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Opponent control of behavior by dorsomedial striatal pathways depends on task demands and internal state

Nature Neuroscience volume 25, pages 345–357 (2022)Cite this article

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Abstract

A classic view of the striatum holds that activity in direct and indirect pathways oppositely modulates motor output. Whether this involves direct control of movement, or reflects a cognitive process underlying movement, remains unresolved. Here we find that strong, opponent control of behavior by the two pathways of the dorsomedial striatum depends on the cognitive requirements of a task. Furthermore, a latent state model (a hidden Markov model with generalized linear model observations) reveals that—even within a single task—the contribution of the two pathways to behavior is state dependent. Specifically, the two pathways have large contributions in one of two states associated with a strategy of evidence accumulation, compared to a state associated with a strategy of repeating previous choices. Thus, both the demands imposed by a task, as well as the internal state of mice when performing a task, determine whether dorsomedial striatum pathways provide strong and opponent control of behavior.

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Fig. 1: Pathway-specific dorsomedial striatum inhibition has no detectable impact on movement in mice navigating a virtual corridor.
Fig. 2: A set of virtual reality T-mazes has similar sensory features and identical motor requirements but different cognitive demands.
Fig. 3: Inhibition of dorsomedial striatum but not nucleus accumbens pathways has strong and opposing influence on choice during an evidence accumulation task, while having weaker effects during task variants with diminished cognitive demands.
Fig. 4: A GLM reveals that sensory evidence, dorsomedial striatum pathway inhibition and trial history predict choice during the evidence accumulation task, but does not precisely recapitulate the shape of the psychometric curve.
Fig. 5: A GLM–HMM better explains choice during the evidence accumulation task than the GLM, particularly on trials with dorsomedial striatum pathway inhibition.
Fig. 6: A GLM–HMM uncovers states during the evidence accumulation task with different weighting on sensory evidence, choice history and dorsomedial striatum pathway inhibition.
Fig. 7: Diversity across sessions in the timing and number of GLM–HMM state transitions.

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Data availability

The data that support the findings of this study are publicly available on figshare at https://doi.org/10.6084/m9.figshare.17299142.v1.

Code availability

Code for general use applications of GLM–HMM analyses developed in this study, including all applications to the present dataset, are available on GitHub at https://github.com/irisstone/glmhmm/. Code to analyze data and regenerate all other plots in this study is publicly available at https://github.com/ssbolkan/BolkanStoneEtAl.

References

  1. Alexander, G. E. & Crutcher, M. D. Functional architecture of basal ganglia circuits: neural substrates of parallel processing. Trends Neurosci. 13, 266–271 (1990).

    Article  CAS  PubMed  Google Scholar 

  2. Kravitz, A. V. et al. Regulation of parkinsonian motor behaviours by optogenetic control of basal ganglia circuitry. Nature 466, 622–626 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Roseberry, T. K. et al. Cell-type-specific control of brainstem locomotor circuits by basal ganglia. Cell 164, 526–537 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Bartholomew, R. A. et al. Striatonigral control of movement velocity in mice. Eur. J. Neurosci. 43, 1097–1110 (2016).

    Article  PubMed  Google Scholar 

  5. Bakhurin, K. I. et al. Opponent regulation of action performance and timing by striatonigral and striatopallidal pathways. eLife 9, e54831 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Lobo, M. K. et al. Cell-type-specific loss of BDNF signaling mimics optogenetic control of cocaine reward. Science 330, 385–390 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kravitz, A. V., Tye, L. D. & Kreitzer, A. C. Distinct roles for direct and indirect pathway striatal neurons in reinforcement. Nat. Neurosci. 15, 816–818 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Yttri, E. A. & Dudman, J. T. Opponent and bidirectional control of movement velocity in the basal ganglia. Nature 533, 402–406 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Nonomura, S. et al. Monitoring and updating of action selection for goal-directed behavior through the striatal direct and indirect pathways. Neuron 99, 1302–1314 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Lee, J., Wang, W. & Sabatini, B. L. Anatomically segregated basal ganglia pathways allow parallel behavioral modulation. Nat. Neurosci. 23, 1388–1398 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Cui, L. et al. Asymmetrical choice-related ensemble activity in direct and indirect-pathway striatal neurons drives perceptual decisions. Preprint at bioRxiv https://doi.org/10.1101/2021.11.16.468594 (2021).

  13. Tang, Y. et al. Opposing regulation of short-term memory by basal ganglia direct and indirect pathways that are coactive during behavior. Preprint at bioRxiv https://doi.org/10.1101/2021.12.15.472735 (2021).

  14. Parker, J. G. et al. Diametric neural ensemble dynamics in parkinsonian and dyskinetic states. Nature 557, 177–182 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Chen, Z. et al. Direct and indirect pathway neurons in ventrolateral striatum differentially regulate licking movement and nigral responses. Cell Rep. 37, 109847 (2021).

    Article  CAS  PubMed  Google Scholar 

  16. Lee, H. J. et al. Activation of direct and indirect pathway medium spiny neurons drives distinct brain-wide responses. Neuron 91, 412–424 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. London, T. D. et al. Coordinated ramping of dorsal striatal pathways preceding food approach and consumption. J. Neurosci. 38, 3547–3558 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Lau, B. & Glimcher, P. W. Value representations in the primate striatum during matching behavior. Neuron 58, 451–463 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ding, L. & Gold, J. I. Separate, causal roles of the caudate in saccadic choice and execution in a perceptual decision task. Neuron 75, 865–874 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Barnes, T. D., Kubota, Y., Hu, D., Jin, D. Z. & Graybiel, A. M. Activity of striatal neurons reflects dynamic encoding and recoding of procedural memories. Nature 437, 1158–1161 (2005).

    Article  CAS  PubMed  Google Scholar 

  23. Yin, H. H. et al. Dynamic reorganization of striatal circuits during the acquisition and consolidation of a skill. Nat. Neurosci. 12, 333–341 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Akhlaghpour, H. et al. Dissociated sequential activity and stimulus encoding in the dorsomedial striatum during spatial working memory. eLife 5, e19507 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Pinto, L. et al. An Accumulation-of-evidence task using visual pulses for mice navigating in virtual reality. Front. Behav. Neurosci. 12, 36 (2018).

