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Maintenance of persistent activity in a frontal thalamocortical loop

Abstract

Persistent neural activity maintains information that connects past and future events. Models of persistent activity often invoke reverberations within local cortical circuits, but long-range circuits could also contribute. Neurons in the mouse anterior lateral motor cortex (ALM) have been shown to have selective persistent activity that instructs future actions. The ALM is connected bidirectionally with parts of the thalamus, including the ventral medial and ventral anterior–lateral nuclei. We recorded spikes from the ALM and thalamus during tactile discrimination with a delayed directional response. Here we show that, similar to ALM neurons, thalamic neurons exhibited selective persistent delay activity that predicted movement direction. Unilateral photoinhibition of delay activity in the ALM or thalamus produced contralesional neglect. Photoinhibition of the thalamus caused a short-latency and near-complete collapse of ALM activity. Similarly, photoinhibition of the ALM diminished thalamic activity. Our results show that the thalamus is a circuit hub in motor preparation and suggest that persistent activity requires reciprocal excitation across multiple brain areas.

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Figure 1: The ALM and thalamus are required for motor preparation.
Figure 2: The ALM and thalamus show similar neural dynamics.
Figure 3: The thalamus drives the ALM.
Figure 4: Comparison of thalamic and cortical input.
Figure 5: Thalamic activity maintains selectivity in the ALM.
Figure 6: The ALM drives the thalamus.

References

  1. Tanji, J. & Evarts, E. V. Anticipatory activity of motor cortex neurons in relation to direction of an intended movement. J. Neurophysiol. 39, 1062–1068 (1976)

    CAS  PubMed  Google Scholar 

  2. Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Ryu, S. I. & Shenoy, K. V. Cortical preparatory activity: representation of movement or first cog in a dynamical machine? Neuron 68, 387–400 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  4. 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 

  5. Murakami, M., Vicente, M. I., Costa, G. M. & Mainen, Z. F. Neural antecedents of self-initiated actions in secondary motor cortex. Nat. Neurosci. 17, 1574–1582 (2014)

    Article  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  Google Scholar 

  7. Funahashi, S., Bruce, C. J. & Goldman-Rakic, P. S. Mnemonic coding of visual space in the monkey’s dorsolateral prefrontal cortex. J. Neurophysiol. 61, 331–349 (1989)

    Article  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  Google Scholar 

  10. Wang, X. J. Decision making in recurrent neuronal circuits. Neuron 60, 215–234 (2008)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Maimon, G. & Assad, J. A. A cognitive signal for the proactive timing of action in macaque LIP. Nat. Neurosci. 9, 948–955 (2006)

    Article  CAS  PubMed  Google Scholar 

  12. Goldman-Rakic, P. S. Cellular basis of working memory. Neuron 14, 477–485 (1995)

    Article  CAS  PubMed  Google Scholar 

  13. Wang, X. J. Synaptic reverberation underlying mnemonic persistent activity. Trends Neurosci. 24, 455–463 (2001)

    Article  CAS  PubMed  Google Scholar 

  14. Zingg, B. et al. Neural networks of the mouse neocortex. Cell 156, 1096–1111 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Crutcher, M. D. & Alexander, G. E. Movement-related neuronal activity selectively coding either direction or muscle pattern in three motor areas of the monkey. J. Neurophysiol. 64, 151–163 (1990)

    Article  CAS  PubMed  Google Scholar 

  16. Gnadt, J. W. & Andersen, R. A. Memory related motor planning activity in posterior parietal cortex of macaque. Exp. Brain Res. 70, 216–220 (1988)

    CAS  PubMed  Google Scholar 

  17. Bruce, C. J. & Goldberg, M. E. Primate frontal eye fields. I. Single neurons discharging before saccades. J. Neurophysiol. 53, 603–635 (1985)

    Article  CAS  PubMed  Google Scholar 

  18. Hernández, A. et al. Decoding a perceptual decision process across cortex. Neuron 66, 300–314 (2010)

    Article  PubMed  CAS  Google Scholar 

  19. Herkenham, M. The afferent and efferent connections of the ventromedial thalamic nucleus in the rat. J. Comp. Neurol. 183, 487–517 (1979)

