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The neuronal logic of how internal states control food choice

Abstract

When deciding what to eat, animals evaluate sensory information about food quality alongside multiple ongoing internal states1,2,3,4,5,6,7,8,9,10. How internal states interact to alter sensorimotor processing and shape decisions such as food choice remains poorly understood. Here we use pan-neuronal volumetric activity imaging in the brain of Drosophila melanogaster to investigate the neuronal basis of internal state-dependent nutrient appetites. We created a functional atlas of the ventral fly brain and find that metabolic state shapes sensorimotor processing across large sections of the neuropil. By contrast, reproductive state acts locally to define how sensory information is translated into feeding motor output. These two states thus synergistically modulate protein-specific food intake and food choice. Finally, using a novel computational strategy, we identify driver lines that label neurons innervating state-modulated brain regions and show that the newly identified ‘borboleta’ region is sufficient to direct food choice towards protein-rich food. We thus identify a generalizable principle by which distinct internal states are integrated to shape decision making and propose a strategy to uncover and functionally validate how internal states shape behaviour.

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Fig. 1: Internal states shape food choice and pan-neuronal volumetric imaging setup.
Fig. 2: A functional atlas of the SEZ.
Fig. 3: Metabolic state selectively changes taste processing in the SEZ.
Fig. 4: Modulations by the metabolic state happen at different levels of processing and increase the efficiency of motor neuron recruitment.
Fig. 5: The reproductive state acts on motor neuron regions.
Fig. 6: Outer borboleta neurons drive protein appetite.

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Data availability

Calcium imaging time series and behavioural data are available at https://github.com/ribeiro-lab/Muench.et.al.2022. TH-F single cell clones in Extended Data Fig. 6 are available from flycircuit.tw and virtualflybrain.org; all IDs are provided in Supplementary Table 4. Braincode cluster information can be downloaded from https://strawlab.org/braincode/r1/clusters/SEZ/CB1/K60_dicedist.

Code availability

Custom R and Python code used for atlas building and data analysis is available at https://github.com/ribeiro-lab/Muench.et.al.2022.

References

  1. Ribeiro, C. & Dickson, B. J. Sex peptide receptor and neuronal TOR/S6K signaling modulate nutrient balancing in Drosophila. Curr. Biol. 20, 1000–1005 (2010).

    Article  CAS  PubMed  Google Scholar 

  2. Corrales-Carvajal, V. M., Faisal, A. A. & Ribeiro, C. Internal states drive nutrient homeostasis by modulating exploration-exploitation trade-off. eLife 5, e19920 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Griffioen-Roose, S. et al. Human protein status modulates brain reward responses to food cues. Am. J. Clin. Nutr. 100, 113–122 (2014).

    Article  PubMed  CAS  Google Scholar 

  4. Hindmarsh Sten, T., Li, R., Otopalik, A. & Ruta, V. Sexual arousal gates visual processing during Drosophila courtship. Nature 595, 549–553 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. Münch, D., Ezra-Nevo, G., Francisco, A. P., Tastekin, I. & Ribeiro, C. Nutrient homeostasis—translating internal states to behavior. Curr. Opin. Neurobiol. 60, 67–75 (2020).

    Article  PubMed  CAS  Google Scholar 

  6. Rolls, E. T. Taste, olfactory, and food reward value processing in the brain. Prog. Neurobiol. 127–128, 64–90 (2015).

    Article  PubMed  Google Scholar 

  7. Root, C. M., Ko, K. I., Jafari, A. & Wang, J. W. Presynaptic facilitation by neuropeptide signaling mediates odor-driven food search. Cell 145, 133–144 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Simpson, S. J. & Abisgold, J. D. Compensation by locusts for changes in dietary nutrients: behavioural mechanisms. Physiol. Entomol. 10, 443–452 (1985).

    Article  Google Scholar 

  9. Steck, K. et al. Internal amino acid state modulates yeast taste neurons to support protein homeostasis in Drosophila. eLife 7, e31625 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Walker, S. J., Corrales-Carvajal, V. M. & Ribeiro, C. Postmating circuitry modulates salt taste processing to increase reproductive output in Drosophila. Curr. Biol. 25, 2621–2630 (2015).

    Article  CAS  PubMed  Google Scholar 

  11. Burgess, C. R., Livneh, Y., Ramesh, R. N. & Andermann, M. L. Gating of visual processing by physiological need. Curr. Opin. Neurobiol. 49, 16–23 (2018).

