@article {69790, title = {An updated catalogue of split-GAL4 driver lines for descending neurons in Drosophila melanogaster}, journal = {bioRxiv}, year = {2025}, month = {2025 Feb 24}, abstract = {

Descending neurons (DNs) occupy a key position in the sensorimotor hierarchy, conveying signals from the brain to the rest of the body below the neck. In Drosophila melanogaster flies, approximately 480 DN cell types have been described from electron-microscopy image datasets. Genetic access to these cell types is crucial for further investigation of their role in generating behaviour. We previously conducted the first large-scale survey of Drosophila melanogaster DNs, describing 98 unique cell types from light microscopy and generating cell-type-specific split-Gal4 driver lines for 65 of them. Here, we extend our previous work, describing the morphology of 137 additional DN types from light microscopy, bringing the total number DN types identified in light microscopy datasets to 235, or nearly 50\%. In addition, we produced 500 new sparse split-Gal4 driver lines and compiled a list of previously published DN lines from the literature for a combined list of 738 split-Gal4 driver lines targeting 171 DN types.

}, doi = {10.1101/2025.02.22.639679}, url = {https://www.biorxiv.org/content/early/2025/02/24/2025.02.22.639679}, author = {Zung, Jessica L. and Namiki, Shigehiro and Meissner, Geoffrey W. and Costa, Marta and Eichler, Katharina and St{\"u}rner, Tomke and Jefferis, Gregory S.X.E. and Managan, Claire and FlyLight Project Team and Korff, Wyatt and Card, Gwyneth M.} } @article {69489, title = {Comparative connectomics of the descending and ascending neurons of the Drosophila nervous system: stereotypy and sexual dimorphism}, journal = {bioRxiv}, year = {2024}, month = {2024 Jun 06}, abstract = {

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.

}, doi = {10.1101/2024.06.04.596633}, url = {https://www.biorxiv.org/content/early/2024/06/06/2024.06.04.596633}, author = {St{\"u}rner, Tomke and Brooks, Paul and Serratosa Capdevila, Laia and Morris, Billy J. and Javier, Alexandre and Fang, Siqi and Gkantia, Marina and Cachero, Sebastian and Beckett, Isabella R. and Champion, Andrew S. and Moitra, Ilina and Richards, Alana and Klemm, Finja and Kugel, Leonie and Namiki, Shigehiro and Cheong, Han S.J. and Kovalyak, Julie and Tenshaw, Emily and Parekh, Ruchi and Schlegel, Philipp and Phelps, Jasper S. and Mark, Brandon and Dorkenwald, Sven and Bates, Alexander S. and Matsliah, Arie and Yu, Szi-Chieh and McKellar, Claire E. and Sterling, Amy and Seung, Sebastian and Murthy, Mala and Tuthill, John and Lee, Wei-Chung A. and Card, Gwyneth M. and Costa, Marta and Jefferis, Gregory S.X.E. and Eichler, Katharina} } @article {69633, title = {A Drosophila computational brain model reveals sensorimotor processing.}, journal = {Nature}, volume = {634}, year = {2024}, month = {2024 Oct 02}, pages = {210-219}, abstract = {

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 brain. Here we create a leaky integrate-and-fire computational model of the entire Drosophila brain, on the basis of neural connectivity and neurotransmitter identity, 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 initiation. In addition, using the model to activate neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing-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 subtypes. 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.

