Striatal activity topographically reflects cortical activity

Abstract

The cortex projects to the dorsal striatum topographically1,2 to regulate behaviour3,4,5, but spiking activity in the two structures has previously been reported to have markedly different relations to sensorimotor events6,7,8,9. Here we show that the relationship between activity in the cortex and striatum is spatiotemporally precise, topographic, causal and invariant to behaviour. We simultaneously recorded activity across large regions of the cortex and across the width of the dorsal striatum in mice that performed a visually guided task. Striatal activity followed a mediolateral gradient in which behavioural correlates progressed from visual cue to response movement to reward licking. The summed activity in each part of the striatum closely and specifically mirrored activity in topographically associated cortical regions, regardless of task engagement. This relationship held for medium spiny neurons and fast-spiking interneurons, whereas the activity of tonically active neurons differed from cortical activity with stereotypical responses to sensory or reward events. Inactivation of the visual cortex abolished striatal responses to visual stimuli, supporting a causal role of cortical inputs in driving the striatum. Striatal visual responses were larger in trained mice than untrained mice, with no corresponding change in overall activity in the visual cortex. Striatal activity therefore reflects a consistent, causal and scalable topographical mapping of cortical activity.

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Fig. 1: The cortex and striatum show spatial gradients of sensorimotor activity during visually guided behaviour.
Fig. 2: Striatal domains are topographically correlated with connected cortical regions.
Fig. 3: Striatal sensorimotor activity reflects associated cortical activity.
Fig. 4: Striatal mirroring of cortical activity is cell-type-specific.
Fig. 5: Striatal stimulus responses increase independently from visual cortex after training.

Data availability

The datasets generated during the current study are available as downloadable files at https://osf.io/x4q26/.

Code availability

The code used to analyse the data are available at https://github.com/petersaj/Peters_et_al_Nature_2021.

References

  1. 1.

    Hintiryan, H. et al. The mouse cortico-striatal projectome. Nat. Neurosci. 19, 1100–1114 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Hunnicutt, B. J. et al. A comprehensive excitatory input map of the striatum reveals novel functional organization. eLife 5, e19103 (2016).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Friedman, A. et al. A corticostriatal path targeting striosomes controls decision-making under conflict. Cell 161, 1320–1333 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Gremel, C. M. et al. Endocannabinoid modulation of orbitostriatal circuits gates habit formation. Neuron 90, 1312–1324 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Znamenskiy, P. & Zador, A. M. Corticostriatal neurons in auditory cortex drive decisions during auditory discrimination. Nature 497, 482–485 (2013).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Fujii, N. & Graybiel, A. M. Time-varying covariance of neural activities recorded in striatum and frontal cortex as monkeys perform sequential-saccade tasks. Proc. Natl Acad. Sci. USA 102, 9032–9037 (2005).

    ADS  CAS  PubMed  Google Scholar 

  7. 7.

    Martiros, N., Burgess, A. A. & Graybiel, A. M. Inversely active striatal projection neurons and interneurons selectively delimit useful behavioral sequences. Curr. Biol. 28, 560–573.e5 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Buch, E. R., Brasted, P. J. & Wise, S. P. Comparison of population activity in the dorsal premotor cortex and putamen during the learning of arbitrary visuomotor mappings. Exp. Brain Res. 169, 69–84 (2006).

    PubMed  Google Scholar 

  9. 9.

    Pidoux, M., Mahon, S., Deniau, J. M. & Charpier, S. Integration and propagation of somatosensory responses in the corticostriatal pathway: an intracellular study in vivo. J. Physiol. (Lond.) 589, 263–281 (2011).

    CAS  Google Scholar 

  10. 10.