    Article  CAS  PubMed  Google Scholar 

  12. Griffioen-Roose, S. et al. Protein status elicits compensatory changes in food intake and food preferences. Am. J. Clin. Nutr. 95, 32–38 (2012).

    Article  CAS  PubMed  Google Scholar 

  13. Inagaki, H. K. et al. Visualizing neuromodulation in vivo: TANGO-mapping of dopamine signaling reveals appetite control of sugar sensing. Cell 148, 583–595 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Vogt, K. et al. Internal state configures olfactory behavior and early sensory processing in Drosophila larvae. Sci. Adv. 7, eabd6900 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  15. Harris, D. T., Kallman, B. R., Mullaney, B. C. & Scott, K. Representations of taste modality in the Drosophila brain. Neuron 86, 1449–1460 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Pacheco, D. A., Thiberge, S. Y., Pnevmatikakis, E. & Murthy, M. Auditory activity is diverse and widespread throughout the central brain of Drosophila. Nat. Neurosci. 24, 93–104 (2021).

    Article  CAS  PubMed  Google Scholar 

  17. Schrödel, T., Prevedel, R., Aumayr, K., Zimmer, M. & Vaziri, A. Brain-wide 3D imaging of neuronal activity in Caenorhabditis elegans with sculpted light. Nat. Methods 10, 1013–1020 (2013).

    Article  PubMed  CAS  Google Scholar 

  18. Portugues, R., Feierstein, C. E., Engert, F. & Orger, M. B. Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior. Neuron 81, 1328–1343 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Mann, K., Gallen, C. L. & Clandinin, T. R. Whole-brain calcium imaging reveals an intrinsic functional network in Drosophila. Curr. Biol. 27, 2389–2396.e4 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Marques, J. C., Li, M., Schaak, D., Robson, D. N. & Li, J. M. Internal state dynamics shape brainwide activity and foraging behaviour. Nature 577, 239–243 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  23. Allen, W. E. et al. Thirst regulates motivated behavior through modulation of brainwide neural population dynamics. Science 364, eaav3932 (2019).

    Article  CAS  Google Scholar 

  24. Simpson, S. J. & Raubenheimer, D. The Nature of Nutrition: a Unifying Framework from Animal Adaptation to Human Obesity (Princeton Univ. Press, 2012).

  25. Carvalho-Santos, Z. et al. Cellular metabolic reprogramming controls sugar appetite in Drosophila. Nat. Metab. 2, 958–973 (2020).

    Article  PubMed  Google Scholar 

  26. Solon-Biet, S. M. et al. The ratio of macronutrients, not caloric intake, dictates cardiometabolic health, aging, and longevity in ad libitum-fed mice. Cell Metab. 19, 418–430 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Leitão-Gonçalves, R. et al. Commensal bacteria and essential amino acids control food choice behavior and reproduction. PLoS Biol. 15, e2000862 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Simpson, S. J. & Simpson, C. L. Mechanisms controlling modulation by haemolymph amino acids of gustatory responsiveness in the locust. J. Exp. Biol. 168, 269–287 (1992).

    Article  CAS  Google Scholar 

  29. Walker, S. J., Goldschmidt, D. & Ribeiro, C. Craving for the future: the brain as a nutritional prediction system. Curr. Opin. Insect Sci. 23, 96–103 (2017).

    Article  PubMed  Google Scholar 

  30. Miroschnikow, A. et al. Convergence of monosynaptic and polysynaptic sensory paths onto common motor outputs in a Drosophila feeding connectome. eLife 7, e40247 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Scott, K. Gustatory processing in Drosophila melanogaster. Annu. Rev. Entomol. 63, 15–30 (2018).

    Article  CAS  PubMed  Google Scholar 

  32. Schwarz, O. et al. Motor control of Drosophila feeding behavior. eLife 6, e19892 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Ito, K. et al. A systematic nomenclature for the insect brain. Neuron 81, 755–765 (2014).

    Article  CAS  PubMed  Google Scholar 

  34. Miyazaki, T. & Ito, K. Neural architecture of the primary gustatory center of Drosophila melanogaster visualized with GAL4 and LexA enhancer-trap systems. J. Comp. Neurol. 518, 4147–4181 (2010).

