Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster.

High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.

Generating parallel representations of position and identity in the olfactory system.

In Drosophila, a dedicated olfactory channel senses a male pheromone, cis-vaccenyl acetate (cVA), promoting female courtship while repelling males. Here, we show that separate cVA-processing streams extract qualitative and positional information. cVA sensory neurons respond to concentration differences in a 5-mm range around a male. Second-order projection neurons encode the angular position of a male by detecting inter-antennal differences in cVA concentration, which are amplified through contralateral inhibition. At the third circuit layer, we identify 47 cell types with diverse input-output connectivity. One population responds tonically to male flies, a second is tuned to olfactory looming, while a third integrates cVA and taste to coincidentally promote female mating. The separation of olfactory features resembles the mammalian what and where visual streams; together with multisensory integration, this enables behavioral responses appropriate to specific ethological contexts.

Integration of Parallel Opposing Memories Underlies Memory Extinction.

Accurately predicting an outcome requires that animals learn supporting and conflicting evidence from sequential experience. In mammals and invertebrates, learned fear responses can be suppressed by experiencing predictive cues without punishment, a process called memory extinction. Here, we show that extinction of aversive memories in Drosophila requires specific dopaminergic neurons, which indicate that omission of punishment is remembered as a positive experience. Functional imaging revealed co-existence of intracellular calcium traces in different places in the mushroom body output neuron network for both the original aversive memory and a new appetitive extinction memory. Light and ultrastructural anatomy are consistent with parallel competing memories being combined within mushroom body output neurons that direct avoidance. Indeed, extinction-evoked plasticity in a pair of these neurons neutralizes the potentiated odor response imposed in the network by aversive learning. Therefore, flies track the accuracy of learned expectations by accumulating and integrating memories of conflicting events.

A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster.

Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-neuron brain is a large but tractable target for comprehensive neural circuit mapping. Only electron microscopy (EM) enables complete, unbiased mapping of synaptic connectivity; however, the fly brain is too large for conventional EM. We developed a custom high-throughput EM platform and imaged the entire brain of an adult female fly at synaptic resolution. To validate the dataset, we traced brain-spanning circuitry involving the mushroom body (MB), which has been extensively studied for its role in learning. All inputs to Kenyon cells (KCs), the intrinsic neurons of the MB, were mapped, revealing a previously unknown cell type, postsynaptic partners of KC dendrites, and unexpected clustering of olfactory projection neurons. These reconstructions show that this freely available EM volume supports mapping of brain-spanning circuits, which will significantly accelerate Drosophila neuroscience. VIDEO ABSTRACT.

Neural circuit mechanisms underlying context-specific halting in Drosophila.

Walking is a complex motor programme involving coordinated and distributed activity across the brain and the spinal cord. Halting appropriately at the correct time is a critical component of walking control. Despite progress in identifying neurons driving halting1-6, the underlying neural circuit mechanisms responsible for overruling the competing walking state remain unclear. Here, using connectome-informed models7-9 and functional studies, we explain two fundamental mechanisms by which Drosophila implement context-appropriate halting. The first mechanism ('walk-OFF') relies on GABAergic neurons that inhibit specific descending walking commands in the brain, whereas the second mechanism ('brake') relies on excitatory cholinergic neurons in the nerve cord that lead to an active arrest of stepping movements. We show that two neurons that deploy the walk-OFF mechanism inhibit distinct populations of walking-promotion neurons, leading to differential halting of forward walking or turning. The brake neurons, by constrast, override all walking commands by simultaneously inhibiting descending walking-promotion neurons and increasing the resistance at the leg joints. We characterized two behavioural contexts in which the distinct halting mechanisms were used by the animal in a mutually exclusive manner: the walk-OFF mechanism was engaged for halting during feeding and the brake mechanism was engaged for halting and stability during grooming.

The fly connectome reveals a path to the effectome.

A goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome1-3 specifies the synaptic paths by which neurons can affect each other, but not how strongly they do affect each other in vivo. To overcome this limitation, we introduce a combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the 'effectome'. Specifically, we propose an estimator for a linear dynamical model of the fly brain that uses stochastic optogenetic perturbation data to estimate causal effects and the connectome as a prior to greatly improve estimation efficiency. We validate our estimator in connectome-based linear simulations and show that it recovers a linear approximation to the nonlinear dynamics of more biophysically realistic simulations. We then analyse the connectome to propose circuits that dominate the dynamics of the fly nervous system. We discover that the dominant circuits involve only relatively small populations of neurons-thus, neuron-level imaging, stimulation and identification are feasible. This approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, we provide evidence that fly whole-brain dynamics are generated by a large collection of small circuits that operate largely independently of each other. This implies that a causal model of a brain can be feasibly obtained in the fly.

Network statistics of the whole-brain connectome of Drosophila.

Brains comprise complex networks of neurons and connections, similar to the nodes and edges of artificial networks. Network analysis applied to the wiring diagrams of brains can offer insights into how they support computations and regulate the flow of information underlying perception and behaviour. The completion of the first whole-brain connectome of an adult fly, containing over 130,000 neurons and millions of synaptic connections1-3, offers an opportunity to analyse the statistical properties and topological features of a complete brain. Here we computed the prevalence of two- and three-node motifs, examined their strengths, related this information to both neurotransmitter composition and cell type annotations4,5, and compared these metrics with wiring diagrams of other animals. We found that the network of the fly brain displays rich-club organization, with a large population (30% of the connectome) of highly connected neurons. We identified subsets of rich-club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex ( https://codex.flywire.ai ) and should serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

A Drosophila computational brain model reveals sensorimotor processing.

The recent assembly of the adult Drosophila melanogaster central brain connectome, containing more than 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain1,2. Here we create a leaky integrate-and-fire computational model of the entire Drosophila brain, on the basis of neural connectivity and neurotransmitter identity3, to study circuit properties of feeding and grooming behaviours. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation4. In addition, using the model to activate neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing5-a testable hypothesis that we validate by optogenetic activation and behavioural studies. Activating different classes of gustatory neurons in the model makes accurate predictions of how several taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit, and accurately describes the circuit response upon activation of different mechanosensory subtypes6-10. Our results demonstrate that modelling brain circuits using only synapse-level connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can describe complete sensorimotor transformations.

Neuronal wiring diagram of an adult brain.

Connections between neurons can be mapped by acquiring and analysing electron microscopic brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative1-6, but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 × 107 chemical synapses7 between 139,255 neurons reconstructed from an adult female Drosophila melanogaster8,9. The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities10-12. Data products are available for download, programmatic access and interactive browsing and have been made interoperable with other fly data resources. We derive a projectome-a map of projections between regions-from the connectome and report on tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine and descending neurons) across both hemispheres and between the central brain and the optic lobes. Tracing from a subset of photoreceptors to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviours. The technologies and open ecosystem reported here set the stage for future large-scale connectome projects in other species.

Whole-brain annotation and multi-connectome cell typing of Drosophila.

The fruit fly Drosophila melanogaster has emerged as a key model organism in neuroscience, in large part due to the concentration of collaboratively generated molecular, genetic and digital resources available for it. Here we complement the approximately 140,000 neuron FlyWire whole-brain connectome1 with a systematic and hierarchical annotation of neuronal classes, cell types and developmental units (hemilineages). Of 8,453 annotated cell types, 3,643 were previously proposed in the partial hemibrain connectome2, and 4,581 are new types, mostly from brain regions outside the hemibrain subvolume. Although nearly all hemibrain neurons could be matched morphologically in FlyWire, about one-third of cell types proposed for the hemibrain could not be reliably reidentified. We therefore propose a new definition of cell type as groups of cells that are each quantitatively more similar to cells in a different brain than to any other cell in the same brain, and we validate this definition through joint analysis of FlyWire and hemibrain connectomes. Further analysis defined simple heuristics for the reliability of connections between brains, revealed broad stereotypy and occasional variability in neuron count and connectivity, and provided evidence for functional homeostasis in the mushroom body through adjustments of the absolute amount of excitatory input while maintaining the excitation/inhibition ratio. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open-source toolchain for brain-scale comparative connectomics.

A bidirectional circuit switch reroutes pheromone signals in male and female brains.

