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Visual comparison of Drosophila brain templates and bridging transformations. The top two rows show four existing templates registered to our JRC 2018 female template, as well as synaptic cleft predictions derived from the FAFB EM volume, transformed into the space of JRC 2018F. The middle four rows show JRC 2018F (second and fourth columns) registered to each of the three templates, along with a close-up around the fan-shaped body and the pedunculus of the mushroom body. The bottom row shows JRC 2018F transformed into the space of FAFB. Scale bars 100 μm. https://doi.org/10.1371/journal.pone.0236495.g003
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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...
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Context 1
... used the algorithm ("ANTs A") that we found performs best overall (see Supplementary Notes A.2 in S2 File). In Fig 3, we show these templates in the space of JRC 2018 and vice versa. Qualitatively, these registrations appear to be about as accurate as those from individuals to templates. ...
Context 2
... automatically aligned the FAFB EM volume and the JRC 2018 female template (see Fig 3). We rendered and blurred (with a 1.0 μm Gaussian kernel) the synapse cleft distance predictions generated by Heinrich et al. [43] at low resolution (1.02 × 1.02 × 1.04 μm) so that the resulting image has an appearance similar to an nc82 or brp-SNAP labeled confocal image. ...
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... Fig 3, we visually compare slices through the JFRC 2010 [6], JFRC 2013 [7], FCWB [28], Tefor [8], and the JRC 2018F brain templates. Note the improved contrast and sharpness of anatomical structures in our JRC 2018F template relative to the others. ...
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... here we put our results in the context of other work attempting to estimate performance of various templates. In Fig 3, we see that the anatomical features in the Drosophila brain in our template are qualitatively more pronounced, having higher contrast than other existing templates. As a result, pixel-based similarity measures used by automatic registration algorithms may be more effective at optimization when the signal for the target image is less obscured by noise of different kinds (e.g., anatomical variability, imaging artifacts). ...
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Maintaining synaptic structure and function over time is vital for overall nervous system function and survival. The processes that underly synaptic development are well understood. However, the mechanisms responsible for sustaining synapses throughout the lifespan of an organism are poorly understood. Here, we demonstrate that a previously unchara...
Citations
... All of the above workflows require identifying similar neuron morphology across large data sets of EM and LM images. Registration of neurons to a common reference alignment space enables spatial matching across biological samples and modalities [39]. Two current solutions to the computational problem of matching neurons across registered imaging modalities are Color Depth MIP Search (abbreviated as CDM Search; [13]) and PatchPerPixMatch (PPPM; [14]). ...
... For the FlyEM Hemibrain data set we relied on the published registration to the JRC2018 Drosophila female brain template [39] (Fig. 4A, D, E). For the FlyEM MANC, we used the published registration to the JRC2018 Drosophila male VNC template [6]. ...
... For the FlyEM MANC, we used the published registration to the JRC2018 Drosophila male VNC template [6]. For the FlyLight data sets, we used CMTK [44] to register all LM images (Fig. 5A, B) to the sexappropriate JRC2018 template [39]. In all cases we subsequently used a bridging transform to move the data into the alignment space of the JRC2018 Unisex template, where neuron matching could be performed across all samples, irrespective of sex. ...
Background
Neuroscience research in Drosophila is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. To exploit this knowledge base, researchers relate a connectome’s structure to neuronal function, often by studying individual neuron cell types. Vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM), enabling the targeting of neurons for experimentation. However, creating a fly line for driving gene expression within a single neuron found in an EM connectome remains a challenge, as it typically requires identifying a pair of driver lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities.
Results
Here, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large data sets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly graphical user interface (GUI), extensible data model, serverless cloud architecture, and massively parallel image search engine.
Conclusions
NeuronBridge fills a critical gap in the Drosophila research workflow and is used by hundreds of neuroscience researchers around the world. We offer our software code, open APIs, and processed data sets for integration and reuse, and provide the application as a service at http://neuronbridge.janelia.org.
... After registration to the JFRC2 template, we performed neuron tracing using the simple neurite tracer in ImageJ (Arshadi et al., 2021). The traced neurons were transformed to the hemibrain space (JRCFIB2018F) via JFRC2010 and JRC2018F using the neuroanatomy toolbox (NAT) Bogovic et al., 2021). We then calculated a similarity score between transformed neurons to all hemibrain neurons using NBLAST (Costa et al., 2016). ...
