Structure of the Zebrafish Locomotor Repertoire Revealed with Unsupervised Behavioral Clustering

The Visual Systems of Zebrafish

The zebrafish visual system has become a paradigmatic preparation for behavioral and systems neuroscience. Around 40 types of retinal ganglion cells (RGCs) serve as matched filters for stimulus features, including light, optic flow, prey, and objects on a collision course. RGCs distribute their signals via axon collaterals to 12 retinorecipient areas in forebrain and midbrain. The major visuomotor hub, the optic tectum, harbors nine RGC input layers that combine information on multiple features. The retinotopic map in the tectum is locally adapted to visual scene statistics and visual subfield-specific behavioral demands. Tectal projections to premotor centers are topographically organized according to behavioral commands. The known connectivity in more than 20 processing streams allows us to dissect the cellular basis of elementary perceptual and cognitive functions. Visually evoked responses, such as prey capture or loom avoidance, are controlled by dedicated multistation pathways that-at least in the larva-resemble labeled lines. This architecture serves the neuronal code's purpose of driving adaptive behavior.

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

Keypoint tracking algorithms can flexibly quantify animal movement from videos obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into discrete actions. This challenge is particularly acute because keypoint data are susceptible to high-frequency jitter that clustering algorithms can mistake for transitions between actions. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ('syllables') from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to identify syllables whose boundaries correspond to natural sub-second discontinuities in pose dynamics. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq also works in multiple species and generalizes beyond the syllable timescale, identifying fast sniff-aligned movements in mice and a spectrum of oscillatory behaviors in fruit flies. Keypoint-MoSeq, therefore, renders accessible the modular structure of behavior through standard video recordings.

Application of machine learning in the study of development, behavior, nerve, and genotoxicity of zebrafish

Machine learning (ML) as a novel model-based approach has been used in studying aquatic toxicology in the environmental field. Zebrafish, as an ideal model organism in aquatic toxicology research, has been widely used to study the toxic effects of various pollutants. However, toxicity testing on organisms may cause significant harm, consume considerable time and resources, and raise ethical concerns. Therefore, ML is used in related research to reduce animal experiments and assist researchers in conducting toxicological research. Although ML techniques have matured in various fields, research on ML-based aquatic toxicology is still in its infancy due to the lack of comprehensive large-scale toxicity databases for environmental pollutants and model organisms. Therefore, to better understand the recent research progress of ML in studying the development, behavior, nerve, and genotoxicity of zebrafish, this review mainly focuses on using ML modeling to assess and predict the toxic effects of zebrafish exposure to different toxic chemicals. Meanwhile, the opportunities and challenges faced by ML in the field of toxicology were analyzed. Finally, suggestions and perspectives were proposed for the toxicity studies of ML on zebrafish in future applications.

Characterizing the structure of mouse behavior using Motion Sequencing

Spontaneous mouse behavior is composed from repeatedly used modules of movement (e.g., rearing, running or grooming) that are flexibly placed into sequences whose content evolves over time. By identifying behavioral modules and the order in which they are expressed, researchers can gain insight into the effect of drugs, genes, context, sensory stimuli and neural activity on natural behavior. Here we present a protocol for performing Motion Sequencing (MoSeq), an ethologically inspired method that uses three-dimensional machine vision and unsupervised machine learning to decompose spontaneous mouse behavior into a series of elemental modules called 'syllables'. This protocol is based upon a MoSeq pipeline that includes modules for depth video acquisition, data preprocessing and modeling, as well as a standardized set of visualization tools. Users are provided with instructions and code for building a MoSeq imaging rig and acquiring three-dimensional video of spontaneous mouse behavior for submission to the modeling framework; the outputs of this protocol include syllable labels for each frame of the video data as well as summary plots describing how often each syllable was used and how syllables transitioned from one to the other. In addition, we provide instructions for analyzing and visualizing the outputs of keypoint-MoSeq, a recently developed variant of MoSeq that can identify behavioral motifs from keypoints identified from standard (rather than depth) video. This protocol and the accompanying pipeline significantly lower the bar for users without extensive computational ethology experience to adopt this unsupervised, data-driven approach to characterize mouse behavior.

