From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response

Vertebrate behavioral thermoregulation: knowledge and future directions

Thermoregulation is critical for survival across species. In animals, the nervous system detects external and internal temperatures, integrates this information with internal states, and ultimately forms a decision on appropriate thermoregulatory actions. Recent work has identified critical molecules and sensory and motor pathways controlling thermoregulation. However, especially with regard to behavioral thermoregulation, many open questions remain. Here, we aim to both summarize the current state of research, the "knowledge," as well as what in our mind is still largely missing, the "future directions." Given the host of circuit entry points that have been discovered, we specifically see that the time is ripe for a neuro-computational perspective on thermoregulation. Such a perspective is largely lacking but is increasingly fueled and made possible by the development of advanced tools and modeling strategies.

Transcriptional control of visual neural circuit development by GS homeobox 1

As essential components of gene expression networks, transcription factors regulate neural circuit assembly. The homeobox transcription factor encoding gene, gs homeobox 1 (gsx1), is expressed in the developing visual system; however, no studies have examined its role in visual system formation. In zebrafish, retinal ganglion cell (RGC) axons that transmit visual information to the brain terminate in ten arborization fields (AFs) in the optic tectum (TeO), pretectum (Pr), and thalamus. Pretectal AFs (AF1-AF9) mediate distinct visual behaviors, yet we understand less about their development compared to AF10 in the TeO. Using gsx1 zebrafish mutants, immunohistochemistry, and transgenic lines, we observed that gsx1 is required for vesicular glutamate transporter, Tg(slc17a6b:DsRed), expression in the Pr, but not overall neuron number. gsx1 mutants have normal eye morphology, yet they exhibit impaired visual ability during prey capture. RGC axon volume in the gsx1 mutant Pr and TeO is reduced, and AF7 that is active during feeding is missing which is consistent with reduced hunting performance. Timed laser ablation of Tg(slc17a6b:DsRed)-positive cells reveals that they are necessary for AF7 formation. This work is the first to implicate gsx1 in establishing cell identity and functional neural circuits in the visual system.

Behavioral effects of visual stimuli in adult zebrafish using a novel eight-tank imaging system

Introduction

Animals respond to various environmental cues. Animal behavior is complex, and behavior analysis can greatly help to understand brain function. Most of the available behavioral imaging setups are expensive, provide limited options for customization, and allow for behavioral imaging of one animal at a time.

Methods

The current study takes advantage of adult zebrafish as a model organism to study behavior in a novel behavioral setup allowing one to concurrently image 8 adult zebrafish.

Results

Our results indicate that adult zebrafish show a unique behavioral profile in response to visual stimuli such as moving lines. In the presence of moving lines, the fish spent more time exploring the tank and spent more time toward the edges of the tanks. In addition, the fish moved and oriented themselves against the direction of the moving lines, indicating a negative optomotor response (OMR). With repeated exposure to moving lines, we observed a reduced optomotor response in adult zebrafish.

Discussion

Our behavioral setup is relatively inexpensive, provides flexibility in the presentation of various animated visual stimuli, and offers improved throughput for analyzing behavior in adult zebrafish. This behavioral setup shows promising potential to quantify various behavioral measures and opens new avenues to understand complex behaviors.

Passive exposure to visual motion leads to short-term changes in the optomotor response of aging zebrafish

Numerous studies have shown that prior visual experiences play an important role in sensory processing and adapting behavior in a dynamic environment. A repeated and passive presentation of visual stimulus is one of the simplest procedures to manipulate acquired experiences. Using this approach, we aimed to investigate exposure-based visual learning of aging zebrafish and how cholinergic intervention is involved in exposure-induced changes. Our measurements included younger and older wild-type zebrafish and achesb55/+ mutants with decreased acetylcholinesterase activity. We examined both within-session and across-day changes in the zebrafish optomotor responses to repeated and passive exposure to visual motion. Our findings revealed short-term (within-session) changes in the magnitude of optomotor response (i.e., the amount of position shift by fish as a response to visual motion) rather than long-term and persistent effects across days. Moreover, the observed short-term changes were age- and genotype-dependent. Compared to the initial presentations of motion within a session, the magnitude of optomotor response to terminal presentations decreased in the older zebrafish. There was a similar robust decrease specific to achesb55/+ mutants. Taken together, these results point to short-term (within-session) alterations in the motion detection of adult zebrafish and suggest differential effects of neural aging and cholinergic system on the observed changes. These findings further provide important insights into adult zebrafish optomotor response to visual motion and contribute to understanding this reflexive behavior in the short- and long-term stimulation profiles.

Ensemble dynamics and information flow deduction from whole-brain imaging data

The recent advancements in large-scale activity imaging of neuronal ensembles offer valuable opportunities to comprehend the process involved in generating brain activity patterns and understanding how information is transmitted between neurons or neuronal ensembles. However, existing methodologies for extracting the underlying properties that generate overall dynamics are still limited. In this study, we applied previously unexplored methodologies to analyze time-lapse 3D imaging (4D imaging) data of head neurons of the nematode Caenorhabditis elegans. By combining time-delay embedding with the independent component analysis, we successfully decomposed whole-brain activities into a small number of component dynamics. Through the integration of results from multiple samples, we extracted common dynamics from neuronal activities that exhibit apparent divergence across different animals. Notably, while several components show common cooperativity across samples, some component pairs exhibited distinct relationships between individual samples. We further developed time series prediction models of synaptic communications. By combining dimension reduction using the general framework, gradient kernel dimension reduction, and probabilistic modeling, the overall relationships of neural activities were incorporated. By this approach, the stochastic but coordinated dynamics were reproduced in the simulated whole-brain neural network. We found that noise in the nervous system is crucial for generating realistic whole-brain dynamics. Furthermore, by evaluating synaptic interaction properties in the models, strong interactions within the core neural circuit, variable sensory transmission and importance of gap junctions were inferred. Virtual optogenetics can be also performed using the model. These analyses provide a solid foundation for understanding information flow in real neural networks.

Mapping the neural dynamics of locomotion across the Drosophila brain

Locomotion engages widely distributed networks of neurons. However, our understanding of the spatial architecture and temporal dynamics of the networks that underpin walking remains incomplete. We use volumetric two-photon imaging to map neural activity associated with walking across the entire brain of Drosophila. We define spatially clustered neural signals selectively associated with changes in either forward or angular velocity, demonstrating that neurons with similar behavioral selectivity are clustered. These signals reveal distinct topographic maps in diverse brain regions involved in navigation, memory, sensory processing, and motor control, as well as regions not previously linked to locomotion. We identify temporal trajectories of neural activity that sweep across these maps, including signals that anticipate future movement, representing the sequential engagement of clusters with different behavioral specificities. Finally, we register these maps to a connectome and identify neural networks that we propose underlie the observed signals, setting a foundation for subsequent circuit dissection. Overall, our work suggests a spatiotemporal framework for the emergence and execution of complex walking maneuvers and links this brain-wide neural activity to single neurons and local circuits.

Structural and Functional Organization of Visual Responses in the Inferior Olive of Larval Zebrafish

The olivo-cerebellar system plays an important role in vertebrate sensorimotor control. Here, we investigate sensory representations in the inferior olive (IO) of larval zebrafish and their spatial organization. Using single-cell labeling of genetically identified IO neurons, we find that they can be divided into at least two distinct groups based on their spatial location, dendritic morphology, and axonal projection patterns. In the same genetically targeted population, we recorded calcium activity in response to a set of visual stimuli using two-photon imaging. We found that most IO neurons showed direction-selective and binocular responses to visual stimuli and that the functional properties were spatially organized within the IO. Light-sheet functional imaging that allowed for simultaneous activity recordings at the soma and axonal level revealed tight coupling between functional properties, soma location, and axonal projection patterns of IO neurons. Taken together, our results suggest that anatomically defined classes of IO neurons correspond to distinct functional types, and that topographic connections between IO and cerebellum contribute to organization of the cerebellum into distinct functional zones.

