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51 Publications

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    12/29/23 | Ketamine modulates a norepinephrine-astroglial circuit to persistently suppress futility-induced passivity.
    Marc Duque , Alex B. Chen , Eric Hsu , Sujatha Narayan , Altyn Rymbek , Shahinoor Begum , Gesine Saher , Adam E. Cohen , David E. Olson , David A. Prober , Dwight E. Bergles , Mark C. Fishman , Florian Engert , Misha B. Ahrens
    bioRxiv. 2023 Dec 29:. doi: 10.1101/2022.12.29.522099

    Mood-altering compounds hold promise for the treatment of many psychiatric disorders, such as depression, but connecting their molecular, circuit, and behavioral effects has been challenging. Here we find that, analogous to effects in rodent learned helplessness models, ketamine pre-exposure persistently suppresses futility-induced passivity in larval zebrafish. While antidepressants are thought to primarily act on neurons, brain-wide imaging in behaving zebrafish showed that ketamine elevates intracellular calcium in astroglia for many minutes, followed by persistent calcium downregulation post-washout. Calcium elevation depends on astroglial α1-adrenergic receptors and is required for suppression of passivity. Chemo-/optogenetic perturbations of noradrenergic neurons and astroglia demonstrate that the aftereffects of glial calcium elevation are sufficient to suppress passivity by inhibiting neuronal-astroglial integration of behavioral futility. Imaging in mouse cortex reveals that ketamine elevates astroglial calcium through conserved pathways, suggesting that ketamine exerts its behavioral effects by persistently modulating evolutionarily ancient neuromodulatory systems spanning neurons and astroglia.

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    03/24/23 | Time-resolved correlation of distributed brain activity tracks E-I balance and accounts for diverse scale-free phenomena.
    Nanda A, Johnson GW, Mu Y, Ahrens MB, Chang C, Englot DJ, Breakspear M, Rubinov M
    Cell Reports. 2023 Mar 24;42(4):112254. doi: 10.1016/j.celrep.2023.112254

    Much of systems neuroscience posits the functional importance of brain activity patterns that lack natural scales of sizes, durations, or frequencies. The field has developed prominent, and sometimes competing, explanations for the nature of this scale-free activity. Here, we reconcile these explanations across species and modalities. First, we link estimates of excitation-inhibition (E-I) balance with time-resolved correlation of distributed brain activity. Second, we develop an unbiased method for sampling time series constrained by this time-resolved correlation. Third, we use this method to show that estimates of E-I balance account for diverse scale-free phenomena without need to attribute additional function or importance to these phenomena. Collectively, our results simplify existing explanations of scale-free brain activity and provide stringent tests on future theories that seek to transcend these explanations.

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    03/15/23 | Fast and sensitive GCaMP calcium indicators for imaging neural populations.
    Zhang Y, Rozsa M, Liang Y, Bushey D, Wei Z, Zheng J, Reep D, Broussard GJ, Tsang A, Tsegaye G, Narayan S, Obara CJ, Lim J, Patel R, Zhang R, Ahrens MB, Turner GC, Wang SS, Korff WL, Schreiter ER, Svoboda K, Hasseman JP, Kolb I, Looger LL
    Nature. 2023 Mar 15:. doi: 10.1038/s41586-023-05828-9

    Calcium imaging with protein-based indicators is widely used to follow neural activity in intact nervous systems, but current protein sensors report neural activity at timescales much slower than electrical signalling and are limited by trade-offs between sensitivity and kinetics. Here we used large-scale screening and structure-guided mutagenesis to develop and optimize several fast and sensitive GCaMP-type indicators. The resulting 'jGCaMP8' sensors, based on the calcium-binding protein calmodulin and a fragment of endothelial nitric oxide synthase, have ultra-fast kinetics (half-rise times of 2 ms) and the highest sensitivity for neural activity reported for a protein-based calcium sensor. jGCaMP8 sensors will allow tracking of large populations of neurons on timescales relevant to neural computation.

