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Context-dependent representations of movement in Drosophila dopaminergic reinforcement pathways

Abstract

Dopamine plays a central role in motivating and modifying behavior, serving to invigorate current behavioral performance and guide future actions through learning. Here we examine how this single neuromodulator can contribute to such diverse forms of behavioral modulation. By recording from the dopaminergic reinforcement pathways of the Drosophila mushroom body during active odor navigation, we reveal how their ongoing motor-associated activity relates to goal-directed behavior. We found that dopaminergic neurons correlate with different behavioral variables depending on the specific navigational strategy of an animal, such that the activity of these neurons preferentially reflects the actions most relevant to odor pursuit. Furthermore, we show that these motor correlates are translated to ongoing dopamine release, and acutely perturbing dopaminergic signaling alters the strength of odor tracking. Context-dependent representations of movement and reinforcement cues are thus multiplexed within the mushroom body dopaminergic pathways, enabling them to coordinately influence both ongoing and future behavior.

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Fig. 1: Compartmentalized DAN activity and dopamine release during reward and locomotion.
Fig. 2: Differential encoding of behavior by mushroom body DANs.
Fig. 3: DAN activity during active odor tracking.
Fig. 4: Mushroom body DAN activity–behavior correlations depend on a fly’s navigational strategy.
Fig. 5: Analysis of dynamic DAN–motor correlations during odor pursuit.
Fig. 6: DAN responses and odor-tracking behavior are altered by satiety state.
Fig. 7: Optogenetic perturbations of DAN subsets acutely influences odor tracking.
Fig. 8: A model depicting how dynamic DAN–motor correlations emerge over different timescales.

Data availability

The data that support the findings of this study are available at

https://github.com/rutalaboratory/Zolin_etal_2021 and upon request.

Code availability

Code used for processing and modeling of the data is available at https://github.com/rutalaboratory/Zolin_etal_2021.

References

  1. Bargmann, C. I. & Marder, E. From the connectome to brain function. Nat. Methods 10, 483–490 (2013).

    CAS  PubMed  Article  Google Scholar 

  2. Schultz, W., Dayan, P. & Montague, P. R. A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).

    CAS  PubMed  Article  Google Scholar 

  3. Watabe-Uchida, M., Eshel, N. & Uchida, N. Neural circuitry of reward prediction error. Annu. Rev. Neurosci. 40, 373–394 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. Waddell, S. Reinforcement signalling in Drosophila; dopamine does it all after all. Curr. Opin. Neurobiol. 23, 324–329 (2013).

    CAS  PubMed  Article  Google Scholar 

  5. Da Silva, J. A., Tecuapetla, F., Paixão, V. & Costa, R. M. Dopamine neuron activity before action initiation gates and invigorates future movements. Nature 554, 244–248 (2018).

    PubMed  Article  CAS  Google Scholar 

  6. Howe, M. W. & Dombeck, D. A. Rapid signalling in distinct dopaminergic axons during locomotion and reward. Nature 535, 505–510 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. Panigrahi, B. et al. Dopamine is required for the neural representation and control of movement vigor. Cell 162, 1418–1430 (2015).

    CAS  PubMed  Article  Google Scholar 

  8. Salamone, J. D. & Correa, M. The mysterious motivational functions of mesolimbic dopamine. Neuron 76, 470–485 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. Beierholm, U. et al. Dopamine modulates reward-related vigor. Neuropsychopharmacology 38, 1495–1503 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. Parker, N. F. et al. Reward and choice encoding in terminals of midbrain dopamine neurons depends on striatal target. Nat. Neurosci. 19, 845–854 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. Engelhard, B. et al. Specialized coding of sensory, motor and cognitive variables in VTA dopamine neurons. Nature 570, 509–513 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. Yves, K., Jérôme, F., Clément, R. & Christian, L. Context-dependent multiplexing by individual VTA dopamine neurons. J. Neurosci. 40, 7489–7509 (2020).

    Article  Google Scholar 

  13. Coddington, L. T. & Dudman, J. T. Review learning from action: reconsidering movement signaling in midbrain dopamine. Neuron Act. Neuron 104, 63–77 (2019).

