The natverse, a versatile toolbox for combining and analysing neuroanatomical data

Taste quality and hunger interactions in a feeding sensorimotor circuit

Taste detection and hunger state dynamically regulate the decision to initiate feeding. To study how context-appropriate feeding decisions are generated, we combined synaptic resolution circuit reconstruction with targeted genetic access to specific neurons to elucidate a gustatory sensorimotor circuit for feeding initiation in adult Drosophila melanogaster. This circuit connects gustatory sensory neurons to proboscis motor neurons through three intermediate layers. Most neurons in this pathway are necessary and sufficient for proboscis extension, a feeding initiation behavior, and respond selectively to sugar taste detection. Pathway activity is amplified by hunger signals that act at select second-order neurons to promote feeding initiation in food-deprived animals. In contrast, the feeding initiation circuit is inhibited by a bitter taste pathway that impinges on premotor neurons, illuminating a local motif that weighs sugar and bitter taste detection to adjust the behavioral outcomes. Together, these studies reveal central mechanisms for the integration of external taste detection and internal nutritive state to flexibly execute a critical feeding decision.

nGauge: Integrated and Extensible Neuron Morphology Analysis in Python

The study of neuron morphology requires robust and comprehensive methods to quantify the differences between neurons of different subtypes and animal species. Several software packages have been developed for the analysis of neuron tracing results stored in the standard SWC format. The packages, however, provide relatively simple quantifications and their non-extendable architecture prohibit their use for advanced data analysis and visualization. We developed nGauge, a Python toolkit to support the parsing and analysis of neuron morphology data. As an application programming interface (API), nGauge can be referenced by other popular open-source software to create custom informatics analysis pipelines and advanced visualizations. nGauge defines an extendable data structure that handles volumetric constructions (e.g. soma), in addition to the SWC linear reconstructions, while remaining lightweight. This greatly extends nGauge's data compatibility.

nAdder: A scale-space approach for the 3D analysis of neuronal traces

Tridimensional microscopy and algorithms for automated segmentation and tracing are revolutionizing neuroscience through the generation of growing libraries of neuron reconstructions. Innovative computational methods are needed to analyze these neuronal traces. In particular, means to characterize the geometric properties of traced neurites along their trajectory have been lacking. Here, we propose a local tridimensional (3D) scale metric derived from differential geometry, measuring for each point of a curve the characteristic length where it is fully 3D as opposed to being embedded in a 2D plane or 1D line. The larger this metric is and the more complex the local 3D loops and turns of the curve are. Available through the GeNePy3D open-source Python quantitative geometry library (https://genepy3d.gitlab.io), this approach termed nAdder offers new means of describing and comparing axonal and dendritic arbors. We validate this metric on simulated and real traces. By reanalysing a published zebrafish larva whole brain dataset, we show its ability to characterize different population of commissural axons, distinguish afferent connections to a target region and differentiate portions of axons and dendrites according to their behavior, shedding new light on the stereotypical nature of neurites' local geometry.

Circadian programming of the ellipsoid body sleep homeostat in Drosophila

Homeostatic and circadian processes collaborate to appropriately time and consolidate sleep and wake. To understand how these processes are integrated, we scheduled brief sleep deprivation at different times of day in Drosophila and find elevated morning rebound compared to evening. These effects depend on discrete morning and evening clock neurons, independent of their roles in circadian locomotor activity. In the R5 ellipsoid body sleep homeostat, we identified elevated morning expression of activity dependent and presynaptic gene expression as well as the presynaptic protein BRUCHPILOT consistent with regulation by clock circuits. These neurons also display elevated calcium levels in response to sleep loss in the morning, but not the evening consistent with the observed time-dependent sleep rebound. These studies reveal the circuit and molecular mechanisms by which discrete circadian clock neurons program a homeostatic sleep center.

Drosophila gustatory projections are segregated by taste modality and connectivity

Gustatory sensory neurons detect caloric and harmful compounds in potential food and convey this information to the brain to inform feeding decisions. To examine the signals that gustatory neurons transmit and receive, we reconstructed gustatory axons and their synaptic sites in the adult Drosophila melanogaster brain, utilizing a whole-brain electron microscopy volume. We reconstructed 87 gustatory projections from the proboscis labellum in the right hemisphere and 57 from the left, representing the majority of labellar gustatory axons. Gustatory neurons contain a nearly equal number of interspersed pre-and post-synaptic sites, with extensive synaptic connectivity among gustatory axons. Morphology- and connectivity-based clustering revealed six distinct groups, likely representing neurons recognizing different taste modalities. The vast majority of synaptic connections are between neurons of the same group. This study resolves the anatomy of labellar gustatory projections, reveals that gustatory projections are segregated based on taste modality, and uncovers synaptic connections that may alter the transmission of gustatory signals.

Smart imaging to empower brain-wide neuroscience at single-cell levels

A deep understanding of the neuronal connectivity and networks with detailed cell typing across brain regions is necessary to unravel the mechanisms behind the emotional and memorial functions as well as to find the treatment of brain impairment. Brain-wide imaging with single-cell resolution provides unique advantages to access morphological features of a neuron and to investigate the connectivity of neuron networks, which has led to exciting discoveries over the past years based on animal models, such as rodents. Nonetheless, high-throughput systems are in urgent demand to support studies of neural morphologies at larger scale and more detailed level, as well as to enable research on non-human primates (NHP) and human brains. The advances in artificial intelligence (AI) and computational resources bring great opportunity to 'smart' imaging systems, i.e., to automate, speed up, optimize and upgrade the imaging systems with AI and computational strategies. In this light, we review the important computational techniques that can support smart systems in brain-wide imaging at single-cell resolution.

Walking strides direct rapid and flexible recruitment of visual circuits for course control in Drosophila

Flexible mapping between activity in sensory systems and movement parameters is a hallmark of motor control. This flexibility depends on the continuous comparison of short-term postural dynamics and the longer-term goals of an animal, thereby necessitating neural mechanisms that can operate across multiple timescales. To understand how such body-brain interactions emerge across timescales to control movement, we performed whole-cell patch recordings from visual neurons involved in course control in Drosophila. We show that the activity of leg mechanosensory cells, propagating via specific ascending neurons, is critical for stride-by-stride steering adjustments driven by the visual circuit, and, at longer timescales, it provides information about the moving body’s state to flexibly recruit the visual circuit for course control. Thus, our findings demonstrate the presence of an elegant stride-based mechanism operating at multiple timescales for context-dependent course control. We propose that this mechanism functions as a general basis for the adaptive control of locomotion.