Functional neuronal circuits emerge in the absence of developmental activity

The complex neuronal circuitry of the brain develops from limited information contained in the genome. After the genetic code instructs the birth of neurons, the emergence of brain regions, and the formation of axon tracts, it is believed that temporally structured spiking activity shapes circuits for behavior. Here, we challenge the learning-dominated assumption that spiking activity is required for circuit formation by quantifying its contribution to the development of visually-guided swimming in the larval zebrafish. We found that visual experience had no effect on the emergence of the optomotor response (OMR) in dark-reared zebrafish. We then raised animals while pharmacologically silencing action potentials with the sodium channel blocker tricaine. After washout of the anesthetic, fish could swim and performed with 75-90% accuracy in the OMR paradigm. Brain-wide imaging confirmed that neuronal circuits came 'online' fully tuned, without requiring activity-dependent plasticity. Thus, complex sensory-guided behaviors can emerge through activity-independent developmental mechanisms.

Predictive neural computations in the cerebellum contribute to motor planning and faster behavioral responses in larval zebrafish

The ability to predict the future based on past experience lies at the core of the brain's ability to adapt behavior. However, the neural mechanisms that participate in generating and updating predictions are not clearly understood. Further, the evolutionary antecedents and the prevalence of predictive processing among vertebrates are even less explored. Here, we show evidence of predictive processing via the involvement of cerebellar circuits in larval zebrafish. We presented stereotyped optic flow stimuli to larval zebrafish to evoke swims and discovered that lesioning the cerebellum abolished prediction-dependent modulation of swim latency. When expectations of optic flow direction did not match with reality, error signals arrive at Purkinje cells via the olivary climbing fibers, whereas granule cells and Purkinje cells encode signals of expectation. Strong neural representations of expectation correlate with faster swim responses and vice versa. In sum, our results show evidence for predictive processing in nonmammalian vertebrates with the involvement of cerebellum, an evolutionarily conserved brain structure.

Bilateral Retinofugal Pathfinding Impairments Limit Behavioral Compensation in Near-Congenital One-Eyed Xenopus laevis

To generate a coherent visual percept, information from both eyes must be appropriately transmitted into the brain, where binocular integration forms the substrate for visuomotor behaviors. To establish the anatomical substrate for binocular integration, the presence of bilateral eyes and interaction of both optic nerves during retinotectal development play a key role. However, the extent to which embryonic monocularly derived visual circuits can convey visuomotor behaviors is unknown. In this study, we assessed the retinotectal anatomy and visuomotor performance of embryonically generated one-eyed tadpoles. In one-eyed animals, the axons of retinal ganglion cells from the singular remaining eye exhibited striking irregularities in their central projections in the brain, generating a noncanonical ipsilateral retinotectal projection. This data is indicative of impaired pathfinding abilities. We further show that these novel projections are correlated with an impairment of behavioral compensation for the loss of one eye.

Mechanical intelligence simplifies control in terrestrial limbless locomotion

Limbless locomotors, from microscopic worms to macroscopic snakes, traverse complex, heterogeneous natural environments typically using undulatory body wave propagation. Theoretical and robophysical models typically emphasize body kinematics and active neural/electronic control. However, we contend that because such approaches often neglect the role of passive, mechanically controlled processes (those involving "mechanical intelligence"), they fail to reproduce the performance of even the simplest organisms. To uncover principles of how mechanical intelligence aids limbless locomotion in heterogeneous terradynamic regimes, here we conduct a comparative study of locomotion in a model of heterogeneous terrain (lattices of rigid posts). We used a model biological system, the highly studied nematode worm Caenorhabditis elegans, and a robophysical device whose bilateral actuator morphology models that of limbless organisms across scales. The robot's kinematics quantitatively reproduced the performance of the nematodes with purely open-loop control; mechanical intelligence simplified control of obstacle navigation and exploitation by reducing the need for active sensing and feedback. An active behavior observed in C. elegans, undulatory wave reversal upon head collisions, robustified locomotion via exploitation of the systems' mechanical intelligence. Our study provides insights into how neurally simple limbless organisms like nematodes can leverage mechanical intelligence via appropriately tuned bilateral actuation to locomote in complex environments. These principles likely apply to neurally more sophisticated organisms and also provide a design and control paradigm for limbless robots for applications like search and rescue and planetary exploration.

Zebrafish optomotor response to second-order motion illustrates that age-related changes in motion detection depend on the activated motion system

Various aspects of visual functioning, including motion perception, change with age. Yet, there is a lack of comprehensive understanding of age-related alterations at different stages of motion processing and in each motion system. To understand the effects of aging on second-order motion processing, we investigated optomotor responses (OMR) in younger and older wild-type (AB-strain) and acetylcholinesterase (achesb55/+) mutant zebrafish. The mutant fish with decreased levels of acetylcholinesterase have been shown to have delayed age-related cognitive decline. Compared to previous results on first-order motion, we found distinct changes in OMR to second-order motion. The polarity of OMR was dependent on age, such that second-order stimulation led to mainly negative OMR in the younger group while older zebrafish had positive responses. Hence, these findings revealed an overall aging effect on the detection of second-order motion. Moreover, neither the genotype of zebrafish nor the spatial frequency of motion significantly changed the response magnitude. Our findings support the view that age-related changes in motion detection depend on the activated motion system.

Zebrafish brain atlases: a collective effort for a tiny vertebrate brain

In the past two decades, digital brain atlases have emerged as essential tools for sharing and integrating complex neuroscience datasets. Concurrently, the larval zebrafish has become a prominent vertebrate model offering a strategic compromise for brain size, complexity, transparency, optogenetic access, and behavior. We provide a brief overview of digital atlases recently developed for the larval zebrafish brain, intersecting neuroanatomical information, gene expression patterns, and connectivity. These atlases are becoming pivotal by centralizing large datasets while supporting the generation of circuit hypotheses as functional measurements can be registered into an atlas' standard coordinate system to interrogate its structural database. As challenges persist in mapping neural circuits and incorporating functional measurements into zebrafish atlases, we emphasize the importance of collaborative efforts and standardized protocols to expand these resources to crack the complex codes of neuronal activity guiding behavior in this tiny vertebrate brain.

Dimensionality reduction reveals separate translation and rotation populations in the zebrafish hindbrain

In many brain areas, neuronal activity is associated with a variety of behavioral and environmental variables. In particular, neuronal responses in the zebrafish hindbrain relate to oculomotor and swimming variables as well as sensory information. However, the precise functional organization of the neurons has been difficult to unravel because neuronal responses are heterogeneous. Here, we used dimensionality reduction methods on neuronal population data to reveal the role of the hindbrain in visually driven oculomotor behavior and swimming. We imaged neuronal activity in zebrafish expressing GCaMP6s in the nucleus of almost all neurons while monitoring the behavioral response to gratings that rotated with different speeds. We then used reduced-rank regression, a method that condenses the sensory and motor variables into a smaller number of "features," to predict the fluorescence traces of all ROIs (regions of interest). Despite the potential complexity of the visuo-motor transformation, our analysis revealed that a large fraction of the population activity can be explained by only two features. Based on the contribution of these features to each ROI's activity, ROIs formed three clusters. One cluster was related to vergent movements and swimming, whereas the other two clusters related to leftward and rightward rotation. Voxels corresponding to these clusters were segregated anatomically, with leftward and rightward rotation clusters located selectively to the left and right hemispheres, respectively. Just as described in many cortical areas, our analysis revealed that single-neuron complexity co-exists with a simpler population-level description, thereby providing insights into the organization of visuo-motor transformations in the hindbrain.