    Article  CAS  PubMed  Google Scholar 

  20. Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  21. Hunnicutt, B. J. et al. A comprehensive thalamocortical projection map at the mesoscopic level. Nat. Neurosci. 17, 1276–1285 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kuramoto, E. et al. Ventral medial nucleus neurons send thalamocortical afferents more widely and more preferentially to layer 1 than neurons of the ventral anterior–ventral lateral nuclear complex in the rat. Cereb. Cortex 25, 221–235 (2015)

    Article  PubMed  Google Scholar 

  23. Tanaka, M. Cognitive signals in the primate motor thalamus predict saccade timing. J. Neurosci. 27, 12109–12118 (2007)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  25. Shibasaki, H. & Hallett, M. What is the Bereitschaftspotential? Clin. Neurophysiol. 117, 2341–2356 (2006)

    Article  PubMed  Google Scholar 

  26. Fried, I., Mukamel, R. & Kreiman, G. Internally generated preactivation of single neurons in human medial frontal cortex predicts volition. Neuron 69, 548–562 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 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)

    Article  ADS  CAS  PubMed  Google Scholar 

  28. Guo, Z. V. et al. Procedures for behavioral experiments in head-fixed mice. PLoS One 9, e88678 (2014)

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  29. Reinhold, K., Lien, A. D. & Scanziani, M. Distinct recurrent versus afferent dynamics in cortical visual processing. Nat. Neurosci. 18, 1789–1797 (2015)

    Article  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  31. Krupa, D. J., Ghazanfar, A. A. & Nicolelis, M. A. Immediate thalamic sensory plasticity depends on corticothalamic feedback. Proc. Natl Acad. Sci. USA 96, 8200–8205 (1999)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhao, S. et al. Cell type-specific channelrhodopsin-2 transgenic mice for optogenetic dissection of neural circuitry function. Nat. Methods 8, 745–752 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Yu, J., Gutnisky, D. A., Hires, S. A. & Svoboda, K. Layer 4 fast-spiking interneurons filter thalamocortical signals during active somatosensation. Nat. Neurosci. 19, 1647–1657 (2016)

    Article  CAS  PubMed  Google Scholar 

  34. Yin, H. H. & Knowlton, B. J. The role of the basal ganglia in habit formation. Nat. Rev. Neurosci. 7, 464–476 (2006)

    Article  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  36. Goldberg, J. H. & Fee, M. S. A cortical motor nucleus drives the basal ganglia-recipient thalamus in singing birds. Nat. Neurosci. 15, 620–627 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Goldberg, J. H., Farries, M. A. & Fee, M. S. Basal ganglia output to the thalamus: still a paradox. Trends Neurosci. 36, 695–705 (2013)

    Article  CAS  PubMed  Google Scholar 

  38. Sherman, S. M . & Guillery, R. W. Functional Connections of Cortical Areas: a New View from the Thalamus. (MIT Press, 2013)

  39. Duan, C. A., Erlich, J. C. & Brody, C. D. Requirement of prefrontal and midbrain regions for rapid executive control of behavior in the rat. Neuron 86, 1491–1503 (2015)

    Article  CAS  PubMed  Google Scholar 

  40. Van der Werf, Y. D., Witter, M. P. & Groenewegen, H. J. The intralaminar and midline nuclei of the thalamus. Anatomical and functional evidence for participation in processes of arousal and awareness. Brain Res. Rev. 39, 107–140 (2002)

    Article  PubMed  Google Scholar 

  41. Saalmann, Y. B. Intralaminar and medial thalamic influence on cortical synchrony, information transmission and cognition. Front. Syst. Neurosci. 8, 83 (2014)

    Article  PubMed  PubMed Central  Google Scholar 

  42. Giber, K. et al. A subcortical inhibitory signal for behavioral arrest in the thalamus. Nat. Neurosci. 18, 562–568 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Isseroff, A., Rosvold, H. E., Galkin, T. W. & Goldman-Rakic, P. S. Spatial memory impairments following damage to the mediodorsal nucleus of the thalamus in rhesus monkeys. Brain Res. 232, 97–113 (1982)

    Article  CAS  PubMed  Google Scholar 

  44. Parnaudeau, S. et al. Inhibition of mediodorsal thalamus disrupts thalamofrontal connectivity and cognition. Neuron 77, 1151–1162 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Hippenmeyer, S. et al. A developmental switch in the response of DRG neurons to ETS transcription factor signaling. PLoS Biol. 3, e159 (2005)