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HS cells receive stride-coupled signals via ascending neurons

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The stride-coupled signals reflect an internal motor context

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Motor context modulates HS cells at multiple timescales

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HS cells drive rapid steering depending on motor context

The Neuronal Circuit of the Dorsal Circadian Clock Neurons in Drosophila melanogaster

Drosophila’s dorsal clock neurons (DNs) consist of four clusters (DN_1as, DN_1ps, DN₂s, and DN₃s) that largely differ in size. While the DN_1as and the DN₂s encompass only two neurons, the DN_1ps consist of ∼15 neurons, and the DN₃s comprise ∼40 neurons per brain hemisphere. In comparison to the well-characterized lateral clock neurons (LNs), the neuroanatomy and function of the DNs are still not clear. Over the past decade, numerous studies have addressed their role in the fly’s circadian system, leading to several sometimes divergent results. Nonetheless, these studies agreed that the DNs are important to fine-tune activity under light and temperature cycles and play essential roles in linking the output from the LNs to downstream neurons that control sleep and metabolism. Here, we used the Flybow system, specific split-GAL4 lines, trans-Tango, and the recently published fly connectome (called hemibrain) to describe the morphology of the DNs in greater detail, including their synaptic connections to other clock and non-clock neurons. We show that some DN groups are largely heterogenous. While certain DNs are strongly connected with the LNs, others are mainly output neurons that signal to circuits downstream of the clock. Among the latter are mushroom body neurons, central complex neurons, tubercle bulb neurons, neurosecretory cells in the pars intercerebralis, and other still unidentified partners. This heterogeneity of the DNs may explain some of the conflicting results previously found about their functionality. Most importantly, we identify two putative novel communication centers of the clock network: one fiber bundle in the superior lateral protocerebrum running toward the anterior optic tubercle and one fiber hub in the posterior lateral protocerebrum. Both are invaded by several DNs and LNs and might play an instrumental role in the clock network.

Olfactory stimuli and moonwalker SEZ neurons can drive backward locomotion in Drosophila

How different sensory stimuli are collected, processed, and further transformed into a coordinated motor response is a fundamental question in neuroscience. In particular, the internal and external conditions that drive animals to switch to backward walking and the mechanisms by which the nervous system supports such behavior are still unknown. In fruit flies, moonwalker descending neurons (MDNs) are considered command-type neurons for backward locomotion as they receive visual and mechanosensory inputs and transmit motor-related signals to downstream neurons to elicit backward locomotion. Whether other modalities converge onto MDNs, which central brain neurons activate MDNs, and whether other retreat-driving pathways exist is currently unknown. Here, we show that olfactory stimulation can elicit MDN-mediated backward locomotion. Moreover, we identify the moonwalker subesophageal zone neurons (MooSEZs), a pair of bilateral neurons, which can trigger straight and rotational backward locomotion. MooSEZs act via postsynaptic MDNs and via other descending neurons. Although they respond to olfactory input, they are not required for odor-induced backward walking. Thus, this work reveals an important modality input to MDNs, a novel set of neurons presynaptic to MDNs driving backward locomotion and an MDN-independent backward locomotion pathway.

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MooSEZs elicit backward locomotion via MDN-dependent and MDN-independent pathways

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MooSEZs are connected to MDNs and other descending neurons

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MooSEZs and MDNs both respond to olfactory input

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MooSEZs can trigger rotational backward locomotion

Connectomic features underlying diverse synaptic connection strengths and subcellular computation

Connectomes generated from electron microscopy images of neural tissue unveil the complex morphology of every neuron and the locations of every synapse interconnecting them. These wiring diagrams may also enable inference of synaptic and neuronal biophysics, such as the functional weights of synaptic connections, but this requires integration with physiological data to properly parameterize. Working with a stereotyped olfactory network in the Drosophila brain, we make direct comparisons of the anatomy and physiology of diverse neurons and synapses with subcellular and subthreshold resolution. We find that synapse density and location jointly predict the amplitude of the somatic postsynaptic potential evoked by a single presynaptic spike. Biophysical models fit to data predict that electrical compartmentalization allows axon and dendrite arbors to balance independent and interacting computations. These findings begin to fill the gap between connectivity maps and activity maps, which should enable new hypotheses about how network structure constrains network function.

Drosophila melanogaster Chemosensory Pathways as Potential Targets to Curb the Insect Menace

Simple Summary

The perception and processing of chemosensory stimuli are indispensable to the survival of living organisms. In insects, olfaction and gustation play a critical role in seeking food, finding mates and avoiding signs of danger. This review aims to present updated information about olfactory and gustatory signaling in the fruit fly Drosophila melanogaster. We have described the mechanisms involved in olfactory and gustatory perceptions at the molecular level, the receptors along with the allied molecules involved, and their signaling pathways in the fruit fly. Due to the magnifying problems of disease-causing insect vectors and crop pests, the applications of chemosensory signaling in controlling pests and insect vectors are also discussed.

Abstract

From a unicellular bacterium to a more complex human, smell and taste form an integral part of the basic sensory system. In fruit flies Drosophila melanogaster, the behavioral responses to odorants and tastants are simple, though quite sensitive, and robust. They explain the organization and elementary functioning of the chemosensory system. Molecular and functional analyses of the receptors and other critical molecules involved in olfaction and gustation are not yet completely understood. Hence, a better understanding of chemosensory cue-dependent fruit flies, playing a major role in deciphering the host-seeking behavior of pathogen transmitting insect vectors (mosquitoes, sandflies, ticks) and crop pests (Drosophila suzukii, Queensland fruit fly), is needed. Using D. melanogaster as a model organism, the knowledge gained may be implemented to design new means of controlling insects as well as in analyzing current batches of insect and pest repellents. In this review, the complete mechanisms of olfactory and gustatory perception, along with their implementation in controlling the global threat of disease-transmitting insect vectors and crop-damaging pests, are explained in fruit flies.

