A brainstem integrator for self-location memory and positional homeostasis in zebrafish.

To track and control self-location, animals integrate their movements through space. Representations of self-location are observed in the mammalian hippocampal formation, but it is unknown if positional representations exist in more ancient brain regions, how they arise from integrated self-motion, and by what pathways they control locomotion. Here, in a head-fixed, fictive-swimming, virtual-reality preparation, we exposed larval zebrafish to a variety of involuntary displacements. They tracked these displacements and, many seconds later, moved toward their earlier location through corrective swimming ("positional homeostasis"). Whole-brain functional imaging revealed a network in the medulla that stores a memory of location and induces an error signal in the inferior olive to drive future corrective swimming. Optogenetically manipulating medullary integrator cells evoked displacement-memory behavior. Ablating them, or downstream olivary neurons, abolished displacement corrections. These results reveal a multiregional hindbrain circuit in vertebrates that integrates self-motion and stores self-location to control locomotor behavior.

Glia Accumulate Evidence that Actions Are Futile and Suppress Unsuccessful Behavior.

When a behavior repeatedly fails to achieve its goal, animals often give up and become passive, which can be strategic for preserving energy or regrouping between attempts. It is unknown how the brain identifies behavioral failures and mediates this behavioral-state switch. In larval zebrafish swimming in virtual reality, visual feedback can be withheld so that swim attempts fail to trigger expected visual flow. After tens of seconds of such motor futility, animals became passive for similar durations. Whole-brain calcium imaging revealed noradrenergic neurons that responded specifically to failed swim attempts and radial astrocytes whose calcium levels accumulated with increasing numbers of failed attempts. Using cell ablation and optogenetic or chemogenetic activation, we found that noradrenergic neurons progressively activated brainstem radial astrocytes, which then suppressed swimming. Thus, radial astrocytes perform a computation critical for behavior: they accumulate evidence that current actions are ineffective and consequently drive changes in behavioral states. VIDEO ABSTRACT.

The Serotonergic System Tracks the Outcomes of Actions to Mediate Short-Term Motor Learning.

To execute accurate movements, animals must continuously adapt their behavior to changes in their bodies and environments. Animals can learn changes in the relationship between their locomotor commands and the resulting distance moved, then adjust command strength to achieve a desired travel distance. It is largely unknown which circuits implement this form of motor learning, or how. Using whole-brain neuronal imaging and circuit manipulations in larval zebrafish, we discovered that the serotonergic dorsal raphe nucleus (DRN) mediates short-term locomotor learning. Serotonergic DRN neurons respond phasically to swim-induced visual motion, but little to motion that is not self-generated. During prolonged exposure to a given motosensory gain, persistent DRN activity emerges that stores the learned efficacy of motor commands and adapts future locomotor drive for tens of seconds. The DRN's ability to track the effectiveness of motor intent may constitute a computational building block for the broader functions of the serotonergic system. VIDEO ABSTRACT.

Fast and sensitive GCaMP calcium indicators for imaging neural populations.

Calcium imaging with protein-based indicators1,2 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 indicators3-8. 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.

A genetically encoded fluorescent sensor for in vivo imaging of GABA.

Current techniques for monitoring GABA (γ-aminobutyric acid), the primary inhibitory neurotransmitter in vertebrates, cannot follow transients in intact neural circuits. To develop a GABA sensor, we applied the design principles used to create the fluorescent glutamate receptor iGluSnFR. We used a protein derived from a previously unsequenced Pseudomonas fluorescens strain and performed structure-guided mutagenesis and library screening to obtain intensity-based GABA sensing fluorescence reporter (iGABASnFR) variants. iGABASnFR is genetically encoded, detects GABA release evoked by electric stimulation of afferent fibers in acute brain slices and produces readily detectable fluorescence increases in vivo in mice and zebrafish. We applied iGABASnFR to track mitochondrial GABA content and its modulation by an anticonvulsant, swimming-evoked, GABA-mediated transmission in zebrafish cerebellum, GABA release events during interictal spikes and seizures in awake mice, and found that GABA-mediated tone decreases during isoflurane anesthesia.

Brain-wide circuit interrogation at the cellular level guided by online analysis of neuronal function.

Whole-brain imaging allows for comprehensive functional mapping of distributed neural pathways, but neuronal perturbation experiments are usually limited to targeting predefined regions or genetically identifiable cell types. To complement whole-brain measures of activity with brain-wide manipulations for testing causal interactions, we introduce a system that uses measured activity patterns to guide optical perturbations of any subset of neurons in the same fictively behaving larval zebrafish. First, a light-sheet microscope collects whole-brain data that are rapidly analyzed by a distributed computing system to generate functional brain maps. On the basis of these maps, the experimenter can then optically ablate neurons and image activity changes across the brain. We applied this method to characterize contributions of behaviorally tuned populations to the optomotor response. We extended the system to optogenetically stimulate arbitrary subsets of neurons during whole-brain imaging. These open-source methods enable delineating the contributions of neurons to brain-wide circuit dynamics and behavior in individual animals.