}, keywords = {Animals, Brain, Computer Simulation, Connectome, Drosophila melanogaster, Feeding Behavior, Female, Grooming, Male, Models, Neurological, Motor Neurons, Optogenetics, Synapses, Taste}, issn = {1476-4687}, doi = {10.1038/s41586-024-07763-9}, url = {https://doi.org/10.1038/s41586-024-07763-9}, author = {Shiu, Philip K and Sterne, Gabriella R and Spiller, Nico and Franconville, Romain and Sandoval, Andrea and Zhou, Joie and Simha, Neha and Kang, Chan Hyuk and Yu, Seongbong and Kim, Jinseop S and Dorkenwald, Sven and Matsliah, Arie and Schlegel, Philipp and Yu, Szi-Chieh and McKellar, Claire E and Sterling, Amy and Costa, Marta and Eichler, Katharina and Bates, Alexander Shakeel and Eckstein, Nils and Funke, Jan and Jefferis, Gregory S X E and Murthy, Mala and Bidaye, Salil S and Hampel, Stefanie and Seeds, Andrew M and Scott, Kristin} } @article {68889, title = {Neuronal wiring diagram of an adult brain.}, journal = {Nature}, volume = {634}, year = {2024}, month = {2024 Oct 02}, pages = {124-138}, abstract = {

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 informative, but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 {\texttimes} 107 chemical synapses between 139,255 neurons reconstructed from an adult female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities. 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.

}, doi = {10.1038/s41586-024-07558-y}, url = {https://doi.org/10.1038/s41586-024-07558-y}, author = {Dorkenwald, Sven and Matsliah, Arie and Sterling, Amy R and Schlegel, Philipp and Yu, Szi-Chieh and McKellar, Claire E and Lin, Albert and Costa, Marta and Eichler, Katharina and Yin, Yijie and Silversmith, Will and Schneider-Mizell, Casey and Jordan, Chris S and Brittain, Derrick and Halageri, Akhilesh and Kuehner, Kai and Ogedengbe, Oluwaseun and Morey, Ryan and Gager, Jay and Kruk, Krzysztof and Perlman, Eric and Yang, Runzhe and Deutsch, David and Bland, Doug and Sorek, Marissa and Lu, Ran and Macrina, Thomas and Lee, Kisuk and Bae, J Alexander and Mu, Shang and Nehoran, Barak and Mitchell, Eric and Popovych, Sergiy and Wu, Jingpeng and Jia, Zhen and Castro, Manuel and Kemnitz, Nico and Ih, Dodam and Bates, Alexander Shakeel and Eckstein, Nils and Funke, Jan and Collman, Forrest and Bock, Davi D and Jefferis, Gregory S X E and Seung, H Sebastian and Murthy, Mala} } @article {69439, title = {Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster}, journal = {Cell}, volume = {187}, year = {2024}, month = {2024 May 09}, pages = {2574-2594.e23}, abstract = {

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.

}, keywords = {Animals, Brain, Connectome, Drosophila melanogaster, gamma-Aminobutyric Acid, Microscopy, Electron, Neural Networks, Computer, Neurons, Neurotransmitter Agents, Synapses}, issn = {1097-4172}, doi = {10.1016/j.cell.2024.03.016}, author = {Eckstein, Nils and Bates, Alexander Shakeel and Champion, Andrew and Du, Michelle and Yin, Yijie and Schlegel, Philipp and Lu, Alicia Kun-Yang and Rymer, Thomson and Finley-May, Samantha and Paterson, Tyler and Parekh, Ruchi and Dorkenwald, Sven and Matsliah, Arie and Yu, Szi-Chieh and McKellar, Claire and Sterling, Amy and Eichler, Katharina and Costa, Marta and Seung, Sebastian and Murthy, Mala and Hartenstein, Volker and Jefferis, Gregory S X E and Funke, Jan} } @article {67793, title = {Circuits for integrating learned and innate valences in the insect brain.}, journal = {eLife}, volume = {10}, year = {2021}, month = {2021 Nov 10}, abstract = {

Animal behavior is shaped both by evolution and by individual experience. Parallel brain pathways encode innate and learned valences of cues, but the way in which they are integrated during action-selection is not well understood. We used electron microscopy to comprehensively map with synaptic resolution all neurons downstream of all Mushroom Body output neurons (encoding learned valences) and characterized their patterns of interaction with Lateral Horn neurons (encoding innate valences) in larva. The connectome revealed multiple types that receive convergent Mushroom Body and Lateral Horn inputs. A subset of these receives excitatory input from positive-valence MB and LH pathways and inhibitory input from negative-valence MB pathways. We confirmed functional connectivity from LH and MB pathways and behavioral roles of two of these neurons. These neurons encode integrated odor value and bidirectionally regulate turning. Based on this we speculate that learning could potentially skew the balance of excitation and inhibition onto these neurons and thereby modulate turning. Together, our study provides insights into the circuits that integrate learned and innate valences to modify behavior.