    Kim, H. F. & Hikosaka, O. Distinct basal ganglia circuits controlling behaviors guided by flexible and stable values. Neuron 79, 1001–1010 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Wang, L., Rangarajan, K. V., Gerfen, C. R. & Krauzlis, R. J. Activation of striatal neurons causes a perceptual decision bias during visual change detection in mice. Neuron 97, 1369–1381.e5 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Guo, L., Walker, W. I., Ponvert, N. D., Penix, P. L. & Jaramillo, S. Stable representation of sounds in the posterior striatum during flexible auditory decisions. Nat. Commun. 9, 1534 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Rueda-Orozco, P. E. & Robbe, D. The striatum multiplexes contextual and kinematic information to constrain motor habits execution. Nat. Neurosci. 18, 453–460 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Tai, L.-H., Lee, A. M., Benavidez, N., Bonci, A. & Wilbrecht, L. Transient stimulation of distinct subpopulations of striatal neurons mimics changes in action value. Nat. Neurosci. 15, 1281–1289 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Yartsev, M. M., Hanks, T. D., Yoon, A. M. & Brody, C. D. Causal contribution and dynamical encoding in the striatum during evidence accumulation. eLife 7, e34929 (2018).

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Kincaid, A. E., Zheng, T. & Wilson, C. J. Connectivity and convergence of single corticostriatal axons. J. Neurosci. 18, 4722–4731 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Huerta-Ocampo, I., Mena-Segovia, J. & Bolam, J. P. Convergence of cortical and thalamic input to direct and indirect pathway medium spiny neurons in the striatum. Brain Struct. Funct. 219, 1787–1800 (2014).

    PubMed  Google Scholar 

  18. 18.

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Emmons, E. B. et al. Rodent medial frontal control of temporal processing in the dorsomedial striatum. J. Neurosci. 37, 8718–8733 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Lemke, S. M., Ramanathan, D. S., Guo, L., Won, S. J. & Ganguly, K. Emergent modular neural control drives coordinated motor actions. Nat. Neurosci. 22, 1122–1131 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Apicella, P. Tonically active neurons in the primate striatum and their role in the processing of information about motivationally relevant events. Eur. J. Neurosci. 16, 2017–2026 (2002).

    PubMed  Google Scholar 

  22. 22.

    Gage, G. J., Stoetzner, C. R., Wiltschko, A. B. & Berke, J. D. Selective activation of striatal fast-spiking interneurons during choice execution. Neuron 67, 466–479 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Stalnaker, T. A., Berg, B., Aujla, N. & Schoenbaum, G. Cholinergic interneurons use orbitofrontal input to track beliefs about current state. J. Neurosci. 36, 6242–6257 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Bolam, J. P., Hanley, J. J., Booth, P. A. C. & Bevan, M. D. Synaptic organisation of the basal ganglia. J. Anat. 196, 527–542 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Kato, S. et al. Selective neural pathway targeting reveals key roles of thalamostriatal projection in the control of visual discrimination. J. Neurosci. 31, 17169–17179 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Ponvert, N. D. & Jaramillo, S. Auditory thalamostriatal and corticostriatal pathways convey complementary information about sound features. J. Neurosci. 39, 271–280 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Burke, D. A., Rotstein, H. G. & Alvarez, V. A. Striatal local circuitry: a new framework for lateral inhibition. Neuron 96, 267–284 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    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).

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Burgess, C. P. et al. High-yield methods for accurate two-alternative visual psychophysics in head-fixed mice. Cell Rep. 20, 2513–2524 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Wekselblatt, J. B., Flister, E. D., Piscopo, D. M. & Niell, C. M. Large-scale imaging of cortical dynamics during sensory perception and behavior. J. Neurophysiol. 115, 2852–2866 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Jun, J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232–236 (2017).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Allen, W. E. et al. Global representations of goal-directed behavior in distinct cell types of mouse neocortex. Neuron 94, 891–907.e6 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Senzai, Y., Fernandez-Ruiz, A. & Buzsáki, G. Layer-specific physiological features and interlaminar interactions in the primary visual cortex of the mouse. Neuron 101, 500–513.e5 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Mallet, N., Le Moine, C., Charpier, S. & Gonon, F. Feedforward inhibition of projection neurons by fast-spiking GABA interneurons in the rat striatum in vivo. J. Neurosci. 25, 3857–3869 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Inokawa, H., Yamada, H., Matsumoto, N., Muranishi, M. & Kimura, M. Juxtacellular labeling of tonically active neurons and phasically active neurons in the rat striatum. Neuroscience 168, 395–404 (2010).

