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184 Janelia Publications

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    12/30/22 | Local shape descriptors for neuron segmentation.
    Sheridan A, Nguyen TM, Deb D, Lee WA, Saalfeld S, Turaga SC, Manor U, Funke J
    Nature Methods. 2022 Dec 30:. doi: 10.1038/s41592-022-01711-z

    We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.

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    11/28/22 | The connectome of an insect brain
    Michael Winding , Benjamin D. Pedigo , Christopher L. Barnes , Heather G. Patsolic , Youngser Park , Tom Kazimiers , Akira Fushiki , Ingrid V. Andrade , Avinash Khandelwal , Javier Valdes-Aleman , Feng Li , Nadine Randel , Elizabeth Barsotti , Ana Correia , Richard D. Fetter , Volker Hartenstein , Carey E. Priebe , Joshua T. Vogelstein , Albert Cardona , Marta Zlatic
    bioRxiv. 2022 Nov 28:. doi: 10.1101/2022.11.28.516756

    Brains contain networks of interconnected neurons, so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an insect brain (Drosophila larva) with rich behavior, including learning, value-computation, and action-selection, comprising 3,013 neurons and 544,000 synapses. We characterized neuron-types, hubs, feedforward and feedback pathways, and cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain’s most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled powerful machine learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.

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    12/22/22 | A brainstem integrator for self-localization and positional homeostasis
    Yang E, Zwart MF, Rubinov M, James B, Wei Z, Narayan S, Vladimirov N, Mensh BD, Fitzgerald JE, Ahrens MB
    Cell. 2022 Dec 22;185(26):5011-5027.e20. doi: 10.1101/2021.11.26.468907

    To accurately track self-location, animals need to integrate their movements through space. In amniotes, representations of self-location have been found in regions such as the hippocampus. It is unknown whether more ancient brain regions contain such representations and by which pathways they may drive locomotion. Fish displaced by water currents must prevent uncontrolled drift to potentially dangerous areas. We found that larval zebrafish track such movements and can later swim back to their earlier location. Whole-brain functional imaging revealed the circuit enabling this process of positional homeostasis. Position-encoding brainstem neurons integrate optic flow, then bias future swimming to correct for past displacements by modulating inferior olive and cerebellar activity. Manipulation of position-encoding or olivary neurons abolished positional homeostasis or evoked behavior as if animals had experienced positional shifts. These results reveal a multiregional hindbrain circuit in vertebrates for optic flow integration, memory of self-location, and its neural pathway to behavior.Competing Interest StatementThe authors have declared no competing interest.

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    12/22/22 | Neuronal cell types, projections, and spatial organization of the central amygdala
    O’Leary TP, Kendrick RM, Bristow BN, Sullivan KE, Wang L, Clements J, Lemire AL, Cembrowski MS
    iScience. 2022 Dec 22;25(12):105497. doi: 10.1016/j.isci.2022.105497

    The central amygdala (CEA) has been richly studied for interpreting function and behavior according to specific cell types and circuits. Such work has typically defined molecular cell types by classical inhibitory marker genes; consequently, whether marker-gene-defined cell types exhaustively cover the CEA and co-vary with connectivity remains unresolved. Here, we combined single-cell RNA sequencing, multiplexed fluorescent in situ hybridization, immunohistochemistry, and long-range projection mapping to derive a “bottom-up” understanding of CEA cell types. In doing so, we identify two major cell types, encompassing one-third of all CEA neurons, that have gone unresolved in previous studies. In spatially mapping these novel types, we identify a non-canonical CEA subdomain associated with Nr2f2 expression and uncover an Isl1-expressing medial cell type that accounts for many long-range CEA projections. Our results reveal new CEA organizational principles across cell types and spatial scales and provide a framework for future work examining cell-type-specific behavior and function.

