memory and decision making. Neuron 76, 1057–1070 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Liebe, S., Hoerzer, G.M., Logothetis, N.K. & Rainer, G. Theta coupling between V4 and prefrontal cortex predicts visual short-term memory performance. Nat. Neurosci. 15, 456–462, S1–S2 (2012).

    CAS  PubMed  Google Scholar 

  46. Jones, M.W. & Wilson, M.A. Theta rhythms coordinate hippocampal-prefrontal interactions in a spatial memory task. PLoS Biol. 3, e402 (2005).

    PubMed  PubMed Central  Google Scholar 

  47. Narayanan, N.S. & Laubach, M. Top-down control of motor cortex ensembles by dorsomedial prefrontal cortex. Neuron 52, 921–931 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Harris, K.D. & Shepherd, G.M. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170–181 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Schmidt, R., Leventhal, D.K., Mallet, N., Chen, F. & Berke, J.D. Canceling actions involves a race between basal ganglia pathways. Nat. Neurosci. 16, 1118–1124 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Fonseca, M.S., Murakami, M. & Mainen, Z.F. Activation of dorsal raphe serotonergic neurons promotes waiting but is not reinforcing. Curr. Biol. 25, 306–315 (2015).

    CAS  PubMed  Google Scholar 

  51. Tsien, J.Z. et al. Subregion- and cell type-restricted gene knockout in mouse brain. Cell 87, 1317–1326 (1996).

    CAS  PubMed  Google Scholar 

  52. Ziv, Y. et al. Long-term dynamics of CA1 hippocampal place codes. Nat. Neurosci. 16, 264–266 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Ahrens, M.B. et al. Brain-wide neuronal dynamics during motor adaptation in zebrafish. Nature 485, 471–477 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Kerlin, A.M., Andermann, M.L., Berezovskii, V.K. & Reid, R.C. Broadly tuned response properties of diverse inhibitory neuron subtypes in mouse visual cortex. Neuron 67, 858–871 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Meyers, E.M., Freedman, D.J., Kreiman, G., Miller, E.K. & Poggio, T. Dynamic population coding of category information in inferior temporal and prefrontal cortex. J. Neurophysiol. 100, 1407–1419 (2008).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank L. Pinto for helping set up the endoscope imaging system, M. Zhang and N. Perwez for technical assistance, University of North Carolina Virus Core and Penn Vector Core for supplying AAV, and V. Jayaraman, R.A. Kerr, D.S. Kim, L.L. Looger and K. Svoboda from the GENIE Project for providing GCaMP6f. This work was supported by the Uehara Memorial Foundation (T.K.), the Human Frontier Science Program (T.K.), NIH R01 EY018861 (Y.D.) and Howard Hughes Medical Institute (Y.D.).

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Authors and Affiliations

  1. Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, California, USA

    Tsukasa Kamigaki & Yang Dan

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  1. Tsukasa Kamigaki
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Contributions

T.K. performed all the experiments and analyzed the data. T.K. and Y.D. conceived and designed the experiments and wrote the manuscript.

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Correspondence to Yang Dan.

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Integrated supplementary information

Supplementary Figure 1 Lick response during the delayed Go versus No-Go task.

Trial-averaged lick rate was averaged across all mice used for pyramidal cell imaging (n = 9). Different colors denote different trial types (blue, Hit; light blue, Miss; magenta, Correct Rejection (CR); orange, False Alarm (FA)). Colored shadings, ± s.e.m.

Supplementary Figure 2 Pupil sizes in Go and No-Go trials.

The averaged pupil sizes during the delay period were not significantly different between Go and No-Go trials (n = 11 sessions from 5 mice, P = 0.76; Wilcoxon signed-rank test). Gray lines, individual sessions; black, mean ± s.e.m.

Supplementary Figure 3 Fractions of significant Go- and NG-preferring cells vs. distance from pia at two different DV levels.

(a) Schematic showing estimated bottom positions of near-vertically implanted lenses at two different DV levels (red lines; 650 and 2150 μm from the dorsal surface). (b) At both DV levels, deep layers had higher fractions of significant NG-preferring cells (P < 0.005, one-way ANOVA). All error bars, ± s.e.m.

Supplementary Figure 4 Decoding of neural activity in pyramidal cells.

A decoding analysis was performed using pyramidal cell activity measured in the imaging experiments and a correlation coefficient-based classifier. Go probability was computed as the proportion of each type of trials classified as Go. Colored shadings, ± s.e.m.

Supplementary Figure 5 Histology.

