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Coupling of activity, metabolism and behaviour across the Drosophila brain

Nature volume 593, pages 244–248 (2021)Cite this article

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Abstract

Coordinated activity across networks of neurons is a hallmark of both resting and active behavioural states in many species1,2,3,4,5. These global patterns alter energy metabolism over seconds to hours, which underpins the widespread use of oxygen consumption and glucose uptake as proxies of neural activity6,7. However, whether changes in neural activity are causally related to metabolic flux in intact circuits on the timescales associated with behaviour is unclear. Here we combine two-photon microscopy of the fly brain with sensors that enable the simultaneous measurement of neural activity and metabolic flux, across both resting and active behavioural states. We demonstrate that neural activity drives changes in metabolic flux, creating a tight coupling between these signals that can be measured across brain networks. Using local optogenetic perturbation, we demonstrate that even transient increases in neural activity result in rapid and persistent increases in cytosolic ATP, which suggests that neuronal metabolism predictively allocates resources to anticipate the energy demands of future activity. Finally, our studies reveal that the initiation of even minimal behavioural movements causes large-scale changes in the pattern of neural activity and energy metabolism, which reveals a widespread engagement of the brain. As the relationship between neural activity and energy metabolism is probably evolutionarily ancient and highly conserved, our studies provide a critical foundation for using metabolic proxies to capture changes in neural activity.

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Fig. 1: Metabolic and neural networks are highly correlated across the brain.
Fig. 2: Simultaneous measurements of neural activity and metabolic flux reveal correlations that are dominated by low frequencies.
Fig. 3: Neural activity drives metabolic flux in the brain.
Fig. 4: Neural activity and metabolic flux are correlated with behaviour in specific regions.

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Data availability

Raw imaging data are available upon request to the corresponding authors.

Code availability

Analysis scripts are available upon request from the corresponding authors.

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Acknowledgements

We thank C. Gallen for providing discussion and support. This work was supported by the Simons foundation (T.R.C. and S.G.), an NSF Career Award 1845166 (S.G.) and the Stanford Wu Tsai neuroscience institute (K.M.)

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Author notes

  1. These authors contributed equally: Kevin Mann, Stephane Deny

Authors and Affiliations

  1. Department of Neurobiology, Stanford University, Stanford, CA, USA

    Kevin Mann, Surya Ganguli & Thomas R. Clandinin

  2. Department of Applied Physics, Stanford University, Stanford, CA, USA

    Stephane Deny & Surya Ganguli

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Contributions

K.M., S.D., S.G. and T.R.C. conceived the project. K.M. collected the data. K.M. and S.D. analysed the data and generated figures. K.M., S.D., S.G. and T.R.C. wrote the paper.

Corresponding authors

Correspondence to Surya Ganguli or Thomas R. Clandinin.

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The authors declare no competing interests.

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Peer review information Nature thanks Marcus Raichle and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Normalized iATPSnFR responses in whole brains to ATP.

Normalized ∆F/F values for different concentrations of ATP measured in whole brains expressing iATPSnFR pan-neuronally. n = 10 flies, mean ± s.e.m.

Extended Data Fig. 2 Example traces and correlations of Pyronic, jRGECO1a and iATPSnFR.

a, Pyronic traces over an imaging session in different regions. b, A pair of traces that exhibit high correlation over time. c, Scatter plot of these two regions demonstrating correlation. d, A pair of traces that exhibit lower correlation over time. e, Scatter plot of these two regions demonstrating correlation. f–j, As in a–e, but with jRGECO1a. k–o, As in a–e, but with iATPSnFR.

Extended Data Fig. 3 Correlation matrices of GCaMP6s, Pyronic and iATPSnFR.

a–c, Correlation matrices for GCaMP6s, Pyronic and iATPSnFR, reproduced and enlarged from Fig. 1, and labelling each individual region.