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    12/20/22 | Behavioral state-dependent modulation of insulin-producing cells in Drosophila.
    Liessem S, Held M, Bisen RS, Haberkern H, Lacin H, Bockemühl T, Ache JM
    Current Biology. 2022 Dec 20:. doi: 10.1016/j.cub.2022.12.005

    Insulin signaling plays a pivotal role in metabolic control and aging, and insulin accordingly is a key factor in several human diseases. Despite this importance, the in vivo activity dynamics of insulin-producing cells (IPCs) are poorly understood. Here, we characterized the effects of locomotion on the activity of IPCs in Drosophila. Using in vivo electrophysiology and calcium imaging, we found that IPCs were strongly inhibited during walking and flight and that their activity rebounded and overshot after cessation of locomotion. Moreover, IPC activity changed rapidly during behavioral transitions, revealing that IPCs are modulated on fast timescales in behaving animals. Optogenetic activation of locomotor networks ex vivo, in the absence of actual locomotion or changes in hemolymph sugar levels, was sufficient to inhibit IPCs. This demonstrates that the behavioral state-dependent inhibition of IPCs is actively controlled by neuronal pathways and is independent of changes in glucose concentration. By contrast, the overshoot in IPC activity after locomotion was absent ex vivo and after starvation, indicating that it was not purely driven by feedforward signals but additionally required feedback derived from changes in hemolymph sugar concentration. We hypothesize that IPC inhibition during locomotion supports mobilization of fuel stores during metabolically demanding behaviors, while the rebound in IPC activity after locomotion contributes to replenishing muscle glycogen stores. In addition, the rapid dynamics of IPC modulation support a potential role of insulin in the state-dependent modulation of sensorimotor processing.

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    12/10/22 | Reward expectations direct learning and drive operant matching in Drosophila
    Adithya E. Rajagopalan , Ran Darshan , Karen L. Hibbard , James E. Fitzgerald , Glenn C. Turner
    bioRxiv. 2022 Dec 10:. doi: 10.1101/2022.05.24.493252

    Foraging animals must use decision-making strategies that dynamically adapt to the changing availability of rewards in the environment. A wide diversity of animals do this by distributing their choices in proportion to the rewards received from each option, Herrnstein’s operant matching law. Theoretical work suggests an elegant mechanistic explanation for this ubiquitous behavior, as operant matching follows automatically from simple synaptic plasticity rules acting within behaviorally relevant neural circuits. However, no past work has mapped operant matching onto plasticity mechanisms in the brain, leaving the biological relevance of the theory unclear. Here we discovered operant matching in Drosophila and showed that it requires synaptic plasticity that acts in the mushroom body and incorporates the expectation of reward. We began by developing a novel behavioral paradigm to measure choices from individual flies as they learn to associate odor cues with probabilistic rewards. We then built a model of the fly mushroom body to explain each fly’s sequential choice behavior using a family of biologically-realistic synaptic plasticity rules. As predicted by past theoretical work, we found that synaptic plasticity rules could explain fly matching behavior by incorporating stimulus expectations, reward expectations, or both. However, by optogenetically bypassing the representation of reward expectation, we abolished matching behavior and showed that the plasticity rule must specifically incorporate reward expectations. Altogether, these results reveal the first synaptic level mechanisms of operant matching and provide compelling evidence for the role of reward expectation signals in the fly brain.

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    12/15/22 | Eye structure shapes neuron function in Drosophila motion vision
    Arthur Zhao , Eyal Gruntman , Aljoscha Nern , Nirmala A. Iyer , Edward M. Rogers , Sanna Koskela , Igor Siwanowicz , Marisa Dreher , Miriam A. Flynn , Connor W. Laughland , Henrique D.F. Ludwig , Alex G. Thomson , Cullen P. Moran , Bruck Gezahegn , Davi D. Bock , Michael B. Reiser
    bioRxiv. 2022 Dec 15:. doi: 10.1101/2022.12.14.520178

    Many animals rely on vision to navigate through their environment. The pattern of changes in the visual scene induced by self-motion is the optic flow1, which is first estimated in local patches by directionally selective (DS) neurons24. But how should the arrays of DS neurons, each responsive to motion in a preferred direction at a specific retinal position, be organized to support robust decoding of optic flow by downstream circuits? Understanding this global organization is challenging because it requires mapping fine, local features of neurons across the animal’s field of view3. In Drosophila, the asymmetric dendrites of the T4 and T5 DS neurons establish their preferred direction, making it possible to predict DS responses from anatomy4,5. Here we report that the preferred directions of fly DS neurons vary at different retinal positions and show that this spatial variation is established by the anatomy of the compound eye. To estimate the preferred directions across the visual field, we reconstructed hundreds of T4 neurons in a full brain EM volume6 and discovered unexpectedly stereotypical dendritic arborizations that are independent of location. We then used whole-head μCT scans to map the viewing directions of all compound eye facets and found a non-uniform sampling of visual space that explains the spatial variation in preferred directions. Our findings show that the organization of preferred directions in the fly is largely determined by the compound eye, exposing an intimate and unexpected connection between the peripheral structure of the eye, functional properties of neurons deep in the brain, and the control of body movements.