    CAS  PubMed  Google Scholar 

  37. 37.

    Yamin, H. G., Stern, E. A. & Cohen, D. Parallel processing of environmental recognition and locomotion in the mouse striatum. J. Neurosci. 33, 473–484 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Schmitzer-Torbert, N. C. & Redish, A. D. Task-dependent encoding of space and events by striatal neurons is dependent on neural subtype. Neuroscience 153, 349–360 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Kim, N. et al. A striatal interneuron circuit for continuous target pursuit. Nat. Commun. 10, 2715 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Benhamou, L., Kehat, O. & Cohen, D. Firing pattern characteristics of tonically active neurons in rat striatum: context dependent or species divergent? J. Neurosci. 34, 2299–2304 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Marche, K., Martel, A.-C. & Apicella, P. Differences between dorsal and ventral striatum in the sensitivity of tonically active neurons to rewarding events. Front. Syst. Neurosci. 11, 52 (2017).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Johansson, Y. & Silberberg, G. The functional organization of cortical and thalamic inputs onto five types of striatal neurons is determined by source and target cell identities. Cell Rep. 30, 1178–1194.e3 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Orsolic, I., Rio, M., Mrsic-Flogel, T. D. & Znamenskiy, P. Mesoscale cortical dynamics reflect the interaction of sensory evidence and temporal expectation during perceptual decision-making. Preprint at https://doi.org/10.1101/552026 (2019).

  44. 44.

    Sales-Carbonell, C. et al. No discrete start/stop signals in the dorsal striatum of mice performing a learned action. Curr. Biol. 28, 3044–3055.e5 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Rothwell, P. E. et al. Input- and output-specific regulation of serial order performance by corticostriatal circuits. Neuron 88, 345–356 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    O’Hare, J. K. et al. Pathway-specific striatal substrates for habitual behavior. Neuron 89, 472–479 (2016).

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Xiong, Q., Znamenskiy, P. & Zador, A. M. Selective corticostriatal plasticity during acquisition of an auditory discrimination task. Nature 521, 348–351 (2015).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Koralek, A. C., Costa, R. M. & Carmena, J. M. Temporally precise cell-specific coherence develops in corticostriatal networks during learning. Neuron 79, 865–872 (2013).

    CAS  PubMed  Google Scholar 

  49. 49.

    Owen, S. F., Berke, J. D. & Kreitzer, A. C. Fast-spiking interneurons supply feedforward control of bursting, calcium, and plasticity for efficient learning. Cell 172, 683–695.e15 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Steinmetz, N. A. et al. Aberrant cortical activity in multiple GCaMP6-expressing transgenic mouse lines. eNeuro 4, ENEURO.0207-17.2017 (2017).

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Bhagat, J., Wells, M. J., Harris, K. D., Carandini, M. & Burgess, C. P. Rigbox: An open-source toolbox for probing neurons and behavior. eNeuro 7, ENEURO.0406-19.2020 (2020).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Wang, Q. et al. The Allen mouse brain common coordinate framework: a 3D reference atlas. Cell 181, 936–953.e20 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Zhuang, J. et al. An extended retinotopic map of mouse cortex. eLife 6, e18372 (2017).

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Rossant, C. et al. Spike sorting for large, dense electrode arrays. Nat. Neurosci. 19, 634–641 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Siegle, J. H. et al. Open Ephys: an open-source, plugin-based platform for multichannel electrophysiology. J. Neural Eng. 14, 045003 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Deligkaris, K., Bullmann, T. & Frey, U. Extracellularly recorded somatic and neuritic signal shapes and classification algorithms for high-density microelectrode array electrophysiology. Front. Neurosci. 10, 421 (2016).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Hill, D. N., Mehta, S. B. & Kleinfeld, D. Quality metrics to accompany spike sorting of extracellular signals. J. Neurosci. 31, 8699–8705 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank C. Reddy, M. Wells, L. Funnell and H. Forrest for mouse husbandry and training; R. Raghupathy, I. Prankerd and D. Orme for histology; D. Cohen for helpful discussions; and NVIDIA for donation of a Titan X GPU. This work was supported by a Newton International Fellowship, EMBO Fellowship (ALTF 1428-2015), and a Human Frontier Science Program Fellowship (LT226/2016-L) to A.J.P., a Wellcome Trust PhD Studentship to J.M.J.F., a Human Frontier Science Program Fellowship (LT001071/2015-L) and Marie Skłodowska-Curie fellowship of the EU Horizon 2020 (656528) to N.A.S., Wellcome Trust grants 205093, 204915 to K.D.H. and M.C. and ERC grant 694401 to K.D.H. M.C. holds the GlaxoSmithKline/Fight for Sight Chair in Visual Neuroscience.