Extended Data Fig. 4 Correspondence of functional networks derived from simultaneous jRGECO1a, Pyronic and iATPSnFR measurements.

a, Left, traces displaying iATPSnFR (green) and corresponding jRGECO1a signal (blue). Right, Pyronic signals (orange) and corresponding jRGECO1a signals (blue) across six different brain regions b, Correlation matrix derived from jRGECO1a in the simultaneous imaging experiments from a and Fig. 2. c, Correlation matrix derived from Pyronic in the simultaneous imaging experiments from a and Fig. 2. d, Scatter plot of the pairwise correlations between jRGECO1a and Pyronic. e–g, As in b–d, but with jRGECO1a and iATPSnFR. n = 23 flies for Pyronic and n = 9 flies for iATPSnFR. h–m, Comparison of jRGECO1a and Pyronic signals within a single brain region (saddle (SAD)). h, Traces of Pyronic and jRGECO1a signals including all frequency components. i, Pairwise comparison of Pyronic and jRGECO1a signals including all frequency components and the correlation between these signals. j, k, As in h, i, but filtered to include only low-frequency (<0.1 Hz) components. l, m, As in h, i, but filtered to include only high-frequency (>0.1 Hz) components.

Extended Data Fig. 5 Neural activity drives metabolic flux in the brain.

a, jRGECO1a (blue), Pyronic (orange) and iATPSnFR (green) traces in three different brain regions before (left) and after (right) application of TTX. b, Region-by-region correlations between jRGECO1a and Pyronic signals (orange) and between jRGECO1a and iATPSnFR signals (green), across all flies, before TTX application (top row) and after TTX application (bottom row). Mean ± s.e.m. c, GCaMP6s response to 100-ms activation pulse in flies that lack CsChrimson. n = 45 ROIs, mean ± s.e.m. d, As in c, but with iATPSnFR. n = 45 ROIs, mean ± s.e.m.

Extended Data Fig. 6 Example model predictions of behaviour and CsChrimson controls.

a, Schematic of the data processing and analysis pipeline used: (i) traces of Pyronic, iATPSnFR, jRGECO1a and behaviour (movement of the legs); (ii) half of the dataset was used to train a logistic regression model relating neural activity and metabolic flux to behaviour; (iii) predicted behavioural outputs were generated using the withheld data and were compared to the actual behaviour during those time periods; and (iv) model prediction was evaluated by correlating predicted behaviour to observed behaviour. b, Left, four example flies showing the prediction based on the model for jRGECO1a (blue) with the corresponding behaviour trace (black). Correlation between signals shown above each trace. Right, weights for each ROI generated by the model shown on right (oriented as in Fig. 4c). c, As in b, but with Pyronic (orange). d, e, As in b, c, but with a different set of four flies, with jRGECO1a (blue), iATPSnFR (green) and behaviour trace (black). f, Correlation between model weights derived from iATPSnFR and jRGECO1a. g, Correlation between model weights derived from Pyronic and jRGECO1a.

Extended Data Fig. 7 Frequency spectra of jRGECO1a, Pyronic, iATPSnFR and behaviour.

Normalized spectra from data presented in Fig. 4.

Extended Data Fig. 8 Correlation of model weights for GCaMP6s and descending-neuron innervation.

a, Model weights for each brain region generated using GCaMP6s. b, The number of descending-neuron processes in each brain region (abbreviations defined as in ref. 32). c, Graphical representation of model weights, similar to Fig. 4c. d, Correlation between model weights and descending-neuron innervation by each region. e, Correlation between model weights derived from GCaMP6s and jRGECO1a.

Extended Data Fig. 9 Changes in correlations across regions during behaviour for both jRGECO1a and Pyronic.

a, Functional connectivity map of jRGECO1a during bouts of rest. b, Functional connectivity map of jRGECO1a during bouts of activity. c, Correlation of functional connectivity maps during resting and behaving bouts. Correlations increase across the vast majority of regions (P = 0.004, n = 12 flies, one-tailed t-test). d–f, As in a–c, but for Pyronic (P = 0.13, n = 7 flies, one-tailed t-test). g–i, As in a–c, but for iATPSnFR (P = 0.38, n = 13 flies, one-tailed t-test).

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Mann, K., Deny, S., Ganguli, S. et al. Coupling of activity, metabolism and behaviour across the Drosophila brain. Nature 593, 244–248 (2021). https://doi.org/10.1038/s41586-021-03497-0

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