}, issn = {2050-084X}, doi = {10.7554/eLife.62567}, author = {Eschbach, Claire and Fushiki, Akira and Winding, Michael and Afonso, Bruno and Andrade, Ingrid V and Cocanougher, Benjamin T and Eichler, Katharina and Gepner, Ruben and Si, Guangwei and Valdes-Aleman, Javier and Fetter, Richard D and Gershow, Marc and Jefferis, Gregory Sxe and Samuel, Aravinthan Dt and Truman, James W and Cardona, Albert and Zlatic, Marta} } @article {65637, title = {Identification of dopaminergic neurons that can both establish associative memory and acutely terminate its behavioral expression.}, journal = {Journal of Neuroscience}, volume = {40}, year = {2020}, month = {2020 Jul 29}, pages = {5990-6006}, abstract = {

An adaptive transition from exploring the environment in search of vital resources to exploiting these resources once the search is successful is important to all animals. Here we study the neuronal circuitry that allows larval of either sex to negotiate this exploration-exploitation transition. We do so by combining Pavlovian conditioning with high-resolution behavioral tracking, optogenetic manipulation of individually identified neurons, and EM-data-based analyses of synaptic organization. We find that optogenetic activation of the dopaminergic neuron DAN-i1 can both establish memory during training, and acutely terminate learned search behavior in a subsequent recall test. Its activation leaves innate behavior unaffected, however. Specifically, DAN-i1 activation can establish associative memories of opposite valence upon paired and unpaired training with odor, and its activation during the recall test can terminate the search behavior resulting from either of these memories. Our results further suggest that in its behavioral significance DAN-i1 activation resembles but does not equal sugar reward. Dendrogram analyses of all the synaptic connections between DAN-i1 and its two main targets, the Kenyon cells and the mushroom body output neuron MBON-i1, further suggest that the DAN-i1 signals during training and during the recall test could be delivered to the Kenyon cells and to MBON-i1, respectively, within previously unrecognized, locally confined branching structures. This would provide an elegant circuit motif to terminate search upon its successful completion.In the struggle for survival animals have to explore their environment in search of food. Once food is found, however, it is adaptive to prioritize exploiting it over continuing a search that would now be as pointless as searching for the glasses you are wearing. This exploration-exploitation trade-off is important for animals and humans, as well as for technical search devices. We investigate which of the only 10,000 neurons of a fruit fly larva can tip the balance in this trade-off, and identify a single dopamine neuron called DAN-i1 that can do so. Given the similarities in dopamine neuron function across the animal kingdom, this may reflect a general principle of how search is terminated once it is successful.

}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.0290-20.2020}, author = {Schleyer, Michael and Weiglein, Ali{\'c}e and Thoener, Juliane and Strauch, Martin and Hartenstein, Volker and Kantar Weigelt, Melisa and Schuller, Sarah and Saumweber, Timo and Eichler, Katharina and Rohwedder, Astrid and Merhof, Dorit and Zlatic, Marta and Thum, Andreas S and Gerber, Bertram} } @article {65373, title = {Recurrent architecture for adaptive regulation of learning in the insect brain.}, journal = {Nature Neuroscience}, volume = {23}, year = {2020}, month = {2020 Mar 23}, pages = {544-55}, abstract = {

Dopaminergic neurons (DANs) drive learning across the animal kingdom, but the upstream circuits that regulate their activity and thereby learning remain poorly understood. We provide a synaptic-resolution connectome of the circuitry upstream of all DANs in a learning center, the mushroom body of Drosophila larva. We discover afferent sensory pathways and a large population of neurons that provide feedback from mushroom body output neurons and link distinct memory systems (aversive and appetitive). We combine this with functional studies of DANs and their presynaptic partners and with comprehensive circuit modeling. We find that DANs compare convergent feedback from aversive and appetitive systems, which enables the computation of integrated predictions that may improve future learning. Computational modeling reveals that the discovered feedback motifs increase model flexibility and performance on learning tasks. Our study provides the most detailed view to date of biological circuit motifs that support associative learning.