Author information

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Authors

Contributions

A.J.P., K.D.H. and M.C. conceived and designed the study. A.J.P. collected and analysed data. J.M.J.F. analysed cell types and single-unit data. N.A.S. developed wide-field imaging and Neuropixels setups. A.J.P., K.D.H. and M.C. wrote the manuscript with input from J.M.J.F. and N.A.S.

Corresponding author

Correspondence to Andrew J. Peters.

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The authors declare no competing interests.

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Peer review information Nature thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Task performance.

a, Timeline of events in a trial. After 0.5 s with no wheel movement, a stimulus appears. The mouse may turn the wheel immediately, but it only becomes yoked to the stimulus after a further 0.5 s, at which time an auditory ‘go’ cue is played. If the mouse drives the stimulus into the centre, a water reward is delivered and a new trial begins after 1 s; if the mouse drives the stimulus off the screen away from the centre, a white noise sound is played and a new trial begins after 2 s. b, Psychometric curve showing task performance: the fraction of choices as a function of stimulus contrast and side. Curve and shaded region show mean ± s.e. across sessions. c, Median reaction time as a function of stimulus contrast and side as in b (mean ± s.e. across each session’s median). Dotted horizontal line indicates time of auditory ‘go’ cue. d, Histogram of times from stimulus to movement onset (reaction time) by trial quartile within sessions (first quarter of trials in the session in black, last quarter in beige; mean ± s.e. across mice). In early trials, mice typically begin moving the wheel before the cue. Later in the sessions, they waited more often for the cue.

Extended Data Fig. 2 Cortical wide-field alignment.

a, Example wide-field images from one mouse, used to align vasculature. b, Retinotopic visual field sign maps corresponding to the sessions in a. c, Retinotopic maps averaged across all sessions for three example mice, used to align wide-field images across mice. d, Retinotopic map averaged across mice and symmetrized, used to align wide-field images to the Allen CCF atlas (used only for figure overlay purposes). e, Cortical seed pixels (left) and corresponding pixel–pixel correlation maps (right). Each pixel–pixel correlation map (right) is made by correlating a given pixel with all other pixels, which reveals clusters of pixels belonging to correlated cortical regions (for example, the central circles corresponding to limb somatomotor cortex). f, Summed edge-filtered pixel–pixel correlation maps (as in e) showing the outlines of correlated clusters of pixels. Each pixel–pixel correlation map (as in e) highlights a correlated cluster of pixels. Edge-filtering each pixel–pixel correlation map then draws a boundary around the highlighted correlated cluster. Summing these edges across all pixel–pixel correlation maps illustrates the boundaries of all correlated clusters of pixels, prominently including limb somatomotor cortex (central circles), visual cortex (posterior lateral triangular regions), retrosplenial cortex (posterior medial region) and orofacial somatomotor cortex (frontal lateral regions). The Allen CCF regions aligned using retinotopy (red) align well to correlation borders, indicating that our alignment methods based on posterior retinotopy also successfully align anterior regions.