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    01/01/23 | Dimensionality reduction of calcium-imaged neuronal population activity
    Tze Hui Koh , William E. Bishop , Takashi Kawashima , Brian B. Jeon , Ranjani Srinivasan , Sandra J. Kuhlman , Misha B. Ahrens , Steven M. Chase , Byron M. Yu
    Nature Computational Science. 2023 Jan 01:. doi: 10.1038/s43588-022-00390-2

    Calcium imaging has been widely adopted for its ability to record from large neuronal populations. To summarize the time course of neural activity, dimensionality reduction methods, which have been applied extensively to population spiking activity, may be particularly useful. However, it is unclear if the dimensionality reduction methods applied to spiking activity are appropriate for calcium imaging. We thus carried out a systematic study of design choices based on standard dimensionality reduction methods. We also developed a novel method to perform deconvolution and dimensionality reduction simultaneously (termed CILDS). CILDS most accurately recovered the single-trial, low-dimensional time courses from calcium imaging that would have been recovered from spiking activity. CILDS also outperformed the other methods on calcium imaging recordings from larval zebrafish and mice. More broadly, this study represents a foundation for summarizing calcium imaging recordings of large neuronal populations using dimensionality reduction in diverse experimental settings.

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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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    10/31/22 | FourierNets enable the design of highly non-local optical encoders for computational imaging
    Diptodip Deb , Zhenfei Jiao , Ruth R Sims , Alex Bo-Yuan Chen , Michael Broxton , Misha Ahrens , Kaspar Podgorski , Srinivas C Turaga , Alice H. Oh , Alekh Agarwal , Danielle Belgrave , Kyunghyun Cho
    Advances in Neural Information Processing Systems. 10/2022:. doi: https://doi.org/10.48550/arXiv.2104.10611

    Differentiable simulations of optical systems can be combined with deep learning-based reconstruction networks to enable high performance computational imaging via end-to-end (E2E) optimization of both the optical encoder and the deep decoder. This has enabled imaging applications such as 3D localization microscopy, depth estimation, and lensless photography via the optimization of local optical encoders. More challenging computational imaging applications, such as 3D snapshot microscopy which compresses 3D volumes into single 2D images, require a highly non-local optical encoder. We show that existing deep network decoders have a locality bias which prevents the optimization of such highly non-local optical encoders. We address this with a decoder based on a shallow neural network architecture using global kernel Fourier convolutional neural networks (FourierNets). We show that FourierNets surpass existing deep network based decoders at reconstructing photographs captured by the highly non-local DiffuserCam optical encoder. Further, we show that FourierNets enable E2E optimization of highly non-local optical encoders for 3D snapshot microscopy. By combining FourierNets with a large-scale multi-GPU differentiable optical simulation, we are able to optimize non-local optical encoders 170× to 7372× larger than prior state of the art, and demonstrate the potential for ROI-type specific optical encoding with a programmable microscope.

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    04/06/22 | Voltage imaging identifies spinal circuits that modulate locomotor adaptation in zebrafish.
    Böhm UL, Kimura Y, Kawashima T, Ahrens MB, Higashijima S, Engert F, Cohen AE
    Neuron. 2022 Apr 06;110(7):1211-1222.e4. doi: 10.1016/j.neuron.2022.01.001

    Motor systems must continuously adapt their output to maintain a desired trajectory. While the spinal circuits underlying rhythmic locomotion are well described, little is known about how the network modulates its output strength. A major challenge has been the difficulty of recording from spinal neurons during behavior. Here, we use voltage imaging to map the membrane potential of large populations of glutamatergic neurons throughout the spinal cord of the larval zebrafish during fictive swimming in a virtual environment. We characterized a previously undescribed subpopulation of tonic-spiking ventral V3 neurons whose spike rate correlated with swimming strength and bout length. Optogenetic activation of V3 neurons led to stronger swimming and longer bouts but did not affect tail beat frequency. Genetic ablation of V3 neurons led to reduced locomotor adaptation. The power of voltage imaging allowed us to identify V3 neurons as a critical driver of locomotor adaptation in zebrafish.