    Article  PubMed  Google Scholar 

  35. Varoquaux, G. et al. A group model for stable multi-subject ICA on fMRI datasets. NeuroImage 51, 288–299 (2010).

    Article  CAS  PubMed  Google Scholar 

  36. Inoshita, T. & Tanimura, T. Cellular identification of water gustatory receptor neurons and their central projection pattern in Drosophila. Proc. Natl Acad. Sci. USA 103, 1094–1099 (2006).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  37. Marella, S. et al. Imaging taste responses in the fly brain reveals a functional map of taste category and behavior. Neuron 49, 285–295 (2006).

    Article  CAS  PubMed  Google Scholar 

  38. Kim, H., Kirkhart, C. & Scott, K. Long-range projection neurons in the taste circuit of Drosophila. eLife 6, e23386 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Liu, Q. et al. Branch-specific plasticity of a bifunctional dopamine circuit encodes protein hunger. Science 356, 534–539 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  40. Yang, Z. et al. A post-ingestive amino acid sensor promotes food consumption in Drosophila. Cell Res. 28, 1013–1025 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Yapici, N., Cohn, R., Schusterreiter, C., Ruta, V. & Vosshall, L. B. A taste circuit that regulates ingestion by integrating food and hunger signals. Cell 165, 715–729 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Panser, K. et al. Automatic segmentation of Drosophila neural compartments using GAL4 expression data reveals novel visual pathways. Curr. Biol. 26, 1943–1954 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Itskov, P. M. et al. Automated monitoring and quantitative analysis of feeding behaviour in Drosophila. Nat. Commun. 5, 4560 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  44. Cabanac, M. Physiological role of pleasure. Science 173, 1103–1107 (1971).

    Article  ADS  CAS  PubMed  Google Scholar 

  45. Livneh, Y. et al. Homeostatic circuits selectively gate food cue responses in insular cortex. Nature 546, 611–616 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  46. Miroschnikow, A., Schlegel, P. & Pankratz, M. J. Making feeding decisions in the Drosophila nervous system. Curr. Biol. 30, R831–R840 (2020).

    Article  CAS  PubMed  Google Scholar 

  47. Tastekin, I. et al. Role of the subesophageal zone in sensorimotor control of orientation in Drosophila larva. Curr. Biol. 25, 1448–1460 (2015).

    Article  CAS  PubMed  Google Scholar 

  48. Vendrell-Llopis, N. & Yaksi, E. Evolutionary conserved brainstem circuits encode category, concentration and mixtures of taste. Sci Rep. 5, 17825 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  49. Yarmolinsky, D. A., Zuker, C. S. & Ryba, N. J. P. Common sense about taste: from mammals to insects. Cell 139, 234–244 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Moore, J. D., Kleinfeld, D. & Wang, F. How the brainstem controls orofacial behaviors comprised of rhythmic actions. Trends Neurosci. 37, 370–380 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Carter, M. E., Soden, M. E., Zweifel, L. S. & Palmiter, R. D. Genetic identification of a neural circuit that suppresses appetite. Nature 503, 111–114 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  52. Marella, S., Mann, K. & Scott, K. Dopaminergic modulation of sucrose acceptance behavior in Drosophila. Neuron 73, 941–950 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Nakamura, K. & Nakamura, Y. Hunger and satiety signaling: modeling two hypothalamomedullary pathways for energy homeostasis. BioEssays 40, 1700252 (2018).

    Article  Google Scholar 

  54. Giza, B. K. & Scott, T. R. Blood glucose selectively affects taste-evoked activity in rat nucleus tractus solitarius. Physiol. Behav. 31, 643–650 (1983).

    CAS  PubMed  Google Scholar 

  55. Wang, K. et al. Neural circuit mechanisms of sexual receptivity in Drosophila females. Nature 589, 577–581 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  57. Lopes, G. et al. Bonsai: an event-based framework for processing and controlling data streams. Front. Neuroinformatics 9, 7 (2015).

    Article  Google Scholar 

  58. Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. Int. J. 29, 162–173 (1996).

    Article  CAS  Google Scholar 

  59. Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54, 2033–2044 (2011).

    Article  PubMed  Google Scholar 

  60. Yushkevich, P. A. et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage 31, 1116–1128 (2006).

    Article  PubMed  Google Scholar 

  61. Abraham, A. et al. Machine learning for neuroimaging with scikit-learn. Front. Neuroinformatics 8, 14 (2014).

    Article  Google Scholar 

  62. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).

  63. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).