Using neuronal models to capture burst-and-glide motion and leadership in fish

While mathematical models, in particular self-propelled particle models, capture many properties of large fish schools, they do not always capture the interactions of smaller shoals. Nor do these models tend to account for the use of intermittent locomotion, often referred to as burst-and-glide, by many species. In this paper, we propose a model of social burst-and-glide motion by combining a well-studied model of neuronal dynamics, the FitzHugh-Nagumo model, with a model of fish motion. We first show that our model can capture the motion of a single fish swimming down a channel. Extending to a two-fish model, where visual stimulus of a neighbour affects the internal burst or glide state of the fish, we observe a rich set of dynamics found in many species. These include: leader-follower behaviour; periodic changes in leadership; apparently random (i.e. chaotic) leadership change; and tit-for-tat turn taking. Moreover, unlike previous studies where a randomness is required for leadership switching to occur, we show that this can instead be the result of deterministic interactions. We give several empirically testable predictions for how bursting fish interact and discuss our results in light of recently established correlations between fish locomotion and brain activity.

The lost art of mathematical modelling

We provide a critique of mathematical biology in light of rapid developments in modern machine learning. We argue that out of the three modelling activities - (1) formulating models; (2) analysing models; and (3) fitting or comparing models to data - inherent to mathematical biology, researchers currently focus too much on activity (2) at the cost of (1). This trend, we propose, can be reversed by realising that any given biological phenomenon can be modelled in an infinite number of different ways, through the adoption of an pluralistic approach, where we view a system from multiple, different points of view. We explain this pluralistic approach using fish locomotion as a case study and illustrate some of the pitfalls - universalism, creating models of models, etc. - that hinder mathematical biology. We then ask how we might rediscover a lost art: that of creative mathematical modelling.

Structure and function in artificial, zebrafish and human neural networks

Network science provides a set of tools for the characterization of the structure and functional behavior of complex systems. Yet a major problem is to quantify how the structural domain is related to the dynamical one. In other words, how the diversity of dynamical states of a system can be predicted from the static network structure? Or the reverse problem: starting from a set of signals derived from experimental recordings, how can one discover the network connections or the causal relations behind the observed dynamics? Despite the advances achieved over the last two decades, many challenges remain concerning the study of the structure-dynamics interplay of complex systems. In neuroscience, progress is typically constrained by the low spatio-temporal resolution of experiments and by the lack of a universal inferring framework for empirical systems. To address these issues, applications of network science and artificial intelligence to neural data have been rapidly growing. In this article, we review important recent applications of methods from those fields to the study of the interplay between structure and functional dynamics of human and zebrafish brain. We cover the selection of topological features for the characterization of brain networks, inference of functional connections, dynamical modeling, and close with applications to both the human and zebrafish brain. This review is intended to neuroscientists who want to become acquainted with techniques from network science, as well as to researchers from the latter field who are interested in exploring novel application scenarios in neuroscience.

Learning and memory formation in zebrafish: Protein dynamics and molecular tools

Research on learning and memory formation at the level of neural networks, as well as at the molecular level, is challenging due to the immense complexity of the brain. The zebrafish as a genetically tractable model organism can overcome many of the current challenges of studying molecular mechanisms of learning and memory formation. Zebrafish have a translucent, smaller and more accessible brain than that of mammals, allowing imaging of the entire brain during behavioral manipulations. Recent years have seen an extensive increase in published brain research describing the use of zebrafish for the study of learning and memory. Nevertheless, due to the complexity of the brain comprising many neural cell types that are difficult to isolate, it has been difficult to elucidate neural networks and molecular mechanisms involved in memory formation in an unbiased manner, even in zebrafish larvae. Therefore, data regarding the identity, location, and intensity of nascent proteins during memory formation is still sparse and our understanding of the molecular networks remains limited, indicating a need for new techniques. Here, we review recent progress in establishing learning paradigms for zebrafish and the development of methods to elucidate neural and molecular networks of learning. We describe various types of learning and highlight directions for future studies, focusing on molecular mechanisms of long-term memory formation and promising state-of-the-art techniques such as cell-type-specific metabolic labeling.

Zebrafish Larvae Position Tracker (Z-LaP Tracker): a high-throughput deep-learning behavioral approach for the identification of calcineurin pathway-modulating drugs using zebrafish larvae

Brain function studies greatly depend on quantification and analysis of behavior. While behavior can be imaged efficiently, the quantification of specific aspects of behavior is labor-intensive and may introduce individual biases. Recent advances in deep learning and artificial intelligence-based tools have made it possible to precisely track individual features of freely moving animals in diverse environments without any markers. In the current study, we developed Zebrafish Larvae Position Tracker (Z-LaP Tracker), a modification of the markerless position estimation software DeepLabCut, to quantify zebrafish larval behavior in a high-throughput 384-well setting. We utilized the high-contrast features of our model animal, zebrafish larvae, including the eyes and the yolk for our behavioral analysis. Using this experimental setup, we quantified relevant behaviors with similar accuracy to the analysis performed by humans. The changes in behavior were organized in behavioral profiles, which were examined by K-means and hierarchical cluster analysis. Calcineurin inhibitors exhibited a distinct behavioral profile characterized by increased activity, acoustic hyperexcitability, reduced visually guided behaviors, and reduced habituation to acoustic stimuli. The developed methodologies were used to identify 'CsA-type' drugs that might be promising candidates for the prevention and treatment of neurological disorders.

Incubator-independent perfusion system integrated with microfluidic device for continuous electrophysiology and microscopy readouts

Advances in primary and stem cell derived neuronal cell culture techniques and abundance of available neuronal cell types have enabledin vitroneuroscience as a substantial approach to modelin vivoneuronal networks. Survival of the cultured neurons is inevitably dependent on the cell culture incubators to provide stable temperature and humidity and to supply required CO₂levels for controlling the pH of culture medium. Therefore, imaging and electrophysiology recordings outside of the incubator are often limited to the short-term experimental sessions. This restricts our understanding of physiological events to the short snapshots of recorded data while the major part of temporal data is neglected. Multiple custom-made and commercially available platforms like integrated on-stage incubators have been designed to enable long-term microscopy. Nevertheless, long-term high-spatiotemporal electrophysiology recordings from developing neuronal networks needs to be addressed. In the present work an incubator-independent polydimethylsiloxane-based double-wall perfusion chamber was designed and integrated with multi-electrode arrays (MEAs) electrophysiology and compartmentalized microfluidic device to continuously record from engineered neuronal networks at sub-cellular resolution. Cell culture media underwent iterations of conditioning to the ambient CO₂and adjusting its pH to physiological ranges to retain a stable pH for weeks outside of the incubator. Double-wall perfusion chamber and an integrated air bubble trapper reduced media evaporation and osmolality drifts of the conditioned media for two weeks. Aligned microchannel-microfluidic device on MEA electrodes allowed neurite growth on top of the planar electrodes and amplified their extracellular activity. This enabled continuous sub-cellular resolution imaging and electrophysiology recordings from developing networks and their growing neurites. The on-chip versatile and self-contained system provides long-term, continuous and high spatiotemporal access to the network data and offers a robustin vitroplatform with many potentials to be applied on advanced cell culture systems including organ-on-chip and organoid models.