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    01/24/22 | Voltage imaging identifies spinal circuits that modulate locomotor adaptation in zebrafish.
    Böhm UL, Kimura Y, Kawashima T, Ahrens MB, Higashijima S, Engert F, Cohen AE
    Neuron. 2022 Jan 24:. doi: 10.1016/j.neuron.2022.01.001

    Motor systems must continuously adapt their output to maintain a desired trajectory. While the spinal circuits underlying rhythmic locomotion are well described, little is known about how the network modulates its output strength. A major challenge has been the difficulty of recording from spinal neurons during behavior. Here, we use voltage imaging to map the membrane potential of large populations of glutamatergic neurons throughout the spinal cord of the larval zebrafish during fictive swimming in a virtual environment. We characterized a previously undescribed subpopulation of tonic-spiking ventral V3 neurons whose spike rate correlated with swimming strength and bout length. Optogenetic activation of V3 neurons led to stronger swimming and longer bouts but did not affect tail beat frequency. Genetic ablation of V3 neurons led to reduced locomotor adaptation. The power of voltage imaging allowed us to identify V3 neurons as a critical driver of locomotor adaptation in zebrafish.

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    10/06/21 | Cre-Dependent Anterograde Transsynaptic Labeling and Functional Imaging in Zebrafish Using VSV With Reduced Cytotoxicity.
    Kler S, Ma M, Narayan S, Ahrens MB, Pan YA
    Frontiers in Neuroanatomy. 2021 Oct 06;15:758350. doi: 10.3389/fnana.2021.758350

    The small size and translucency of larval zebrafish () have made it a unique experimental system to investigate whole-brain neural circuit structure and function. Still, the connectivity patterns between most neuronal types remain mostly unknown. This gap in knowledge underscores the critical need for effective neural circuit mapping tools, especially ones that can integrate structural and functional analyses. To address this, we previously developed a vesicular stomatitis virus (VSV) based approach called Tracer with Restricted Anterograde Spread (TRAS). TRAS utilizes lentivirus to complement replication-incompetent VSV (VSVΔG) to allow restricted (monosynaptic) anterograde labeling from projection neurons to their target cells in the brain. Here, we report the second generation of TRAS (TRAS-M51R), which utilizes a mutant variant of VSVΔG [VSV(M51R)ΔG] with reduced cytotoxicity. Within the primary visual pathway, we found that TRAS-M51R significantly improved long-term viability of transsynaptic labeling (compared to TRAS) while maintaining anterograde spread activity. By using Cre-expressing VSV(M51R)ΔG, TRAS-M51R could selectively label excitatory ( positive) and inhibitory ( positive) retinorecipient neurons. We further show that these labeled excitatory and inhibitory retinorecipient neurons retained neuronal excitability upon visual stimulation at 5-8 days post fertilization (2-5 days post-infection). Together, these findings show that TRAS-M51R is suitable for neural circuit studies that integrate structural connectivity, cell-type identity, and neurophysiology.

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    04/21/21 | Programmable 3D snapshot microscopy with Fourier convolutional networks
    Deb D, Jiao Z, Chen AB, Broxton M, Ahrens MB, Podgorski K, Turaga SC

    3D snapshot microscopy enables fast volumetric imaging by capturing a 3D volume in a single 2D camera image and performing computational reconstruction. Fast volumetric imaging has a variety of biological applications such as whole brain imaging of rapid neural activity in larval zebrafish. The optimal microscope design for this optical 3D-to-2D encoding is both sample- and task-dependent, with no general solution known. Deep learning based decoders can be combined with a differentiable simulation of an optical encoder for end-to-end optimization of both the deep learning decoder and optical encoder. This technique has been used to engineer local optical encoders for other problems such as depth estimation, 3D particle localization, and lensless photography. However, 3D snapshot microscopy is known to require a highly non-local optical encoder which existing UNet-based decoders are not able to engineer. We show that a neural network architecture based on global kernel Fourier convolutional neural networks can efficiently decode information from multiple depths in a volume, globally encoded across a 3D snapshot image. We show in simulation that our proposed networks succeed in engineering and reconstructing optical encoders for 3D snapshot microscopy where the existing state-of-the-art UNet architecture fails. We also show that our networks outperform the state-of-the-art learned reconstruction algorithms for a computational photography dataset collected on a prototype lensless camera which also uses a highly non-local optical encoding.

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