  26. Owen, S. F., Liu, M. H. & Kreitzer, A. C. Thermal constraints on in vivo optogenetic manipulations. Nat. Neurosci. 22, 1061–1065 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Cruz, B. F., Soares, S. & Paton, J. J. Striatal circuits support broadly opponent aspects of action suppression and production. Preprint at bioRxiv https://doi.org/10.1101/2020.06.30.180539 (2020).

  28. Kupchik, Y. M. et al. Coding the direct/indirect pathways by D1 and D2 receptors is not valid for accumbens projections. Nat. Neurosci. 18, 1230–1232 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bengio, Y. & Frasconi, P. An input output HMM architecture. Adv. Neural Inf. Process. Syst. 7, 427–234 (1994).

    Google Scholar 

  30. Escola, S., Fontanini, A., Katz, D. & Paninski, L. Hidden Markov models for the stimulus-response relationships of multistate neural systems. Neural Comput. 23, 1071–1132 (2011).

    Article  PubMed  Google Scholar 

  31. Calhoun, A. J., Pillow, J. W. & Murthy, M. Unsupervised identification of the internal states that shape natural behavior. Nat. Neurosci. 22, 2040–2049 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ashwood, Z. C. et al. Mice alternate between discrete strategies during perceptual decision-making. Nat. Neurosci. 25, 201–212 (2022).

    Article  CAS  PubMed  Google Scholar 

  33. Donahue, C. H., Liu, M. & Kreitzer, A. C. Distinct value encoding in striatal direct and indirect pathways during adaptive learning. Preprint at bioRxiv https://doi.org/10.1101/277855 (2018).

  34. Shin, J. H., Kim, D. & Jung, M. W. Differential coding of reward and movement information in the dorsomedial striatal direct and indirect pathways. Nat. Commun. 9, 404 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Delevich, K., Hoshal, B., Collins, A. G. & Wilbrecht, L. Choice suppression is achieved through opponent but not independent function of the striatal indirect pathway in mice. Preprint at bioRxiv https://doi.org/10.1101/675850 (2020).

  36. Frank, M. J. & Badre, D. Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: computational analysis. Cereb. Cortex 22, 509–526 (2012).

    Article  PubMed  Google Scholar 

  37. Cui, G. et al. Concurrent activation of striatal direct and indirect pathways during action initiation. Nature 494, 238–242 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Barbera, G. et al. Spatially compact neural clusters in the dorsal striatum encode locomotion relevant information. Neuron 92, 202–213 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Sippy, T., Lapray, D., Crochet, S. & Petersen, C. C. H. Cell-type-specific sensorimotor processing in striatal projection neuorns during goal-directed behavior. Neuron 88, 298–305 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Jin, X., Tecuapetla, F. & Costa, R. M. Basal ganglia subcircuits distinctively encode the parsing and concatenation of action sequences. Nat. Neurosci. 17, 423–430 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Soares-Cunha, C. et al. Activation of D2 dopamine receptor-expressing neurons in the nucleus accumbens increases motivation. Nat. Commun. 7, 1–11 (2016).

    Article  Google Scholar 

  42. Cole, S. L., Robinson, M. J. F. & Berridge, K. C. Optogenetic self-stimulation in the nucleus accumbens: D1 reward versus D2 ambivalence. PLoS ONE 13, e0207694 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Vicente, A. M., Galvão-Ferreira, P., Tecuapetla, F. & Costa, R. M. Direct and indirect dorsolateral striatum pathways reinforce different action strategies. Curr. Biol. 26, R267–R269 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Tecuapetla, F., Jin, X., Lima, S. Q. & Costa, R. M. Complementary contributions of striatal projection pathways to action initiation and execution. Cell 166, 703–715 (2016).

    Article  CAS  PubMed  Google Scholar 

  45. Geddes, C. E., Li, H. & Jin, X. Optogenetic editing reveals the hierarchical organization of learned action sequences. Cell 174, 32–43 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 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 98, 669 (2018).

    Article  CAS  PubMed  Google Scholar 

  47. Peak, J., Chieng, B., Hart, G. & Balleine, B. W. Striatal direct and indirect pathway neurons differentially control the encoding and updating of goal-directed learning. eLife 9, e58544 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Eldar, E., Morris, G. & Niv, Y. The effects of motivation on response rate: a hidden semi-Markov model analysis of behavioral dynamics. J. Neurosci. Methods 201, 251–261 (2011).

    Article  PubMed  Google Scholar 

  49. Ahilan, S. et al. Learning to use past evidence in a sophisticated world. PLoS Comput. Biol. 15, e1007093 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Goshen, I. et al. Dynamics of retrieval strategies for remote memories. Cell 147, 678–689 (2011).

    Article  CAS  PubMed  Google Scholar 

  51. Fetsch, C. R. et al. Focal optogenetic suppression in macaque area MT biases direction discrimination and decision confidence, but only transiently. eLife 7, e36523 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Roy, N. A. et al. Extracting the dynamics of behavior in sensory decision-making experiments. Neuron 109, 597–610 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Aronov, D. & Tank, D. W. Engagement of neural circuits underlying 2D spatial navigation in a rodent virtual reality system. Neuron 84, 442–456 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Hanks, T. D. et al. Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature 520, 220–223 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wichmann, F. A. & Hill, N. J. The psychometric function: I. Fitting, sampling, and goodness of fit. Percept. Psychophys. 63, 1293–1313 (2001).

    Article  CAS  PubMed  Google Scholar 

  56. Pillow, J. W., Ahmadian, Y. & Paninski, L. Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains. Neural Comput. 23, 1–45 (2011).

    Article  PubMed  Google Scholar 

  57. Bishop, C. M. Chapter 13: Sequential Data. in Pattern Recognition and Machine Learning (Information Science and Statistics) (eds. Jordan, M., Kleinberg, J. & Schölkopf, B.) 605-652 (Springer-Verlag, 2006).