    CAS  Article  Google Scholar 

  14. Berke, J. D. What does dopamine mean? Nat. Neurosci. 21, 787–793 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. Watabe-Uchida, M. & Uchida, N. Multiple dopamine systems: weal and woe of dopamine. Cold Spring Harb. Symp. Quant. Biol. LXXXIII, 037648 (2019).

    Google Scholar 

  16. Aso, Y. et al. The neuronal architecture of the mushroom body provides a logic for associative learning. eLife 3, e04577 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  17. Claridge-Chang, A. et al. Writing memories with light-addressable reinforcement circuitry. Cell 139, 405–415 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Liu, C. et al. A subset of dopamine neurons signals reward for odour memory in Drosophila. Nature 488, 512–516 (2012).

    CAS  PubMed  Article  Google Scholar 

  19. Aso, Y. et al. Specific dopaminergic neurons for the formation of labile aversive memory. Curr. Biol. 20, 1445–1451 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. Yamagata, N. et al. Distinct dopamine neurons mediate reward signals for short- and long-term memories. Proc. Natl Acad. Sci. USA 112, 578–583 (2015).

    CAS  PubMed  Article  Google Scholar 

  21. Aso, Y. et al. Three dopamine pathways induce aversive odor memories with different stability. PLoS Genet. 8, e1002768 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. Aso, Y. & Rubin, G. M. Dopaminergic neurons write and update memories with cell-type-specific rules. eLife 5, 1–15 (2016).

    Article  CAS  Google Scholar 

  23. Aso, Y. et al. Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila. eLife 3, e04580 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  24. Burke, C. J. et al. Layered reward signalling through octopamine and dopamine in Drosophila. Nature 492, 433–437 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. Hige, T., Aso, Y., Modi, M. N., Rubin, G. M. & Turner, G. C. Heterosynaptic plasticity underlies aversive olfactory learning in Drosophila. Neuron 88, 985–998 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Cohn, R., Morantte, I. & Ruta, V. Coordinated and compartmentalized neuromodulation shapes sensory processing in Drosophila. Cell 163, 1742–1755 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. Waddell, S. Neural plasticity: dopamine tunes the mushroom body output network. Curr. Biol. 26, R109–R112 (2016).

    CAS  PubMed  Article  Google Scholar 

  28. Handler, A. et al. Distinct dopamine receptor pathways underlie the temporal sensitivity of associative learning. Cell 178, 60–75 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  29. Berry, J. A., Cervantes-Sandoval, I., Chakraborty, M. & Davis, R. L. Sleep facilitates memory by blocking dopamine neuron-mediated forgetting. Cell 161, 1656–1667 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Aimon, S. et al. Fast near-whole–brain imaging in adult Drosophila during responses to stimuli and behavior. PLoS Biol. 17, e2006732 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  31. Siju, K. P. et al. Valence and state-dependent population coding in dopaminergic neurons in the fly mushroom body. Curr. Biol. 30, 809277 (2020).

    Article  CAS  Google Scholar 

  32. Li, F. et al. The connectome of the adult Drosophila mushroom body provides insights into function. eLife 9, e62576 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  33. Pnevmatikakis, E. A. et al. Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron 89, 285–299 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. Patriarchi, T. et al. Ultrafast neuronal imaging of dopamine dynamics with designed genetically encoded sensors. Science 360, eaat4422 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  35. Dana, H. et al. Sensitive red protein calcium indicators for imaging neural activity. eLife 5, e12727 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  36. Hamid, A. A. et al. Mesolimbic dopamine signals the value of work. Nat. Neurosci. 19, 117–126 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  37. Coddington, L. T. & Dudman, J. T. The timing of action determines reward prediction signals in identified midbrain dopamine neurons. Nat. Neurosci. 21, 1563–1573 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. Saunders, B. T., Richard, J. M., Margolis, E. B. & Janak, P. H. Dopamine neurons create Pavlovian conditioned stimuli with circuit-defined motivational properties. Nat. Neurosci. 21, 1072–1083 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. Hughes, R. N. et al. Ventral tegmental dopamine neurons control the impulse vector during motivated behavior. Curr. Biol. 30, 2681–2694 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. Syed, E. C. J. et al. Action initiation shapes mesolimbic dopamine encoding of future rewards. Nat. Neurosci. 19, 34–36 (2016).

