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29 Janelia Publications

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    03/16/18 | Functional architecture of reward learning in mushroom body extrinsic neurons of larval Drosophila.
    Saumweber T, Rohwedder A, Schleyer M, Eichler K, Chen Y, Aso Y, Cardona A, Eschbach C, Kobler O, Voigt A, Durairaja A, Mancini N, Zlatic M, Truman JW, Thum AS, Gerber B
    Nature Communications. 2018 Mar 16;9(1):1104. doi: 10.1038/s41467-018-03130-1

    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.

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    03/12/18 | Nociceptive interneurons control modular motor pathways to promote escape behavior in.
    Burgos A, Honjo K, Ohyama T, Qian CS, Shin GJ, Gohl DM, Silies M, Tracey WD, Zlatic M, Cardona A, Grueber WB
    eLife. 2018 Mar 12;7:. doi: 10.7554/eLife.26016

    Rapid and efficient escape behaviors in response to noxious sensory stimuli are essential for protection and survival. Yet, how noxious stimuli are transformed to coordinated escape behaviors remains poorly understood. Inlarvae, noxious stimuli trigger sequential body bending and corkscrew-like rolling behavior. We identified a population of interneurons in the nerve cord of, termed Down-and-Back (DnB) neurons, that are activated by noxious heat, promote nociceptive behavior, and are required for robust escape responses to noxious stimuli. Electron microscopic circuit reconstruction shows that DnBs are targets of nociceptive and mechanosensory neurons, are directly presynaptic to pre-motor circuits, and link indirectly to Goro rolling command-like neurons. DnB activation promotes activity in Goro neurons, and coincident inactivation of Goro neurons prevents the rolling sequence but leaves intact body bending motor responses. Thus, activity from nociceptors to DnB interneurons coordinates modular elements of nociceptive escape behavior.

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    03/09/18 | NeuroStorm: accelerating brain science discovery in the cloud.
    Kiar G, Anderson RJ, Baden A, Badea A, Bridgeford EW, Champion A, Chandrashekar J, Collman F, Duderstadt B, Evans AC, Engert F, Falk B, Glatard T, Roncal WG, Kennedy DN, Maitlin-Shepard , Marren RA, Nnaemeka O, Perlman E, Seshamani S
    arXiv. 2018 Mar 09:

    Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales. However, our ability to capitalize on existing datasets, tools, and intellectual capacities is hampered by technical challenges. The key barriers to accelerating scientific discovery correspond to the FAIR data principles: findability, global access to data, software interoperability, and reproducibility/re-usability. We conducted a hackathon dedicated to making strides in those steps. This manuscript is a technical report summarizing these achievements, and we hope serves as an example of the effectiveness of focused, deliberate hackathons towards the advancement of our quickly-evolving field.

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    12/20/17 | Divergent connectivity of homologous command-like neurons mediates segment-specific touch responses in Drosophila.
    Takagi S, Cocanougher BT, Niki S, Miyamoto D, Kohsaka H, Kazama H, Fetter RD, Truman JW, Zlatic M, Cardona A, Nose A
    Neuron. 2017 Dec 20;96(6):1373-87. doi: 10.1016/j.neuron.2017.10.030

    Animals adaptively respond to a tactile stimulus by choosing an ethologically relevant behavior depending on the location of the stimuli. Here, we investigate how somatosensory inputs on different body segments are linked to distinct motor outputs in Drosophila larvae. Larvae escape by backward locomotion when touched on the head, while they crawl forward when touched on the tail. We identify a class of segmentally repeated second-order somatosensory interneurons, that we named Wave, whose activation in anterior and posterior segments elicit backward and forward locomotion, respectively. Anterior and posterior Wave neurons extend their dendrites in opposite directions to receive somatosensory inputs from the head and tail, respectively. Downstream of anterior Wave neurons, we identify premotor circuits including the neuron A03a5, which together with Wave, is necessary for the backward locomotion touch response. Thus, Wave neurons match their receptive field to appropriate motor programs by participating in different circuits in different segments.