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    12/15/22 | Neural circuit mechanisms for transforming learned olfactory valences into wind-oriented movement
    Yoshinori Aso , Daichi Yamada , Daniel Bushey , Karen Hibbard , Megan Sammons , Hideo Otsuna , Yichun Shuai , Toshihide Hige
    bioRxiv. 2022 Dec 15:. doi: 10.1101/2022.12.21.521497

    How memories are used by the brain to guide future action is poorly understood. In olfactory associative learning in Drosophila, multiple compartments of the mushroom body act in parallel to assign valence to a stimulus. Here, we show that appetitive memories stored in different compartments induce different levels of upwind locomotion. Using a photoactivation screen of a new collection of split-GAL4 drivers and EM connectomics, we identified a cluster of neurons postsynaptic to the mushroom body output neurons (MBONs) that can trigger robust upwind steering. These UpWind Neurons (UpWiNs) integrate inhibitory and excitatory synaptic inputs from MBONs of appetitive and aversive memory compartments, respectively. After training, disinhibition from the appetitive-memory MBONs enhances the response of UpWiNs to reward-predicting odors. Blocking UpWiNs impaired appetitive memory and reduced upwind locomotion during retrieval. Photoactivation of UpWiNs also increased the chance of returning to a location where activation was initiated, suggesting an additional role in olfactory navigation. Thus, our results provide insight into how learned abstract valences are gradually transformed into concrete memory-driven actions through divergent and convergent networks, a neuronal architecture that is commonly found in the vertebrate and invertebrate brains.

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    12/15/22 | Neural coding of distinct motor patterns during Drosophila courtship song
    Hiroshi M. Shiozaki , Kaiyu Wang , Joshua L. Lillvis , Min Xu , Barry J. Dickson , David L. Stern
    bioRxiv. 2022 Dec 15:. doi: 10.1101/2022.12.14.520499

    Animals flexibly switch between different actions by changing neural activity patterns for motor control. Courting Drosophila melanogaster males produce two different acoustic signals, pulse and sine song, each of which can be promoted by artificial activation of distinct neurons. However, how the activity of these neurons implements flexible song production is unknown. Here, we developed an assay to record neuronal calcium signals in the ventral nerve cord, which contains the song motor circuit, in singing flies. We found that sine-promoting neurons, but not pulse-promoting neurons, show strong activation during sine song. In contrast, both pulse- and sine-promoting neurons are active during pulse song. Furthermore, population calcium imaging in the song circuit suggests that sine song involves activation of a subset of neurons that are also active during pulse song. Thus, differential activation of overlapping, rather than distinct, neural populations underlies flexible motor actions during acoustic communication in D. melanogaster.

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    12/14/22 | Neuromuscular embodiment of feedback control elements in flight.
    Whitehead SC, Leone S, Lindsay T, Meiselman MR, Cowan NJ, Dickinson MH, Yapici N, Stern DL, Shirangi T, Cohen I
    Science Advances. 2022 Dec 14;8(50):eabo7461. doi: 10.1126/sciadv.abo7461

    While insects such as are flying, aerodynamic instabilities require that they make millisecond time scale adjustments to their wing motion to stay aloft and on course. These stabilization reflexes can be modeled as a proportional-integral (PI) controller; however, it is unclear how such control might be instantiated in insects at the level of muscles and neurons. Here, we show that the b1 and b2 motor units-prominent components of the fly's steering muscle system-modulate specific elements of the PI controller: the angular displacement (integral) and angular velocity (proportional), respectively. Moreover, these effects are observed only during the stabilization of pitch. Our results provide evidence for an organizational principle in which each muscle contributes to a specific functional role in flight control, a finding that highlights the power of using top-down behavioral modeling to guide bottom-up cellular manipulation studies.

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