    CAS  PubMed  Article  Google Scholar 

  41. Baker, K. L. et al. Algorithms for olfactory search across species. J. Neurosci. 38, 9383–9389 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. Bell, W. J. & Kramer, E. Sex pheromone-stimulated orientation of the American cockroach on a servosphere apparatus. J. Chem. Ecol. 6, 287–295 (1980).

    Article  Google Scholar 

  43. Tsao, C. H., Chen, C. C., Lin, C. H., Yang, H. Y. & Lin, S. Drosophila mushroom bodies integrate hunger and satiety signals to control innate food-seeking behavior. eLife 7, 1–35 (2018).

    Article  Google Scholar 

  44. Sayin, S. et al. A neural circuit arbitrates between persistence and withdrawal in hungry Drosophila. Neuron 104, 544–558 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Yu, Y. et al. Regulation of starvation-induced hyperactivity by insulin and glucagon signaling in adult Drosophila. eLife 5, 1–19 (2016).

    Google Scholar 

  46. Landayan, D., Feldman, D. S. & Wolf, F. W. Satiation state-dependent dopaminergic control of foraging in Drosophila. Sci. Rep. 8, 5777 (2018).

  47. Dayan, P. & Balleine, B. W. Reward, motivation, and reinforcement learning. Neuron 36, 285–298 (2002).

    CAS  PubMed  Article  Google Scholar 

  48. Pang, R., van Breugel, F., Dickinson, M., Riffell, J. A. & Fairhall, A. History dependence in insect flight decisions during odor tracking. PLoS Comput. Biol. 14, 1–26 (2018).

    Article  CAS  Google Scholar 

  49. Jiang, L. & Litwin-Kumar, A. Models of heterogeneous dopamine signaling in an insect learning and memory center. PLoS Comput. Biol. 17, e1009205 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. Takemura, S. et al. A connectome of a learning and memory center in the adult Drosophila brain. eLife 6, e26975 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  51. Yamamoto, K. & Vernier, P. The evolution of dopamine systems in chordates. Front. Neuroanat. 5, 1–21 (2011).

    Article  CAS  Google Scholar 

  52. Barron, A. B., Søvik, E. & Cornish, J. L. The roles of dopamine and related compounds in reward-seeking behavior across animal phyla. Front. Behav. Neurosci. 4, 163 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  53. Green, J. et al. A neural circuit architecture for angular integration in Drosophila. Nature 546, 101–106 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. Moore, R. J. D. et al. FicTrac: a visual method for tracking spherical motion and generating fictive animal paths. J. Neurosci. Methods 225, 106–119 (2014).

    PubMed  Article  Google Scholar 

  55. Pnevmatikakis, E. A. & Giovannucci, A. NoRMCorre: an online algorithm for piecewise rigid motion correction of calcium imaging data. J. Neurosci. Methods 291, 83–94 (2017).

    CAS  PubMed  Article  Google Scholar 

  56. Giovannucci, A. et al. CaImAn an open source tool for scalable calcium imaging data analysis. eLife 8, e38173 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  57. Meissner, G. W. et al. An image resource of subdivided Drosophila GAL4-driver expression patterns for neuron-level searches. Preprint at https://www.biorxiv.org/content/10.1101/2020.05.29.080473v1 (2020).

  58. Clements, J. et al. neuPrint: analysis tools for EM connectomics. Preprint at https://www.biorxiv.org/content/10.1101/2020.01.16.909465v1 (2020).

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Acknowledgements

We thank S. R. Datta, B. Noro, A. Handler and members of the Ruta lab for valuable discussions and comments on the manuscript. We also thank C. Dan, V. Jayaraman and L. Tian for developing the dLight sensor flies and J. Petrillo and P. Stock for technical advice. Stocks from the Bloomington Drosophila Stock Center (NIH P40OD018537) were used in this study. This work was supported by the National Institutes of Health (R01NS113103 and DP2NS087942 (to V.R) and T32GM007739 to the Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program (to A.Z. and A.S.)); by a Kavli Neural Systems Institute Fellowship (to A.S.); and by the Simons Collaboration on the Global Brain (to V.R. and A.F.).