Publisher Correction: A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters.

In the version of this article originally published, the bottom of Figure 4f,g was partially truncated in the PDF. The error has been corrected in the PDF version of this article.

A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters.

We developed a new way to engineer complex proteins toward multidimensional specifications using a simple, yet scalable, directed evolution strategy. By robotically picking mammalian cells that were identified, under a microscope, as expressing proteins that simultaneously exhibit several specific properties, we can screen hundreds of thousands of proteins in a library in just a few hours, evaluating each along multiple performance axes. To demonstrate the power of this approach, we created a genetically encoded fluorescent voltage indicator, simultaneously optimizing its brightness and membrane localization using our microscopy-guided cell-picking strategy. We produced the high-performance opsin-based fluorescent voltage reporter Archon1 and demonstrated its utility by imaging spiking and millivolt-scale subthreshold and synaptic activity in acute mouse brain slices and in larval zebrafish in vivo. We also measured postsynaptic responses downstream of optogenetically controlled neurons in C. elegans.

Multi-scale approaches for high-speed imaging and analysis of large neural populations.

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution. Here we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques based on simple local averaging provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified at fine scale (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover denoised fluorescence traces and deconvolved neural activity of each individual neuron from coarse scale data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to "zoom out" by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution.

Brain-wide neuronal dynamics during motor adaptation in zebrafish.

A fundamental question in neuroscience is how entire neural circuits generate behaviour and adapt it to changes in sensory feedback. Here we use two-photon calcium imaging to record the activity of large populations of neurons at the cellular level, throughout the brain of larval zebrafish expressing a genetically encoded calcium sensor, while the paralysed animals interact fictively with a virtual environment and rapidly adapt their motor output to changes in visual feedback. We decompose the network dynamics involved in adaptive locomotion into four types of neuronal response properties, and provide anatomical maps of the corresponding sites. A subset of these signals occurred during behavioural adjustments and are candidates for the functional elements that drive motor learning. Lesions to the inferior olive indicate a specific functional role for olivocerebellar circuitry in adaptive locomotion. This study enables the analysis of brain-wide dynamics at single-cell resolution during behaviour.

Mapping brain activity at scale with cluster computing.

Understanding brain function requires monitoring and interpreting the activity of large networks of neurons during behavior. Advances in recording technology are greatly increasing the size and complexity of neural data. Analyzing such data will pose a fundamental bottleneck for neuroscience. We present a library of analytical tools called Thunder built on the open-source Apache Spark platform for large-scale distributed computing. The library implements a variety of univariate and multivariate analyses with a modular, extendable structure well-suited to interactive exploration and analysis development. We demonstrate how these analyses find structure in large-scale neural data, including whole-brain light-sheet imaging data from fictively behaving larval zebrafish, and two-photon imaging data from behaving mouse. The analyses relate neuronal responses to sensory input and behavior, run in minutes or less and can be used on a private cluster or in the cloud. Our open-source framework thus holds promise for turning brain activity mapping efforts into biological insights.

Whole-brain functional imaging at cellular resolution using light-sheet microscopy.

Brain function relies on communication between large populations of neurons across multiple brain areas, a full understanding of which would require knowledge of the time-varying activity of all neurons in the central nervous system. Here we use light-sheet microscopy to record activity, reported through the genetically encoded calcium indicator GCaMP5G, from the entire volume of the brain of the larval zebrafish in vivo at 0.8 Hz, capturing more than 80% of all neurons at single-cell resolution. Demonstrating how this technique can be used to reveal functionally defined circuits across the brain, we identify two populations of neurons with correlated activity patterns. One circuit consists of hindbrain neurons functionally coupled to spinal cord neuropil. The other consists of an anatomically symmetric population in the anterior hindbrain, with activity in the left and right halves oscillating in antiphase, on a timescale of 20 s, and coupled to equally slow oscillations in the inferior olive.

Calcium imaging of neural circuits with extended depth-of-field light-sheet microscopy.