A behavioral and modeling study of control algorithms underlying the translational optomotor response in larval zebrafish with implications for neural circuit function

The optomotor response (OMR) is central to the locomotory behavior in diverse animal species including insects, fish and mammals. Furthermore, the study of the OMR in larval zebrafish has become a key model system for investigating the neural basis of sensorimotor control. However, a comprehensive understanding of the underlying control algorithms is still outstanding. In fish it is often assumed that the OMR, by reducing average optic flow across the retina, serves to stabilize position with respect to the ground. Yet the degree to which this is achieved, and how it could emerge from the intermittent burst dynamics of larval zebrafish swimming, are unclear. Here, we combine detailed computational modeling with a new approach to free-swimming experiments in which we control the amount of visual feedback produced by a given motor effort by varying the height of the larva above a moving grid stimulus. We develop an account of underlying feedback control mechanisms that describes both the bout initiation process and the control of swim speed during bouts. We observe that the degree to which fish stabilize their position is only partial and height-dependent, raising questions about its function. We find the relative speed profile during bouts follows a fixed temporal pattern independent of absolute bout speed, suggesting that bout speed and bout termination are not separately controlled. We also find that the reverse optic flow, experienced when the fish is swimming faster than the stimulus, plays a minimal role in control of the OMR despite carrying most of the sensory information about self-movement. These results shed new light on the underlying dynamics of the OMR in larval zebrafish and will be crucial for future work aimed at identifying the neural basis of this behavior.

An optofluidic platform for interrogating chemosensory behavior and brainwide neural representation in larval zebrafish

Studying chemosensory processing desires precise chemical cue presentation, behavioral response monitoring, and large-scale neuronal activity recording. Here we present Fish-on-Chips, a set of optofluidic tools for highly-controlled chemical delivery while simultaneously imaging behavioral outputs and whole-brain neuronal activities at cellular resolution in larval zebrafish. These include a fluidics-based swimming arena and an integrated microfluidics-light sheet fluorescence microscopy (µfluidics-LSFM) system, both of which utilize laminar fluid flows to achieve spatiotemporally precise chemical cue presentation. To demonstrate the strengths of the platform, we used the navigation arena to reveal binasal input-dependent behavioral strategies that larval zebrafish adopt to evade cadaverine, a death-associated odor. The µfluidics-LSFM system enables sequential presentation of odor stimuli to individual or both nasal cavities separated by only ~100 µm. This allowed us to uncover brainwide neural representations of cadaverine sensing and binasal input summation in the vertebrate model. Fish-on-Chips is readily generalizable and will empower the investigation of neural coding in the chemical senses.

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.

Zebrafish - The Neurobehavioural Model in Trend

Zebrafish (Danio rerio) is currently in vogue as a prevalently used experimental model for studies concerning neurobehavioural disorders and associated fields. Since the 1960s, this model has succeeded in breaking most barriers faced in the hunt for an experimental model. From its appearance to its high parity with human beings genetically, this model renders itself as an advantageous experimental lab animal. Neurobehavioural disorders have always posed an arduous task in terms of their detection as well as in determining their exact etiology. They are still, in most cases, diseases of interest for inventing or discovering novel pharmacological interventions. Thus, the need for a harbinger experimental model for studying neurobehaviours is escalating. Ensuring the same model is used for studying several neuro-studies conserves the results from inter-species variations. For this, we need a model that satisfies all the pre-requisite conditions to be made the final choice of model for neurobehavioural studies. This review recapitulates the progress of zebrafish as an experimental model with its most up-to-the-minute advances in the area. Various tests, assays, and responses employed using zebrafish in screening neuroactive drugs have been tabulated effectively. The tools, techniques, protocols, and apparatuses that bolster zebrafish studies are discussed. The probable research that can be done using zebrafish has also been briefly outlined. The various breeding and maintenance methods employed, along with the information on various strains available and most commonly used, are also elaborated upon, supplementing Zebrafish's use in neuroscience.

Automated synapse-level reconstruction of neural circuits in the larval zebrafish brain

Dense reconstruction of synaptic connectivity requires high-resolution electron microscopy images of entire brains and tools to efficiently trace neuronal wires across the volume. To generate such a resource, we sectioned and imaged a larval zebrafish brain by serial block-face electron microscopy at a voxel size of 14 × 14 × 25 nm3. We segmented the resulting dataset with the flood-filling network algorithm, automated the detection of chemical synapses and validated the results by comparisons to transmission electron microscopic images and light-microscopic reconstructions. Neurons and their connections are stored in the form of a queryable and expandable digital address book. We reconstructed a network of 208 neurons involved in visual motion processing, most of them located in the pretectum, which had been functionally characterized in the same specimen by two-photon calcium imaging. Moreover, we mapped all 407 presynaptic and postsynaptic partners of two superficial interneurons in the tectum. The resource developed here serves as a foundation for synaptic-resolution circuit analyses in the zebrafish nervous system.

Review on data analysis methods for mesoscale neural imaging in vivo

Significance: Mesoscale neural imaging in vivo has gained extreme popularity in neuroscience for its capacity of recording large-scale neurons in action. Optical imaging with single-cell resolution and millimeter-level field of view in vivo has been providing an accumulated database of neuron-behavior correspondence. Meanwhile, optical detection of neuron signals is easily contaminated by noises, background, crosstalk, and motion artifacts, while neural-level signal processing and network-level coordinate are extremely complicated, leading to laborious and challenging signal processing demands. The existing data analysis procedure remains unstandardized, which could be daunting to neophytes or neuroscientists without computational background. Aim: We hope to provide a general data analysis pipeline of mesoscale neural imaging shared between imaging modalities and systems. Approach: We divide the pipeline into two main stages. The first stage focuses on extracting high-fidelity neural responses at single-cell level from raw images, including motion registration, image denoising, neuron segmentation, and signal extraction. The second stage focuses on data mining, including neural functional mapping, clustering, and brain-wide network deduction. Results: Here, we introduce the general pipeline of processing the mesoscale neural images. We explain the principles of these procedures and compare different approaches and their application scopes with detailed discussions about the shortcomings and remaining challenges. Conclusions: There are great challenges and opportunities brought by the large-scale mesoscale data, such as the balance between fidelity and efficiency, increasing computational load, and neural network interpretability. We believe that global circuits on single-neuron level will be more extensively explored in the future.

From calcium imaging to graph topology

Systems neuroscience is facing an ever-growing mountain of data. Recent advances in protein engineering and microscopy have together led to a paradigm shift in neuroscience; using fluorescence, we can now image the activity of every neuron through the whole brain of behaving animals. Even in larger organisms, the number of neurons that we can record simultaneously is increasing exponentially with time. This increase in the dimensionality of the data is being met with an explosion of computational and mathematical methods, each using disparate terminology, distinct approaches, and diverse mathematical concepts. Here we collect, organize, and explain multiple data analysis techniques that have been, or could be, applied to whole-brain imaging, using larval zebrafish as an example model. We begin with methods such as linear regression that are designed to detect relations between two variables. Next, we progress through network science and applied topological methods, which focus on the patterns of relations among many variables. Finally, we highlight the potential of generative models that could provide testable hypotheses on wiring rules and network progression through time, or disease progression. While we use examples of imaging from larval zebrafish, these approaches are suitable for any population-scale neural network modeling, and indeed, to applications beyond systems neuroscience. Computational approaches from network science and applied topology are not limited to larval zebrafish, or even to systems neuroscience, and we therefore conclude with a discussion of how such methods can be applied to diverse problems across the biological sciences.

Synaptic balancing: A biologically plausible local learning rule that provably increases neural network noise robustness without sacrificing task performance

We introduce a novel, biologically plausible local learning rule that provably increases the robustness of neural dynamics to noise in nonlinear recurrent neural networks with homogeneous nonlinearities. Our learning rule achieves higher noise robustness without sacrificing performance on the task and without requiring any knowledge of the particular task. The plasticity dynamics—an integrable dynamical system operating on the weights of the network—maintains a multiplicity of conserved quantities, most notably the network’s entire temporal map of input to output trajectories. The outcome of our learning rule is a synaptic balancing between the incoming and outgoing synapses of every neuron. This synaptic balancing rule is consistent with many known aspects of experimentally observed heterosynaptic plasticity, and moreover makes new experimentally testable predictions relating plasticity at the incoming and outgoing synapses of individual neurons. Overall, this work provides a novel, practical local learning rule that exactly preserves overall network function and, in doing so, provides new conceptual bridges between the disparate worlds of the neurobiology of heterosynaptic plasticity, the engineering of regularized noise-robust networks, and the mathematics of integrable Lax dynamical systems.