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Acknowledgements

We thank the entire BRAINCoGs team as well as the laboratories of I.B.W. and J.W.P. for feedback on this work. We thank S. Stein and S. Baptista for technical support in animal training, and C. Kopecs for technical assistance. This work was supported by grants from the National Institutes of Health R01 DA047869 (to I.B.W.), F32MH118792 (to S.S.B.), F32NS101871 (to L.P.), K99MH120047 (to L.P.), U19 NS104648-01 (to J.W.P. and I.B.W.) and ARO W911NF1710554 (to I.B.W.), the Brain Research Foundation (to I.B.W.), Simons Collaboration on the Global Brain (to J.W.P. and I.B.W.), 1R01MH106689 (to I.B.W.) and the New York Stem Cell Foundation (to I.B.W.). I.B.W. is an NYSCF–Robertson Investigator.

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Author notes

  1. These authors contributed equally: Scott S. Bolkan, Iris R. Stone.

Authors and Affiliations

  1. Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA

    Scott S. Bolkan, Iris R. Stone, Lucas Pinto, Zoe C. Ashwood, Jorge M. Iravedra Garcia, Alison L. Herman, Priyanka Singh, Akhil Bandi, Julia Cox, Christopher A. Zimmerman, Jounhong Ryan Cho, Ben Engelhard, Jonathan W. Pillow & Ilana B. Witten

  2. Department of Psychology, Princeton University, Princeton, NJ, USA

    Jonathan W. Pillow & Ilana B. Witten

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Contributions

S.S.B. performed the experiments. I.R.S. developed the GLM–HMM. S.S.B. and I.R.S. analyzed the data with guidance from J.W.P. and I.B.W. L.P., Z.C.A. and B.E. provided technical and analysis support. J.M.I., A.L.H. and P.S. aided behavioral training. A.B. performed histology. J.C. provided support for electrophysiology. C.A.Z. and J.R.C. provided support for the virtual corridor. S.S.B. and I.B.W. conceived the experimental work. S.S.B., I.R.S., J.W.P. and I.B.W. interpreted the results and wrote the manuscript.

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Correspondence to Jonathan W. Pillow or Ilana B. Witten.

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Extended data

Extended Data Fig. 1 Optogenetic inhibition of DMS pathways is effective, generating little post-inhibitory rebound, nor excitation during the inhibition period.

(a) Schematic of viral delivery of AAV5-eF1a-DIO-NpHR to the dorsomedial striatum (DMS) of A2a-Cre or D1R-Cre mice. (b,i) Schematic of electrophysiological recording and laser delivery (532-nm, 5-mW) to the DMS in awake, head-fixed mice ambulating on a running wheel. (b,ii) Example recording electrode tracks and cre-dependent NpHR expression in an A2a-Cre mouse targeting the indirect pathway of the DMS. (b,iii) As in b,ii but in a D1R-Cre mouse targeting the DMS direct pathway. (b,iv) Schematic of silicon optrode recording tip, including tapered optical fiber coupled to a 32-channel silicon probe. (c) Two example peristimulus time histograms (PSTH) (top) and raster plots of trial-by-trial spike times (bottom) from single neurons recorded from the DMS of an A2a-Cre mouse. Inset at top displays average spike waveform (black) and 100 randomly sampled spike waveforms (grey) for each neuron. A trial consisted of 5-s without laser (pre, −5 to 0-s), 5-s of 532-nm light (5-mW) delivery (on, 0 to 5-s), followed by a 10-s ITI (40 trials per recording site). The first 2-s following laser offset (post, 5-7-s) was used to assess post-inhibitory effects. (d) Left: Histogram of change in average firing rate (on-pre, Hz) for all neurons (n = 60) recorded from the DMS of A2a-Cre mice (n = 3). Colors indicate non-significant (black, n = 38 neurons), significantly decreased (red, n = 18 neurons) or increased (green, n = 4 neurons) changes in firing rate determined via paired, two-tailed signrank comparison of average across-trial baseline (pre) or laser (on) firing rates. A Bonferroni-corrected significance threshold was used to account for multiple neuron comparisons (p < 0.00083, or p = 0.05/60 neuron comparisons). Right: same as left but for change in firing rate (post-pre, Hz): non-significant (n = 55 neurons), significantly decreased (n = 4) or increased (n = 1). Insets display pie-chart summaries of the proportion of non-significant (black unfilled), significantly decreased (red) or increased (green) neurons. (e) Left: Mean  ±1 s.e.m. z-scored firing rate across all non-significantly modulated on vs pre (black, n = 38) or significantly decreased on vs pre (red, n = 18) neurons recorded from A2a-Cre mice. Right: same as left but for all non-significantly modulated post vs pre (black, n = 55) or significantly decreased post vs pre (red, n = 4) neurons. (f) Same as c but for example neurons recorded from the DMS of D1R-Cre mice. (g) Same as d but for all neurons (n = 50) recorded from the DMS of D1R-Cre mice (n = 2). Left (on-pre): non-significant (n = 27), significantly decreased (n = 21), or increased (n = 2). Right (post-pre): non-significant (n = 46), significantly decreased (n = 2) or increased (n = 2). A Bonferroni-corrected significance threshold was used to account for multiple neuron comparisons (p < 0.001, or p = 0.05/50 neuron comparisons). (h) same as e but for neurons recorded from the DMS of D1R-Cre mice.

Extended Data Fig. 2 Non-significant motor effects of DMS pathway inhibition compared to non-opsin expressing controls during virtual corridor navigation.