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Madisen, L. et al. A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing. Nat. Neurosci. 15, 793–802 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Vue, T. Y. et al. Sonic hedgehog signaling controls thalamic progenitor identity and nuclei specification in mice. J. Neurosci. 29, 4484–4497 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Petreanu, L., Mao, T., Sternson, S. M. & Svoboda, K. The subcellular organization of neocortical excitatory connections. Nature 457, 1142–1145 (2009)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  49. Viswanathan, S. et al. High-performance probes for light and electron microscopy. Nat. Methods 12, 568–576 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Tsuriel, S., Gudes, S., Draft, R. W., Binshtok, A. M. & Lichtman, J. W. Multispectral labeling technique to map many neighboring axonal projections in the same tissue. Nat. Methods 12, 547–552 (2015)

    Article  CAS  PubMed  Google Scholar 

  51. Gerfen, C. R., Paletzki, R. & Heintz, N. GENSAT BAC Cre-recombinase driver lines to study the functional organization of cerebral cortical and basal ganglia circuits. Neuron 80, 1368–1383 (2013)

    Article  CAS  PubMed  Google Scholar 

  52. Wan, Y., Otsuna, H., Chien, C. B. & Hansen, C. An interactive visualization tool for multi-channel confocal microscopy data in neurobiology research. IEEE Trans. Vis. Comput. Graph. 15, 1489–1496 (2009)

    Article  PubMed  PubMed Central  Google Scholar 

  53. Di Chiara, G., Morelli, M., Porceddu, M. L. & Gessa, G. L. Role of thalamic gamma-aminobutyrate in motor functions: catalepsy and ipsiversive turning after intrathalamic muscimol. Neuroscience 4, 1453–1465 (1979)

    Article  CAS  PubMed  Google Scholar 

  54. Gulcebi, M. I. et al. Topographical connections of the substantia nigra pars reticulata to higher-order thalamic nuclei in the rat. Brain Res. Bull. 87, 312–318 (2012)

    Article  PubMed  Google Scholar 

  55. Margrie, T. W., Brecht, M. & Sakmann, B. In vivo, low-resistance, whole-cell recordings from neurons in the anaesthetized and awake mammalian brain. Pflugers Arch. 444, 491–498 (2002)

    Article  CAS  PubMed  Google Scholar 

  56. Barthó, P. et al. Ongoing network state controls the length of sleep spindles via inhibitory activity. Neuron 82, 1367–1379 (2014)

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Rossi, M. A., Fan, D., Barter, J. W. & Yin, H. H. Bidirectional modulation of substantia nigra activity by motivational state. PLoS One 8, e71598 (2013)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  58. Teeters, J. L. et al. Neurodata without borders: creating a common data format for neurophysiology. Neuron 88, 629–634 (2015)

    Article  CAS  PubMed  Google Scholar 

  59. Chen, J. & Kriegstein, A. R. A GABAergic projection from the zona incerta to cortex promotes cortical neuron development. Science 350, 554–558 (2015)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  60. Shepherd, G. M. Corticostriatal connectivity and its role in disease. Nat. Rev. Neurosci. 14, 278–291 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank N. Li, J. Yu, J. Goldberg, X.-J. Wang, A. Hantman, J. Phillips, G. Shepherd and N. Yamawaki for comments on the manuscript, L. Walendy, T. Pluntke and M. Inagaki for animal training, T. Harris, B. Barbarits, J. J. Jun and W.-L. Sun for help with silicon probe recordings and spike sorting, M. Economo for help with image processing and A. Hu for help with histology. This work was funded by the Howard Hughes Medical Institute. H.K.I. and K.D. are Helen Hay Whitney Foundation postdoctoral fellows. K.D. is supported by the Simons Collaboration on the Global Brain.

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Authors and Affiliations

Authors

Contributions

Z.V.G., H.K.I. and K.S. conceived the project. Z.V.G. and H.K.I. performed extracellular electrophysiology and optogenetic experiments. H.K.I. performed whole-cell recordings. Z.V.G., H.K.I. and C.R.G. performed anatomical experiments. K.D. and S.D. performed network modelling. Z.V.G., H.K.I. and K.S. analysed data. Z.V.G., H.K.I. and K.S. wrote the paper, with input from all the authors.

Corresponding author

Correspondence to Karel Svoboda.

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The authors declare no competing financial interests.