Descending neurons coordinate anterior grooming behavior in Drosophila

The brain coordinates the movements that constitute behavior, but how descending neurons convey the myriad of commands required to activate the motor neurons of the limbs in the right order and combinations to produce those movements is not well understood. For anterior grooming behavior in the fly, we show that its component head sweeps and leg rubs can be initiated separately, or as a set, by different descending neurons. Head sweeps and leg rubs are mutually exclusive movements of the front legs that normally alternate, and we show that circuits in the ventral nerve cord as well as in the brain can resolve competing commands. Finally, the left and right legs must work together to remove debris. The coordination for leg rubs can be achieved by unilateral activation of a single descending neuron, while a similar manipulation of a different descending neuron decouples the legs to produce single-sided head sweeps. Taken together, these results demonstrate that distinct descending neurons orchestrate the complex alternation between the movements that make up anterior grooming.

Transforming representations of movement from body- to world-centric space

When an animal moves through the world, its brain receives a stream of information about the body's translational velocity from motor commands and sensory feedback signals. These incoming signals are referenced to the body, but ultimately, they must be transformed into world-centric coordinates for navigation1,2. Here we show that this computation occurs in the fan-shaped body in the brain of Drosophila melanogaster. We identify two cell types, PFNd and PFNv3-5, that conjunctively encode translational velocity and heading as a fly walks. In these cells, velocity signals are acquired from locomotor brain regions6 and are multiplied with heading signals from the compass system. PFNd neurons prefer forward-ipsilateral movement, whereas PFNv neurons prefer backward-contralateral movement, and perturbing PFNd neurons disrupts idiothetic path integration in walking flies7. Downstream, PFNd and PFNv neurons converge onto hΔB neurons, with a connectivity pattern that pools together heading and translation direction combinations corresponding to the same movement in world-centric space. This network motif effectively performs a rotation of the brain's representation of body-centric translational velocity according to the current heading direction. Consistent with our predictions, we observe that hΔB neurons form a representation of translational velocity in world-centric coordinates. By integrating this representation over time, it should be possible for the brain to form a working memory of the path travelled through the environment8-10.

Mesoscale microscopy and image analysis tools for understanding the brain

Over the last ten years, developments in whole-brain microscopy now allow for high-resolution imaging of intact brains of small animals such as mice. These complex images contain a wealth of information, but many neuroscience laboratories do not have all of the computational knowledge and tools needed to process these data. We review recent open source tools for registration of images to atlases, and the segmentation, visualisation and analysis of brain regions and labelled structures such as neurons. Since the field lacks fully integrated analysis pipelines for all types of whole-brain microscopy analysis, we propose a pathway for tool developers to work together to meet this challenge.

Desmosomal connectomics of all somatic muscles in an annelid larva

Cells form networks in animal tissues through synaptic, chemical, and adhesive links. Invertebrate muscle cells often connect to other cells through desmosomes, adhesive junctions anchored by intermediate filaments. To study desmosomal networks, we skeletonised 853 muscle cells and their desmosomal partners in volume electron microscopy data covering an entire larva of the annelid Platynereis. Muscle cells adhere to each other, to epithelial, glial, ciliated, and bristle-producing cells and to the basal lamina, forming a desmosomal connectome of over 2000 cells. The aciculae - chitin rods that form an endoskeleton in the segmental appendages - are highly connected hubs in this network. This agrees with the many degrees of freedom of their movement, as revealed by video microscopy. Mapping motoneuron synapses to the desmosomal connectome allowed us to infer the extent of tissue influenced by motoneurons. Our work shows how cellular-level maps of synaptic and adherent force networks can elucidate body mechanics.

The lateral posterior clock neurons (LPN) of Drosophila melanogaster express three neuropeptides and have multiple connections within the circadian clock network and beyond

Drosophila's lateral posterior neurons (LPNs) belong to a small group of circadian clock neurons that is so far not characterized in detail. Thanks to a new highly specific split-Gal4 line, here we describe LPNs' morphology in fine detail, their synaptic connections, daily bimodal expression of neuropeptides, and propose a putative role of this cluster in controlling daily activity and sleep patterns. We found that the three LPNs are heterogeneous. Two of the neurons with similar morphology arborize in the superior medial and lateral protocerebrum and most likely promote sleep. One unique, possibly wakefulness-promoting, neuron with wider arborizations extends from the superior lateral protocerebrum toward the anterior optic tubercle. Both LPN types exhibit manifold connections with the other circadian clock neurons, especially with those that control the flies' morning and evening activity (M- and E-neurons, respectively). In addition, they form synaptic connections with neurons of the mushroom bodies, the fan-shaped body, and with many additional still unidentified neurons. We found that both LPN types rhythmically express three neuropeptides, Allostatin A, Allostatin C, and Diuretic Hormone 31 with maxima in the morning and the evening. The three LPN neuropeptides may, furthermore, signal to the insect hormonal center in the pars intercerebralis and contribute to rhythmic modulation of metabolism, feeding, and reproduction. We discuss our findings in the light of anatomical details gained by the recently published hemibrain of a single female fly on the electron microscopic level and of previous functional studies concerning the LPN. This article is protected by copyright. All rights reserved.

FlyWire: online community for whole-brain connectomics

Due to advances in automated image acquisition and analysis, whole-brain connectomes with 100,000 or more neurons are on the horizon. Proofreading of whole-brain automated reconstructions will require many person-years of effort, due to the huge volumes of data involved. Here we present FlyWire, an online community for proofreading neural circuits in a Drosophila melanogaster brain and explain how its computational and social structures are organized to scale up to whole-brain connectomics. Browser-based three-dimensional interactive segmentation by collaborative editing of a spatially chunked supervoxel graph makes it possible to distribute proofreading to individuals located virtually anywhere in the world. Information in the edit history is programmatically accessible for a variety of uses such as estimating proofreading accuracy or building incentive systems. An open community accelerates proofreading by recruiting more participants and accelerates scientific discovery by requiring information sharing. We demonstrate how FlyWire enables circuit analysis by reconstructing and analyzing the connectome of mechanosensory neurons.