Extended Data Fig. 3 Striatal recording locations and electrophysiological borders.

a, Top, wide-field images used to approximate probe location (red line); middle and bottom, horizontal and coronal views of the brain with wide-field-estimated probe location (red line) and histologically verified probe location (green line). Black outline, brain; blue outline, dorsal striatum; purple outline, ventral striatum. Wide-field-estimated probe locations closely match histologically verified probe locations. b, Wide-field-estimated probe location of all trained mice plotted in Allen CCF coordinates. c, Example histology showing GCaMP6s fluorescence (green) and dye from the probe (red). d, Example multiunit correlation matrix by location along the probe for several sessions in the mouse from c, with the borders of the striatum approximated medially by the lack of spikes in the ventricle and laterally by the sudden drop in local multiunit correlation. Dye from c corresponds to session 1 in d and histology-validated regions are labelled.

Extended Data Fig. 4 Relationship of cortical spiking with cortical fluorescence and with striatal spiking.

a, Example triple recording with wide-field imaging, VISam electrophysiology and striatal electrophysiology during the task. b, Deconvolution kernel obtained by predicting cortical multiunit spikes from cortical fluorescence around the probe (black, mean; grey, individual sessions). c, Current source density (CSD) from average stimulus responses aligned and averaged across sessions, used to identify superficial and deep cortical layers. Horizontal dashed line represents the estimated border between superficial and deep layers. d, Correlation of VISam spiking with deconvolved fluorescence (green) and DMS spiking (black) (mean ± s.e. across sessions). Cortical fluorescence and striatal spiking are both correlated to cortical spiking along a similar depth profile (correlation between fluorescence and striatal depth profiles compared to depth-shifted distribution, r = 0.57 ± 0.14 (mean ± s.e.) across 10 sessions, P = 9.0 × 10−4). e, Correlation of VISam spiking with fluorescence deconvolved with a kernel created only using superficial spikes (blue) or deep spikes (orange) (mean ± s.e. across sessions). Inset, deconvolution kernels as in b created using only superficial or deep spikes. Kernels created from superficial or deep spikes are not different (two-way ANOVA on time and depth, depth P = 1 across 10 sessions) and correlation between spikes and deconvolved fluorescence does not depend on kernel (two-way ANOVA on depth and kernel, kernel P = 0.97), indicating the deconvolution kernel is related to GcaMP6s dynamics consistently across depths. f, Cross-correlation of multiunit activity across superficial cortex, deep cortex and DMS. Inset, zoomed-in plot. Deep cortical spiking leads striatal spiking by about 3 ms (orange vertical line).

Extended Data Fig. 5 Striatal activity during trials with ipsilateral stimuli and ipsilaterally orienting movements.

a, Activity for each striatal domain across all trials from all sessions with ipsilateral stimuli, ipsilaterally orienting movements and rewards, formatted as in Fig. 3a. Trials are sorted vertically by reaction time. Blue line, stimulus onset; orange curve, movement onset; yellow line, ‘go’ cue. Activity within each time point is smoothed with a running average of 100 trials to display across-trial trends. b, Prediction of activity in each striatal domain by summing kernels for task events, formatted as in Fig. 3c. c, Prediction of striatal activity from cortical activity, formatted as in Fig. 3d. d, Trial-averaged activity in each striatal domain (black), predicted from task events (blue), and predicted from cortical activity (green), aligned to stimulus (blue line), movement (orange line) and reward (cyan line) (mean ± s.e. across sessions), formatted as in Fig. 3e.

Extended Data Fig. 6 Visual responses in dorsomedial striatum do not depend on upcoming movement choice and responses to the auditory ‘go’ cue are suppressed by ongoing movement.

a, Curves show average stimulus response (0–0.2 s after stimulus onset) in DMS, as a function of contrast and side, for trials with <500-ms reaction times and contralateral-orienting (purple) or ipsilateral-orienting (orange) movements (mean ± s.e. across sessions). Movement choice does not affect stimulus responses, indicating that stimulus responses are purely sensory, rather than linked to decisions (two-way ANOVA on stimulus and choice, interaction P = 0.56 for 77 sessions). b, ‘Go’ cue kernel (lag = 50 ms after cue shown) obtained when fitting cortical activity from task events, for trials with movement onset before the cue (top) and after the cue (bottom). c, ‘Go’ cue kernel obtained when fitting activity in each striatal domain from task events as in Fig. 3b, for trials with movement onset before the cue (black) and after the cue (grey). In both parietal cortex and DMS, responses to the ‘go’ cue are much larger when the mouse is not moving.