(a) Schematic of virtual corridor and unilateral delivery of 532-nm light (5-mW) restricted to 0-200 cm. (b) Difference in average y-velocity (cm/s) during laser on and off trials (on-off) for mice receiving indirect (n = 7 mice, n = 1,712 laser off and n = 1,288 laser on trials) or direct (n = 6 mice, n = 1,088 laser off and n = 757 laser on trials) pathway inhibition of the DMS, or DMS illumination alone (no opsin; n = 5 mice, n = 1,178 laser off and n = 827 laser on trials). p-value denotes significance of one-way ANOVA of group on delta y-velocity (p = 0.98, F2,13 = 0.02). (c) Same as b but for difference in x-position (cm, on-off) contralateral to the laser hemisphere (p = 0.60, F2,13 = 0.53). (d) Same as c but for difference in view angle (deg, on-off) contralateral to the laser hemisphere (p = 0.20, F2,13 = 1.90). (e) Same as c but for difference in mean standard deviation in view angle (deg, on-off). The mean of the standard deviation in view angles sampled in 5-cm steps from 0-300 cm were calculated per trial, and then averaged across all laser off (or on) trials for a mouse (p = 0.94, F2,16 = 0.06). Indirect: n = 7 mice, n = 2,109 laser off and n = 1,574 laser on trials; direct: n = 6 mice, n = 1,330 laser off and n = 930 laser on trials; no opsin: n = 6 mice, n = 1,688 laser off and n = 1,199 laser on trials). (f) As in e but for difference in total distance travelled (cm, on-off) to complete a trial (p = 0.93, F2,16 = 0.08). (g) As in e but for the difference in percentage of trials with excess travel (defined as >10% of corridor length to reward, or >330 cm) (p = 0.76, F2,18 = 0.28). Throughout solid black lines indicate mean ±1 s.e.m. across mice and transparent ‘x’ denote individual mouse mean throughout.

Extended Data Fig. 3 Similar motor performance across three virtual reality T-mazes.

Schematic of three virtual reality (VR)-based T-mazes that differ in task requirements. (b) Average y-velocity (cm/s) of mice during the cue region (0-200 cm) of the accumulation of evidence task (black, n = 32 mice, n = 52,381 trials), no distractors (ctrl #1) task (magenta: n = 32 mice, 56,783 trials), or permanent cues (ctrl #2) task (cyan: n = 20 mice, n = 27,870 trials). Solid bars denote mean ±1 s.e.m. across mice while transparent ‘x’ denotes individual mouse mean. p-value denotes one-way ANOVA of task on y-velocity (p = 0.51, F2,80 = 0.67). (c) Same as b but for average x-position (cm) during the cue region (0-200 cm) on left and right choice trials. p-value denotes one-way ANOVA of task on x-position (left choice: p = 0.50, F2,80 = 0.70; right choice: p = 0.37, F2,80 = 1.0). (d) Same as b but for average view angle (degrees) during the cue region (0-200 cm) on left and right choice trials (left choice: p = 0.53, F2,80 = 0.64; right choice: p = 0.70, F2,80 = 0.37). (e) As in b but for average percent of trials with excess travel (defined as travel >10% of maze stem, or >330 cm). Accumulation of evidence: n = 32 mice, n = 53,833 trials; control #2 (no distractors): n = 32 mice, n = 60,074 trials; control #2 (permanent cues): n = 20 mice, n = 29,192 trials. p-value denotes one-way ANOVA of task on excess travel (p = 0.06, F2,81 = 2.9). (f) As in b but for mean standard deviation in view angle (degrees) per trial (n as in e). p-value denotes one-way ANOVA of task on view angle deviation (p = 0.07, F2,81 = 2.8). (g) Average accuracy of decoding left/right choice based on the trial-by-trial x-position (cm) of mice as a function of y-position in the maze (0-300 cm in 25-cm bins). Training and test trial sets were selected within individual mice (80% train, 5-fold cross-validation, re-sampled 10 times). Left: Each ‘x’ depicts decoding accuracy at each y-position bin for individual mice performing the evidence accumulation (black), no distractors (ctrl #1, magenta), or permanent cues (ctrl #2, cyan) tasks. Right: Group mean and ±1 s.e.m. across mice for each task (n as in b). (h) Same as f but for average accuracy of decoding left/right choice based on the trial-by-trial view angle (degrees) of mice (n as in b). (i) Average accuracy of decoding left/right choice based on the trial-by-trial x-position (cm) of mice as a function of y-position in the maze (0-300 cm in 25-cm bins). Training trial sets were randomly selected across all mice (50% total trials, re-sampled 50 times) performing either the accumulation of evidence (left, AoE, black), no distractors (middle, ctrl#1, magenta), or permanent cues (right, ctrl#2, cyan) tasks. Testing trial sets were the 50% of held-out trials in the task used for training, or all trials in the alternate tasks. Top: Each ‘x’ depicts average decoding accuracy across all training/tests sets at each y-position bin for individual mice performing the evidence accumulation (black), no distractors (ctrl #1, magenta), or permanent cues (ctrl #2, cyan) tasks. Right: Group mean and ±1 s.e.m. across mice for each task (n as in a). (j) Same as I but for average accuracy of decoding left/right choice based on the trial-by-trial view angle (degrees) of mice (n as in b).

Extended Data Fig. 4 Effects of pathway-specific DMS and NAc inhibition on psychometric performance across virtual reality tasks.

(a) Schematic of unilateral indirect pathway DMS inhibition with choice defined ipsilateral or contralateral to the hemisphere receiving 532-nm laser illumination. (b) Schematic of three virtual reality based decision-making tasks (left: accumulation of evidence; middle: control #1, no distractors; right: control #2, permanent cues) and laser illumination restricted to the cue region (0-200 cm). (c) Percent of contralateral choice trials as a function of the difference in sensory cues (contralateral-ipsilateral) binned in increments of 5 from −15 to 15. Transparent lines indicate individual mouse mean during laser off (grey) and on (green) trials for mice receiving indirect-pathway DMS inhibition during the evidence accumulation (black, left), no distractors (magenta, ctrl#1, middle), or permanent cues (cyan, ctrl#2, right). Thick lines indicate mean ±1 s.e.m. across mice at each evidence bin during laser off (black) and on (green) trials. (d) Same as a but for mice receiving unilateral direct pathway DMS inhibition. (e) same as b. (f) Same as c but for mice receiving direct pathway DMS inhibition. (g) Same as a but for mice receiving unilateral DMS illumination in the absence of NpHR (no opsin). (h) Same as b. (i) same as c but for mice receiving unilateral DMS illumination in the absence of NpHR (no opsin). (j) Schematic of unilateral inhibition of NAc indirect (left) or direct (middle) pathway, or NAc illumination in the absence of NpHR (no opsin). (k) Schematic of accumulation of evidence task and delivery of 532-nm light during the cue region (0-200 cm). (l) As in c but for psychometric comparison between groups receiving NAc indirect or direct pathway inhibition, or NAc illumination in the absence of NpHR (no opsin).