Synaptic targets of photoreceptors specialized to detect color and skylight polarization in Drosophila

Color and polarization provide complementary information about the world and are detected by specialized photoreceptors. However, the downstream neural circuits that process these distinct modalities are incompletely understood in any animal. Using electron microscopy, we have systematically reconstructed the synaptic targets of the photoreceptors specialized to detect color and skylight polarization in Drosophila, and we have used light microscopy to confirm many of our findings. We identified known and novel downstream targets that are selective for different wavelengths or polarized light, and followed their projections to other areas in the optic lobes and the central brain. Our results revealed many synapses along the photoreceptor axons between brain regions, new pathways in the optic lobes, and spatially segregated projections to central brain regions. Strikingly, photoreceptors in the polarization-sensitive dorsal rim area target fewer cell types, and lack strong connections to the lobula, a neuropil involved in color processing. Our reconstruction identifies shared wiring and modality-specific specializations for color and polarization vision, and provides a comprehensive view of the first steps of the pathways processing color and polarized light inputs.

Niwaki Instead of Random Forests: Targeted Serial Sectioning Scanning Electron Microscopy With Reimaging Capabilities for Exploring Central Nervous System Cell Biology and Pathology

Ultrastructural analysis of discrete neurobiological structures by volume scanning electron microscopy (SEM) often constitutes a "needle-in-the-haystack" problem and therefore relies on sophisticated search strategies. The appropriate SEM approach for a given relocation task not only depends on the desired final image quality but also on the complexity and required accuracy of the screening process. Block-face SEM techniques like Focused Ion Beam or serial block-face SEM are "one-shot" imaging runs by nature and, thus, require precise relocation prior to acquisition. In contrast, "multi-shot" approaches conserve the sectioned tissue through the collection of serial sections onto solid support and allow reimaging. These tissue libraries generated by Array Tomography or Automated Tape Collecting Ultramicrotomy can be screened at low resolution to target high resolution SEM. This is particularly useful if a structure of interest is rare or has been predetermined by correlated light microscopy, which can assign molecular, dynamic and functional information to an ultrastructure. As such approaches require bridging mm to nm scales, they rely on tissue trimming at different stages of sample processing. Relocation is facilitated by endogenous or exogenous landmarks that are visible by several imaging modalities, combined with appropriate registration strategies that allow overlaying images of various sources. Here, we discuss the opportunities of using multi-shot serial sectioning SEM approaches, as well as suitable trimming and registration techniques, to slim down the high-resolution imaging volume to the actual structure of interest and hence facilitate ambitious targeted volume SEM projects.

A projectome of the bumblebee central complex

Insects have evolved diverse and remarkable strategies for navigating in various ecologies all over the world. Regardless of species, insects share the presence of a group of morphologically conserved neuropils known collectively as the central complex (CX). The CX is a navigational center, involved in sensory integration and coordinated motor activity. Despite the fact that our understanding of navigational behavior comes predominantly from ants and bees, most of what we know about the underlying neural circuitry of such behavior comes from work in fruit flies. Here, we aim to close this gap, by providing the first comprehensive map of all major columnar neurons and their projection patterns in the CX of a bee. We find numerous components of the circuit that appear to be highly conserved between the fly and the bee, but also highlight several key differences which are likely to have important functional ramifications.

Classification and genetic targeting of cell types in the primary taste and premotor center of the adult Drosophila brain

Neural circuits carry out complex computations that allow animals to evaluate food, select mates, move toward attractive stimuli, and move away from threats. In insects, the subesophageal zone (SEZ) is a brain region that receives gustatory, pheromonal, and mechanosensory inputs and contributes to the control of diverse behaviors, including feeding, grooming, and locomotion. Despite its importance in sensorimotor transformations, the study of SEZ circuits has been hindered by limited knowledge of the underlying diversity of SEZ neurons. Here, we generate a collection of split-GAL4 lines that provides precise genetic targeting of 138 different SEZ cell types in adult Drosophila melanogaster, comprising approximately one third of all SEZ neurons. We characterize the single-cell anatomy of these neurons and find that they cluster by morphology into six supergroups that organize the SEZ into discrete anatomical domains. We find that the majority of local SEZ interneurons are not classically polarized, suggesting rich local processing, whereas SEZ projection neurons tend to be classically polarized, conveying information to a limited number of higher brain regions. This study provides insight into the anatomical organization of the SEZ and generates resources that will facilitate further study of SEZ neurons and their contributions to sensory processing and behavior.

A unified platform to manage, share, and archive morphological and functional data in insect neuroscience

Insect neuroscience generates vast amounts of highly diverse data, of which only a small fraction are findable, accessible and reusable. To promote an open data culture, we have therefore developed the InsectBrainDatabase (IBdb), a free online platform for insect neuroanatomical and functional data. The IBdb facilitates biological insight by enabling effective cross-species comparisons, by linking neural structure with function, and by serving as general information hub for insect neuroscience. The IBdb allows users to not only effectively locate and visualize data, but to make them widely available for easy, automated reuse via an application programming interface. A unique private mode of the database expands the IBdb functionality beyond public data deposition, additionally providing the means for managing, visualizing, and sharing of unpublished data. This dual function creates an incentive for data contribution early in data management workflows and eliminates the additional effort normally associated with publicly depositing research data.

Insect neuroscience, like any field in the natural sciences, generates vast amounts of data. Currently, only a fraction are publicly available, and even less are reusable. This is because insect neuroscience data come in many formats and from many species. Some experiments focus on what insect brains look like (morphology), while others focus on how insect brains work (function). Some data come in the form of high-speed video, while other data contain voltage traces from individual neurons. Sharing is not as simple as uploading the raw files to the internet.

To get a clear picture of how insect brains work, researchers need a way to cross-reference and connect different experiments. But, as it stands, there is no dedicated place for insect neuroscientists to share and explore such a diverse body of work. The community needs an open data repository that can link different types of data across many species, and can evolve as more data become available. Above all, this repository needs to be easy for researchers to use.

To meet these specifications, Heinze et al. developed the Insect Brain Database. The database organizes data into three categories: species, brain structures, and neuron types. Within these categories, each entry has its own profile page. These pages bring different experiments together under one heading, allowing researchers to combine and compare data of different types. As researchers add more experiments, the profile pages will grow and evolve. To make the data easy to navigate, Heinze et al. developed a visual search tool. A combination of 2D and 3D images allow users to explore the data by anatomical location, without the need for expert knowledge. Researchers also have the option to upload their work in private mode, allowing them to securely share unpublished data.

The Insect Brain Database brings data together in a way that is accessible not only to researchers, but also to students, and non-scientists. It will help researchers to find related work, to reuse existing data, and to build an open data culture. This has the potential to drive new discoveries combining research across the whole of the insect neuroscience field.