}, issn = {1546-1726}, doi = {10.1038/s41593-020-0607-9}, author = {Eschbach, Claire and Fushiki, Akira and Winding, Michael and Schneider-Mizell, Casey M and Shao, Mei and Arruda, Rebecca and Eichler, Katharina and Valdes-Aleman, Javier and Ohyama, Tomoko and Thum, Andreas S and Gerber, Bertram and Fetter, Richard D and Truman, James W and Litwin-Kumar, Ashok and Cardona, Albert and Zlatic, Marta and Cardona, Albert and Zlatic, Marta} } @article {49093, title = {Functional architecture of reward learning in mushroom body extrinsic neurons of larval Drosophila.}, journal = {Nature Communications}, volume = {9}, year = {2018}, month = {2018 Mar 16}, pages = {1104}, abstract = {

The brain adaptively integrates present sensory input, past experience, and options for future action. The insect mushroom body exemplifies how a central brain structure brings about such integration. Here we use a combination of systematic single-cell labeling, connectomics, transgenic silencing, and activation experiments to study the mushroom body at single-cell resolution, focusing on the behavioral architecture of its input and output neurons (MBINs and MBONs), and of the mushroom body intrinsic APL neuron. Our results reveal the identity and morphology of almost all of these 44 neurons in stage 3 Drosophila larvae. Upon an initial screen, functional analyses focusing on the mushroom body medial lobe uncover sparse and specific functions of its dopaminergic MBINs, its MBONs, and of the GABAergic APL neuron across three behavioral tasks, namely odor preference, taste preference, and associative learning between odor and taste. Our results thus provide a cellular-resolution study case of how brains organize behavior.

}, issn = {2041-1723}, doi = {10.1038/s41467-018-03130-1}, author = {Saumweber, Timo and Rohwedder, Astrid and Schleyer, Michael and Eichler, Katharina and Chen, Yi-chun and Aso, Yoshinori and Cardona, Albert and Eschbach, Claire and Kobler, Oliver and Voigt, Anne and Durairaja, Archana and Mancini, Nino and Zlatic, Marta and Truman, James W and Thum, Andreas S and Gerber, Bertram} } @article {48720, title = {The complete connectome of a learning and memory centre in an insect brain.}, journal = {Nature}, volume = {548}, year = {2017}, month = {2017 Aug 09}, pages = {175-182}, abstract = {

Associating stimuli with positive or negative reinforcement is essential for survival, but a complete wiring diagram of a higher-order circuit supporting associative memory has not been previously available. Here we reconstruct one such circuit at synaptic resolution, the Drosophila larval mushroom body. We find that most Kenyon cells integrate random combinations of inputs but that a subset receives stereotyped inputs from single projection neurons. This organization maximizes performance of a model output neuron on a stimulus discrimination task. We also report a novel canonical circuit in each mushroom body compartment with previously unidentified connections: reciprocal Kenyon cell to modulatory neuron connections, modulatory neuron to output neuron connections, and a surprisingly high number of recurrent connections between Kenyon cells. Stereotyped connections found between output neurons could enhance the selection of learned behaviours. The complete circuit map of the mushroom body should guide future functional studies of this learning and memory centre.