The Drosophila sex pheromone cVA elicits different behaviors in males and females. First- and second-order olfactory neurons show identical pheromone responses, suggesting that sex genes differentially wire circuits deeper in the brain. Using in vivo whole-cell electrophysiology, we now show that two clusters of third-order olfactory neurons have dimorphic pheromone responses. One cluster responds in females; the other responds in males. These clusters are present in both sexes and share a common input pathway, but sex-specific wiring reroutes pheromone information. Regulating dendritic position, the fruitless transcription factor both connects the male-responsive cluster and disconnects the female-responsive cluster from pheromone input. Selective masculinization of third-order neurons transforms their morphology and pheromone responses, demonstrating that circuits can be functionally rewired by the cell-autonomous action of a switch gene. This bidirectional switch, analogous to an electrical changeover switch, provides a simple circuit logic to activate different behaviors in males and females.

Olfactory receptor and circuit evolution promote host specialization.

The evolution of animal behaviour is poorly understood1,2. Despite numerous correlations between interspecific divergence in behaviour and nervous system structure and function, demonstrations of the genetic basis of these behavioural differences remain rare3-5. Here we develop a neurogenetic model, Drosophila sechellia, a species that displays marked differences in behaviour compared to its close cousin Drosophila melanogaster6,7, which are linked to its extreme specialization on noni fruit (Morinda citrifolia)8-16. Using calcium imaging, we identify olfactory pathways in D. sechellia that detect volatiles emitted by the noni host. Our mutational analysis indicates roles for different olfactory receptors in long- and short-range attraction to noni, and our cross-species allele-transfer experiments demonstrate that the tuning of one of these receptors is important for species-specific host-seeking. We identify the molecular determinants of this functional change, and characterize their evolutionary origin and behavioural importance. We perform circuit tracing in the D. sechellia brain, and find that receptor adaptations are accompanied by increased sensory pooling onto interneurons as well as species-specific central projection patterns. This work reveals an accumulation of molecular, physiological and anatomical traits that are linked to behavioural divergence between species, and defines a model for investigating speciation and the evolution of the nervous system.

Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set.

We develop an automatic method for synaptic partner identification in insect brains and use it to predict synaptic partners in a whole-brain electron microscopy dataset of the fruit fly. The predictions can be used to infer a connectivity graph with high accuracy, thus allowing fast identification of neural pathways. To facilitate circuit reconstruction using our results, we develop CIRCUITMAP, a user interface add-on for the circuit annotation tool CATMAID.

BAcTrace, a tool for retrograde tracing of neuronal circuits in Drosophila.

Animal behavior is encoded in neuronal circuits in the brain. To elucidate the function of these circuits, it is necessary to identify, record from and manipulate networks of connected neurons. Here we present BAcTrace (Botulinum-Activated Tracer), a genetically encoded, retrograde, transsynaptic labeling system. BAcTrace is based on Clostridium botulinum neurotoxin A, Botox, which we engineered to travel retrogradely between neurons to activate an otherwise silent transcription factor. We validated BAcTrace at three neuronal connections in the Drosophila olfactory system. We show that BAcTrace-mediated labeling allows electrophysiological recording of connected neurons. Finally, in a challenging circuit with highly divergent connections, BAcTrace correctly identified 12 of 16 connections that were previously observed by electron microscopy.

An olfactory receptor for food-derived odours promotes male courtship in Drosophila.

Many animals attract mating partners through the release of volatile sex pheromones, which can convey information on the species, gender and receptivity of the sender to induce innate courtship and mating behaviours by the receiver. Male Drosophila melanogaster fruitflies display stereotyped reproductive behaviours towards females, and these behaviours are controlled by the neural circuitry expressing male-specific isoforms of the transcription factor Fruitless (FRU(M)). However, the volatile pheromone ligands, receptors and olfactory sensory neurons (OSNs) that promote male courtship have not been identified in this important model organism. Here we describe a novel courtship function of Ionotropic receptor 84a (IR84a), which is a member of the chemosensory ionotropic glutamate receptor family, in a previously uncharacterized population of FRU(M)-positive OSNs. IR84a-expressing neurons are activated not by fly-derived chemicals but by the aromatic odours phenylacetic acid and phenylacetaldehyde, which are widely found in fruit and other plant tissues that serve as food sources and oviposition sites for drosophilid flies. Mutation of Ir84a abolishes both odour-evoked and spontaneous electrophysiological activity in these neurons and markedly reduces male courtship behaviour. Conversely, male courtship is increased--in an IR84a-dependent manner--in the presence of phenylacetic acid but not in the presence of another fruit odour that does not activate IR84a. Interneurons downstream of IR84a-expressing OSNs innervate a pheromone-processing centre in the brain. Whereas IR84a orthologues and phenylacetic-acid-responsive neurons are present in diverse drosophilid species, IR84a is absent from insects that rely on long-range sex pheromones. Our results suggest a model in which IR84a couples food presence to the activation of the fru(M) courtship circuitry in fruitflies. These findings reveal an unusual but effective evolutionary solution to coordinate feeding and oviposition site selection with reproductive behaviours through a specific sensory pathway.