Re-emergence and diversification of a specialized antennal lobe morphology in ithomiine butterflies

How an organism's sensory system functions is central to how it navigates its environment. The insect olfactory system is a prominent model for investigating how ecological factors impact sensory reception and processing. Notably, work in Lepidoptera led to the discovery of vastly expanded structures, termed macroglomerular complexes (MGCs), within the primary olfactory processing centre. MGCs typically process pheromonal cues, are usually larger in males, and provide classic examples of how variation in the size of neural structures reflects the importance of sensory cues. Though prevalent across moths, MGCs were lost during the origin of butterflies, consistent with evidence that courtship initiation in butterflies is primarily reliant on visual cues, rather than long distance chemical signals. However, an MGC was recently described in a species of ithomiine butterfly, suggesting that this once lost neural adaptation has re-emerged in this tribe. Here, we show that MGC-like morphologies are widely distributed across ithomiines, but vary in both their structure and prevalence of sexual dimorphism. Based on this interspecific variation we suggest that the ithomiine MGC is involved in processing both plant and pheromonal cues, which have similarities in their chemical constitution, and co-evolved with an increased importance of plant derived chemical compounds.

The connectome predicts resting-state functional connectivity across the Drosophila brain

Anatomical connectivity can constrain both a neural circuit's function and its underlying computation. This principle has been demonstrated for many small, defined neural circuits. For example, connectome reconstructions have informed models for direction selectivity in the vertebrate retina1,2 as well as the Drosophila visual system.3 In these cases, the circuit in question is relatively compact, well-defined, and has known functions. However, how the connectome constrains global properties of large-scale networks, across multiple brain regions or the entire brain, is incompletely understood. As the availability of partial or complete connectomes expands to more systems and species4-8 it becomes critical to understand how this detailed anatomical information can inform our understanding of large-scale circuit function.9,10 Here, we use data from the Drosophila connectome4 in conjunction with whole-brain in vivo imaging11 to relate structural and functional connectivity in the central brain. We find a strong relationship between resting-state functional correlations and direct region-to-region structural connectivity. We find that the relationship between structure and function varies across the brain, with some regions displaying a tight correspondence between structural and functional connectivity whereas others, including the mushroom body, are more strongly dependent on indirect connections. Throughout this work, we observe features of structural and functional networks in Drosophila that are strikingly similar to those seen in mammalian cortex, including in the human brain. Given the vast anatomical and functional differences between Drosophila and mammalian nervous systems, these observations suggest general principles that govern brain structure, function, and the relationship between the two.

Developmental Changes in Pyramidal Cell Morphology in Multiple Visual Cortical Areas Using Cluster Analysis

Neuronal morphology is characterized by salient features such as complex axonal and dendritic arbors. In the mammalian brain, variations in dendritic morphology among cell classes, brain regions, and animal species are thought to underlie known differences in neuronal function. In this work, we obtained a large dataset from http://neuromorpho.org/ comprising layer III pyramidal cells in different cortical areas of the ventral visual pathway (V1, V2, V4, TEO, and TE) of the macaque monkey at different developmental stages. We performed an in depth quantitative analysis of pyramidal cell morphology throughout development in an effort to determine which aspects mature early in development and which features require a protracted period of maturation. We were also interested in establishing if developmental changes in morphological features occur simultaneously or hierarchically in multiple visual cortical areas. We addressed these questions by performing principal component analysis (PCA) and hierarchical clustering analysis on relevant morphological features. Our analysis indicates that the maturation of pyramidal cell morphology is largely based on early development of topological features in most visual cortical areas. Moreover, the maturation of pyramidal cell morphology in V1, V2, V4, TEO, and TE is characterized by unique developmental trajectories.

Information flow, cell types and stereotypy in a full olfactory connectome

The hemibrain connectome provides large-scale connectivity and morphology information for the majority of the central brain of Drosophila melanogaster. Using this data set, we provide a complete description of the Drosophila olfactory system, covering all first, second and lateral horn-associated third-order neurons. We develop a generally applicable strategy to extract information flow and layered organisation from connectome graphs, mapping olfactory input to descending interneurons. This identifies a range of motifs including highly lateralised circuits in the antennal lobe and patterns of convergence downstream of the mushroom body and lateral horn. Leveraging a second data set we provide a first quantitative assessment of inter- versus intra-individual stereotypy. Comparing neurons across two brains (three hemispheres) reveals striking similarity in neuronal morphology across brains. Connectivity correlates with morphology and neurons of the same morphological type show similar connection variability within the same brain as across two brains.

SNT: a unifying toolbox for quantification of neuronal anatomy

SNT is an end-to-end framework for neuronal morphometry and whole-brain connectomics that supports tracing, proof-editing, visualization, quantification and modeling of neuroanatomy. With an open architecture, a large user base, community-based documentation, support for complex imagery and several model organisms, SNT is a flexible resource for the broad neuroscience community. SNT is both a desktop application and multi-language scripting library, and it is available through the Fiji distribution of ImageJ.

Visualizing anatomically registered data with brainrender

Three-dimensional (3D) digital brain atlases and high-throughput brain-wide imaging techniques generate large multidimensional datasets that can be registered to a common reference frame. Generating insights from such datasets depends critically on visualization and interactive data exploration, but this a challenging task. Currently available software is dedicated to single atlases, model species or data types, and generating 3D renderings that merge anatomically registered data from diverse sources requires extensive development and programming skills. Here, we present brainrender: an open-source Python package for interactive visualization of multidimensional datasets registered to brain atlases. Brainrender facilitates the creation of complex renderings with different data types in the same visualization and enables seamless use of different atlas sources. High-quality visualizations can be used interactively and exported as high-resolution figures and animated videos. By facilitating the visualization of anatomically registered data, brainrender should accelerate the analysis, interpretation, and dissemination of brain-wide multidimensional data.

Selective dendritic localization of mRNA in Drosophila mushroom body output neurons

Memory-relevant neuronal plasticity is believed to require local translation of new proteins at synapses. Understanding this process requires the visualization of the relevant mRNAs within these neuronal compartments. Here, we used single-molecule fluorescence in situ hybridization to localize mRNAs at subcellular resolution in the adult Drosophila brain. mRNAs for subunits of nicotinic acetylcholine receptors and kinases could be detected within the dendrites of co-labeled mushroom body output neurons (MBONs) and their relative abundance showed cell specificity. Moreover, aversive olfactory learning produced a transient increase in the level of CaMKII mRNA within the dendritic compartments of the γ5β'2a MBONs. Localization of specific mRNAs in MBONs before and after learning represents a critical step towards deciphering the role of dendritic translation in the neuronal plasticity underlying behavioral change in Drosophila.

Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic and synaptomic era

In recent years, a wealth of Drosophila neuroscience data have become available including cell type and connectome/synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fruit fly brain, we have developed FlyBrainLab, a unique open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab's User Interface, Utilities Libraries and Circuit Libraries bring together neuroanatomical, neurogenetic and electrophysiological datasets with computational models of different researchers for validation and comparison within the same platform. Seeking to transcend the limitations of the connectome/synaptome, FlyBrainLab also provides libraries for molecular transduction arising in sensory coding in vision/olfaction. Together with sensory neuron activity data, these libraries serve as entry points for the exploration, analysis, comparison, and evaluation of circuit functions of the fruit fly brain.

Dopaminergic mechanism underlying reward-encoding of punishment omission during reversal learning in Drosophila

Animals form and update learned associations between otherwise neutral sensory cues and aversive outcomes (i.e., punishment) to predict and avoid danger in changing environments. When a cue later occurs without punishment, this unexpected omission of aversive outcome is encoded as reward via activation of reward-encoding dopaminergic neurons. How such activation occurs remains unknown. Using real-time in vivo functional imaging, optogenetics, behavioral analysis and synaptic reconstruction from electron microscopy data, we identify the neural circuit mechanism through which Drosophila reward-encoding dopaminergic neurons are activated when an olfactory cue is unexpectedly no longer paired with electric shock punishment. Reduced activation of punishment-encoding dopaminergic neurons relieves depression of olfactory synaptic inputs to cholinergic neurons. Synaptic excitation by these cholinergic neurons of reward-encoding dopaminergic neurons increases their odor response, thus decreasing aversiveness of the odor. These studies reveal how an excitatory cholinergic relay from punishment- to reward-encoding dopaminergic neurons encodes the absence of punishment as reward, revealing a general circuit motif for updating aversive memories that could be present in mammals.

Leucokinin and Associated Neuropeptides Regulate Multiple Aspects of Physiology and Behavior in Drosophila

Leucokinins (LKs) constitute a family of neuropeptides identified in numerous insects and many other invertebrates. LKs act on G-protein-coupled receptors that display only distant relations to other known receptors. In adult Drosophila, 26 neurons/neurosecretory cells of three main types express LK. The four brain interneurons are of two types, and these are implicated in several important functions in the fly's behavior and physiology, including feeding, sleep-metabolism interactions, state-dependent memory formation, as well as modulation of gustatory sensitivity and nociception. The 22 neurosecretory cells (abdominal LK neurons, ABLKs) of the abdominal neuromeres co-express LK and a diuretic hormone (DH44), and together, these regulate water and ion homeostasis and associated stress as well as food intake. In Drosophila larvae, LK neurons modulate locomotion, escape responses and aspects of ecdysis behavior. A set of lateral neurosecretory cells, ALKs (anterior LK neurons), in the brain express LK in larvae, but inconsistently so in adults. These ALKs co-express three other neuropeptides and regulate water and ion homeostasis, feeding, and drinking, but the specific role of LK is not yet known. This review summarizes Drosophila data on embryonic lineages of LK neurons, functional roles of individual LK neuron types, interactions with other peptidergic systems, and orchestrating functions of LK.

Auditory activity is diverse and widespread throughout the central brain of Drosophila

Sensory pathways are typically studied by starting at receptor neurons and following postsynaptic neurons into the brain. However, this leads to a bias in analyses of activity toward the earliest layers of processing. Here, we present new methods for volumetric neural imaging with precise across-brain registration to characterize auditory activity throughout the entire central brain of Drosophila and make comparisons across trials, individuals and sexes. We discover that auditory activity is present in most central brain regions and in neurons responsive to other modalities. Auditory responses are temporally diverse, but the majority of activity is tuned to courtship song features. Auditory responses are stereotyped across trials and animals in early mechanosensory regions, becoming more variable at higher layers of the putative pathway, and this variability is largely independent of ongoing movements. This study highlights the power of using an unbiased, brain-wide approach for mapping the functional organization of sensory activity.

An unbiased template of the Drosophila brain and ventral nerve cord

The fruit fly Drosophila melanogaster is an important model organism for neuroscience with a wide array of genetic tools that enable the mapping of individual neurons and neural subtypes. Brain templates are essential for comparative biological studies because they enable analyzing many individuals in a common reference space. Several central brain templates exist for Drosophila, but every one is either biased, uses sub-optimal tissue preparation, is imaged at low resolution, or does not account for artifacts. No publicly available Drosophila ventral nerve cord template currently exists. In this work, we created high-resolution templates of the Drosophila brain and ventral nerve cord using the best-available technologies for imaging, artifact correction, stitching, and template construction using groupwise registration. We evaluated our central brain template against the four most competitive, publicly available brain templates and demonstrate that ours enables more accurate registration with fewer local deformations in shorter time.

The connectome of the adult Drosophila mushroom body provides insights into function

Making inferences about the computations performed by neuronal circuits from synapse-level connectivity maps is an emerging opportunity in neuroscience. The mushroom body (MB) is well positioned for developing and testing such an approach due to its conserved neuronal architecture, recently completed dense connectome, and extensive prior experimental studies of its roles in learning, memory, and activity regulation. Here, we identify new components of the MB circuit in Drosophila, including extensive visual input and MB output neurons (MBONs) with direct connections to descending neurons. We find unexpected structure in sensory inputs, in the transfer of information about different sensory modalities to MBONs, and in the modulation of that transfer by dopaminergic neurons (DANs). We provide insights into the circuitry used to integrate MB outputs, connectivity between the MB and the central complex and inputs to DANs, including feedback from MBONs. Our results provide a foundation for further theoretical and experimental work.