Extended Data Fig. 7 Task kernels for cortical activity associated with each striatal domain match task kernels for striatal activity.

a, Cortical maps used to define cortical activity associated with each striatal domain (from Fig. 2f, producing activity in Fig. 3d). b, Temporal kernels obtained when fitting cortical activity from task events for stimuli (left), movements (middle), and outcome (right) (mean ± s.e. across sessions), formatted as in Fig. 3b. c, Correlation of task kernels for striatal and cortical activity. Columns from left to right: correlation of associated striatal and cortical kernels, within the same session; correlation of striatal kernels for different domains within the same session; correlation of striatal kernels from different sessions but the same striatal domain, and correlation of cortical kernels obtained from different sessions, but associated with the same striatal domain. Grey lines show single sessions; black points and error bars show mean ± s.e. across mice. The kernels obtained for associated striatal domains and cortical regions are more correlated than kernels for different striatal domains (signed-rank test, P = 6.1 × 10−5 for 15 mice), indicating task kernels are domain-specific and shared between associated cortical and striatal regions. Correlations are also higher between associated striatal and cortical activity within-sessions, than between kernels fit to the same striatal domain on different sessions (signed-rank test, P = 1.2 × 10−4), indicating that differences between cortical and striatal task responses are smaller than session-to-session variability. d, Cross-validated fraction of variance explained by task events for striatal activity versus associated cortical activity. Small dots, sessions; large dots, mean ± s.e. across sessions; colour, striatal domain. Explained variance from task events is correlated between the cortex and striatum (correlation, r = 0.68, P = 1.14 × 10−10 across 77 sessions).

Extended Data Fig. 8 Prediction of striatal activity from subregions of cortex, from other striatal domains, and from the cortex during passive periods.

a, Activity in each striatal domain predicted from subregions of cortex (indicated by white regions in diagrams below x-axis) or from the other two striatal domains (far right). Each curve shows the relative cross-validated fraction of explained variance ((R2region − R2full cortex)/R2full cortex) for the colour-coded striatal domain (mean ± s.e. across sessions). Predictions are best from the associated cortical regions (two-way ANOVA on session and cortical subregion, subregion DMS P = 4.6 × 10−3, DCS P = 4.8 × 10−5, DLS P = 3.1 × 10−85 across 77 sessions) and striatal activity is less well predicted from other striatal domains than from cortex (signed-rank test, DMS P = 1.6 × 10−10, DCS P = 2.1 × 10−5, DLS P = 8.4 × 10−10 across 77 sessions). b, Example wheel trace, deconvolved cortical fluorescence, visual cortical electrophysiology and striatal electrophysiology session in the passive context (viewing visual noise stimuli), showing coherent low-frequency oscillations in VISam and DMS (formatted as in Extended Data Fig. 4a, from the same session session). c, Cross-validated fraction of striatal variance explained from cortex in task versus passive contexts. Small dots, sessions; large dots, mean ± s.e. across sessions; colour, striatal domain. DMS is predicted slightly better from cortex in the passive state, but DCS and DLS are predicted slightly worse the passive state (signed-rank test, DMS P = 6.2 × 10−7, DCS P = 1.9 × 10−4, DLS P = 1.6 × 10−5 across 77 sessions). d, Variance of striatal activity across task and passive states, legend as in b. During the passive state, DMS exhibits more variance and DCS and DLS exhibit less variance (signed-rank test, DMS P = 3.0 × 10−6, DCS P = 1.3 × 10−4, DLS P = 1.1 × 10−10 across 77 sessions), matching the differences in predictability between states. e, Cross-validated fraction of striatal explained variance from the cortex vs. variance of striatal activity during task performance, legend as in b. Cortex-explained variance is consistently related to activity variance across domains (ANCOVA, domain P = 0.22, domain-activity variance interaction P = 0.76).