Extended Data Fig. 5 Effects of pathway-specific DMS inhibition on choice are larger in the most demanding task, and stronger than effects of pathway-specific NAc inhibition.

(a) Schematic of three virtual reality based decision-making tasks (left: accumulation of evidence; middle: control #1, no distractors; right: control #2, permanent cues). (b) Schematic of unilateral indirect pathway DMS inhibition with choice defined ipsilateral or contralateral to the hemisphere receiving 532-nm laser illumination (top). Difference in choice bias (%, contralateral - ipsilateral) between laser on and off trials (on-off) in mice performing the accumulation of evidence (AoE, black), no distractors (ctrl #1, magenta), or permanent cues (ctrl #2, cyan) tasks. p-value denotes one-way ANOVA of task on delta (on-off) choice bias (p = 1.0 ×10−5, F2,22 = 20.2). Post-hoc comparisons reflect unpaired, two-tailed Wilcoxon ranksum tests on delta (on-off) choice bias (AoE, n = 11, vs ctrl #1, n = 7: p = 8.0 ×10−4, z = 3.4; AoE vs ctrl #2, n = 7: p = 0.001, z = 3.3). (c) Same as b but for direct pathway DMS inhibition. p-value denotes one-way ANOVA of task on delta (on-off) choice bias (p = 0.001, F2,23 = 9.4). Post-hoc comparisons reflect two-tailed, unpaired Wilcoxon ranksum tests (AoE, n = 10, vs ctrl #1, n = 9: p = 0.002, z = −3.0; AoE vs ctrl #2, n = 7: p = 0.005, z = −2.8). (d) Same as b but for DMS illumination in the absence of NpHR (no opsin). p-value denotes one-way ANOVA of task on delta (on-off) choice bias (p = 0.09, F2,16 = 2.8). Post-hoc comparisons reflect two-tailed, unpaired Wilcoxon ranksum tests (AoE, n = 11, vs ctrl #1, n = 4: p = 0.65, z = 0.46; AoE vs ctrl #2, n = 6: p = 0.06, z = 1.8). (e) Schema of evidence accumulation task (left), unilateral inhibition of indirect pathway in the DMS (middle left) or NAc (middle right), and delta (on-off) choice bias in mice receiving indirect pathway DMS (n = 11) or NAc (n = 9) inhibition (right). Statistical comparison reflects two-tailed, unpaired Wilcoxon ranksum test (DMS vs NAc: p = 2.6 ×10−4, z = 3.6). (f) Same as e but for direct pathway DMS (n = 10) or NAc (n = 10) inhibition. Statistical comparison reflects two-tailed, unpaired Wilcoxon ranksum test (DMS vs NAc: p = 1.8 × 10−4, z = −3.7). Throughout solid bars denote mean ±1 s.e.m. across mice and transparent ‘x’ denote individual mouse means.

Extended Data Fig. 6 Inhibition of DMS pathways has limited impact on motor performance across VR-based decision-making tasks.