GeNePy3D: a quantitative geometry python toolbox for large scale bioimaging

The advent of large-scale fluorescence and electronic microscopy techniques along with maturing image analysis is giving life sciences a deluge of geometrical objects in 2D/3D(+t) to deal with. These objects take the form of large scale, localised, precise, single cell, quantitative data such as cells' positions, shapes, trajectories or lineages, axon traces in whole brains atlases or varied intracellular protein localisations, often in multiple experimental conditions. The data mining of those geometrical objects requires a variety of mathematical and computational tools of diverse accessibility and complexity. Here we present a new Python library for quantitative 3D geometry called GeNePy3D which helps handle and mine information and knowledge from geometric data, providing a unified application programming interface (API) to methods from several domains including computational geometry, scale space methods or spatial statistics. By framing this library as generically as possible, and by linking it to as many state-of-the-art reference algorithms and projects as needed, we help render those often specialist methods accessible to a larger community. We exemplify the usefulness of the  GeNePy3D toolbox by re-analysing a recently published whole-brain zebrafish neuronal atlas, with other applications and examples available online. Along with an open source, documented and exemplified code, we release reusable containers to allow for convenient and wide usability and increased reproducibility.

GeNePy3D: a quantitative geometry python toolbox for bioimaging

The advent of large-scale fluorescence and electronic microscopy techniques along with maturing image analysis is giving life sciences a deluge of geometrical objects in 2D/3D(+t) to deal with. These objects take the form of large scale, localised, precise, single cell, quantitative data such as cells' positions, shapes, trajectories or lineages, axon traces in whole brains atlases or varied intracellular protein localisations, often in multiple experimental conditions. The data mining of those geometrical objects requires a variety of mathematical and computational tools of diverse accessibility and complexity. Here we present a new Python library for quantitative 3D geometry called GeNePy3D which helps handle and mine information and knowledge from geometric data, providing a unified application programming interface (API) to methods from several domains including computational geometry, scale space methods or spatial statistics. By framing this library as generically as possible, and by linking it to as many state-of-the-art reference algorithms and projects as needed, we help render those often specialist methods accessible to a larger community. We exemplify the usefulness of the  GeNePy3D toolbox by re-analysing a recently published whole-brain zebrafish neuronal atlas, with other applications and examples available online. Along with an open source, documented and exemplified code, we release reusable containers to allow for convenient and wide usability and increased reproducibility.

The neural basis for a persistent internal state in Drosophila females

Sustained changes in mood or action require persistent changes in neural activity, but it has been difficult to identify the neural circuit mechanisms that underlie persistent activity and contribute to long-lasting changes in behavior. Here, we show that a subset of Doublesex+ pC1 neurons in the Drosophila female brain, called pC1d/e, can drive minutes-long changes in female behavior in the presence of males. Using automated reconstruction of a volume electron microscopic (EM) image of the female brain, we map all inputs and outputs to both pC1d and pC1e. This reveals strong recurrent connectivity between, in particular, pC1d/e neurons and a specific subset of Fruitless+ neurons called aIPg. We additionally find that pC1d/e activation drives long-lasting persistent neural activity in brain areas and cells overlapping with the pC1d/e neural network, including both Doublesex+ and Fruitless+ neurons. Our work thus links minutes-long persistent changes in behavior with persistent neural activity and recurrent circuit architecture in the female brain.

The Drosophila Split Gal4 System for Neural Circuit Mapping

The diversity and dense interconnectivity of cells in the nervous system present a huge challenge to understanding how brains work. Recent progress toward such understanding, however, has been fuelled by the development of techniques for selectively monitoring and manipulating the function of distinct cell types-and even individual neurons-in the brains of living animals. These sophisticated techniques are fundamentally genetic and have found their greatest application in genetic model organisms, such as the fruit fly Drosophila melanogaster. Drosophila combines genetic tractability with a compact, but cell-type rich, nervous system and has been the incubator for a variety of methods of neuronal targeting. One such method, called Split Gal4, is playing an increasingly important role in mapping neural circuits in the fly. In conjunction with functional perturbations and behavioral screens, Split Gal4 has been used to characterize circuits governing such activities as grooming, aggression, and mating. It has also been leveraged to comprehensively map and functionally characterize cells composing important brain regions, such as the central complex, lateral horn, and the mushroom body-the latter being the insect seat of learning and memory. With connectomics data emerging for both the larval and adult brains of Drosophila, Split Gal4 is also poised to play an important role in characterizing neurons of interest based on their connectivity. We summarize the history and current state of the Split Gal4 method and indicate promising areas for further development or future application.

Hormonal axes in Drosophila: regulation of hormone release and multiplicity of actions

Hormones regulate development, as well as many vital processes in the daily life of an animal. Many of these hormones are peptides that act at a higher hierarchical level in the animal with roles as organizers that globally orchestrate metabolism, physiology and behavior. Peptide hormones can act on multiple peripheral targets and simultaneously convey basal states, such as metabolic status and sleep-awake or arousal across many central neuronal circuits. Thereby, they coordinate responses to changing internal and external environments. The activity of neurosecretory cells is controlled either by (1) cell autonomous sensors, or (2) by other neurons that relay signals from sensors in peripheral tissues and (3) by feedback from target cells. Thus, a hormonal signaling axis commonly comprises several components. In mammals and other vertebrates, several hormonal axes are known, such as the hypothalamic-pituitary-gonad axis or the hypothalamic-pituitary-thyroid axis that regulate reproduction and metabolism, respectively. It has been proposed that the basic organization of such hormonal axes is evolutionarily old and that cellular homologs of the hypothalamic-pituitary system can be found for instance in insects. To obtain an appreciation of the similarities between insect and vertebrate neurosecretory axes, we review the organization of neurosecretory cell systems in Drosophila. Our review outlines the major peptidergic hormonal pathways known in Drosophila and presents a set of schemes of hormonal axes and orchestrating peptidergic systems. The detailed organization of the larval and adult Drosophila neurosecretory systems displays only very basic similarities to those in other arthropods and vertebrates.

Distinct subpopulations of mechanosensory chordotonal organ neurons elicit grooming of the fruit fly antennae

Diverse mechanosensory neurons detect different mechanical forces that can impact animal behavior. Yet our understanding of the anatomical and physiological diversity of these neurons and the behaviors that they influence is limited. We previously discovered that grooming of the Drosophila melanogaster antennae is elicited by an antennal mechanosensory chordotonal organ, the Johnston's organ (JO) (Hampel et al., 2015). Here, we describe anatomically and physiologically distinct JO mechanosensory neuron subpopulations that each elicit antennal grooming. We show that the subpopulations project to different, discrete zones in the brain and differ in their responses to mechanical stimulation of the antennae. Although activation of each subpopulation elicits antennal grooming, distinct subpopulations also elicit the additional behaviors of wing flapping or backward locomotion. Our results provide a comprehensive description of the diversity of mechanosensory neurons in the JO, and reveal that distinct JO subpopulations can elicit both common and distinct behavioral responses.