  64. Ho, J., Tumkaya, T., Aryal, S., Choi, H. & Claridge-Chang, A. Moving beyond P values: data analysis with estimation graphics. Nat. Methods 16, 565–566 (2019).

    Article  CAS  PubMed  Google Scholar 

  65. Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).

    Article  Google Scholar 

  66. Schmid, B. et al. 3Dscript: animating 3D/4D microscopy data using a natural-language-based syntax. Nat. Methods 16, 278 (2019).

    Article  CAS  PubMed  Google Scholar 

  67. Jenett, A. et al. A Gal4-driver line resource for Drosophila neurobiology. Cell Rep. 2, 991–1001 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Milyaev, N. et al. The Virtual Fly Brain browser and query interface. Bioinformatics 28, 411–415 (2012).

    Article  CAS  PubMed  Google Scholar 

  69. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  70. Chiang, A.-S. et al. Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution. Curr. Biol. 21, 56 (2011).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank C. Baltazar and L. Serra for technical assistance; A. Laborde, A. Ostrovsky, J. Baúto and the Champalimaud Software Platform for assistance in establishing the registration workflow on the HPC cluster; the Champalimaud Fly Platform for support with acquiring anatomical images; T. Fujiwara, R. Benton, D. Anderson and N. Yapici for sharing fly lines; K. Steck for the Ir76b-Gal4 immunostaining; G. Costa for assistance with illustrations; and I. Taştekin, S. Walker, G. Ezra-Nevo, M. Orger, E. Chiappe, S. Aimon, T. Tanimura and the whole Behaviour and Metabolism laboratory for many fruitful discussions, valuable feedback throughout the project, and comments on the manuscript. Further fly lines were obtained from the Bloomington Drosophila Stock Center (NIH P40OD018537) and the VDRC. D.M. was supported by a DFG Research Fellowship (MU 4116/1-1). D.G. was supported by a doctoral fellowship PD/BD/114273/2016 from the Portuguese Foundation for Science and Technology (FCT). The project leading to these results has received funding from la Caixa Banking Foundation to C.R. under the project code LCF/PR/HR17/52150002, a grant funded under the Lisbon Regional Operational Programme (POR) by national funds through the FCT–Fundação para a Ciência e Tecnologia, I.P. and co-financed by the European Regional Development Fund (ERDF) under the project 02/SAICT/300081/2017, and supported by the research infrastructure Congento, co-financed by Lisboa Regional Operational Programme (Lisboa2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) and FCT–Fundação para a Ciência e Tecnologia (Portugal) under the project LISBOA-01-0145-FEDER-022170. Research at the Centre for the Unknown is supported by the Fundação Champalimaud.

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Contributions

D.M. and C.R. conceived and designed the project, interpreted data, and wrote the manuscript. D.M. performed experiments and data analysis. D.G. performed and analysed video tracking experiments and performed bootstrapping analysis.

Corresponding authors

Correspondence to Daniel Münch or Carlos Ribeiro.

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Nature thanks Stephen Simpson, Emre Yaksi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Experimental design.

a, Per recording two consecutive taste stimulations lasting 5 s and 10 s respectively were applied to the proboscis. b, Experiments were performed in flies in four different internal state combinations. Experiments started with a recording without any stimulation and were followed by stimulations with three different taste stimuli (water, sucrose & yeast) that were applied in randomized order.

Extended Data Fig. 2 Extracted independent components.

a, Maximum intensity projections of the 80 independent components identified by the spatial ICA algorithm35. b Consecutive sections of the resulting binary SEZ atlas after performing the atlas building steps. For details see Methods. c, Maximum intensity projections of the resulting independent components after running the ICA on temporally shuffled data using the exact same settings as in a. d, as in b but atlas building performed on data plotted in c. Scale bars correspond to 50 µm.

Extended Data Fig. 3 Region IDs of the functional SEZ atlas.

Five slices of the functional SEZ atlas, ordered from anterior (top) to posterior (bottom), indicating the corresponding atlas region numbers.

Extended Data Fig. 4 Stimulus response maps at different metabolic states.

Stimulus-response maps for no stimulus and three different stimulations. Colors indicate median ΔF/F response values across animals during the first stimulation. Numbers indicate slice numbers.