Author information

Authors and Affiliations

Authors

Contributions

A.Z. performed DAN imaging and behavioral experiments, with assistance from R.C. R.C. designed and created the closed-loop system and wrote custom code for data analysis, with assistance from A.Z. R.P. performed analysis and modeling, with assistance from A.F. A.S. performed functional characterization of different DAN subsets and patterns of dopamine release. A.Z. and V.R. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Vanessa Ruta.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Bernardo Sabatini and the other, 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

Extended Data Fig. 1 Compartmentalized DAN activity and dopamine release coordinately represent reward and locomotion.

(a) Schematic depicting experimental system and definition of the quantified parameters of locomotion. (b) Comparison of maximum DAN activity measured by sytGCaMP6s expressed in DANs (left) and dopamine release measured by dLight expressed in Kenyon cell (KCs) (right) in response to ingestion of a sucrose reward (R) or during locomotion (L). Signals normalized by subtracting the median fluorescence during the 5 min trial. Paired two-sided t-test with Bonferroni correction, p < 0.05(*), see Supplementary Table 2. (c) Correlated and compartmentalized sytGCaMP6s activity in γ lobe DANs (top) and KC dLight expression reflecting dopamine release (bottom) during periods of spontaneous locomotion and sucrose ingestion. Multiple clustering algorithms identify each compartment as a relatively homogenous unit, with stronger correlations within than across compartments. Left: pixels color coded by k-means clustering analysis. Middle: pixels color coded by CNMF clustering analysis. Right: pixel-by-pixel cross-correlation (Pearson correlation coefficient) for the same animal. (d) Anatomic reconstructions of γ4 DAN subpopulations from hemibrain connectome. Upper and lower axonal commissures that DANs use to innervate the lobes highlighted in red and green, respectively. (e) Presynaptic distribution of DANs following upper (red) and lower (green) commissure within the γ4 compartment. (f) Overlay of forward velocity (black) and activity of either the MB312B + γ4 DANs (top, which follow the upper commissure, red) or MB316B + γ4 DANs (bottom, which follow the lower commissure, green) expressing GCaMP6f during locomotion and sucrose ingestion (maroon bar). (g) Average MB312B (top, upper commissure, red) or MB316B (bottom, lower commissure, green) responses aligned to the beginning of sucrose ingestion (maroon bar). N for MB312B = 6 animals, 10 sucrose presentations. N for MB316B = 5 animals, 14 sucrose presentations. (h) Heat map of maximum ΔF/Fo for MB312B (top, upper commissure) or MB316B (bottom, lower commissure) during locomotion (middle) or sugar ingestion (right) overlaid on GCaMP fluorescence (left) highlights that MB312B + DANs are active during locomotion but not reward ingestion while MB316B + DANs display multiplexed activity during both contexts.

Extended Data Fig. 2 Multiplexed and correlated activity in γ4 DAN subsets.