The precise pattern of synaptic connections in a neural network entirely determines the network’s function. Learning rules are programs for modifying synapses to allow the network to attain some functional goal. By carefully tweaking synaptic connectivity, neural networks are able learn new tasks, form new memories, and stabilize neural activity. Due to the complexity of network structure, there are many different synaptic weight patterns that can in principle perform the same computation. These different solutions, however, may be more or less desirable in other other ways, such as their vulnerability to unreliable components in the network. In this paper, we present a synaptic learning rule which locates robust solutions among many synaptic weight configurations that equivalently perform the same task. Our biologically plausible rule relies on information which is entirely locally available to each neuron and possesses a number of desirable mathematical properties. Our analysis reveals that networks are most robust when the aggregate incoming and outgoing synapses are balanced in every neuron. Intriguingly, our learning rule makes predictions which resemble widely experimentally observed patterns of compensatory heterosynaptic plasticity in cortical dendrites.

Brain-wide perception of the emotional valence of light is regulated by distinct hypothalamic neurons

Salient sensory stimuli are perceived by the brain, which guides both the timing and outcome of behaviors in a context-dependent manner. Light is such a stimulus, which is used in treating mood disorders often associated with a dysregulated hypothalamic-pituitary-adrenal stress axis. Relationships between the emotional valence of light and the hypothalamus, and how they interact to exert brain-wide impacts remain unclear. Employing larval zebrafish with analogous hypothalamic systems to mammals, we show in free-swimming animals that hypothalamic corticotropin releasing factor (CRFHy) neurons promote dark avoidance, and such role is not shared by other hypothalamic peptidergic neurons. Single-neuron projection analyses uncover processes extended by individual CRFHy neurons to multiple targets including sensorimotor and decision-making areas. In vivo calcium imaging uncovers a complex and heterogeneous response of individual CRFHy neurons to the light or dark stimulus, with a reduced overall sum of CRF neuronal activity in the presence of light. Brain-wide calcium imaging under alternating light/dark stimuli further identifies distinct and distributed photic response neuronal types. CRFHy neuronal ablation increases an overall representation of light in the brain and broadly enhances the functional connectivity associated with an exploratory brain state. These findings delineate brain-wide photic perception, uncover a previously unknown role of CRFHy neurons in regulating the perception and emotional valence of light, and suggest that light therapy may alleviate mood disorders through reducing an overall sum of CRF neuronal activity.

Modulation of calcineurin signaling during development

Calcineurin signaling pathways are suppressed in Down syndrome (trisomy 21), by overexpression of genes that are located on chromosome 21. Two key genes are the regulator of calcineurin 1 (RCAN1), also called the Down syndrome critical region 1 (DSCR1), and the dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A). The suppressed calcineurin pathway may potentially be restored using small-molecule DYRK inhibitors, which have been proposed as therapeutics in Down syndrome. However, little is known about the benefits and risks of such treatments during various stages of embryonic development, fetal development, and childhood. We examined the modulation of calcineurin signaling during development, using zebrafish as a model system. To mimic suppressed calcineurin signaling in Down syndrome, zebrafish were exposed to the calcineurin inhibitors cyclosporine and tacrolimus during development. We found that suppression of calcineurin signaling changed specific larval behaviors, including activity and responses to acoustic and visual stimuli, depending on the period of exposure. Cotreatment with the DYRK inhibitor proINDY restored a few of these behaviors but also induced a range of adverse side effects including decreased activity and reduced optomotor responses to visual stimuli. Based on these results, we conclude that proINDY has limited benefits and substantial risks when used during development. We propose that zebrafish is an efficient model system for preliminary safety and efficacy tests of other DYRK inhibitors that aim to restore calcineurin signaling during neural development.

The emergence and influence of internal states

Animal behavior is shaped by a variety of "internal states"-partially hidden variables that profoundly shape perception, cognition, and action. The neural basis of internal states, such as fear, arousal, hunger, motivation, aggression, and many others, is a prominent focus of research efforts across animal phyla. Internal states can be inferred from changes in behavior, physiology, and neural dynamics and are characterized by properties such as pleiotropy, persistence, scalability, generalizability, and valence. To date, it remains unclear how internal states and their properties are generated by nervous systems. Here, we review recent progress, which has been driven by advances in behavioral quantification, cellular manipulations, and neural population recordings. We synthesize research implicating defined subsets of state-inducing cell types, widespread changes in neural activity, and neuromodulation in the formation and updating of internal states. In addition to highlighting the significance of these findings, our review advocates for new approaches to clarify the underpinnings of internal brain states across the animal kingdom.

Beyond the behavioural phenotype: Uncovering mechanistic foundations in aquatic eco-neurotoxicology

During the last decade, there has been an increase in awareness of how anthropogenic pollution can alter behavioural traits of diverse aquatic organisms. Apart from understanding profound ecological implications, alterations in neuro-behavioural indices have emerged as sensitive and physiologically integrative endpoints in chemical risk assessment. Accordingly, behavioural ecotoxicology and broader eco-neurotoxicology are becoming increasingly popular fields of research that span a plethora of fundamental laboratory experimentations as well as applied field-based studies. Despite mounting interest in aquatic behavioural ecotoxicology studies, there is, however, a considerable paucity in deciphering the mechanistic foundations underlying behavioural alterations upon exposure to pollutants. The behavioural phenotype is indeed the highest-level integrative neurobiological phenomenon, but at its core lie myriads of intertwined biochemical, cellular, and physiological processes. Therefore, the mechanisms that underlie changes in behavioural phenotypes can stem among others from dysregulation of neurotransmitter pathways, electrical signalling, and cell death of discrete cell populations in the central and peripheral nervous systems. They can, however, also be a result of toxicity to sensory organs and even metabolic dysfunctions. In this critical review, we outline why behavioural phenotyping should be the starting point that leads to actual discovery of fundamental mechanisms underlying actions of neurotoxic and neuromodulating contaminants. We highlight potential applications of the currently existing and emerging neurobiology and neurophysiology analytical strategies that should be embraced and more broadly adopted in behavioural ecotoxicology. Such strategies can provide new mechanistic discoveries instead of only observing the end sum phenotypic effects.

Loss-of-Function Models of the Metabotropic Glutamate Receptor Genes Grm8a and Grm8b Display Distinct Behavioral Phenotypes in Zebrafish Larvae (Danio rerio)

Members of the family of metabotropic glutamate receptors are involved in the pathomechanism of several disorders of the nervous system. Besides the well-investigated function of dysfunctional glutamate receptor signaling in neurodegenerative diseases, neurodevelopmental disorders (NDD), like autism spectrum disorders (ASD) and attention-deficit and hyperactivity disorder (ADHD) might also be partly caused by disturbed glutamate signaling during development. However, the underlying mechanism of the type III metabotropic glutamate receptor 8 (mGluR8 or GRM8) involvement in neurodevelopment and disease mechanism is largely unknown. Here we show that the expression pattern of the two orthologs of human GRM8, grm8a and grm8b, have evolved partially distinct expression patterns in the brain of zebrafish (Danio rerio), especially at adult stages, suggesting sub-functionalization of these two genes during evolution. Using double in situ hybridization staining in the developing brain we demonstrate that grm8a is expressed in a subset of gad1a-positive cells, pointing towards glutamatergic modulation of GABAergic signaling. Building on this result we generated loss-of-function models of both genes using CRISPR/Cas9. Both mutant lines are viable and display no obvious gross morphological phenotypes making them suitable for further analysis. Initial behavioral characterization revealed distinct phenotypes in larvae. Whereas grm8a mutant animals display reduced swimming velocity, grm8b mutant animals show increased thigmotaxis behavior, suggesting an anxiety-like phenotype. We anticipate that our two novel metabotropic glutamate receptor 8 zebrafish models may contribute to a deeper understanding of its function in normal development and its role in the pathomechanism of disorders of the central nervous system.