Aggression involves both sexually monomorphic and dimorphic actions. How the brain implements these two types of actions is poorly understood. We found that a set of neurons, which we call CL062, previously shown to mediate male aggression also mediate female aggression. These neurons elicit aggression acutely and without the presence of a target. Although the same set of actions is elicited in males and females, the overall behavior is sexually dimorphic. The CL062 neurons do not express fruitless, a gene required for sexual dimorphism in flies, and expressed by most other neurons important for controlling fly aggression. Connectomic analysis suggests that these neurons have limited connections with fruitless expressing neurons that have been shown to be important for aggression, and signal to different descending neurons. Thus, CL062 is part of a monomorphic circuit for aggression that functions parallel to the known dimorphic circuits.
... 6,215 brains and 6,213 VNCs were included. Images were aligned to the JRC2018 Unisex template (Bogovic et al., 2020) and segmented using Direction-Selective Local Thresholding (DSLT; Kawase et al., 2015). DSLT weight value, neuron volume percentage, MIP ratio against the aligned template, and MIPweight ratio were adjusted to refine segmentation. ...
... Spatial distribution of cell type lines. (A and B) Images of one male and one female sample from 3029 cell type rescreening lines were aligned to JRC2018 Unisex(Bogovic et al., 2020), segmented from background (see Methods), binarized, overlaid, and maximum intensity projected, such that brightness indicates the number of lines with expression in the (A) female and (B) male CNS. (C) Female image stack minus male, maximum intensity projected. ...
Techniques that enable precise manipulations of subsets of neurons in the fly central nervous system have greatly facilitated our understanding of the neural basis of behavior. Split-GAL4 driver lines allow specific targeting of cell types in Drosophila melanogaster and other species. We describe here a collection of 3063 lines targeting a range of cell types in the adult Drosophila central nervous system and 1020 lines characterized in third-instar larvae. These tools enable functional, transcriptomic, and proteomic studies based on precise anatomical targeting. NeuronBridge and other search tools relate light microscopy images of these split-GAL4 lines to connectomes reconstructed from electron microscopy images. The collections are the result of screening over 77,000 split hemidriver combinations. In addition to images and fly stocks for these well-characterized lines, we make available 300,000 new 3D images of other split-GAL4 lines.
... We acquired z-stacks of each VNC on a confocal microscope (Zeiss 510; Zeiss). We aligned the expression pattern in the VNC using the Computational Morphometry Toolkit (CMTK; Neuroimaging Informatics Tools and Resources Clearinghouse) to a female VNC template (Bogovic et al. 2020) in Fiji (Schindelin et al. 2012). ...
The sense of proprioception is mediated by internal mechanosensory neurons that detect joint position and movement. To support a diverse range of functions, from stabilizing posture to coordinating movements, proprioceptive feedback to limb motor control circuits must be tuned in a context-dependent manner. How proprioceptive feedback signals are tuned to match behavioral demands remains poorly understood. Using calcium imaging in behaving Drosophila, we find that the axons of position-encoding leg proprioceptors are active across behaviors, whereas the axons of movement-encoding leg proprioceptors are suppressed during walking and grooming. Using connectomics, we identify a specific class of interneurons that provide GABAergic presynaptic inhibition to the axons of movement-encoding proprioceptors. These interneurons are active during self-generated but not passive leg movements and receive input from descending neurons, suggesting they are driven by predictions of leg movement originating in the brain. Predictively suppressing expected proprioceptive feedback provides a mechanism to attenuate reflexes that would otherwise interfere with voluntary movement.
... Then, each anatomical scan was aligned (affine and non-linear) to the FDA using the tdTomato channel, and the transforms were applied to the GCaMP6f channel. The FDA contains anatomical region delineations 51,52 . ...
The ability to act voluntarily is fundamental to animal behavior. For example, self-directed movements are critical to exploration, particularly in the absence of external sensory signals that could shape a trajectory. However, how neural networks might plan future changes in direction in the absence of salient sensory cues is unknown. Here we use volumetric two-photon imaging to map neural activity associated with walking across the entire brain of the fruit fly Drosophila, register these signals across animals with micron precision, and generate a dataset of ~20 billion neural measurements across thousands of bouts of voluntary movements. We define spatially clustered neural signals selectively associated with changes in forward and angular velocity, and reveal that turning is associated with widespread asymmetric activity between brain hemispheres. Strikingly, this asymmetry in interhemispheric dynamics emerges more than 10 seconds before a turn within a specific brain region associated with motor control, the Inferior Posterior Slope (IPS). This early, local difference in neural activity predicts the direction of future turns on a trial-by-trial basis, revealing long-term motor planning. As the direction of each turn is neither trained, nor guided by external sensory cues, it must be internally determined. We therefore propose that this pre-motor center contains a neural substrate of volitional action.