(a) MB312B + γ4 DANs (upper commissure) expressing GCaMP6f fluorescence (left) with functionally correlated and spatially adjacent pixels clustered into single ROIs by CNMF analysis (middle). Right: representative ROIs whose activity is plotted in (c). Similar results observed in N = 6 animals. (b) Same as in (a) but for MB316B + γ4 DANs (lower commissure). Right: representative ROIs plotted in (d). Similar results observed in N = 5 animals. (c) Net motion (top row, black) aligned to the activity in representative CNMF-generated-ROIs from (a) (2nd, 3rd, and 4th rows, shades of green), total MB312B + DAN GCaMP activity (5th row), the average CNMF-generated-ROI activity (bottom row), and the activity in all ROIs (heatmap) from a representative experiment in a MB312B > GCaMP6f individual. Maroon bars indicate period of sucrose ingestion. (d) As in (c) but for MB316B + γ4 DANs (upper commissure). Maroon bars indicate period of sucrose ingestion. (e) Cytoplasmic GCaMP6f activity in MB312B + γ4 DAN soma (shades of green) in representative examples during sugar ingestion (left) and spontaneous movement (right) aligned to forward velocity (top row, black). Different shades of green indicate different γ4 DAN soma recorded from the same animal. Maroon bars indicate period of sucrose ingestion. (f) As in (e) but recording from MB316 + γ4 DAN soma. (g) Motor-associated signals across individual γ4 DANs is highly correlated. Cytoplasmic GCaMP6f activity in MB312B + γ4 DAN soma measured with volumetric imaging during spontaneous bouts of locomotion. For three flies: top row shows a representative bout of forward velocity (black), middle row shows cytoplasmic GCaMP6s fluorescence (shades of green indicate different γ4 DAN soma), and bottom row is heatmap depicting the cross-correlation (Pearson correlation coefficient) between GCaMP6s signals in different γ4 DANs during spontaneous locomotion in a 5 min trial.

Extended Data Fig. 3 Variability of DAN - behavior correlations.

(a) Top: average motion (black) ± 95% confidence interval (CI, obscured by average line) as animals initiate locomotion. Bottom: heat map of ΔF/Fo in γ DANs aligned to movement initiation. Rows (bouts) ordered by average γ2 (left) or γ4 (right) ΔF/Fo. Dashed lines indicate 20% of trials with highest or lowest average ΔF/Fo. N = 53 animals, 1060 starts. (b) DAN activity and parameters of locomotion during spontaneous movement initiation in which γ2 and γ4 were most differentially active). Left: average γ2 ΔF/Fo (top), motion (2nd row), acceleration (3rd), forward velocity (4th), and |angular velocity| (bottom) ± 95% CI as animals initiated locomotion. 20% of bouts of movement initiation with highest (dark) and lowest (lighter) average γ2 ΔF/Fo as indicated by lines in (a). Right: as left but for bouts of movement initiation with highest (lighter) and lowest (dark) average γ4 ΔF/Fo. N = 212 bouts. (c) As in (b) but for flies walking in non-odorized air in closed-loop. N = 91 bouts. (d) γ2 (top) and γ4 (bottom) DAN activity vs different behavioral variables. N = 1060 bouts. All Pearson correlation coefficients are either weak (|r| < 0.18) or not significant (no Bonferroni correction). (e) Comparisons of average DAN ΔF/Fo during the onset of locomotion. Pearson correlation coefficient (r) indicated where relationship is statistically significant (p < 0.00001, Bonferroni correction, see Supplementary Table 2). N = 1060 starts. (f) Pearson correlation coefficient between change in DAN activity and net motion during bouts of movement initiation for flies walking in clean air in closed-loop. Columns (flies) ordered by average γ4-motion correlation. N = 32 animals, 452 starts. (g) Filters predicting DAN activity from forward velocity (top) or |angular velocity|(bottom) in open loop (OL, as in Fig. 1f , light lines) or closed-loop (CL) in clean air ± 95% CI. OL: N = 66 animals, 119 5-minute trials. CL: N = 20 animals, 32 5-minute trials. (h) Comparison of γ Kenyon cell activity during presentation of apple cider vinegar from indicated angles. Average ΔF/Fo (dark line) ± 95% CI aligned to odor onset. Right: average ΔF/Fo during odor presentation from indicated angles. N = 16 animals, 3 odor presentations per orientation (total 144 odor presentations). One-way ANOVA followed by Tukey’s multiple comparison test; no statistical significance observed.

Extended Data Fig. 4 Rapidly fluctuating network correlations between DANs and different behavioral variables.