Motion vision: Course control in the developing visual system

As we move around, the image pattern on our retina is constantly changing. Nervous systems have evolved to detect such global 'optic flow' patterns. A new study reveals how optic flow is encoded in the larval zebrafish brain and could be used for the estimation of self-motion.

Geometric framework to predict structure from function in neural networks

Neural computation in biological and artificial networks relies on the nonlinear summation of many inputs. The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function, but quantitative links between neural network structure and function are complex and subtle. For example, many networks can give rise to similar functional responses, and the same network can function differently depending on context. Whether certain patterns of synaptic connectivity are required to generate specific network-level computations is largely unknown. Here we introduce a geometric framework for identifying synaptic connections required by steady-state responses in recurrent networks of threshold-linear neurons. Assuming that the number of specified response patterns does not exceed the number of input synapses, we analytically calculate the solution space of all feedforward and recurrent connectivity matrices that can generate the specified responses from the network inputs. A generalization accounting for noise further reveals that the solution space geometry can undergo topological transitions as the allowed error increases, which could provide insight into both neuroscience and machine learning. We ultimately use this geometric characterization to derive certainty conditions guaranteeing a nonzero synapse between neurons. Our theoretical framework could thus be applied to neural activity data to make rigorous anatomical predictions that follow generally from the model architecture.

PyZebrascope: An Open-Source Platform for Brain-Wide Neural Activity Imaging in Zebrafish

Understanding how neurons interact across the brain to control animal behaviors is one of the central goals in neuroscience. Recent developments in fluorescent microscopy and genetically-encoded calcium indicators led to the establishment of whole-brain imaging methods in zebrafish, which record neural activity across a brain-wide volume with single-cell resolution. Pioneering studies of whole-brain imaging used custom light-sheet microscopes, and their operation relied on commercially developed and maintained software not available globally. Hence it has been challenging to disseminate and develop the technology in the research community. Here, we present PyZebrascope, an open-source Python platform designed for neural activity imaging in zebrafish using light-sheet microscopy. PyZebrascope has intuitive user interfaces and supports essential features for whole-brain imaging, such as two orthogonal excitation beams and eye damage prevention. Its camera module can handle image data throughput of up to 800 MB/s from camera acquisition to file writing while maintaining stable CPU and memory usage. Its modular architecture allows the inclusion of advanced algorithms for microscope control and image processing. As a proof of concept, we implemented a novel automatic algorithm for maximizing the image resolution in the brain by precisely aligning the excitation beams to the image focal plane. PyZebrascope enables whole-brain neural activity imaging in fish behaving in a virtual reality environment. Thus, PyZebrascope will help disseminate and develop light-sheet microscopy techniques in the neuroscience community and advance our understanding of whole-brain neural dynamics during animal behaviors.

A robust receptive field code for optic flow detection and decomposition during self-motion

The perception of optic flow is essential for any visually guided behavior of a moving animal. To mechanistically predict behavior and understand the emergence of self-motion perception in vertebrate brains, it is essential to systematically characterize the motion receptive fields (RFs) of optic-flow-processing neurons. Here, we present the fine-scale RFs of thousands of motion-sensitive neurons studied in the diencephalon and the midbrain of zebrafish. We found neurons that serve as linear filters and robustly encode directional and speed information of translation-induced optic flow. These neurons are topographically arranged in pretectum according to translation direction. The unambiguous encoding of translation enables the decomposition of translational and rotational self-motion information from mixed optic flow. In behavioral experiments, we successfully demonstrated the predicted decomposition in the optokinetic and optomotor responses. Together, our study reveals the algorithm and the neural implementation for self-motion estimation in a vertebrate visual system.

Transcriptomic underpinnings of high and low mirror aggression zebrafish behaviours

Background

Aggression is an adaptive behaviour that animals use to protect offspring, defend themselves and obtain resources. Zebrafish, like many other animals, are not able to recognize themselves in the mirror and typically respond to their own reflection with aggression. However, mirror aggression is not an all-or-nothing phenomenon, with some individuals displaying high levels of aggression against their mirror image, while others show none at all. In the current work, we have investigated the genetic basis of mirror aggression by using a classic forward genetics approach - selective breeding for high and low mirror aggression zebrafish (HAZ and LAZ).

Results

We characterized AB wild-type zebrafish for their response to the mirror image. Both aggressive and non-aggressive fish were inbred over several generations. We found that HAZ were on average more aggressive than the corresponding LAZ across generations and that the most aggressive adult HAZ were less anxious than the least aggressive adult LAZ after prolonged selective breeding. RNAseq analysis of these fish revealed that hundreds of protein-encoding genes with important diverse biological functions such as arsenic metabolism (as3mt), cell migration (arl4ab), immune system activity (ptgr1), actin cytoskeletal remodelling (wdr1), corticogenesis (dgcr2), protein dephosphorylation (ublcp1), sialic acid metabolism (st6galnac3) and ketone body metabolism (aacs) were differentially expressed between HAZ and LAZ, suggesting a strong genetic contribution to this phenotype. DAVID pathway analysis showed that a number of diverse pathways are enriched in HAZ over LAZ including pathways related to immune function, oxidation-reduction processes and cell signalling. In addition, weighted gene co-expression network analysis (WGCNA) identified 12 modules of highly correlated genes that were significantly associated with aggression duration and/or experimental group.

Conclusions

The current study shows that selective breeding based of the mirror aggression phenotype induces strong, heritable changes in behaviour and gene expression within the brain of zebrafish suggesting a strong genetic basis for this behaviour. Our transcriptomic analysis of fish selectively bred for high and low levels of mirror aggression revealed specific transcriptomic signatures induced by selective breeding and mirror aggression and thus provides a large and novel resource of candidate genes for future study.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01298-z.

Novel use of FDA-approved drugs identified by cluster analysis of behavioral profiles

Repurposing FDA-approved drugs is an efficient and cost-effective approach in the development of therapeutics for a broad range of diseases. However, prediction of function can be challenging, especially in the brain. We screened a small-molecule library with FDA-approved drugs for effects on behavior. The studies were carried out using zebrafish larvae, imaged in a 384-well format. We found that various drugs affect activity, habituation, startle responses, excitability, and optomotor responses. The changes in behavior were organized in behavioral profiles, which were examined by hierarchical cluster analysis. One of the identified clusters includes the calcineurin inhibitors cyclosporine (CsA) and tacrolimus (FK506), which are immunosuppressants and potential therapeutics in the prevention of Alzheimer's disease. The calcineurin inhibitors form a functional cluster with seemingly unrelated drugs, including bromocriptine, tetrabenazine, rosiglitazone, nebivolol, sorafenib, cabozantinib, tamoxifen, meclizine, and salmeterol. We propose that drugs with 'CsA-type' behavioral profiles are promising candidates for the prevention and treatment of Alzheimer's disease.