(a) Mean ±1 s.e.m.. y-velocity (cm/s) as a function of y-position (0- 300 cm in 25 cm bins) during laser off (black) or laser on (green) trials across mice receiving DMS indirect pathway inhibition during the evidence accumulation (left: n = 11 mice, n = 16,935 laser off and n = 3,390 laser on trials), no distractors (middle, ctrl #1: n = 7 mice, n = 13,706 laser off and n = 3,288 laser on trials) or permanent cues (right, ctrl #2: n = 6 mice, n = 4,033 laser off and n = 929 laser on trials). (b) Same as a but for mice receiving direct pathway inhibition during the evidence accumulation (left: n = 10 mice, n = 14,030 laser off and n = 3,103 laser on trials), no distractors (middle, ctrl #2: n = 8 mice, n = 14,647 laser off and n = 3,682 laser on trials) or permanent cues (right, ctrl #3: n = 7 mice, n = 6,061 laser off and n = 1,494 laser on trials) tasks. (c) Same as a but for mice receiving DMS illumination in the absence of NpHR (no opsin) during the evidence accumulation (left: n = 11 mice, n = 21,422 laser off and n = 5,113 laser on trials), no distractors (middle, ctrl #1: n = 4 mice, n = 3,654 laser off and n = 901 laser on trials), or permanent cues (right, ctrl #2: n = 4 mice, n = 3,975 laser off and n = 923 laser on trials) tasks. (d) Mean ±1 s.e.m. in delta (on-off) distance (cm) traveled (left) and delta (on-off) trials (%) with excess travel greater than 10% of maze stem (or > 330 cm) (right) in mice receiving indirect pathway inhibition during the evidence accumulation (black, n = 11 mice, n = 22,090 laser off and n = 4,378 laser on trials), no distractors (magenta, n = 7 mice, n = 14,826 laser off and n = 3,591 laser on trials), or permanent cues (n = 6 mice, n = 4,447 laser off and n = 1050 laser on trials) tasks. p-value denotes one-way ANOVA of task on delta (on-off) distance (p = 0.45, F2,22 = 0.81) or excess travel (p = 0.52, F2,22 = 0.66). (e) Same as d but for delta (on-off) distance (cm) traveled (left) or delta percent trials with excess travel (right) in mice receiving direct pathway inhibition during the evidence accumulation (black, n = 10 mice, n = 20,914 laser off and n = 4,721 laser on trials), no distractors (magenta, n = 9 mice, n = 15,778 laser off and n = 3,992 laser on trials), or permanent cues (n = 7 mice, n = 6,430 laser off and n = 1,591 laser on trials) tasks. p-value denotes one-way ANOVA of task on delta (on-off) distance (p = 0.13, F2,23 = 2.2) or excess travel (p = 0.50, F2,23 = 0.71). (f) Same as d but for delta (on-off) in distance (cm) traveled (left) or percent trials with excess travel (right) in mice receiving DMS illumination in the absence of NpHR (no opsin) during the evidence accumulation (black, n = 11 mice, n = 28,557 laser off and n = 6,772 laser on trials), no distractors (magenta, n = 5 mice, n = 4,118 laser off and n = 1,002 laser on trials), or permanent cues (n = 6 mice, n = 4,360 laser off and n = 1,038 laser on trials) tasks. p-value denotes one-way ANOVA of task on delta (on-off) distance (p = 0.06, F2,19 = 3.3) or excess travel (p = 0.23, F2,19 = 1.6). (g) Same as d but for delta (on-off) in per-trial standard deviation in view angle in mice receiving DMS indirect pathway inhibition across tasks (p = 0.34, F2,22 = 1.1, n as in d). (h) Same as g but for mice receiving DMS direct pathway inhibition across tasks (p = 0.27, F2,23 = 1.4, n as in e). (i) Same as g but for mice receiving DMS illumination (no opsin) in the absence of NpHR (p = 0.03, F2,19 = 4.3, n as in f). (j) Delta (on-off) x-position (cm) (left) or view angle (degrees) (right) during the cue region (0-200 cm) in mice receiving DMS indirect pathway inhibition during the accumulation of evidence (black), no distractors (control #1, magenta), or permanent cues (control #2, cyan) tasks (n as in a). One-way ANOVA of task on delta (on-off) x-position (p = 0.01, F2,22 = 5.6). Post-hoc, two-tailed, unpaired Wilcoxon ranksum test on delta (on-off) x-position (AoE v control #1: p = 0.2, z = 1.3; AoE v control #2: p = 0.004, z = 2.9; control #1 v control #2: p = 0.13, z = 1.5). One-way ANOVA of task on delta (on-off) view angle (p = 0.14, F2,22 = 2.2). Post-hoc, two-tailed, unpaired Wilcoxon ranksum test on delta (on-off) view angle (AoE v control #1: p = 0.58, z = 0.5; AoE v control #2: p = 0.24, z = 1.78; control #1 v control #2: p = 0.52, z = 0.6). (k) Same as j but for mice receiving DMS direct pathway inhibition (n as in b). One-way ANOVA of task on delta (on-off) x-position (p = 0.08, F2,23 = 2.8). Post-hoc, two-tailed unpaired Wilcoxon ranksum test on delta (on-off) x-position (AoE v control #1: p = 0.13, z = −1.5; AoE v control #2: p = 0.1, z = −1.6; control #1 v control #2: p = 0.46, z = −0.7). One-way ANOVA of task on delta (on-off) view angle (p = 0.02, F2,23 = 3.6). Post-hoc, two-tailed, unpaired Wilcoxon ranksum test on delta (on-off) view angle (AoE v control #1: p = 0.21, z = −1.3; AoE v control #2: p = 0.03, z = −2.1; control #1 v control #2: p = 0.24, z = −1.6). (l) Same as j but for mice receiving DMS illumination in the absence of NpHR (no opsin, n as in c). One-way ANOVA of task on delta (on-off) x-position (p = 0.24, F2,18 = 1.54). Post-hoc, two-tailed, unpaired Wilcoxon ranksum test on delta (on-off) x-position (AoE v control #1: p = 0.21, z = −1.24; AoE v control #2: p = 0.51, z = 0.06; control #1 v control #2: p = 0.04, z = 2.0). One-way ANOVA of task on delta (on-off) view angle (p = 0.23, F2,18 = 1.56). Post-hoc, two-tailed, unpaired Wilcoxon ranksum test on delta (on-off) view angle (AoE v control #1: p = 0.84, z = 0.19; AoE v control #2: p = 0.20, z = 1.2; control #1 v control #2: p = 0.24, z = 1.7). Throughout solid bars denote mean ±1 s.e.m. and transparent ‘x’ indicates individual mouse mean.

Extended Data Fig. 7 Model selection and control data analyses for the GLM-HMM.

(a) Comparison of the log-likelihood of the data using GLM-HMMs with different numbers of states for mice inhibited in the DMS direct pathway (dark gray), or indirect pathway (light gray), and mice without DMS opsin (black). All values are relative to the log-likelihood of the standard GLM (1-state GLM-HMM). Values are calculated in bits per session (see Methods). Solid curves denote mean  ±s.e.m. of five different test sets. Held-out data for test sets was selected as a random 20% of sessions, using an approximately equal number of sessions for each mouse. (b) Same as a but with different numbers of previous choice covariates using a three-state GLM-HMM. (c) Comparison of the log-likelihood of simulated data using GLM-HMMs with different numbers of states. Data was simulated from a two-state GLM-HMM that had been fit to data for mice inhibited in the indirect pathway of the DMS and then cross-validation performed either on the entire simulated dataset (~54000 trials, left) or a subset of 5% of the data (2600 trials, right). All values are relative to the log-likelihood of a GLM (one-state GLM-HMM). Values are calculated in bits per session (see Methods). Solid curves denote the average of five different test sets. Held-out data for test sets was selected as a random 20% of sessions. Performing cross validation on a small subset of the data serves to demonstrate that the log-likelihood does in fact decrease as the model starts to overfit. This is difficult to see with large datasets where overfitting is less of a concern and therefore the log-likelihood begins to flatten rather than decrease. (d) Fitted GLM weights for the four-state GLM-HMM using aggregated data from all mice inhibited in the indirect pathway of the DMS. Error bars denote (±1) posterior standard deviation for each weight. The magnitude of the weight represents the relative importance of that covariate in predicting choice, whereas the sign of the weight indicates the side bias. (e) Same as d but for mice inhibited in the DMS direct pathway. (f) GLM weights fitted to a concatenated dataset consisting of the indirect, direct, and control (no opsin) groups. Solid lines on the left connect covariates that are shared across groups. Horizontal marks on the right denote laser weights, which were learned separately for each group. Error bars denote (±1) posterior standard deviation of each weight. (g) Percent of contralateral choice based on the difference in contralateral versus ipsilateral cues in each trial for mice in the control (no opsin) group. To compute psychometric functions, trials were assigned to each state by taking the maximum of the model’s posterior state probabilities on each trial. Error bars denote ±1 s.e.m. for light-off (solid) and light-on (dotted) trials. Solid curves denote logistic fits to the concatenated data across mice for light-off (solid) and light-on (dotted) trials. (h) Same as f but for data simulated from the model fit to mice in the control group (see Methods).