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Reviewer Information Nature thanks D. J. Simons and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 The ALM makes reciprocal connections with multiple cortical and thalamic areas.

a, Co-injection of the anterograde tracer (AAV2/1-CAG-GFP) and retrograde tracer (WGA–Alexa555)50. b, Retrograde and anterograde labelling in the contralateral ALM, ipsilateral M1 and ipsilateral somatosensory cortex (S1/S2). Dashed boxes indicate magnified images on the right. Green, anterograde label (GFP); magenta, retrograde label (WGA–Alexa555); blue, Nissl stain. c, Thalamus (as in b). Anterograde labelling in the ipsilateral thalamus (with a weak contralateral projection); retrograde labelling was limited to the ipsilateral thalamus (top left). Confocal image of the thalamus (top right). Four coronal sections of ipsilateral thalamus (bottom left) and corresponding Allen Reference Atlas sections (http://mouse.brain-map.org/static/atlas) (bottom middle). Separate anterograde and retrograde label (bottom right). CM, centromedian nucleus of the thalamus; em, external medullary lamina of the thalamus; fr, fasciculus retroflexus; im, internal medullary lamina of the thalamus; IMD, intermediodorsal nucleus of the thalamus; MD, medial dorsal nucleus of the thalamus; ml, medial lemniscus; mtt, mammillothalamic tract; PO, posterior nucleus of the thalamus; RT; thalamic reticular nucleus, ZI, zona incerta. d, Number of neurons labelled by retrograde injection into the left ALM in cortical and subcortical areas. 38,062 (contra ALM); 26,599 (M1); 17,375 (thalamus); 2,532 (basolateral amygdala(BLA)); 1,312 (pallidum and basal forebrain); 427 (locus coeruleus (LC)); 377 (dorsal raphe nucleus (DRN)); 263 (ventral tegmental area (VTA)); and 59 (hypothalamus (HY)). For cortical areas we limit the neuron counting to the regions manipulated in the photoinhibition experiments (Fig. 4 and Methods). In subcortical areas we counted all neurons. e, 3D reconstruction. Left, anterograde GFP signal. Right, anterograde GFP signal (green) overlaid with heatmap representing density of retrogradely labelled neurons. f, Additional experiments using anterograde (AAV2/1-CAG-Flag) and retrograde (RetroBeads) tracers (Methods). Left, injection in the ALM. Retrograde labelling (red) is spatially restricted to the centre of the ALM (with some spreading to layer (L)1 and the pia). The three other panels show the thalamus. g, Retrograde tracer injection in ALM only rarely labelled zona incerta neurons (total count, 31 ± 2 per brain); none of these were positive for somatostatin (a marker for cortex projecting GABAergic zona incerta neurons, data not shown)59. This excludes the possibility that zona incerta GABAergic neurons directly inhibit the ALM during optogenetic manipulation of the thalamus.

Extended Data Figure 2 Optical fibre locations and thalamus photoinhibition.

a, Left, schematic of thalamus photoinhibition through an optical fibre. Right, optical fibre locations were overlaid on a coronal section of the Allen Reference Atlas (n = 7 mice). b, Schematic of thalamus recording during photoinhibition using an optrode. c, Top, PSTH of putative thalamic neurons recorded by an optrode during control (black) and photoinhibition (blue) conditions in Gad2-IRES-Cre mice. Virus expressing ChR2 in a Cre-dependent manner was injected in the VM/VAL projection zone of TRN. The magnitude of photoinhibition depends on the overlap of light intensity and axonal ChR2 expression. The fibre optic was 1 mm dorsal of the VM/VAL, which probably explains why the photoinhibition was stronger 1 mm from the fibre than closer to the fibre output. Averaging window, 100 ms. Bottom, normalized spike rate (mean spike rate during photoinhibition divided by mean spike rate during control) versus distance from the optical fibre. Error bars indicate s.d. n = 26, 41, 17 cells at a distance of 0.6, 0.8, 1.0 mm, respectively. Laser power at the tip of optical fibre, 10 mW. d, Top, PSTH of thalamic neurons recorded by an optrode during control (black) and photoinhibition (blue) conditions in VGAT–ChR2–EYFP mice. Averaging window, 100 ms. Bottom, normalized spike rate (mean spike rate during photoinhibition divided by mean spike rate during control) versus distance from optical fibre. Error bars indicate s.d. n = 34, 42, 38 cells; at a distances of 0.6, 0.8, 1.0 mm, respectively. Silencing extended beyond the VM/VAL and included other thalamic nuclei that project to ALM and nearby cortical areas. Silencing using VGAT–ChR2–EYFP (d) was more potent than with Gad2-IRES-Cre mice (c). Laser power at the tip of optical fibre, 10 mW. e, PSTH of ALM neurons during control (black) and thalamus photoinhibition (blue) conditions. Laser power at the tip of optical fibre 10 mW, n = 314 cells. Averaging window, 100 ms.