The Neuroanatomical Ultrastructure and Function of a Biological Ring Attractor

Neural representations of head direction (HD) have been discovered in many species. Theoretical work has proposed that the dynamics associated with these representations are generated, maintained, and updated by recurrent network structures called ring attractors. We evaluated this theorized structure-function relationship by performing electron-microscopy-based circuit reconstruction and RNA profiling of identified cell types in the HD system of Drosophila melanogaster. We identified motifs that have been hypothesized to maintain the HD representation in darkness, update it when the animal turns, and tether it to visual cues. Functional studies provided support for the proposed roles of individual excitatory or inhibitory circuit elements in shaping activity. We also discovered recurrent connections between neuronal arbors with mixed pre- and postsynaptic specializations. Our results confirm that the Drosophila HD network contains the core components of a ring attractor while also revealing unpredicted structural features that might enhance the network's computational power.

Retinotectal circuitry of larval zebrafish is adapted to detection and pursuit of prey

Retinal axon projections form a map of the visual environment in the tectum. A zebrafish larva typically detects a prey object in its peripheral visual field. As it turns and swims towards the prey, the stimulus enters the central, binocular area, and seemingly expands in size. By volumetric calcium imaging, we show that posterior tectal neurons, which serve to detect prey at a distance, tend to respond to small objects and intrinsically compute their direction of movement. Neurons in anterior tectum, where the prey image is represented shortly before the capture strike, are tuned to larger object sizes and are frequently not direction-selective, indicating that mainly interocular comparisons serve to compute an object's movement at close range. The tectal feature map originates from a linear combination of diverse, functionally specialized, lamina-specific, and topographically ordered retinal ganglion cell synaptic inputs. We conclude that local cell-type composition and connectivity across the tectum are adapted to the processing of location-dependent, behaviorally relevant object features.

A Neural Network for Wind-Guided Compass Navigation

Spatial maps in the brain are most accurate when they are linked to external sensory cues. Here, we show that the compass in the Drosophila brain is linked to the direction of the wind. Shifting the wind rightward rotates the compass as if the fly were turning leftward, and vice versa. We describe the mechanisms of several computations that integrate wind information into the compass. First, an intensity-invariant representation of wind direction is computed by comparing left-right mechanosensory signals. Then, signals are reformatted to reduce the coding biases inherent in peripheral mechanics, and wind cues are brought into the same circular coordinate system that represents visual cues and self-motion signals. Because the compass incorporates both mechanosensory and visual cues, it should enable navigation under conditions where no single cue is consistently reliable. These results show how local sensory signals can be transformed into a global, multimodal, abstract representation of space.

Complete Connectomic Reconstruction of Olfactory Projection Neurons in the Fly Brain

Nervous systems contain sensory neurons, local neurons, projection neurons, and motor neurons. To understand how these building blocks form whole circuits, we must distil these broad classes into neuronal cell types and describe their network connectivity. Using an electron micrograph dataset for an entire Drosophila melanogaster brain, we reconstruct the first complete inventory of olfactory projections connecting the antennal lobe, the insect analog of the mammalian olfactory bulb, to higher-order brain regions in an adult animal brain. We then connect this inventory to extant data in the literature, providing synaptic-resolution "holotypes" both for heavily investigated and previously unknown cell types. Projection neurons are approximately twice as numerous as reported by light level studies; cell types are stereotyped, but not identical, in cell and synapse numbers between brain hemispheres. The lateral horn, the insect analog of the mammalian cortical amygdala, is the main target for this olfactory information and has been shown to guide innate behavior. Here, we find new connectivity motifs, including axo-axonic connectivity between projection neurons, feedback, and lateral inhibition of these axons by a large population of neurons, and the convergence of different inputs, including non-olfactory inputs and memory-related feedback onto third-order olfactory neurons. These features are less prominent in the mushroom body calyx, the insect analog of the mammalian piriform cortex and a center for associative memory. Our work provides a complete neuroanatomical platform for future studies of the adult Drosophila olfactory system.

The Wiring Logic of an Identified Serotonergic Neuron That Spans Sensory Networks

Serotonergic neurons project widely throughout the brain to modulate diverse physiological and behavioral processes. However, a single-cell resolution understanding of the connectivity of serotonergic neurons is currently lacking. Using a whole-brain EM dataset of a female Drosophila, we comprehensively determine the wiring logic of a broadly projecting serotonergic neuron (the CSDn) that spans several olfactory regions. Within the antennal lobe, the CSDn differentially innervates each glomerulus, yet surprisingly, this variability reflects a diverse set of presynaptic partners, rather than glomerulus-specific differences in synaptic output, which is predominately to local interneurons. Moreover, the CSDn has distinct connectivity relationships with specific local interneuron subtypes, suggesting that the CSDn influences distinct aspects of local network processing. Across olfactory regions, the CSDn has different patterns of connectivity, even having different connectivity with individual projection neurons that also span these regions. Whereas the CSDn targets inhibitory local neurons in the antennal lobe, the CSDn has more distributed connectivity in the LH, preferentially synapsing with principal neuron types based on transmitter content. Last, we identify individual novel synaptic partners associated with other sensory domains that provide strong, top-down input to the CSDn. Together, our study reveals the complex connectivity of serotonergic neurons, which combine the integration of local and extrinsic synaptic input in a nuanced, region-specific manner.SIGNIFICANCE STATEMENT All sensory systems receive serotonergic modulatory input. However, a comprehensive understanding of the synaptic connectivity of individual serotonergic neurons is lacking. In this study, we use a whole-brain EM microscopy dataset to comprehensively determine the wiring logic of a broadly projecting serotonergic neuron in the olfactory system of Drosophila Collectively, our study demonstrates, at a single-cell level, the complex connectivity of serotonergic neurons within their target networks, identifies specific cell classes heavily targeted for serotonergic modulation in the olfactory system, and reveals novel extrinsic neurons that provide strong input to this serotonergic system outside of the context of olfaction. Elucidating the connectivity logic of individual modulatory neurons provides a ground plan for the seemingly heterogeneous effects of modulatory systems.