Extended Data Fig. 5 Reproducibility of stimulus induced response patterns.

a, Correlation matrix of yeast induced response patterns across animals. b, as a but with identities of atlas regions shuffled before calculating correlation coefficients. c, as previous matrices but with no taste stimulation performed. a-c and e, Colors indicate Pearson correlation coefficient (r), point size represents absolute r. d, Correlations of repeated stimulations within animals. Response patterns during the first stimulation were correlated to those during the second stimulation (stimulation 2), post stimulation (response magnitude of the last 5 timeframes of a recording), and second stimulation after shuffling region identities (stimulation 2 shuffled). n indicates number of animals. See Extended Data Fig. 1a for a stimulation scheme. e, as a but across animals in different metabolic states. As artifacts in region 35 lead to NaN values, we removed this region from this analysis.

Source data

Extended Data Fig. 6 Neurons described to be involved in feeding regulation, in relation to borboleta and sensory regions.

a, Maximum intensity projections of neuronal populations (blue) and inner- (green) and outer borboleta regions (magenta) registered to the JFRC2 Drosophila standard brain. b, Intersection of inner-, and outer borboleta regions with the corresponding neuronal populations shown in a, green pixels indicate overlap with the inner borboleta region, magenta pixels indicate overlap with the outer borboleta region. The only overlap we observed with the borboleta region was of Ir76b with the inner borboleta. This is likely due to neuronal tracts including sensory neurons passing through the inner borboleta region. c, Intersection of the AMS1 and PMS4 sensory regions with the corresponding neuronal populations shown in a, yellow pixels indicate overlap with AMS1, red pixels indicate overlap with PMS4. Scale bars correspond to 50 µm.

Extended Data Fig. 7 Changes in response probability for different taste stimuli.

The change in response probability across SEZ regions upon protein deprivation for different food taste stimuli, mapped onto the SEZ atlas. Only changes in response probability that exceeded ± 0.3 are plotted.

Extended Data Fig. 8 Response modulation for all atlas regions by metabolic state.

Boxplots of yeast (a) and sucrose (b) stimulus-induced responses across all atlas regions, in mated fully fed or protein deprived females. The data is ordered according to the difference of median values between corresponding fully fed (gray) and protein-deprived (red) flies stimulated with yeast. yellow = sensory regions, blue = motor regions, arrowheads indicate borboleta regions. Points indicate ΔF/F response values in different experiments. Boxplots indicate median and quartiles. Wilcoxon rank-sum test, n = 7–10, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001. All P-values and sample sizes are given in Tables S1 and S2.

Source data

Extended Data Fig. 9 Responses to high sucrose concentration are not modulated by metabolic state.

Boxplot of response magnitudes upon stimulation with 1M sucrose in different metabolic states in the PMS4 sensory region and the outer borboleta region. Points indicate ΔF/F response values in each experiment. Boxplots indicate median and quartiles. Wilcoxon rank-sum test, numbers indicate P-values, n indicates number of animals.

Source data

Extended Data Fig. 10 Responses to low (2%) yeast concentration are modulated by metabolic state.

Boxplot of response magnitudes upon stimulation with 2% yeast in different metabolic states in the AMS1 and PMS4 sensory regions and the outer borboleta region. Points indicate ΔF/F response values in each experiment. Boxplots indicate median and quartiles. Wilcoxon rank-sum test, numbers indicate P-values, n indicates number of animals.

Source data

Extended Data Fig. 11 Change in response onset upon protein deprivation.

Bootstrapped mean differences in response onset upon protein deprivation. Quantification was performed for regions active in fully fed and deprived state, (see Fig. 4g). Data is ordered according to mean difference in response onset. Points indicate the mean of bootstrapped differences of means between fully fed and deprived flies. Bars indicate 95% confidence intervals of the bootstrapped distributions derived from 10000 bootstrapped samples (see Methods). Shaded regions indicated density plots of the bootstrapped distributions. Colors indicate region categories (motor blue, sensory yellow, other gray).

Extended Data Fig. 12 Response modulation for all atlas regions by reproductive state.

Boxplots of yeast stimulus-induced responses across all atlas regions, in protein-deprived virgin or mated females. The data is ordered according to the difference of median values between corresponding virgin, protein-deprived flies (gray) and mated, protein-deprived flies (purple). yellow = sensory regions, blue = motor regions, arrowheads indicate borboleta regions. Points indicate ΔF/F response values in different experiments. Boxplots indicate median and quartiles. Wilcoxon rank-sum test, n = 8-9, * P < 0.05, ** P < 0.01, *** P < 0.001. All P-values and sample sizes are given in Table S3.

