Coupling of activity, metabolism and behaviour across the Drosophila brain

Abstract

Coordinated activity across networks of neurons is a hallmark of both resting and active behavioural states in many species^1-5. 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 activity^6,7. 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.

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. ). 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).

Fig. 1 |. Metabolic and neural networks are highly correlated across the brain.

a, Top left, schematic of the preparation that allows two-photon imaging across the fly brain. Top right, cartoon of the imaged region of the fly brain. Bottom, schematic of a neuronal process, denoting the metabolic pathways that lead to ATP production, and the sensors that were used to measure changes in intracellular calcium concentration (GCaMP6s and jRGECO1a), pyruvate concentration (Pyronic) and ATP concentration (iATPSnFR). CAC, citric acid cycle. b–d, Matrices of pairwise correlations between brain regions. b, GCaMP6s. c, Pyronic. d, iATPSnFR. e–g, Scatter plots of the pairwise correlations between matrices. e, Pyronic versus GCaMP6s. f, iATPSnFR versus GCaMP6s. g, iATPSnFR versus Pyronic. n = 12 flies for GCaMP6s, n = 10 for Pyronic, n = 10 for iATPSNFr.

Fig. 2 |. Simultaneous measurements of neural activity and metabolic flux reveal correlations that are dominated by low frequencies.

a–f, Comparison of jRGECO1a and iATPSnFR signals within a single brain region (right superior medial protocerebrum (SMP-R)). a, Traces of iATPSnFR and jRGECO1a signals including all frequency components. b, Pairwise comparison of iATPSnFR and jRGECO1a signals, including all frequency components and the correlation between these signals. c, d, As in a, b, but filtered to include only low-frequency (<0.1 Hz) components. e, f, As in a, b, but filtered to include only high-frequency (>0.1 Hz) components. g, Pairwise correlations between iATPSnFR and jRGECO1a signals measured in each brain region, as a function of frequency (green trace) and shuffle control in which pairwise comparisons were done between brain regions with identities that have been shuffled (black trace). n = 24 flies, mean ± s.e.m. (shading). ROI, region of interest. h, As in g, but with Pyronic (orange). n = 24 flies, mean ± s.e.m. (shading).

Fig. 3 |. Neural activity drives metabolic flux in the brain.

a, Comparison of the variance of the signals of each ROI before and after TTX application, jRGECO1a (blue) (n = 20 flies), Pyronic (orange) (n = 8 flies) and iATPSnFR (green) (n = 20 flies), mean ± s.e.m. for each region. b, The relative reduction in signal power caused by TTX application, as a function of frequency across all brain regions and flies (n = 54 regions, mean ± 95% confidence interval (shading)). c, Correlation of the correlation maps between flies before and after TTX application, across all brain regions, for jRGECO1a (calcium) (blue dots), Pyronic (pyruvate) (orange dots) and for iATPSnFR (ATP) (green dots) (mean ± 95% confidence interval) ***P < 0.0004, **P < 0.005. d, Schematic of optogenetic stimulation-imaging protocol. Top, cartoon of the imaged fly brain showing the whole-mounted brain and a detailed view of antennal lobe (AL) and imaged projection neurons (PN), with multiple stimulation ROIs indicated by black circles. Bottom, example of stimulation-imaging protocol, with CsChrimson activation (black) and imaging responses of either GCaMP6s (blue) or iATPSnFR (green). e, Left, GCaMP6s response to 10-ms CsChrimson activation (stimulation window denoted by black tick, not to scale). Frames collected during the stimulation window are not shown, as optical stimulation produces a large artefact (n = 141 ROIs, mean ± s.e.m.). Right, 10 s of imaging data from left (box) with exponential fit (black). f, As in e, but with a 50-ms activation pulse (n = 77 ROIs). g, As in e, but with iATPSnFR (n = 124 ROIs, mean ± s.e.m). h, As in g, but with a 50-ms activation pulse (n = 123 ROIs). i, Normalized spectrum of calcium signals from data collected for Fig. 4 (blue), with linear fit (red). AU, arbitrary units. j, Autocorrelation of calcium signal from data collected for Fig. 4.

Fig. 4 |. Neural activity and metabolic flux are correlated with behaviour in specific regions.

a, Receiver–operator curve (ROC) showing a good prediction of behaviour across all flies, using models based on jRGECO1a (blue line) (area under curve (AUC) = 0.82, P < 0.0001, one-tailed t-test against 0.5), and poorer but significant prediction of behaviour using Pyronic (orange line) (AUC = 0.54, P < 0.05, one-tailed t-test against 0.5), and iATPSnFR (green line) (AUC = 0.59, P < 0.001) mean ± s.e.m. b, Comparisons of correlations between predictions of behaviour based on jRGECO1a (blue line), Pyronic (orange line) and iATPSnFR (green line) across a range of frequencies. c, Average weights of each ROI generated from the logistic regression model when computed at the peak frequency of correlation for metabolic flux and behaviour (0.04 Hz) mean ± s.e.m. Images are sagittal (left), coronal (middle) and axial (right) views of the central brain, and coloured by weight. A, anterior; D, dorsal; P, posterior; V, ventral. n = 12 flies for jRGECO1a, n = 8 flies for Pyronic, n = 13 flies for iATPSnFR.