Extended Data Fig. 8 GLM-HMM state 3 is associated with indicators of task disengagement.

(a) The mean posterior probability of each state over the first and last 50 trials of a session, averaged across all sessions for mice inhibited in the indirect pathway of the DMS (n = 271 sessions). (b) Same as a but for mice receiving DMS direct pathway inhibition (n = 266 sessions). (c) Mean  ±s.e.m. of the cumulative reward received in a session prior to transitions into state 1 (n = 142), state 2 (n = 85), or state 3 (n = 237) in the indirect pathway group. One-way ANOVA of transition state on cumulative reward (p = 1.0 ×10−6; F2,460 = 14.2). Unpaired, two-tailed Wilcoxon ranksum comparison between transition types (state 1 vs 2: p = 0.96, z = −0.03; state 2 vs 3: p = 0, z = −3.6; state 1 vs 3: p = 0, z = −4.5). (d) Mean ±s.e.m. of the reward rate (uL/min) in a session prior to transitions into each state for the indirect pathway group. Reward rate was calculated as the sum of reward received from the start of the session up to the transition trial divided by the sum of the duration of all trials from the start of the session up to the transition trial. One-way ANOVA of transition state on reward rate (p = 4.1 ×10−14; F2,460 = 32.9). Unpaired, two-tailed Wilcoxon ranksum comparison between transition types (state 1 vs 2: p = 0.55, z = −0.6; state 2 vs 3: p = 0, z = −4.9; state 1 vs 3: p = 0, z = −7.4). (e) Same as c but for the direct pathway group (state 1: n = 140; state 2: n = 29; state 3: n = 159). One-way ANOVA of transition state on cumulative reward (p = 0.14; F2,325 = 1.99). Unpaired, two-tailed Wilcoxon ranksum comparison between transition types (state 1 vs 2: p = 0.35, z = −0.9; state 2 vs 3: p = 0.78, z = −0.27; state 1 vs 3: p = 0.08, z = −1.74). (f) Same as d but for the direct pathway group. One-way ANOVA of transition state on reward rate (p = 8.7 ×10−10; F2,325 = 22.6). Unpaired, two-tailed Wilcoxon ranksum comparison between transition types (state 1 vs 2: p = 0.49, z = 0.69; state 2 vs 3: p = 0.0, z = −4.2; state 1 vs 3: p = 0.0, z = −5.9). (g) The mean posterior probability of each state aligned  ±25 trials to trials in which reward was received for the indirect pathway group. (h) Same as g but state probability aligned to trials with excess travel (defined as 10% greater than the maze stem, or 330 cm). (i) Same as g but for the direct pathway group. (j) Same as h but for the direct pathway group.

Extended Data Fig. 9 Model simulations recapitulate transition and state characteristics of real data.

(a) Transition probabilities of the model fit to data from mice inhibited in the DMS indirect pathway (black) and from five simulated datasets generated from the model fit to mice inhibited in the DMS indirect pathway (gray), shown separately for diagonal (left) and off-diagonal (right) probabilities. (b) Same as a but for mice inhibited in the direct pathway of the DMS. (c) The posterior probability of each state over the first and last 50 trials of a session, averaged across all sessions for mice inhibited in the indirect pathway of the DMS (n = 271). Dark lines denote average for real data (same as Fig. 7E) and faded lines indicate averages for each of the five simulations. (d) Same as c but for mice inhibited in the direct pathway of the DMS (dark lines are the same as shown in Fig. 7 F). (e) Dwell times showing the average consecutive number of trials that mice inhibited in the DMS indirect pathway spent in each state for real data (left; range 39-86 trials, average session length 202 trials, same as shown in Fig. 7g) and one simulated dataset (right; range 60–71 trials, average session length 202 trials). Black dots show averages for individual mice (n = 13). We removed the last run in each session (including any run that lasted the entire session length) from the analysis, as the termination of the session prematurely truncated the length of those runs. (f) Same as e but without removing the last run in each session for real data (left; range 51–118 trials, average session length 202 trials) and one simulated dataset (right; range 65–93 trials, average session length 202 trials). (g) Same as e but for mice inhibited in the direct pathway of the DMS for real data (left; range 52–59 trials, average session length 185 trials, same as shown in Fig. 7g) and one simulated dataset (right; range 61–66 trials, average session length 185 trials). Black dots show averages for individual mice (n = 13). (h) Same as g but without removing the last run in each session for real data (left; 67–89 trials, average session length 185 trials) and one simulated dataset (right; range 74–110 trials, average session length 185 trials).

Extended Data Fig. 10 Comparison of motor performance across GLM-HMM states with and without pathway-specific DMS inhibition.