... R84C10 showed the sparsest expression with all three lines labeling a set of neurons that seemed to overlap in the dorsal fan-shaped body (dFB) of the CX. To better compare their expression, we overlaid the registered expression patterns of these driver lines on a template brain (JRC2018Unisex; Court et al., 2023;Bogovic et al., 2020) ( Fig. 3b), which revealed that they indeed had overlapping projections to the FB layer 6 (FB6) and the Superior Neuropils (SNP), with cell bodies located in the lateral posterior part of the protocerebrum. ...
Foraging animals must balance the costs of exploring their surroundings with the potential benefits of finding nutritional resources. Each time an animal encounters a food source it must decide whether to initiate feeding or continue searching for potentially better options. Experimental evidence and patch foraging models predict that this decision depends on both nutritional state and the density of available resources in the environment. How the brain integrates such internal and external states to adapt the so-called exploration-exploitation trade-off remains poorly understood. We use video-based tracking to show that Drosophila regulates the decision to engage with food patches based on nutritional state and travel time between food patches, the latter being a measure of food patch density in the environment. To uncover the neuronal basis of this decision process, we performed a neurogenetic silencing screen of more than 400 genetic driver lines with sparse expression patterns in the fly brain. We identified a population of neurons in the central complex that acts as a key regulator of the decision to engage with a food patch. We show that manipulating the activity of these neurons alters the probability to engage, that their activity is modulated by the protein state of the animal, and that silencing these neurons perturbs the ability of the animal to adjust foraging decisions to the fly's travel time between food patches. Taken together, our results reveal a neuronal substrate that integrates nutritional state and patch density information to control a specific foraging decision, and therefore provide an important step towards a mechanistic explanation of the cognitive computations that resolve complex cost-benefit trade-offs.
... The adult fruit fly represents the current frontier for whole brain connectomics. With 127,978 neurons, the newly completed full adult female brain connectome is intermediate in log scale between the first connectome of C. elegans (302 neurons 4,5 ) and the mouse (10^8 neurons), a desirable but currently intractable target 6 . The availability of a complete adult fly brain connectome now allows brain-spanning circuits to be mapped and linked to circuit dynamics and behaviour as has long been possible for the nematode and more recently the Drosophila larva (3016 neurons 7 ). ...
... The remaining neurons are typeable even if they cannot be unambiguously linked to previously reported cell types, and we provide a path for this below ("Toward multi-connectome cell typing"). In total, we collected over 720k annotations for all 127 We find striking stereotypy in the number of central brain intrinsic neurons: e.g. ...
... In more detail, since the hemibrain and FAFB datasets are different shapes a non-rigid transform already exists between them. This passes via the symmetric JRC2018F light level template 127 . Therefore if you wish to superimpose the (true) RHS of hemibrain and FAFB you can use the existing transforms, but apply a simple mirror flip within the JRC2018F space. ...
The fruit fly Drosophila melanogaster combines surprisingly sophisticated behaviour with a highly tractable nervous system. A large part of the fly's success as a model organism in modern neuroscience stems from the concentration of collaboratively generated molecular genetic and digital resources. As presented in our FlyWire companion paper ¹ , this now includes the first full brain connectome of an adult animal. Here we report the systematic and hierarchical annotation of this ~130,000-neuron connectome including neuronal classes, cell types and developmental units (hemilineages). This enables any researcher to navigate this huge dataset and find systems and neurons of interest, linked to the literature through the Virtual Fly Brain database ² . Crucially, this resource includes 4,179 cell types of which 3,166 consensus cell types are robustly defined by comparison with a second dataset, the "hemibrain" connectome ³ . Comparative analysis showed that cell type counts and strong connections were largely stable, but connection weights were surprisingly variable within and across animals. Further analysis defined simple heuristics for connectome interpretation: connections stronger than 10 unitary synapses or providing >1% of the input to a target cell are highly conserved. Some cell types showed increased variability across connectomes: the most common cell type in the mushroom body, required for learning and memory, is almost twice as numerous in FlyWire than in the hemibrain. We find evidence for functional homeostasis through adjustments of the absolute amount of excitatory input while maintaining the excitation-inhibition ratio. Finally, and surprisingly, about one third of the cell types recorded in the hemibrain connectome could not be robustly identified in the FlyWire connectome, cautioning against defining cell types based on single connectomes. We propose that a cell type should be robust to inter-individual variation, and therefore defined as a group of cells that are more similar to cells in a different brain than to any other cell in the same brain. We show that this new definition can be consistently applied to whole connectome datasets. 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.