Source data

Extended Data Fig. 13 Yeast stimulus response maps for four internal states.

Stimulus-response maps for yeast stimulations in four internal states (fully fed & virgin, fully fed & mated, protein deprived & virgin, protein deprived & mated). Colors indicate mean ΔF/F response values across animals during the first stimulation.

Extended Data Fig. 14 Change in response magnitude upon mating.

Bootstrapped mean differences in response magnitude upon mating, for fully fed, black and protein deprived females, red. Plotted for regions 19, 44, 73, 74 (motor). Points indicate the mean of bootstrapped differences of means between virgin and mated flies. Bars indicate 95% confidence intervals of the bootstrapped distributions derived from 10000 bootstrapped samples (see Methods). Shaded regions indicated density plots of the bootstrapped distributions.

Source data

Extended Data Fig. 15 Anatomy of borboleta-innervating Gal4 lines.

a, Anatomy of three Gal4 lines labeling neurons that innervate the outer borboleta region. top: maximum intensity projections of full confocal brain scans, bottom: zoom into the SEZ region. b, like a for inner borboleta lines. Scale bars correspond to 50 µm. Gal4 expression pattern (UAS-CD8::GFP) is shown in green, nc82 neuropil staining is shown in magenta.

Extended Data Fig. 16 Video tracking reveals effects of borboleta activation.

Total duration of micromovements on yeast spots as measured by image-based tracking of mated female fully fed flies maintained for 2 h at restrictive temperature. Points indicate the total duration of micromovements flies performed on yeast food spots. Boxplots indicate median and quartiles. Wilcoxon rank-sum test, numbers indicate P-values, n indicates number of animals. Some outliers were excluded from plotting due to scaling.

Source data

Extended Data Fig. 17 Sucrose feeding effects observed in borboleta activation experiments.

Number of sips from 20 mM sucrose as measured by the flyPAD assay in mated fully fed female flies maintained for 2h at restrictive temperature. These data correspond to the experiments shown in Fig. 6. The genotype of the Gal4 lines used to drive TRPA1 expression is depicted below the plots with empty depicting the matching empty-Gal4 control. Outer borboleta lines in magenta and inner borboleta lines in green. Points indicate the average number of sips on sucrose for each fly. Boxplots indicate median and quartiles. Wilcoxon rank-sum test, numbers indicate P-values, n indicates number of animals.

Extended Data Fig. 18 Feeding microstructure effects observed in borboleta activation experiments.

a and b, Feeding microstructure parameters for control flies (57C10-Gal4,UAS-GCaMP6s) as measured using the flyPAD assay. Points indicate the mean inter-burst interval in seconds (a) and sips per feeding burst (b). Boxplots indicate median and quartiles. Wilcoxon rank-sum test, numbers indicate P-values, n indicates number of animals. c, Mean inter-burst interval as measured by the flyPAD assay from 10% yeast (top) and 20 mM sucrose (bottom), by mated female fully fed flies maintained for 2h at restrictive temperature. The genotype of the Gal4 lines used to drive TRPA1 expression is depicted below the plots with empty depicting the matching empty-Gal4 control. Results for lines showing expression in the outer borboleta are plotted in magenta and for lines showing expression in the inner borboleta are plotted in green. Points indicate the mean inter-burst interval in seconds for each fly. Wilcoxon rank-sum test. d, As c but for the number of sips per burst. All plots correspond to the analysis of the experiments shown in Fig. 6. Wilcoxon rank-sum test. Some outliers were excluded from plotting due to scaling.

Source data

Supplementary information

Supplementary Tables

Supplementary Tables 1–4.

Reporting Summary

Supplementary Data 1

3D NifTI image stack of the binary SEZ atlas used for signal extraction

Supplementary Data 2

3D NifTI image stack of the SEZ standard used for alignments.

Supplementary Video 1

Volume rendering of volumetric imaging data. Example recording showing the SEZ response upon yeast stimulation in a protein-deprived fly. The recording is looped and the z-plane is changed from anterior to posterior to show responses across different SEZ layers. The stimulation period is indicated in the upper left corner. Playback speed is 10×.

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Source data for Figs. 1, 3–6 and Extended Data Figs. 5, 8–12, 14 and 16–18

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Münch, D., Goldschmidt, D. & Ribeiro, C. The neuronal logic of how internal states control food choice. Nature 607, 747–755 (2022). https://doi.org/10.1038/s41586-022-04909-5

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