(a) Schematic denoting analysis of motor performance across GLM-HMM states on laser off trials only (panels b-g) in mice unilaterally coupled to a fiberoptic for indirect pathway inhibition. (b) Average y-velocity (cm/s) during laser off trials as a function of y-position in the maze (0-300 cm in 25-cm bins) in indirect pathway mice across GLM-HMM states (state 1, blue: n = 13,394 trials; state 2, yellow: n = 13,570 trials; state 3, red: n = 16,982 trials). (c) As in b but for average x-position (cm) on ipsilateral or contralateral choice trials (n as in b). (d) As in c but for average view angle (degrees) on ipsilateral and contralateral choice trials (n as in b). (e) Mean per-trial standard deviation in view angle during laser off trials across GLM-HMM states (state 1, blue: n = 13,854 trials; state 2, yellow: n = 14,201 trials; state 3, red: n = 18,258 trials). p-value denotes one-way repeated measures ANOVA of state on view angle deviation (p = 0.06, F2,24 = 3.2). (f) As in e but for average distance traveled (cm) per trial. p-value denotes one-way repeated measures ANOVA of state on distance (p = 0.02, F2,24 = 5.0, n as in e). (g) As in e but for average percent of trials with excess travel. p-value denotes one-way repeated measures ANOVA of state on excess travel (p = 0.0004, F2,24 = 10.9, n as in e). (h) Schematic denoting analysis of effects of indirect pathway DMS inhibition on motor performance across GLM-HMM states in i-n. (i) As in b but for average y-velocity on laser off (black) or laser on (green) trials across GLM-HMM states (n of laser off trials as in b-g, n of laser on trials: state 1, blue: n = 2,302 trials; state 2, yellow: n = 1,858 trials; state 3, red: n = 3,005 trials). (j) As in c but for delta (on-off) x-position (cm) during the cue region (0-200 cm) across GLM-HMM states in mice with indirect pathway inhibition. p-value denotes one-way repeated measures ANOVA of state on delta x-position (p = 3.2×10−4, F2,24 = 11.4, n as in i). Post-hoc comparisons reflect two-tailed, paired Willcoxon signed rank tests between states (state 1 vs state 3: p = 0.07, z = 1.7; state 1 vs state 2: p = 0.006, z = 2.7; state 2 vs state 3: p = 0.03, z = 2.4). (k) As in j but for delta (on-off) view angle (degrees). p-value denotes one-way repeated measures ANOVA of state on delta view angle (p = 1.2×10−5, F2,26 = 18.7, n as in i). Post-hoc comparisons reflect two-tailed, paired Willcoxon signed rank tests between states (state 1 vs state 3: p = 0.009, z = 2.6; state 1 vs state 2: p = 0.001, z = −3.18; state 2 vs state 3: p = 0.002, z = −3.1). (l) Same as e but for delta (on-off) mean per-trial view angle standard deviation across GLM-HMM states in mice with indirect pathway inhibition (n of laser off trials as in e-g, n of laser on trials: state 1, blue: n = 2,887 trials; state 2, yellow: n = 2,713 trials; state 3, red: n = 2,970 trials). p-value denotes one-way repeated measures ANOVA of state on delta view angle deviation (p = 0.97, F2,24 = 0.03, n as in l). (m) Same as f but for delta (on-off) in mean per-trial distance (cm) traveled across GLM-HMM states with indirect pathway inhibition (p = 0.68, F2,24 = 0.38, n as in l). (n) Same as g but for delta (on-off) in percent of trials with excess travel across GLM-HMM states with direct pathway inhibition (p = 0.08, F2,24 = 2.8, n as in l). (o) As in a but schematic denoting analysis of motor performance across GLM-HMM states on laser off trials only in mice unilaterally coupled to a fiberoptic for direct pathway inhibition in p-u. (p) As in b but for y-velocity (cm/s) on laser off trials across GLM-HMM states in direct pathway mice (state 1, blue: n = 12,294 laser off and n = 2,302 laser on trials; state 2, yellow: n = 9,201 laser off and n = 1,858 laser on trials; state 3, red: n = 16,239 laser off and n = 3,005 laser on trials). (q) As in c but x-position (cm) for direct pathway mice (n as in p). (r) As in d but for view angle (degrees) for direct pathway mice (n as in p). (s) As in e but for mean per-trial view angle standard deviation across GLM-HMM states in direct pathway mice (state 1, blue: n = 13,403 laser off and n = 2,508 laser on trials; state 2, yellow: n = 9,555 laser off and n = 1,969 laser on trials; state 3, red: n = 18,292 laser off and n = 3,450 laser on trials). p-value denotes one-way repeated measures ANOVA of state on per-trial view angle standard deviation (p = 0.12, F2,24 = 2.3). (t) As in f but for distance (cm) in direct pathway mice (p = 0.1, F2,24 = 2.5). (u) As in g but for percent trials with excess travel in direct pathway mice (p = 0.14, F2,24 = 2.1). (v) As in h but schematic denoting analysis of effects of direct pathway DMS inhibition on motor performance across GLM-HMM states in w-bb. (w) As in i but for the mean y-velocity (cm/s) on laser on (green) and off (black) trials across GLM-HMM states in direct pathway mice. (x) As in j but for the delta (on-off) x-position (cm) across GLM-HMM states in direct pathway mice. p-value denotes one-way repeated measures ANOVA of state on delta x-position (p = 7.9×10−5, F2,24 = 14.9). Posthoc comparisons reflect two-tailed, paired Willcoxon signed rank tests between states (state 1 vs state 3: p = 0.06, z = 1.8; state 1 vs state 2: p = 0.005, z = 2.8; state 2 vs state 3: p = 0.005, z = 2.8). (y) As in k but for delta (on-off) view angle (degrees) across GLM-HMM states in direct pathway mice. p-value denotes one-way repeated measures ANOVA of state on delta view angle (p = 2.6×10−4, F2,24 = 12.3). Posthoc comparisons reflect two-tailed, paired Willcoxon signed rank tests between states (state 1 vs state 3: p = 0.03, z = 2.3; state 1 vs state 2: p = 0.003, z = 2.98; state 2 vs state 3: p = 0.03, z = 2.1). (z) As in l but for delta (on-off) mean per-trial view angle standard deviation (degrees) in direct pathway mice (p = 0.40, F2,24 = 0.94, n as in s-u). (aa) as in m but for delta (on-off) in mean distance (cm) traveled in direct pathway mice (p = 0.43, F2,24 = 0.89). (bb) as in n but for delta (on-off) in percent trials with excess travel in direct pathway mice (p = 0.90, F2,24 = 0.1). Throughout solid colored bars denote mean ±1 s.e.m. while transparent grey lines reflect individual mouse mean.

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Bolkan, S.S., Stone, I.R., Pinto, L. et al. Opponent control of behavior by dorsomedial striatal pathways depends on task demands and internal state. Nat Neurosci 25, 345–357 (2022). https://doi.org/10.1038/s41593-022-01021-9

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