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2268 Publications

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    Card LabFly Functional ConnectomeFly Facility
    01/04/23 | Synaptic gradients transform object location to action.
    Dombrovski M, Peek MY, Park J, Vaccari A, Sumathipala M, Morrow C, Breads P, Zhao A, Kurmangaliyev YZ, Sanfilippo P, Rehan A, Polsky J, Alghailani S, Tenshaw E, Namiki S, Zipursky SL, Card GM
    Nature. 2023 Jan 04;613(7944):534-42. doi: 10.1038/s41586-022-05562-8

    To survive, animals must convert sensory information into appropriate behaviours. Vision is a common sense for locating ethologically relevant stimuli and guiding motor responses. How circuitry converts object location in retinal coordinates to movement direction in body coordinates remains largely unknown. Here we show through behaviour, physiology, anatomy and connectomics in Drosophila that visuomotor transformation occurs by conversion of topographic maps formed by the dendrites of feature-detecting visual projection neurons (VPNs) into synaptic weight gradients of VPN outputs onto central brain neurons. We demonstrate how this gradient motif transforms the anteroposterior location of a visual looming stimulus into the fly's directional escape. Specifically, we discover that two neurons postsynaptic to a looming-responsive VPN type promote opposite takeoff directions. Opposite synaptic weight gradients onto these neurons from looming VPNs in different visual field regions convert localized looming threats into correctly oriented escapes. For a second looming-responsive VPN type, we demonstrate graded responses along the dorsoventral axis. We show that this synaptic gradient motif generalizes across all 20 primary VPN cell types and most often arises without VPN axon topography. Synaptic gradients may thus be a general mechanism for conveying spatial features of sensory information into directed motor outputs.

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    01/02/23 | Time-tagged ticker tapes for intracellular recordings.
    Lin D, Li X, Moult E, Park P, Tang B, Shen H, Grimm JB, Falco N, Jia BZ, Baker D, Lavis LD, Cohen AE
    Nature Biotechnology. 2023 Jan 02:. doi: 10.1038/s41587-022-01524-7

    Recording transcriptional histories of a cell would enable deeper understanding of cellular developmental trajectories and responses to external perturbations. Here we describe an engineered protein fiber that incorporates diverse fluorescent marks during its growth to store a ticker tape-like history. An embedded HaloTag reporter incorporates user-supplied dyes, leading to colored stripes that map the growth of each individual fiber to wall clock time. A co-expressed eGFP tag driven by a promoter of interest records a history of transcriptional activation. High-resolution multi-spectral imaging on fixed samples reads the cellular histories, and interpolation of eGFP marks relative to HaloTag timestamps provides accurate absolute timing. We demonstrate recordings of doxycycline-induced transcription in HEK cells and cFos promoter activation in cultured neurons, with a single-cell absolute accuracy of 30-40 minutes over a 12-hour recording. The protein-based ticker tape design we present here could be generalized to achieve massively parallel single-cell recordings of diverse physiological modalities.

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    01/01/23 | Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations.
    Malin-Mayor C, Hirsch P, Guignard L, McDole K, Wan Y, Lemon WC, Kainmueller D, Keller PJ, Preibisch S, Funke J
    Nature Biotechnology. 2023 Jan 01;41(1):44-49. doi: 10.1038/s41587-022-01427-7

    We present a method to automatically identify and track nuclei in time-lapse microscopy recordings of entire developing embryos. The method combines deep learning and global optimization. On a mouse dataset, it reconstructs 75.8% of cell lineages spanning 1 h, as compared to 31.8% for the competing method. Our approach improves understanding of where and when cell fate decisions are made in developing embryos, tissues, and organs.

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    01/01/23 | Hippocampal spatial representations exhibit a hyperbolic geometry that expands with experience.
    Zhang H, Rich PD, Lee AK, Sharpee TO
    Nature Neuroscience. 2023 Jan 01;26(1):131-139. doi: 10.1038/s41593-022-01212-4

    Daily experience suggests that we perceive distances near us linearly. However, the actual geometry of spatial representation in the brain is unknown. Here we report that neurons in the CA1 region of rat hippocampus that mediate spatial perception represent space according to a non-linear hyperbolic geometry. This geometry uses an exponential scale and yields greater positional information than a linear scale. We found that the size of the representation matches the optimal predictions for the number of CA1 neurons. The representations also dynamically expanded proportional to the logarithm of time that the animal spent exploring the environment, in correspondence with the maximal mutual information that can be received. The dynamic changes tracked even small variations due to changes in the running speed of the animal. These results demonstrate how neural circuits achieve efficient representations using dynamic hyperbolic geometry.

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    01/01/23 | Structured cerebellar connectivity supports resilient pattern separation.
    Nguyen TM, Thomas LA, Rhoades JL, Ricchi I, Yuan XC, Sheridan A, Hildebrand DG, Funke J, Regehr WG, Lee WA
    Nature. 2023 Jan 01;613(7944):543-549. doi: 10.1038/s41586-022-05471-w

    The cerebellum is thought to help detect and correct errors between intended and executed commands and is critical for social behaviours, cognition and emotion. Computations for motor control must be performed quickly to correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise. Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the network's first layer. However, maximizing encoding capacity reduces the resilience to noise. To understand how neuronal circuits address this fundamental trade-off, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy and convolutional neural network-based image segmentation. We found that both the input and output layers of the circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest that these redundant, non-random connectivity motifs increase the resilience to noise at a negligible cost to the overall encoding capacity. This work reveals how neuronal network structure can support a trade-off between encoding capacity and redundancy, unveiling principles of biological network architecture with implications for the design of artificial neural networks.

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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 | Desmosomal connectomics of all somatic muscles in an annelid larva.
    Jasek S, Verasztó C, Brodrick E, Shahidi R, Kazimiers T, Kerbl A, Jékely G
    eLife. 2022 Dec 20;11:. doi: 10.7554/eLife.71231

    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 . 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.

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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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