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    Fetter LabCardona Lab
    10/23/17 | Conserved neural circuit structure across Drosophila larva development revealed by comparative connectomics.
    Gerhard S, Andrade I, Fetter RD, Cardona A, Schneider-Mizell CM
    eLife. 2017 Oct 23;6:e29089. doi: 10.7554/eLife.29089

    During postembryonic development, the nervous system must adapt to a growing body. How changes in neuronal structure and connectivity contribute to the maintenance of appropriate circuit function remains unclear. In a previous paper (Schneider-Mizell et al., 2016), we measured the cellular neuroanatomy underlying synaptic connectivity in Drosophila. Here, we examined how neuronal morphology and connectivity change between 1st instar and 3rd instar larval stages using serial section electron microscopy. We reconstructed nociceptive circuits in a larva of each stage and found consistent topographically arranged connectivity between identified neurons. Five-fold increases in each size, number of terminal dendritic branches, and total number of synaptic inputs were accompanied by cell-type specific connectivity changes that preserved the fraction of total synaptic input associated with each presynaptic partner. We propose that precise patterns of structural growth act to conserve the computational function of a circuit, for example determining the location of a dangerous stimulus.

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    08/09/17 | The complete connectome of a learning and memory centre in an insect brain.
    Eichler K, Li F, Litwin-Kumar A, Park Y, Andrade I, Schneider-Mizell CM, Saumweber T, Huser A, Eschbach C, Gerber B, Fetter RD, Truman JW, Priebe CE, Abbott LF, Thum AS, Zlatic M, Cardona A
    Nature. 2017 Aug 09;548(7666):175-182. doi: 10.1038/nature23455

    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.

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    08/08/17 | Organization of the drosophila larval visual circuit.
    Larderet I, Fritsch PM, Gendre N, Neagu-Maier GL, Fetter RD, Schneider-Mizell CM, Truman JW, Zlatic M, Cardona A, Sprecher SG
    eLife. 2017 Aug 8:e28387. doi: 10.7554/eLife.28387

    Visual systems transduce, process and transmit light-dependent environmental cues. Computation of visual features depends on photoreceptor neuron types (PR) present, organization of the eye and wiring of the underlying neural circuit. Here, we describe the circuit architecture of the visual system of Drosophila larvae by mapping the synaptic wiring diagram and neurotransmitters. By contacting different targets, the two larval PR-subtypes create two converging pathways potentially underlying the computation of ambient light intensity and temporal light changes already within this first visual processing center. Locally processed visual information then signals via dedicated projection interneurons to higher brain areas including the lateral horn and mushroom body. The stratified structure of the larval optic neuropil (LON) suggests common organizational principles with the adult fly and vertebrate visual systems. The complete synaptic wiring diagram of the LON paves the way to understanding how circuits with reduced numerical complexity control wide ranges of behaviors.

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    05/09/17 | Semiparametric spectral modeling of the Drosophila connectome.
    Priebe CE, Park Y, Tang M, Athreya A, Lyzinski V, Vogelstein JT, Qin Y, Cocanougher B, Eichler K, Zlatic M, Cardona A
    arXiv. 2017 May 9:1705.03297

    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.

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    03/29/17 | Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.
    Arganda-Carreras I, Kaynig V, Rueden C, Eliceiri KW, Schindelin J, Cardona A, Seung HS
    Bioinformatics (Oxford, England). 2017 Mar 29;33(15):2424-6. doi: 10.1093/bioinformatics/btx180

    Summary: State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This processis time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leveragesa limited number of manual annotations in order to train a classifier and segment the remaining dataautomatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers.

    Availability and Implementation: TWS is distributed as open-source software as part of the Fiji image processing distribution of ImageJ at http://imagej.net/Trainable_Weka_Segmentation.

    Contact: ignacio.arganda@ehu.eus.

    Supplementary information: Supplementary data are available at Bioinformatics online.

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    01/31/17 | Multicut brings automated neurite segmentation closer to human performance.
    Beier T, Pape C, Rahaman N, Prange T, Berg S, Bock DD, Cardona A, Knott GW, Plaza SM, Scheffer LK, Koethe U, Kreshuk A, Hamprecht FA
    Nature Methods. 2017 Jan 31;14(2):101-102. doi: 10.1038/nmeth.4151