Noninvasive Electrophysiology: Emerging Prospects in Aquatic Neurotoxicity Testing

The significance of neurotoxicological risks associated with anthropogenic pollution is gaining increasing recognition worldwide. In this regard, perturbations in behavioral traits upon exposure to environmentally relevant concentrations of neurotoxic and neuro-modulating contaminants have been linked to diminished ecological fitness of many aquatic species. Despite an increasing interest in behavioral testing in aquatic ecotoxicology there is, however, a notable gap in understanding of the neurophysiological foundations responsible for the altered behavioral phenotypes. One of the canonical approaches to explain the mechanisms of neuro-behavioral changes is functional analysis of neuronal transmission. In aquatic animals it requires, however, invasive, complex, and time-consuming electrophysiology techniques. In this perspective, we highlight emerging prospects of noninvasive, in situ electrophysiology based on multielectrode arrays (MEAs). This technology has only recently been pioneered for the detection and analysis of transient electrical signals in the central nervous system of small model organisms such as zebrafish. The analysis resembles electroencephalography (EEG) applications and provides an appealing strategy for mechanistic explorative studies as well as routine neurotoxicity risk assessment. We outline the prospective future applications and existing challenges of this emerging analytical strategy that is poised to bring new vistas for aquatic ecotoxicology such as greater mechanistic understanding of eco-neurotoxicity and thus more robust risk assessment protocols.

A Model of Discovery: The Role of Imaging Established and Emerging Non-mammalian Models in Neuroscience

Rodents have been the dominant animal models in neurobiology and neurological disease research over the past 60 years. The prevalent use of rats and mice in neuroscience research has been driven by several key attributes including their organ physiology being more similar to humans, the availability of a broad variety of behavioral tests and genetic tools, and widely accessible reagents. However, despite the many advances in understanding neurobiology that have been achieved using rodent models, there remain key limitations in the questions that can be addressed in these and other mammalian models. In particular, in vivo imaging in mammals at the cell-resolution level remains technically difficult and demands large investments in time and cost. The simpler nervous systems of many non-mammalian models allow for precise mapping of circuits and even the whole brain with impressive subcellular resolution. The types of non-mammalian neuroscience models available spans vertebrates and non-vertebrates, so that an appropriate model for most cell biological questions in neurodegenerative disease likely exists. A push to diversify the models used in neuroscience research could help address current gaps in knowledge, complement existing rodent-based bodies of work, and bring new insight into our understanding of human disease. Moreover, there are inherent aspects of many non-mammalian models such as lifespan and tissue transparency that can make them specifically advantageous for neuroscience studies. Crispr/Cas9 gene editing and decreased cost of genome sequencing combined with advances in optical microscopy enhances the utility of new animal models to address specific questions. This review seeks to synthesize current knowledge of established and emerging non-mammalian model organisms with advances in cellular-resolution in vivo imaging techniques to suggest new approaches to understand neurodegeneration and neurobiological processes. We will summarize current tools and in vivo imaging approaches at the single cell scale that could help lead to increased consideration of non-mammalian models in neuroscience research.

High activity and high functional connectivity are mutually exclusive in resting state zebrafish and human brains

Background

The structural connectivity of neurons in the brain allows active neurons to impact the physiology of target neuron types with which they are functionally connected. While the structural connectome is at the basis of functional connectome, it is the functional connectivity measured through correlations between time series of individual neurophysiological events that underlies behavioral and mental states. However, in light of the diverse neuronal cell types populating the brain and their unique connectivity properties, both neuronal activity and functional connectivity are heterogeneous across the brain, and the nature of their relationship is not clear. Here, we employ brain-wide calcium imaging at cellular resolution in larval zebrafish to understand the principles of resting state functional connectivity.

Results

We recorded the spontaneous activity of >12,000 neurons in the awake resting state forebrain. By classifying their activity (i.e., variances of ΔF/F across time) and functional connectivity into three levels (high, medium, low), we find that highly active neurons have low functional connections and highly connected neurons are of low activity. This finding holds true when neuronal activity and functional connectivity data are classified into five instead of three levels, and in whole brain spontaneous activity datasets. Moreover, such activity-connectivity relationship is not observed in randomly shuffled, noise-added, or simulated datasets, suggesting that it reflects an intrinsic brain network property. Intriguingly, deploying the same analytical tools on functional magnetic resonance imaging (fMRI) data from the resting state human brain, we uncover a similar relationship between activity (signal variance over time) and functional connectivity, that is, regions of high activity are non-overlapping with those of high connectivity.

Conclusions

We found a mutually exclusive relationship between high activity (signal variance over time) and high functional connectivity of neurons in zebrafish and human brains. These findings reveal a previously unknown and evolutionarily conserved brain organizational principle, which has implications for understanding disease states and designing artificial neuronal networks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01286-3.

Synaptic dysfunction connects autism spectrum disorder and sleep disturbances: A perspective from studies in model organisms

Sleep disturbances (SD) accompany many neurodevelopmental disorders, suggesting SD is a transdiagnostic process that can account for behavioral deficits and influence underlying neuropathogenesis. Autism Spectrum Disorder (ASD) comprises a complex set of neurodevelopmental conditions characterized by challenges in social interaction, communication, and restricted, repetitive behaviors. Diagnosis of ASD is based primarily on behavioral criteria, and there are no drugs that target core symptoms. Among the co-occurring conditions associated with ASD, SD are one of the most prevalent. SD often arises before the onset of other ASD symptoms. Sleep interventions improve not only sleep but also daytime behaviors in children with ASD. Here, we examine sleep phenotypes in multiple model systems relevant to ASD, e.g., mice, zebrafish, fruit flies and worms. Given the functions of sleep in promoting brain connectivity, neural plasticity, emotional regulation and social behavior, all of which are of critical importance in ASD pathogenesis, we propose that synaptic dysfunction is a major mechanism that connects ASD and SD. Common molecular targets in this interplay that are involved in synaptic function might be a novel avenue for therapy of individuals with ASD experiencing SD. Such therapy would be expected to improve not only sleep but also other ASD symptoms.

Genoarchitecture of the Early Postmitotic Pretectum and the Role of Wnt Signaling in Shaping Pretectal Neurochemical Anatomy in Zebrafish

The pretectum has a distinct nuclear arrangement and complex neurochemical anatomy. While previous genoarchitectural studies have described rostrocaudal and dorsoventral progenitor domains and subdomains in different species, the relationship between these early partitions and its later derivatives in the mature anatomy is less understood. The signals and transcription factors that control the establishment of pretectal anatomy are practically unknown. We investigated the possibility that some aspects of the development of pretectal divisions are controlled by Wnt signaling, focusing on the transitional stage between neurogenesis and histogenesis in zebrafish. Using several molecular markers and following the prosomeric model, we identified derivatives from each rostrocaudal pretectal progenitor domain and described the localization of gad1b-positive GABAergic and vglut2.2-positive glutamatergic cell clusters. We also attempted to relate these clusters to pretectal nuclei in the mature brain. Then, we examined the influence of Wnt signaling on the size of neurochemically distinctive pretectal areas, using a chemical inhibitor of the Wnt pathway and the CRISPR/Cas9 approach to knock out genes that encode the Wnt pathway mediators, Lef1 and Tcf7l2. The downregulation of the Wnt pathway led to a decrease in two GABAergic clusters and an expansion of a glutamatergic subregion in the maturing pretectum. This revealed an instructive role of the Wnt signal in the development of the pretectum during neurogenesis. The molecular anatomy presented here improves our understanding of pretectal development during early postmitotic stages and support the hypothesis that Wnt signaling is involved in shaping the neurochemical organization of the pretectum.

Genetic and Neurological Deficiencies in the Visual System of mct8 Mutant Zebrafish

Thyroid hormones (THs; T3 and T4) enter cells using specific transporters and regulate development and metabolism. Mutation in the TH transporter monocarboxylate transporter 8 (MCT8, SLC16A2) is associated with brain hypothyroidism and neurological impairment. We established mct8 mutant (mct8-/-) zebrafish as a model for MCT8 deficiency, which causes endocrinological, neurological, and behavioral alterations. Here, we profiled the transcriptome of mct8-/- larvae. Among hundreds of differentially expressed genes, the expression of a cluster of vision-related genes was distinct. Specifically, the expression of the opsin 1 medium wave sensitive 2 (opn1mw2) decreased in two mct8 mutants: mct8-/- and mct8^-25bp-/- larvae, and under pharmacological inhibition of TH production. Optokinetic reflex (OKR) assays showed a reduction in the number of conjugated eye movements, and live imaging of genetically encoded Ca²⁺ indicator revealed altered neuronal activity in the pretectum area of mct8^-25bp-/- larvae. These results imply that MCT8 and THs regulate the development of the visual system and suggest a mechanism to the deficiencies observed in the visual system of MCT8-deficiency patients.