(a) Representative traces from two flies showing the net motion of each animal (top), overlay of γ DAN activity (colored) and either forward velocity (middle rows, black) or turning velocity (bottom rows, black) during a period of continuous locomotion (epoch shown by gray dashed box in top trace). DAN activity is normalized to minimum and maximum values during the selected bout of walking. (b) Average activity of γ DANs aligned to increases in forward velocity during bouts of continuous movement. N = 9,772 movements in 74 flies. (c) Average activity of γ DANs aligned to increases in turning velocity during bouts of continuous movement. N = 11,667 movements in 74 flies. (d) Left: overlay of DAN activity in different compartments during epochs designated in (a). Top: same epoch as left panel of (a). Bottom: same epoch as right panel of (a). Middle: running cross-correlation between pairs of γ DANs for the traces at left. Right: histograms of running correlation. (e) Histogram of running cross-correlation between pairs of γ DANs for all flies. Shuffled controls (random 1-20 sec temporal shift) in black. N = 74 animals, 178 5-minute trials. (f) Partial correlations between γ DANs to control for potential relationships that arise from common behavioral signals. N = 74 animals, 178 5-minute trials. ANOVA followed by Tukey’s multiple comparison test. Data labeled with different letters are significantly different from each other (p < 0.00001). (g) Proportion of the variance (R2) in net motion (left), forward velocity (middle), and |angular velocity| (right) explained by individual and all DANs. N = 66 animals, 119 5-minute trials. ANOVA followed by Tukey’s multiple comparison test. Data labeled with different letters are significantly different from each other (p < 0.0005). (h) No significant relationships are apparent between intercompartmental correlations and behavioral parameters. All Pearson correlation coefficients are either weak (|r|<0.1) or not significant, see Supplementary Table 2.

Extended Data Fig. 5 DAN-motor correlations vary across conditions.

(a) Same analysis as in Fig. 3f  but offset by 15 sec such that animals were walking only in clean air. N = 26 flies, 143 epochs. (b) Same analysis as in Fig. 3c but offset by 15 sec such that animals were walking in clean air. Fisher r-to-z transformation indicates no significant differences in correlation coefficients between upwind displacement and Δ|heading| in and out of odor (z = -1.32). N = 26 flies, 143 odor presentations. (c) Average γ DAN ΔF/Fo shows no correlations with an animal’s net displacement (left) or total scalar distance traveled (right) during odor presentations. Displacement was normalized (divided by) an individual’s average walking speed. Pearson correlation coefficient (r) indicated where relationship is statistically significant (p < 0.055, Bonferroni correction). N = 26 flies, 143 epochs. (d) ΔF/Fo of DANs in the γ2 vs γ4 compartments during odor presentation. Pearson coefficient (r) indicated where relationship is significant (p < 0.0001, see Supplementary Table 2). N = 26 flies, 143 odor presentations. (e) Same analysis as in Fig. 4d but offset by 15 sec such that flies were walking in clean air. N = 22 flies, 52 odor presentations. (f) Same analysis as in Fig. 4b but offset by 15 sec such that animals were walking in clean air. N = 22 flies, 52 odor presentations. (g) Filters predicting DAN activity from |heading| (top) or forward velocity (bottom) as animals walked in clean air, under low (lighter) or high (darker) airflow conditions. ± 95% confidence interval obscured by thickness of the data line. (h) Average γ DAN ΔF/Fo plotted as a function of upwind displacement (left), average Δ|heading| (middle), and average Δforward velocity (right) during odor presentation from Fig. 3f however here data from the low airflow context was subsampled such that the variance of the Δ|heading| was statistically equal to that of the high airflow context. Top: histogram showing distribution of behavioral variables. Pearson coefficient (r) indicates where relationship between subsampled variables is significant (p < 0.05 with Bonferroni, see Supplementary Table 2). Nlow airflow = 135 odor presentations. (i) Same as (h) with data from the high airflow context subsampled such that the variance of the Δforward velocity was statistically equal to that of the low airflow context. Nhigh airflow = 50 odor presentations.

Extended Data Fig. 6 Analysis of dynamic DAN-motor correlations.