(a) Optogenetic stimulation site in an example SST-ChR2 mouse. Yellow arrowhead, position of optic fiber tip. (b,c,d) similar to (a) but for PV-, VIP-, CaMKIIα-ChR2 mice. (e) Recording site in an example SST-ChR2 mouse. White, Dil labelling. Yellow arrowhead, position of electrode tip. (f) same as (e) but for a VIP-ChR2 mouse. Scale bars, 500 μm.

Supplementary Figure 6 Spike sorting in an example recording session.

(a) Spike waveform projections in feature space. Each axis denotes a principal component (PC) derived from the spike waveforms. Note that clustering was performed using additional PCs to those shown here; Unit 2 (red) was well separated from the unsorted spikes (gray) in other projections. (b) Left, spike waveform of each unit (gray, individual spikes; colored, average). Right, auto-correlograms.

Supplementary Figure 7 Effect of SST neuron activation on dmPFC activity.

(a) Activity of a significant Go-preferring (upper) and a significant NG-preferring cell (lower) with or without SST stimulation. (b) Laser stimulation of SST neurons significantly reduced the Euclidean distance between Go and NoGo activity. The black horizontal bars on the top indicate the analysis periods including baseline (1 s before the beginning of sample), sample, delay, and post-delay (0.5 s after the end of delay) periods. **P < 0.0001 for sample, delay, and post-delay periods; P = 0.49 for the baseline period; bootstrap. Shadings and error bars, 95% confidence intervals (bootstrap).

Supplementary Figure 8 Behavioral effects of optogenetic activation of SST and PV neurons in the vibrissa primary somatosensory cortex (vS1).

(a,b) Whole-trial stimulation of SST (a) or PV (b) neurons in the vS1 did not affect the behavioral performance (SST: P = 0.91, 1.0, and 0.91 for correct response, Hit, and False-Alarm (FA) rates, respectively; PV: P = 0.81, 0.38, and 0.81 for correct response, Hit, and FA rates, respectively; Wilcoxon signed-rank test). (c,d) SST (c) or PV (d) neuron activation in the vS1 during the early delay period did not change the behavioral performance (SST: P = 0.81, 0.81, and 1.0 for correct response, Hit, and FA rates, respectively; PV: P = 0.44, 1.0, and 0.31 for correct response, Hit, and FA rates, respectively). Error bars, ± s.e.m.

Supplementary Figure 9 Behavioral effect of optogenetic activation of VIP neurons in the vibrissa primary somatosensory cortex (vS1).

(a) Whole-trial activation of VIP neurons in the vS1 did not affect the behavioral performance (P = 0.50, 0.25, and 0.57 for correct response, Hit, and FA rates, respectively; Wilcoxon signed-rank test). (b) Activation of VIP neurons in the vS1 during the early delay period did not change the performance (P = 0.64, 0.55, and 1.0 for correct response, Hit, and FA rates, respectively). Error bars, ± s.e.m.

Supplementary Figure 10 Behavioral effects of optogenetic silencing of VIP, SST or PV neurons in the vS1.

(a) Arch-mediated silencing of VIP neurons in the vS1 did not change the performance (P = 0.58, 0.58, and 0.69 for correct response, Hit, and FA rates, respectively). (b) Arch-mediated silencing of SST neurons in the vS1 did not change the behavioral performance (P = 0.31, 0.31, and 0.31 for correct response, Hit, and FA rates, respectively). (c) Arch- or halorhodopsin (Halo)-mediated silencing of PV neurons in the vS1 did not change the performance (P = 0.38, 1.0, and 0.38 for correct response, Hit, and FA rates, respectively). Error bars, ± s.e.m.

Supplementary Figure 11 Behavioral effect of optogenetic activating CaMKIIα-expressing pyramidal neurons in the dmPFC.

ChR2-mediated activation of pyramidal cells impaired the behavioral performance (P = *0.039, **0.0078, and 0.64 for correct response, Hit, and FA rates, respectively; Wilcoxon signed-rank test). Error bars, ± s.e.m.

Supplementary Figure 12 Distribution of firing rate changes induced by VIP neuron activation.

For each neuron group (Go-preferring, NG-preferring, and unmodulated cells), the firing rate difference between laser-on and -off trials divided by their sum was calculated in Go and No-Go trials. Arrowhead, mean of the population. *P < 0.05; ** P < 0.001; *** P < 0.0001, paired t-test.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12 (PDF 1306 kb)

Supplementary Methods Checklist (PDF 513 kb)

A mouse performing the delayed Go/No-Go auditory task.

The target and non-target tones used in the task are also included. Lick responses were detected with an infra-red beam running between the optic fibers near the mouth. Play speed, 1x. (MP4 12860 kb)

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Kamigaki, T., Dan, Y. Delay activity of specific prefrontal interneuron subtypes modulates memory-guided behavior. Nat Neurosci 20, 854–863 (2017). https://doi.org/10.1038/nn.4554

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