... last accessed on 5 May 2023, (version 1.6.4)). Subsequently, brains were registered to the JRC2018 unisex 63× brain template [74] using the "CMTK (Computational Morphometry Toolkit) registration runner" macro for FIJI (written by Sándor Kovács, using CMTK by Thorsten Rohlfing and Munger by Gregory Jefferis) running on an IBM x3850 x5 two-node 80-core server. ...
... Acquired images of brains of SS56217>DsRed flies were registered to Janelia Research Campus Unisex Drosophila Template brain (JRC2018) [74] using the anti-Brp channel as a reference. ...
Parkinson’s disease (PD) often displays a strong unilateral predominance in arising symptoms. PD is correlated with dopamine neuron (DAN) degeneration in the substantia nigra pars compacta (SNPC), and in many patients, DANs appear to be affected more severely on one hemisphere than the other. The reason for this asymmetric onset is far from being understood. Drosophila melanogaster has proven its merit to model molecular and cellular aspects of the development of PD. However, the cellular hallmark of the asymmetric degeneration of DANs in PD has not yet been described in Drosophila. We ectopically express human α-synuclein (hα-syn) together with presynaptically targeted syt::HA in single DANs that innervate the Antler (ATL), a symmetric neuropil located in the dorsomedial protocerebrum. We find that expression of hα-syn in DANs innervating the ATL yields asymmetric depletion of synaptic connectivity. Our study represents the first example of unilateral predominance in an invertebrate model of PD and will pave the way to the investigation of unilateral predominance in the development of neurodegenerative diseases in the genetically versatile invertebrate model Drosophila.
... First, we generated a 'mean brain' volume by iteratively aligning and averaging a collection of high-resolution, in vivo anatomical scans of the volume of interest ( Figure 2E, n=11 flies). Next, we used the syt1GCaMP6f channel of the mean brain to align to the JRC2018 template brain ( Figure 2F; Bogovic et al., 2020). Finally, we generated a glomerulus map using locations of the presynaptic T-bars belonging to LC and LPLC neurons, which we extracted from the hemibrain connectome (Scheffer et al., 2020), and aligned it to the JRC2018 template brain. ...
Natural vision is dynamic: as an animal moves, its visual input changes dramatically. How can the visual system reliably extract local features from an input dominated by self-generated signals? In Drosophila, diverse local visual features are represented by a group of projection neurons with distinct tuning properties. Here we describe a connectome-based volumetric imaging strategy to measure visually evoked neural activity across this population. We show that local visual features are jointly represented across the population, and that a shared gain factor improves trial-to-trial coding fidelity. A subset of these neurons, tuned to small objects, is modulated by two independent signals associated with self-movement, a motor-related signal and a visual motion signal associated with rotation of the animal. These two inputs adjust the sensitivity of these feature detectors across the locomotor cycle, selectively reducing their gain during saccades and restoring it during intersaccadic intervals. This work reveals a strategy for reliable feature detection during locomotion.
... We then manually reconstructed all branches of one DNx01 neuron using CATMAID (Saalfeld et al., 2009;Schneider-Mizell et al., 2016) as described in Phelps et al., 2021. The reconstructed neuron was then registered to the female VNC standard template (Bogovic et al., 2020) using an elastix-based atlas registration as described in Phelps et al., 2021. ...
Deciphering how the brain regulates motor circuits to control complex behaviors is an important, long-standing challenge in neuroscience. In the fly, Drosophila melanogaster , this is coordinated by a population of ~ 1100 descending neurons (DNs). Activating only a few DNs is known to be sufficient to drive complex behaviors like walking and grooming. However, what additional role the larger population of DNs plays during natural behaviors remains largely unknown. For example, they may modulate core behavioral commands or comprise parallel pathways that are engaged depending on sensory context. We evaluated these possibilities by recording populations of nearly 100 DNs in individual tethered flies while they generated limb-dependent behaviors, including walking and grooming. We found that the largest fraction of recorded DNs encode walking while fewer are active during head grooming and resting. A large fraction of walk-encoding DNs encode turning and far fewer weakly encode speed. Although odor context does not determine which behavior-encoding DNs are recruited, a few DNs encode odors rather than behaviors. Lastly, we illustrate how one can identify individual neurons from DN population recordings by using their spatial, functional, and morphological properties. These results set the stage for a comprehensive, population-level understanding of how the brain’s descending signals regulate complex motor actions.