}, issn = {1476-4687}, doi = {10.1038/nature23455}, author = {Eichler, Katharina and Li, Feng and Litwin-Kumar, Ashok and Park, Youngser and Andrade, Ingrid and Schneider-Mizell, Casey M and Saumweber, Timo and Huser, Annina and Eschbach, Claire and Gerber, Bertram and Fetter, Richard D and Truman, James W and Priebe, Carey E and Abbott, L F and Thum, Andreas S and Zlatic, Marta and Cardona, Albert} } @article {48642, title = {The Ol1mpiad: concordance of behavioural faculties of stage 1 and stage 3 Drosophila larvae.}, journal = {The Journal of Experimental Biology}, volume = {220}, year = {2017}, month = {2017 Jul 01}, pages = {2452-2475}, abstract = {

Mapping brain function to brain structure is a fundamental task for neuroscience. For such an endeavour, the Drosophila larva is simple enough to be tractable, yet complex enough to be interesting. It features about 10,000 neurons and is capable of various taxes, kineses and Pavlovian conditioning. All its neurons are currently being mapped into a light-microscopical atlas, and Gal4 strains are being generated to experimentally access neurons one at a time. In addition, an electron microscopic reconstruction of its nervous system seems within reach. Notably, this electron microscope-based connectome is being drafted for a stage 1 larva - because stage 1 larvae are much smaller than stage 3 larvae. However, most behaviour analyses have been performed for stage 3 larvae because their larger size makes them easier to handle and observe. It is therefore warranted to either redo the electron microscopic reconstruction for a stage 3 larva or to survey the behavioural faculties of stage 1 larvae. We provide the latter. In a community-based approach we called the Ol1mpiad, we probed stage 1 Drosophila larvae for free locomotion, feeding, responsiveness to substrate vibration, gentle and nociceptive touch, burrowing, olfactory preference and thermotaxis, light avoidance, gustatory choice of various tastants plus odour-taste associative learning, as well as light/dark-electric shock associative learning. Quantitatively, stage 1 larvae show lower scores in most tasks, arguably because of their smaller size and lower speed. Qualitatively, however, stage 1 larvae perform strikingly similar to stage 3 larvae in almost all cases. These results bolster confidence in mapping brain structure and behaviour across developmental stages.

}, issn = {1477-9145}, doi = {10.1242/jeb.156646}, author = {Almeida-Carvalho, Maria J and Berh, Dimitri and Braun, Andreas and Chen, Yi-chun and Eichler, Katharina and Eschbach, Claire and Fritsch, Pauline M J and Gerber, Bertram and Hoyer, Nina and Jiang, Xiaoyi and Kleber, J{\"o}rg and Kl{\"a}mbt, Christian and K{\"o}nig, Christian and Louis, Matthieu and Michels, Birgit and Miroschnikow, Anton and Mirth, Christen and Miura, Daisuke and Niewalda, Thomas and Otto, Nils and Paisios, Emmanouil and Pankratz, Michael J and Petersen, Meike and Ramsperger, Noel and Randel, Nadine and Risse, Benjamin and Saumweber, Timo and Schlegel, Philipp and Schleyer, Michael and Soba, Peter and Sprecher, Simon G and Tanimura, Teiichi and Thum, Andreas S and Toshima, Naoko and Truman, Jim W and Yarali, Ayse and Zlatic, Marta} } @article {48495, title = {Semiparametric spectral modeling of the Drosophila connectome.}, journal = {arXiv}, year = {2017}, month = {2017 May 9}, pages = {1705.03297}, abstract = {

We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. Motivated by a thorough exploratory data analysis of the network via Gaussian mixture modeling (GMM) in the adjacency spectral embedding (ASE) representation space, we introduce the latent structure model (LSM) for network modeling and inference. LSM is a generalization of the stochastic block model (SBM) and a special case of the random dot product graph (RDPG) latent position model, and is amenable to semiparametric GMM in the ASE representation space. The resulting connectome code derived via semiparametric GMM composed with ASE captures latent connectome structure and elucidates biologically relevant neuronal properties.

}, url = {https://arxiv.org/abs/1705.03297}, author = {Priebe, Carey E. and Park, Youngser and Tang, Minh and Athreya, Avanti and Lyzinski, Vince and Vogelstein, Joshua T and Qin, Yichen and Cocanougher, Benjamin and Eichler, Katharina and Zlatic, Marta and Cardona, Albert} }