Ultrafast tissue staining with chemical tags.

Genetically encoded fluorescent proteins and immunostaining are widely used to detect cellular and subcellular structures in fixed biological samples. However, for thick or whole-mount tissue, each approach suffers from limitations, including limited spectral flexibility and lower signal or slow speed, poor penetration, and high background labeling, respectively. We have overcome these limitations by using transgenically expressed chemical tags for rapid, even, high-signal and low-background labeling of thick biological tissues. We first construct a platform of widely applicable transgenic Drosophila reporter lines, demonstrating that chemical labeling can accelerate staining of whole-mount fly brains by a factor of 100. Using viral vectors to deliver chemical tags into the mouse brain, we then demonstrate that this labeling strategy works well in mice. Thus this tag-based approach drastically improves the speed and specificity of labeling genetically marked cells in intact and/or thick biological samples.

From connectome to effectome: learning the causal interaction map of the fly brain.

A long-standing goal of neuroscience is to obtain a causal model of the nervous system. This would allow neuroscientists to explain animal behavior in terms of the dynamic interactions between neurons. The recently reported whole-brain fly connectome [1-7] specifies the synaptic paths by which neurons can affect each other but not whether, or how, they do affect each other in vivo. To overcome this limitation, we introduce a novel combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the "effectome". Specifically, we propose an estimator for a dynamical systems model of the fly brain that uses stochastic optogenetic perturbation data to accurately estimate causal effects and the connectome as a prior to drastically improve estimation efficiency. We then analyze the connectome to propose circuits that have the greatest total effect on the dynamics of the fly nervous system. We discover that, fortunately, the dominant circuits significantly involve only relatively small populations of neurons-thus imaging, stimulation, and neuronal identification are feasible. Intriguingly, we find that this approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, our analyses of the connectome provide evidence that global dynamics of the fly brain are generated by a large collection of small and often anatomically localized circuits operating, largely, independently of each other. This in turn implies that a causal model of a brain, a principal goal of systems neuroscience, can be feasibly obtained in the fly.

Network Statistics of the Whole-Brain Connectome of Drosophila.

Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

Comparative connectomics of the descending and ascending neurons of the Drosophila nervous system: stereotypy and sexual dimorphism.

In most complex nervous systems there is a clear anatomical separation between the nerve cord, which contains most of the final motor outputs necessary for behaviour, and the brain. In insects, the neck connective is both a physical and information bottleneck connecting the brain and the ventral nerve cord (VNC, spinal cord analogue) and comprises diverse populations of descending (DN), ascending (AN) and sensory ascending neurons, which are crucial for sensorimotor signalling and control. Integrating three separate EM datasets, we now provide a complete connectomic description of the ascending and descending neurons of the female nervous system of Drosophila and compare them with neurons of the male nerve cord. Proofread neuronal reconstructions have been matched across hemispheres, datasets and sexes. Crucially, we have also matched 51% of DN cell types to light level data defining specific driver lines as well as classifying all ascending populations. We use these results to reveal the general architecture, tracts, neuropil innervation and connectivity of neck connective neurons. We observe connected chains of descending and ascending neurons spanning the neck, which may subserve motor sequences. We provide a complete description of sexually dimorphic DN and AN populations, with detailed analysis of circuits implicated in sex-related behaviours, including female ovipositor extrusion (DNp13), male courtship (DNa12/aSP22) and song production (AN hemilineage 08B). Our work represents the first EM-level circuit analyses spanning the entire central nervous system of an adult animal.