(a) Average predicted γ2 odor responses generated by applying high airflow filters to low airflow behavioral data, plotted as a function of upwind displacement (left), average |heading| (middle), and average forward velocity (right) during odor presentation under low airflow conditions. Best fit line and Pearson coefficient (r) indicated where relationship is significant (p < 0.0001, Bonferroni correction, see Supplementary Table 2). N = 26 flies, 143 odor presentations. (b) As in (a) but predicted DAN odor responses generated from applying low airflow filters to high airflow behavioral data plotted against behavior under high airflow conditions. Best fit line and Pearson coefficient (r) indicated where relationship is significant (p < 0.0001, Bonferroni correction). N = 22 flies, 52 odor presentations. (c,d) Same as (a,b) except for γ3 DAN odor responses. N = 26 flies, 143 odor presentations (c), N = 22 flies, 52 odor presentations (d). (e) Average predicted DAN odor responses generated by applying filters derived as animals walk in clean airflow to the behavioral data plotted as a function of upwind displacement (left), average |heading| (middle), and average forward velocity (right) as animals walked in clean air, under low airflow. Best fit line and Pearson coefficient (r) indicated where relationship is significant (p < 0.01 with Bonferroni correction, see Supplementary Table 2). N = 26 flies, 143 odor presentations. (f) Same as (e) except under high airflow. N = 22 flies, 52 odor presentations.

Extended Data Fig. 7 Cross-correlation analysis between DAN activity and behavior during odor pursuit.

(a) Organization of cross-correlation matrix comparing DAN activity to past, present, and future behavior, in and out of odor. (b) Auto-correlation of forward velocity (left) and |heading| (right) during the 10 sec prior to odor and the 10 sec of odor presentation. Colored points indicate statistically significant correlations (Pearson correlation coefficient, p < 0.05, no Bonferroni correction). N = 26 flies, 143 odor presentations. Note the correlation between an animal’s current and past or future forward velocity extend throughout the trial, while the correlation between an animal’s current and past or future heading lasts < 3 sec. (c,d) Cross-correlation matrix between forward velocity (left) or |heading| (right) and γ DAN activity during the 10 sec prior to odor onset and the 10 sec during odor presentation under low (c) and high (d) airflow conditions. Only relationships that are statistically significant by Pearson cross correlation (p < 0.05, no Bonferroni correction, see Supplementary Table 2) are shown in color indicated by green-magenta scale. N = 26 flies, 143 odor presentations (c), N = 22 flies, 52 odor presentations (d). (e,f) Same analysis as in (c,d) but over a 20-sec period during which only clean air is presented to the animal. Colored points indicate statistically significant correlations (p < 0.05, no Bonferroni correction). N = 26 flies, 143 odor presentations (e), N = 22 flies, 52 clean air epochs (f).

Extended Data Fig. 8 Correlations between DAN activity and current and future behavior emerge during odor tracking.

(a) Representative trial showing fictive 2D trajectory, forward velocity, |heading|, and γ DAN activity in which the fly reorients and tracks upwind in response to apple cider vinegar in the low airflow context. Black trajectories indicate clean air, orange indicates time of odor presentation. Shaded areas represent epochs used in nested linear model (b). (b) A nested linear model predicting γ DAN activity during the initial phase of odor presentation under low airflow conditions (t = 1-4 sec after odor onset) based on an animal’s average heading 10 sec prior to odor onset (ho), initial Δforward velocity (t = 1-4 sec, ΔV1-4), initial Δ|heading| (t = 1-4 sec, Δh1-4), and future Δ|heading| (t = 7-10 sec, Δh7-10, a time window when behavioral autocorrelations are no longer relevant). Fraction of DAN variance explained as a function of which predictors were included in the model, for odor presentation (colored lines) and same temporal epochs offset 10 sec prior to the odor presentation (black) when the fly walked in clean air. F-test, p < 0.05 (*), p < 0.01 (**) with colored asterisk depicting significant differences in odor and black asterisk depicting significant differences in clean air. N = 26 flies, 143 odor presentations. (c) Same as (b) except under high flow conditions. N = 22 flies, 52 odor presentations.

Extended Data Fig. 9 DAN-movement relationships during odor tracking in low airflow conditions are comparable in starved and fed animals.