Extended Data Figure 3 Effects of thalamic muscimol infusions on behaviour.

a, Muscimol infusion locations (red crosses) near the VM/VAL. Sites from left (n = 3) and right (n = 3) hemispheres were mapped onto the left hemisphere. b, Small amounts of muscimol (1.5–5 ng) infused near the VM/VAL produced ipsilateral bias. Left, performance change in contra trials after muscimol infusion. Right, performance change in ipsi-trials after muscimol infusion. Each line represents an infusion site (n = 6, same mice as in a). *P < 0.05, paired t-test. c, Muscimol infusion locations in the anterior part of the thalamus (red crosses). Sites from left (n = 2) and right (n = 2) hemispheres were mapped onto the left hemisphere. d, Muscimol infusions in the anterior part of the thalamus (around 1.1–1.6 mm anterior to the centre of VM/VAL; same mice as in c). Note that much higher muscimol concentrations (10 times of those used near the VM/VAL), did not affect behaviour. e, Muscimol infusion locations in the dorsal part of the thalamus (red crosses). Sites from left (n = 2) and right (n = 2) hemispheres were mapped onto the left hemisphere. f, Muscimol infusions in the dorsal part of the thalamus (around 0.2–0.5 mm dorsal to medial dorsal thalamus, same mice as in e). Note that much higher muscimol concentrations (10 times of those used near the VM/VAL), did not affect behaviour.

Extended Data Figure 4 Recording sites and neuron types recorded in the ALM, thalamus and SNr.

a, Example electrode tracks in ALM labelled with DiI. b, Single-unit classification in the ALM. Left, putative fast-spiking (FS) interneurons (red, n = 166) and putative pyramidal neurons (blue, n = 1,006) were separated on the basis of the histogram of spike widths3 (Methods). A small subset of neurons with intermediate spike durations were not classified (brown, n = 42). Right, mean spike waveform of each unit. c, Left, average population selectivity in spike rate of ALM neurons. To compute population selectivity, we first determined each neuron’s preferred trial type using spike counts from half of the trials; selectivity was calculated as the spike rate difference between the preferred and non-preferred trial types for the other half of trials. The s.e.m. was estimated by bootstrapping over neurons. Averaging window, 200 ms. Right, population response correlation of ALM neurons. The smoothed response was mean subtracted and normalized to the variance during the entire trial epoch. The Pearson’s correlation at a particular time was calculated between the population response vector at that time point and the population response vector at cue onset27. d, Example electrode tracks in the VM/VAL. e, Single-unit classification of neurons in thalamus. Left, putative thalamic neurons (blue, n = 672) were selected on the basis of the histogram of spike widths (Methods). Right, mean spike waveform of each unit. f, Average population selectivity in spike rate (left) and population correlation (right) of VM/VAL neurons. g, Additional electrode tracks in the thalamus (n = 10 mice). Electrode tracks were used to determine whether recorded neurons were in the VM/VAL. h, Example electrode tracks in the SNr. i, Single-unit classification in SNr. Left, putative GABAergic neurons (red, n = 181) were selected on the basis of the histogram of spike widths and their high spike rates (Methods). Right, mean spike waveform of each unit. j, Spike rate of single units in the SNr. Putative GABAergic neurons have a mean spike rate of 40.9 ± 21.5 (mean ± s.d., n = 181). The other neurons have a mean spike rate of 23.4 ± 17.0 (mean ± s.d., n = 46).