Uncovering multiscale structure in the variability of larval zebrafish navigation

Animals chain movements into long-lived motor strategies, resulting in variability that ultimately reflects the interplay between internal states and environmental cues. To reveal structure in such variability, we build models that bridges across time scales that enable a quantitative comparison of behavioral phenotypes among individuals. Applied to larval zebrafish exposed to diverse sensory cues, we uncover a hierarchy of long-lived motor strategies, dominated by changes in orientation distinguishing cruising and wandering strategies. Environmental cues induce preferences along these modes at the population level: while fish cruise in the light, they wander in response to aversive (dark) stimuli or in search for prey. Our method enables us to encode the behavioral dynamics of each individual fish in the transitions among coarse-grained motor strategies. By doing so, we uncover a hierarchical structure to the phenotypic variability that corresponds to exploration-exploitation trade-offs. Within a wide range of sensory cues, a major source of variation among fish is driven by prior and immediate exposure to prey that induces exploitation phenotypes. However, a large degree of variability is unexplained by environmental cues, pointing to hidden states that override the sensory context to induce contrasting exploration-exploitation phenotypes. Altogether, our approach extracts the timescales of motor strategies deployed during navigation, exposing undiscovered structure among individuals and pointing to internal states tuned by prior experience.

Key HPI axis receptors facilitate light adaptive behavior in larval zebrafish

The vertebrate stress response (SR) is mediated by the hypothalamic-pituitary-adrenal (HPA) axis and contributes to generating context appropriate physiological and behavioral changes. Although the HPA axis plays vital roles both in stressful and basal conditions, research has focused on the response under stress. To understand broader roles of the HPA axis in a changing environment, we characterized an adaptive behavior of larval zebrafish during ambient illumination changes. Genetic abrogation of glucocorticoid receptor (nr3c1) decreased basal locomotor activity in light and darkness. Some key HPI axis receptors (mc2r [ACTH receptor], nr3c1), but not nr3c2 (mineralocorticoid receptor), were required to adapt to light more efficiently but became dispensable when longer illumination was provided. Such light adaptation was more efficient in dimmer light. Our findings show that the HPI axis contributes to the SR, facilitating the phasic response and maintaining an adapted basal state, and that certain adaptations occur without HPI axis activity.

High-resolution tracking of unconfined zebrafish behavior reveals stimulatory and anxiolytic effects of psilocybin

Serotonergic psychedelics are emerging therapeutics for psychiatric disorders, yet their underlying mechanisms of action in the brain remain largely elusive. Here, we developed a wide-field behavioral tracking system for larval zebrafish and investigated the effects of psilocybin, a psychedelic serotonin receptor agonist. Machine learning analyses of precise body kinematics identified latent behavioral states reflecting spontaneous exploration, visually-driven rapid swimming, and irregular swim patterns following stress exposure. Using this method, we found that acute psilocybin treatment has two behavioral effects: [i] facilitation of spontaneous exploration ("stimulatory") and [ii] prevention of irregular swim patterns following stress exposure ("anxiolytic"). These effects differed from the effect of acute SSRI treatment and were rather similar to the effect of ketamine treatment. Neural activity imaging in the dorsal raphe nucleus suggested that psilocybin inhibits serotonergic neurons by activating local GABAergic neurons, consistent with psychedelic-induced suppression of serotonergic neurons in mammals. These findings pave the way for using larval zebrafish to elucidate neural mechanisms underlying the behavioral effects of serotonergic psychedelics.

Bioelectric control of locomotor gaits in the walking ciliate Euplotes

Diverse animal species exhibit highly stereotyped behavioral actions and locomotor sequences as they explore their natural environments. In many such cases, the neural basis of behavior is well established, where dedicated neural circuitry contributes to the initiation and regulation of certain response sequences. At the microscopic scale, single-celled eukaryotes (protists) also exhibit remarkably complex behaviors and yet are completely devoid of nervous systems. Here, to address the question of how single cells control behavior, we study locomotor patterning in the exemplary hypotrich ciliate Euplotes, a highly polarized cell, which actuates a large number of leg-like appendages called cirri (each a bundle of ∼25-50 cilia) to swim in fluids or walk on surfaces. As it navigates its surroundings, a walking Euplotes cell is routinely observed to perform side-stepping reactions, one of the most sophisticated maneuvers ever observed in a single-celled organism. These are spontaneous and stereotyped reorientation events involving a transient and fast backward motion followed by a turn. Combining high-speed imaging with simultaneous time-resolved electrophysiological recordings, we show that this complex coordinated motion sequence is tightly regulated by rapid membrane depolarization events, which orchestrate the activity of different cirri on the cell. Using machine learning and computer vision methods, we map detailed measurements of cirri dynamics to the cell's membrane bioelectrical activity, revealing a differential response in the front and back cirri. We integrate these measurements with a minimal model to understand how Euplotes-a unicellular organism-manipulates its membrane potential to achieve real-time control over its motor apparatus.