(a) Linear filters predicting DAN activity using forward velocity (Vf, left) or |heading| (|h|, right) in fed (colored lines) and starved (black dashed lines) flies during odor tracking over a 4 second window. N = 10 flies, 49-53 odor presentations. (b) Average predicted DAN activity generated by applying linear filters from fed animals plotted as a function of upwind displacement (left), average |heading| (middle), and average forward velocity (right) during odor in fed individuals. Best fit line and Pearson correlation coefficient (r) indicated where relationship is statistically significant (p < 0.0001 with Bonferroni correction, see Supplementary Table 2). (c,d) Average predicted γ2 DAN odor responses generated by applying filters derived from fed (c) or starved (d) animals to behavioral data from starved (c) or fed (d) animals, plotted as a function of average |heading| (left) or average forward velocity (right) during odor presentation. Best fit line and Pearson correlation coefficient (r) indicated where relationship is statistically significant (p < 0.0005 with Bonferroni correction, see Supplementary Table 2). N = 10 flies, 49 (fed) and 53 (starved) odor presentations. (e,f) Same as (c,d) but for γ3 DANs. (g,h) Same as (c,d) but for γ4 DANs.

Extended Data Fig. 10 Optogenetic inhibition or excitation of PAM DANs bidirectionally influences upwind tracking behavior.

(a) Average upwind velocity during odor presentations preceding optogenetic inhibition (-) and during odor presentations paired with optogenetic inhibition (+) for the indicated genotypes in starved animals. PAM DANs (MB042B driver) > GtACR1 (N = 63, top left), PAM DANs (MB196B driver) > GtACR1 (N = 49, top middle), PAM DANs MB042B-Gal4 parental controls (N = 33, top right), PPL DANs (MB504B driver) > GtACR1 (N = 30, bottom left), γ4 DANs (MB312B driver) > GtACR1 (N = 54, bottom middle), UAS-GtACR1 parental controls (N = 48, bottom right). Paired two-sided t-test with Bonferroni correction, p < 10−5(**), see Supplementary Table 2. (b) Top: average upwind speed in odor presentations preceding optogenetic activation (-) and in odor predsentations paired with optogenetic activation (+) in fed PAM DANs (MB042B driver) > CsChrimson flies (left) and UAS-CsChrimson parental controls (right). N = 60 paired cohorts of PAM > CsChrimson and parental control animals assayed together during a single experiment. Bottom: average upwind speed of fed animals in clean air preceding optogenetic activation (-) and with optogenetic activation (+) for fed PAM DANs (MB042B driver) > CsChrimson flies (left) and UAS-Chrimson parental controls (right). N = 44 paired cohorts of PAM > CsChrimson and parental control animals assayed together during a single experiment. Paired two-sided t-test with Bonferroni correction, p < 10−5(**), see Supplementary Table 2.

Supplementary information

Supplementary Information

Supplementary Figs. 1–4 and Supplementary Tables 1 and 2.

Reporting Summary

Supplementary Video 1

Supplementary Video 1. Movement- and reward-related compartmentalized dopamine release across the mushroom body γ lobe. Right: overlay of net motion (white) and concurrent compartmentalized Kenyon cell-dLight activity (colored) during spontaneous tethered locomotion and sucrose ingestion. Left (top): sideview of a tethered fly walking spontaneously and ingesting 1 M sucrose. Left (bottom): Kenyon cell-dLight fluorescence across both γ lobes of the mushroom body.

Supplementary Video 2

Supplementary Video 2: γ4 DAN odor responses correlate with reorientation behaviors that lead to upwind tracking. Left: fictive two-dimentaional path of a tethered fly during locomotion in the closed-loop virtual environment under low-airflow conditions in air and in ACV (onset indicated by ‘odor’ in red). Right (top): concurrent recording of a tethered fly walking in the closed-loop system imaged from behind. Air tube can be seen in the background and foreground rotating in response to the fly’s movements. Right (bottom): concurrent DAN>sytGCaMP6s activity in the mushroom body γ lobe. Compartments are outlined in white.

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Zolin, A., Cohn, R., Pang, R. et al. Context-dependent representations of movement in Drosophila dopaminergic reinforcement pathways. Nat Neurosci 24, 1555–1566 (2021). https://doi.org/10.1038/s41593-021-00929-y

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