Extended Data Figure 5 Hyperpolarization of ALM neurons during thalamus photoinhibition is caused by loss of excitation.

a, b, ALM neuron during thalamus photoinhibition. Top, PSTH during control (a) and photoinhibition (b) trials. Bottom, Vm during each trial type (10 trials each). Red and blue lines, trial averaged Vm. ch, Vm changes in ALM neurons after thalamus photoinhibition (non-behaving animals). In this experiment thalamic photoinhibition was low (Cre-dependent ChR2-AAV injected near the VM/VAL projection zone of the TRN in Gad2-IRES-Cre mice). Photoinhibition is much more potent in VGAT–ChR2 mice, because the vast majority of TRN and SNr neurons are ChR2+. c, Schematic. d, Vm changes after light onset. Average control, black; average photoinhibition, blue; n = 14 cells. Thin lines, individual neurons. Consistent with data from behaving VGAT–ChR2 mice (Fig. 3g), we observed significant hyperpolarization after light onset. e, Same as d during negative current injection (n = 9 cells). Vm is near the reversal potential for inhibitory currents, and excitatory currents were amplified. f, Same as d during positive current injection (n = 6 cells). Vm is near the reversal potential for excitatory currents, and the inhibitory currents are amplified. g, Input resistance was similar during positive and negative current injections (P = 0.05, rank-sum test). h, Relationship between Vm in non-photoinhibition condition versus Vm changes with photoinhibition (ΔVm). Vm and ΔVm were calculated between 100–120 ms after the onset of light. We plotted data from positive and negative current injections, because the input resistances were similar (see g). Slope of linear regression (dashed line) is larger than zero (P < 0.0001, bootstrapped), indicating that hyperpolarization is mainly caused by loss of excitation. Black circles, cells with significant change of Vm. in, The time course of Vm change in ALM neurons during photoactivation of local parvalbumin+ (PV+) neurons expressing ChR2. This experiment shows that silencing by increased inhibition can be distinguished from loss of excitation with our method. Panels are as in ch. i, Schematic. j, n = 7 cells. k, l, Hyperpolarization was reduced during negative current injection (n = 5 cells, k), and enhanced during positive current injection (n = 5 cells, l). m, Input resistances during positive and negative current injections were similar (P = 0.662, rank-sum test). n, The slope of linear regression is smaller than zero (P < 0.0001, bootstrapped), which indicates that hyperpolarization was mainly due to increased inhibition. Note that the effect of current injection is opposite from that of thalamic inactivation (compare with h).

Extended Data Figure 6 Onset of Vm changes after thalamic and cortical photoinhibition.

a, Contributions to the time of detected Vm change in the ALM after photoinhibition of the thalamus. The time between photostimulus onset and silencing in thalamus is T1 = 2.5 ± 0.8 ms (Fig. 3f). The propagation delay from thalamus to the thalamic terminals in the ALM is T2 = 3.6 ms (see c). An additional T3 = 1.8 ms is required to hyperpolarize the Vm of ALM neurons, because of the synaptic and membrane time constants. T1 + T2 + T3 explains the measured latency (7.9 ± 1.7 ms). T2 + T3 is defined as the latency difference. b, The time course of Vm change in ALM neurons after thalamic photoinhibition (same as Fig. 3g). Other panels in this figure (c, e, f) follow the same format. c, The time course of Vm change in ALM neurons after thalamus photoactivation in non-behaving naive Olig3-Cre47 × Ai32 mice (labelling the thalamus specifically, n = 9 cells). Since we used a high laser power intensity (10 mW), we assume spikes were generated in the thalamus within 1 ms. This time provides an estimate for the conduction delay of thalamocortical neurons (T2). d, Model-based estimation of the time required to depolarize (black) or hyperpolarize (blue) ALM neurons (T3). Left, schematic. Middle, mean Vm traces. Right, latency (mean ± s.e.m., n = 300 per condition). Conduction delay was set to zero. Traces or plots with a different colour indicate data with different fractions of activated/inhibited neurons: 10–100% (from lighter to darker). Even when all the input neurons were inhibited, we expect to observe a latency of 1.8 ± 0.7 ms (mean ± s.e.m.). See Supplementary Information for details. e, The time course of Vm change in M1 putative pyramidal neurons after thalamus photoinhibition during the delay epoch in behaving mice (n = 9 cells). As it takes 2.5 ± 0.8 ms to reduce spike rates in thalALM after photostimulation onset, we estimate that it takes 8.5 ms for the thalALM to affect M1 activity. f, The time course of Vm change in ALM neurons after M1 photoinhibition during the delay epoch in behaving mice (n = 11 cells). As it takes 8.1 ± 1.2 ms to silence the cortex (Fig. 6e), this implies it takes approximately 5.8 ms for changes in M1 activity to affect ALM activity. g, Summary of measured latencies. Time required to inhibit input structures is subtracted to show T2 + T3.