Avobenzone and nanoplastics affect the development of zebrafish nervous system and retinal system and inhibit their locomotor behavior

The use of cosmetics is growing with each passing day, arousing widespread attention to their ingredients. Avobenzone (AVO) and nanoplastics (NPs) are typical ingredients in cosmetics, which coexist in the aquatic environment and have a combined effect on aquatic organisms. In this study, the accumulation of AVO and NPs in zebrafish larvae and effects on gene expression and enzymatic activity related to nervous functions, and locomotor behavior were investigated. AVO and NPs accumulated continuously in zebrafish, and the combined exposure enhanced AVO accumulation. After recovery, the accumulated concentrations of AVO and NPs in zebrafish remained unchanged, suggesting that AVO and NPs could not be eliminated in 72 h. The genes regulated nervous system development were affected mainly by AVO exposure, while the genes regulated retinal system development were affected by NPs exposure. Single and combined exposures of AVO and NPs affected the activities of acetylcholinesterase and antioxidant enzymes in zebrafish, and superoxide dismutase activity could not return to normal level after 72 h of recovery period. The locomotor activity of zebrafish larvae was significantly inhibited by AVO and NPs, which might be related to the alterations in functions of nervous system development and retinal system development as well as the interference of neurotransmitter system and antioxidant system.

Input from torus longitudinalis drives binocularity and spatial summation in zebrafish optic tectum

Background

A continued effort in neuroscience aims to understand the way brain circuits consisting of diverse neuronal types generate complex behavior following sensory input. A common feature of vertebrate visual systems is that lower-order and higher-order visual areas are reciprocally connected. Feedforward projections confer visual responsiveness to higher-order visual neurons while feedback projections likely serve to modulate responses of lower-order visual neurons in a context-dependent manner. Optic tectum is the largest first-order visual brain area in zebrafish and is reciprocally connected with the torus longitudinalis (TL), a second-order visual brain area that does not receive retinal input. A functional role for feedback projections from TL to tectum has not been identified. Here we aim to understand how this feedback contributes to visual processing.

Results

In this study, we demonstrate that TL feedback projections to tectum drive binocular integration and spatial summation in a defined tectal circuit. We performed genetically targeted, cell type-specific functional imaging in tectal pyramidal neurons (PyrNs) and their two input neuron populations: retinal ganglion cells (RGCs) and neurons in TL. We find that PyrNs encode gradual changes in scene luminance using a complement of three distinct response classes that encode different light intensity ranges. Functional imaging of RGC inputs to tectum suggest that these response classes originate in the retina and RGC input specifies PyrN functional classes. In contrast, TL input serves to endow PyrNs with large, compound receptive fields that span both retinal hemifields.

Conclusions

These findings reveal a novel role for the zebrafish TL in driving binocular integration and spatial summation in tectal PyrNs. The neural circuit we describe generates a population of tectal neurons with large receptive fields tailored for detecting changes in the visual scene.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-021-01222-x.

Fourier Motion Processing in the Optic Tectum and Pretectum of the Zebrafish Larva

In the presence of moving visual stimuli, the majority of animals follow the Fourier motion energy (luminance), independently of other stimulus features (edges, contrast, etc.). While the behavioral response to Fourier motion has been studied in the past, how Fourier motion is represented and processed by sensory brain areas remains elusive. Here, we investigated how visual moving stimuli with or without the first Fourier component (square-wave signal or missing fundamental signal) are represented in the main visual regions of the zebrafish brain. First, we monitored the larva's optokinetic response (OKR) induced by square-wave and missing fundamental signals. Then, we used two-photon microscopy and GCaMP6f zebrafish larvae to monitor neuronal circuit dynamics in the optic tectum and the pretectum. We observed that both the optic tectum and the pretectum circuits responded to the square-wave gratings. However, only the pretectum responded specifically to the direction of the missing-fundamental signal. In addition, a group of neurons in the pretectum responded to the direction of the behavioral output (OKR), independently of the type of stimulus presented. Our results suggest that the optic tectum responds to the different features of the stimulus (e.g., contrast, spatial frequency, direction, etc.), but does not respond to the direction of motion if the motion information is not coherent (e.g., the luminance and the edges and contrast in the missing-fundamental signal). On the other hand, the pretectum mainly responds to the motion of the stimulus based on the Fourier energy.

A zebrafish model for calcineurin-dependent brain function

Small-molecule modulators of calcineurin signaling have been proposed as potential therapeutics in Down syndrome and Alzheimer's disease. Models predict that in Down syndrome, suppressed calcineurin-NFAT signaling may be mitigated by proINDY, which activates NFAT, the nuclear factor of activated T-cells. Conversely, elevated calcineurin signaling in Alzheimer's disease may be suppressed with the calcineurin inhibitors cyclosporine and tacrolimus. Such small-molecule treatments may have both beneficial and adverse effects. The current study examines the effects of proINDY, cyclosporine and tacrolimus on behavior, using zebrafish larvae as a model system. To suppress calcineurin signaling, larvae were treated with cyclosporine and tacrolimus. We found that these calcineurin inhibitors induced hyperactivity, suppressed visually-guided behaviors, acoustic hyperexcitability and reduced habituation to acoustic stimuli. To activate calcineurin-NFAT signaling, larvae were treated with proINDY. ProINDY treatment reduced activity and stimulated visually-guided behaviors, opposite to the behavioral changes induced by calcineurin inhibitors. The opposing effects suggest that activity and visually-guided behaviors are regulated by the calcineurin-NFAT signaling pathway. A central role of calcineurin-NFAT signaling is further supported by co-treatments of calcineurin inhibitors and proINDY, which had therapeutic effects on activity and visually-guided behaviors. However, these co-treatments adversely increased excitability, suggesting that some behaviors are regulated by other calcineurin signaling pathways. Overall, the developed methodologies provide an efficient high-throughput platform for the evaluation of modulators of calcineurin signaling that restore neural function, while avoiding adverse side effects, in a complex neural system.

Orphan cytochrome P450 20a1 CRISPR/Cas9 mutants and neurobehavioral phenotypes in zebrafish

Orphan cytochrome P450 (CYP) enzymes are those for which biological substrates and function(s) are unknown. Cytochrome P450 20A1 (CYP20A1) is the last human orphan P450 enzyme, and orthologs occur as single genes in every vertebrate genome sequenced to date. The occurrence of high levels of CYP20A1 transcripts in human substantia nigra and hippocampus and abundant maternal transcripts in zebrafish eggs strongly suggest roles both in the brain and during early embryonic development. Patients with chromosome 2 microdeletions including CYP20A1 show hyperactivity and bouts of anxiety, among other conditions. Here, we created zebrafish cyp20a1 mutants using CRISPR/Cas9, providing vertebrate models with which to study the role of CYP20A1 in behavior and other neurodevelopmental functions. The homozygous cyp20a1 null mutants exhibited significant behavioral differences from wild-type zebrafish, both in larval and adult animals. Larval cyp20a1-/- mutants exhibited a strong increase in light-simulated movement (i.e., light-dark assay), which was interpreted as hyperactivity. Further, the larvae exhibited mild hypoactivity during the adaptation period of the optomotor assays. Adult cyp20a1 null fish showed a pronounced delay in adapting to new environments, which is consistent with an anxiety paradigm. Taken together with our earlier morpholino cyp20a1 knockdown results, the results described herein suggest that the orphan CYP20A1 has a neurophysiological role.