Extended Data Fig. 9 Visual cortical inactivation selectively eliminates striatal visual responses.

a, Cortical unit firing rate change across topical muscimol application. Horizontal dotted line indicates bottom edge of cortex. Topical muscimol effectively silences the full cortical depth. b, Deconvolved cortical fluorescence standard deviation (top) and retinotopic visual field sign (bottom) before and after muscimol application. Muscimol was centred on VISam and spread laterally to other visual areas. c, Relative firing rate change in each striatal domain before and after cortical inactivation (dots are sessions). Firing rate increases slightly after cortical inactivation (signed-rank test, DMS P = 0.04, DCS P = 0.02, DLS P = 0.02 across 22 sessions). d, Passive responses to visual stimuli in cortical regions of interest (left) and corresponding striatal domains (right) before (black) and after (yellow) inactivation of visual cortex. Muscimol reduced the stimulus response in VISam and DMS proportionally within each session (correlation between fractional reduction of each area: r = 0.48, P = 0.04 across 22 sessions). e, Psychometric curve (left) and median reaction (right) as a function of stimulus contrast and side as in Extended Data Fig. 1b, before (black) and after (yellow) muscimol in visual cortex (mean ± s.e. across sessions). Task performance becomes worse asymmetrically across stimuli (two-way ANOVA on stimulus and condition, interaction P = 0.04 across 22 sessions) and reaction times become longer across stimuli (two-way ANOVA on stimulus and condition, condition P = 1.1 × 10−26 across 22 sessions). f, Kernels using task events to predict striatal activity before (black) and after (yellow) visual cortical muscimol (mean ± s.e. across sessions). Stimulus kernel weights decrease after muscimol while other kernel weights do not change significantly (two-way ANOVA on regressor and condition, condition effect on stimuli regressors DMS P = 2.0 × 10−7, DCS P = 8.4 × 10−6, DLS P = 0.03, P > 0.05 for other domains and regressors across 22 sessions).

Extended Data Fig. 10 Identifying striatal cell types with electrophysiology, and unidentified interneuron activity.

a, Electrophysiological properties used to classify striatal cell types. Striatal cells were identified as MSNs, FSIs, TANs and a fourth class of unidentified interneurons, according to waveform duration, length of post-spike suppression, and fraction of long interspike intervals. b, Histogram of firing rates across all units within each cell type. c, Number of units in each striatal domain classified as each cell type. d, Averaged and smoothed firing rates (lines) and raster plots across trials (dots) for one example cell of each type in each domain, aligned to the indicated task event. Top row, DMS; middle row, DCS; bottom row, DLS. e, Waveform and autocorrelogram of unidentified interneurons (mean ± s.d. across cells). f, Heat maps—spiking in individual cells aligned to contralateral stimuli (left), contralaterally orienting movements (middle) and rewards (right)—averaged across trials with reaction times less than 500 ms, maximum-normalized and sorted by time of maximum activity using half of the trials and plotting the other half of trials, formatted as in Fig. 4b. Line plots, average activity across neurons, formatted as in Fig. 4c. g, Correlation of the activity of each neuron (rows within heat maps of c) with the average activity within cell types or cortical activity from an region of interest corresponding to each domain, calculated from nonoverlapping sessions to account for interneuron sparsity (mean ± s.e. across sessions), formatted as in Fig. 4d. Unidentified interneurons were equally correlated to other unidentified interneurons, MSNs, FSIs and cortical activity (two-way ANOVA on firing rate and type, type P = 0.56 across 77 sessions) and uncorrelated to TAN activity (two-way ANOVA on firing rate and type, type P = 2.7 × 10−12 across 77 sessions). h, Activity during passive stimulus presentations in untrained (black) and trained (yellow) mice (mean ± s.e. across sessions), activity increases in DMS and DCS (time window 0–0.2 s, rank-sum test, DMS: P = 5.5 × 10−4, DCS: P = 1.4 × 10−4 across 77 sessions).

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Peters, A.J., Fabre, J.M.J., Steinmetz, N.A. et al. Striatal activity topographically reflects cortical activity. Nature (2021). https://doi.org/10.1038/s41586-020-03166-8

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