Extended Data Figure 7 Effects of low thalamus inhibition on ALM selectivity and models of thalamo-ALM interactions.

a, Average population PSTH (top left and middle) and population selectivity (bottom left and middle) of contra-preferring ALM neurons. Here, contra-preferring neurons are defined as neurons with significantly higher spike rates during the delay epoch of contra trials compared to ipsi trials (t-test, P < 0.05). We included neurons with spike rates higher than 2 spikes per s during both control and inactivation conditions. Selectivity was calculated as the spike rate difference between the contra and ipsi trial types. Averaging window, 200 ms. Average population PSTH (top middle) and selectivity (bottom middle) of contra-preferring ALM neurons during low thalamic photoinhibition. Average spike rate changes (top right) and average selectivity changes (bottom right) caused by low thalamic photoinhibition. The s.e.m. was estimated by bootstrapping over neurons. Blue, mean ± s.e.m. (bootstrap) of contra trials; red, mean ± s.e.m. of ipsi trials. b, The same plot as in a for ipsi-preferring neurons. ce, We analysed model networks to better understand the possible interactions between the ALM and thalamus. Top, the models consist of two neurons (left- and right-preferring neurons, blue and red, respectively) in both the thalamus and ALM. Thalamus to ALM connections were either non-selective (c, d) or selective (e). Activity of the right (blue) and left (red)-preferring neurons during a lick right trial are plotted (second to fourth rows). Selective sensory input enters the ALM during the sample epoch, and selective activity is maintained during the delay epoch without sustained input (second row from the top). The models were tested in response to non-selective thalamic photoinhibition that was either high (third row) or low (fourth row). During high thalamus photoinhibition, activities of the right and left preferring neurons were reduced to zero in all models (consistent with Fig. 3). During low thalamus photoinhibition, selectivity was reduced to zero without large changes in mean spike rate in both nonlinear models (d, e) (consistent with Fig. 5), but not in a linear model (c). See Supplementary Information for details.

Extended Data Figure 8 Modulation of thalamic activity by ALM photoinhibition is localized.

a, VM/VAL recordings during ALM photoinhibition. b, PSTH of thalamic neurons averaged during control (black) and photoinhibition (light blue). Neurons were grouped by distance to the centre of VM/VAL. Distance <0.5 mm, n = 250; 0.5 ≤ distance < 1.0 mm, n = 160; distance ≥ 1.0 mm, n = 46. Averaging window, 100 ms. c, Locations of recorded neurons in the thalamus, projected to the example coronal section. Colour code shows the spike rate during ALM photoinhibition normalized to control (the first 100 ms of photoinhibition, see Methods). Same data as in Fig. 6d. dg, Comparison of the effects of photoinhibition of ALM versus vM1 on VM/VAL activity. Labelling corticothalamic projections from ALM (data from mouse connectivity map of the Allen Brain Atlas ID 263242463, http://connectivity.brain-map.org/)20 (see also Extended Data Figs 1, 9). e, Labelling corticothalamic projections from vM1 (data from mouse connectivity map of the Allen Brain Atlas ID 168162771)20. f, ALM photoinhibition. PSTH of VM/VAL neurons averaged during control (black) and ALM photoinhibition (blue). The s.e.m. was estimated by bootstrapping over neurons (n = 46 cells from 3 mice.). The s.e.m. for photoinhibition conditions are not displayed for clarity. Averaging window, 100 ms. g, vM1 photoinhibition. PSTH of VM/VAL neurons averaged during control (black) and vM1 photoinhibition (blue) conditions. Photoinhibiting the vM1 produced a lower reduction in VM/VAL activity. The s.e.m. was estimated by bootstrapping over neurons (n = 46 cells from 3 mice). The s.e.m. for photoinhibition conditions are not displayed for clarity. Averaging window, 100 ms. h, i, Absence of long-range GABAergic projections from the ALM in the thalamus. h, GABAergic neurons labelled with GFP in the ALM. Left, AAV2/1-CAG-flex-EGFP was injected into the ALM in a Gad2-IRES-Cre mouse. Middle, confocal images showing GABAergic neurons expressing EGFP. Same neurons as on the left. Right, magnified view of the boxed region in the middle, showing labelled axons of GABAergic neurons. i, Absence of GABAergic axons in the VM. Left, VM and the mammilothalamic tract (mtt). Middle, confocal image of the region on the left. Laser power was 10× higher compared to h. Images were contrast-enhanced to show small structures. Right, magnified view of the indicated region in the middle. No labelled axonal processes were detected in the thalamus.