Increasing the volumetric imaging speed of light-sheet microscopy will improve its ability to detect fast changes in neural activity. Here, a system is introduced for brain-wide imaging of neural activity in the larval zebrafish by coupling structured illumination with cubic phase extended depth-of-field (EDoF) pupil encoding. This microscope enables faster light-sheet imaging and facilitates arbitrary plane scanning-removing constraints on acquisition speed, alignment tolerances, and physical motion near the sample. The usefulness of this method is demonstrated by performing multi-plane calcium imaging in the fish brain with a 416×832×160 µm field of view at 33 Hz. The optomotor response behavior of the zebrafish is monitored at high speeds, and time-locked correlations of neuronal activity are resolved across its brain.

Identification of nonvisual photomotor response cells in the vertebrate hindbrain.

Nonvisual photosensation enables animals to sense light without sight. However, the cellular and molecular mechanisms of nonvisual photobehaviors are poorly understood, especially in vertebrate animals. Here, we describe the photomotor response (PMR), a robust and reproducible series of motor behaviors in zebrafish that is elicited by visual wavelengths of light but does not require the eyes, pineal gland, or other canonical deep-brain photoreceptive organs. Unlike the relatively slow effects of canonical nonvisual pathways, motor circuits are strongly and quickly (seconds) recruited during the PMR behavior. We find that the hindbrain is both necessary and sufficient to drive these behaviors. Using in vivo calcium imaging, we identify a discrete set of neurons within the hindbrain whose responses to light mirror the PMR behavior. Pharmacological inhibition of the visual cycle blocks PMR behaviors, suggesting that opsin-based photoreceptors control this behavior. These data represent the first known light-sensing circuit in the vertebrate hindbrain.

Astrocyte Calcium Signaling.

Astrocytes are predominant glial cells that tile the central nervous system and participate in well-established functional and morphological interactions with neurons, blood vessels, and other glia. These ubiquitous cells display rich intracellular Ca2+ signaling, which has now been studied for over 30 years. In this review, we provide a summary and perspective of recent progress concerning the study of astrocyte intracellular Ca2+ signaling as well as discussion of its potential functions. Progress has occurred in the areas of imaging, silencing, activating, and analyzing astrocyte Ca2+ signals. These insights have collectively permitted exploration of the relationships of astrocyte Ca2+ signals to neural circuit function and behavior in a variety of species. We summarize these aspects along with a framework for mechanistically interpreting behavioral studies to identify directly causal effects. We finish by providing a perspective on new avenues of research concerning astrocyte Ca2+ signaling.

Norepinephrine changes behavioral state via astroglial purinergic signaling.

Both neurons and glia communicate via diffusible neuromodulatory substances, but the substrates of computation in such neuromodulatory networks are unclear. During behavioral transitions in the larval zebrafish, the neuromodulator norepinephrine drives fast excitation and delayed inhibition of behavior and circuit activity. We find that the inhibitory arm of this feedforward motif is implemented by astroglial purinergic signaling. Neuromodulator imaging, behavioral pharmacology, and perturbations of neurons and astroglia reveal that norepinephrine triggers astroglial release of adenosine triphosphate, extracellular conversion into adenosine, and behavioral suppression through activation of hindbrain neuronal adenosine receptors. This work, along with a companion piece by Lefton and colleagues demonstrating an analogous pathway mediating the effect of norepinephrine on synaptic connectivity in mice, identifies a computational and behavioral role for an evolutionarily conserved astroglial purinergic signaling axis in norepinephrine-mediated behavioral and brain state transitions.

Imaging the extracellular matrix in live tissues and organisms with a glycan-binding fluorophore.

All multicellular systems produce and dynamically regulate extracellular matrices (ECM) that play important roles in both biochemical and mechanical signaling. Though the spatial arrangement of these extracellular assemblies is critical to their biological functions, visualization of ECM structure is challenging, in part because the biomolecules that compose the ECM are difficult to fluorescently label individually and collectively. Here, we present a cell-impermeable small molecule fluorophore, termed Rhobo6, that turns on and red shifts upon reversible binding to glycans. Given that most ECM components are densely glycosylated, the dye enables wash-free visualization of ECM, in systems ranging from in vitro substrates to in vivo mouse mammary tumors. Relative to existing techniques, Rhobo6 provides a broad substrate profile, superior tissue penetration, nonperturbative labeling, and negligible photobleaching. This work establishes a straightforward method for imaging the distribution of ECM in live tissues and organisms, lowering barriers for investigation of extracellular biology.

Fast, Accurate, and Versatile Data Analysis Platform for the Quantification of Molecular Spatiotemporal Signals.

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce Activity Quantification and Analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine learning techniques. It decomposes complex live imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, and imaging modalities. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, and distinct sensorimotor signal propagation patterns in the mouse spinal cord.

Time-resolved correlation of distributed brain activity tracks E-I balance and accounts for diverse scale-free phenomena.

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.