Extended Data Figure 9 Thalamic regions that are connected reciprocally with the ALM (thalALM) receive input from multiple brain areas.

RetroBeads were injected into the thalALM (AP −1.5, ML 0.85, DV −4.0 mm from bregma, mainly in the VM). Magenta, retrograde labelling; blue, Nissl staining. a, Coronal sections. Dashed boxes indicate location of magnified images in bg. b, Labelling in the ALM. Overall labelling was much stronger in the ipsilateral ALM. Labelling in the ALM was observed on both sides in L6, whereas labelling in L5 was seen only in the ipsilateral ALM. L6 neurons are corticothalamic neurons, whereas the L5 neurons correspond to pyramidal-tract neurons that send a branch to the thalamus60. In addition to the ALM, labelling was observed in M1, S2 and weakly in other cortical areas (see a). c, Labelling in the ipsilateral TRN. d, Labelling in the ipsilateral superior colliculus (SC). e, Labelling in the ipsilateral SNr, in three coronal sections. Labelling was observed throughout the SNr from the caudal to the rostral end, consistent with a previous report54. f, Labelling in the ipsilateral pedunculopontine nucleus (PPN). g, Labelling in the contralateral deep cerebellar nuclei. DN, dentate nucleus; FN, fastigial nucleus; IP, interposed nucleus.

Extended Data Figure 10 The effect of ALM photoinhibition on SNr activity.

a, Schematic of SNr recording during ALM photoinhibition. Because the SNr→thalamus projection is inhibitory (red arrow), the SNr could contribute to VM/VAL inhibition, if ALM photoinhibition activates the SNr. We used multi-shank silicon probes (spanning 600 μm, medial to lateral) to survey a large part of the SNr (medial, lateral, rostral and caudal). b, SNr population selectivity. Selectivity is the difference in spike rate between the preferred and non-preferred trial type, normalized to the peak selectivity. Only putative GABAergic neurons with significant trial selectivity are shown (n = 152 out of 181, t-test, P < 0.05). The scale bar on the right indicates selectivity type: neurons showing preparatory activity only (white); both preparatory activity and peri-movement activity (grey); peri-movement activity only (black). Averaging window, 200 ms. SNr selectivity is similar to the ALM and VM/VAL (Fig. 2). c, Scatter plot of SNr GABAergic neurons (n = 181; spikes measured for 100 ms, starting 20 ms after photostimulus onset; Methods). Filled circles, neurons that were significantly modulated by ALM photoinhibition (P < 0.05, t-test). Photoinhibition of ALM changed only a relatively small fraction of SNr neurons (48 out of 181 significantly inhibited; 23 out of 181 significantly activated; P < 0.05, t-test). Moreover, neurons that decreased their activity were more numerous than neurons that increased their activity (bootstrapping over neurons; P < 0.01, Methods). Overall, inhibiting the ALM reduced SNr activity by 3.6 spikes per s (8.3% of control activity measured for 100 ms, starting 20 ms after photostimulus onset). This reduction in neural activity in the SNr is expected to increase thalALM activity. d, The time course of SNr GABAergic neurons during ALM photoinhibition. Left, significantly inhibited neurons (n = 48). Right, significantly excited neurons (n = 23). The s.e.m. was estimated by bootstrapping over neurons. Top, averaging window, 100 ms. Bottom, bin size, 1 ms. SNr neurons were affected by ALM photoinhibition with a relatively long latency difference (15.2 ± 4.6 ms (mean ± s.e.m.), P < 0.05, t-test), longer than for reducing thalALM activity (10.9 ± 2.9 ms; Fig. 6e). These data indicate that the ALM to SNr pathway does not contribute to the early phase of VM/VAL inhibition after ALM photoinhibition.

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Guo, Z., Inagaki, H., Daie, K. et al. Maintenance of persistent activity in a frontal thalamocortical loop. Nature 545, 181–186 (2017). https://doi.org/10.1038/nature22324

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