The preoptic area and dorsal habenula jointly support homeostatic navigation in larval zebrafish

Animals must maintain physiological processes within an optimal temperature range despite changes in their environment. Through behavioral assays, whole-brain functional imaging, and neural ablations, we show that larval zebrafish, an ectothermic vertebrate, achieves thermoregulation through homeostatic navigation-non-directional and directional movements toward the temperature closest to its physiological setpoint. A brain-wide circuit encompassing several brain regions enables this behavior. We identified the preoptic area of the hypothalamus (PoA) as a key brain structure in triggering non-directional reorientation when thermal conditions are worsening. This result shows an evolutionary conserved role of the PoA as principal thermoregulator of the brain also in ectotherms. We further show that the habenula (Hb)-interpeduncular nucleus (IPN) circuit retains a short-term memory of the sensory history to support the generation of coherent directed movements even in the absence of continuous sensory cues. We finally provide evidence that this circuit may not be exclusive for temperature but may convey a more abstract representation of relative valence of physiologically meaningful stimuli regardless of their specific identity to enable homeostatic navigation.

Multisensory strategies for postural compensation after lateral line loss

To control elevation underwater, aquatic vertebrates integrate multisensory information (e.g., vestibular, visual, proprioceptive) to guide posture and swim kinematics. Here we characterized how larval zebrafish changed posture and locomotive strategies after imposed instability (decreased buoyancy) in the presence and absence of visual cues. We discovered that larvae sank more after acute loss of lateral line (flow-sensing) hair cells. In response, larvae engaged different compensatory strategies, depending on whether they were in the light or dark. In the dark, larvae swam more frequently, engaging their trunk to steer their nose up and climb more effectively. However, in the light, larvae climbed more often, engaging both pectoral fins and trunk to elevate. We conclude that larvae sense instability and use vestibular and visual information as available to control posture and trajectory. Our work is a step towards understanding the multisensory neural computations responsible for control strategies that allow orientation and navigation in depth.

All-optical interrogation of brain-wide activity in freely swimming larval zebrafish

We introduce an all-optical technique that enables volumetric imaging of brain-wide calcium activity and targeted optogenetic stimulation of specific brain regions in unrestrained larval zebrafish. The system consists of three main components: a 3D tracking module, a dual-color fluorescence imaging module, and a real-time activity manipulation module. Our approach uses a sensitive genetically encoded calcium indicator in combination with a long Stokes shift red fluorescence protein as a reference channel, allowing the extraction of Ca2+ activity from signals contaminated by motion artifacts. The method also incorporates rapid 3D image reconstruction and registration, facilitating real-time selective optogenetic stimulation of different regions of the brain. By demonstrating that selective light activation of the midbrain regions in larval zebrafish could reliably trigger biased turning behavior and changes of brain-wide neural activity, we present a valuable tool for investigating the causal relationship between distributed neural circuit dynamics and naturalistic behavior.

Pose analysis in free-swimming adult zebrafish, Danio rerio : "fishy" origins of movement design

Movement requires maneuvers that generate thrust to either make turns or move the body forward in physical space. The computational space for perpetually controlling the relative position of every point on the body surface can be vast. We hypothesize the evolution of efficient design for movement that minimizes active (neural) control by leveraging the passive (reactive) forces between the body and the surrounding medium at play. To test our hypothesis, we investigate the presence of stereotypical postures during free-swimming in adult zebrafish, Danio rerio . We perform markerless tracking using DeepLabCut, a deep learning pose estimation toolkit, to track geometric relationships between body parts. To identify putative clusters of postural configurations obtained from twelve freely behaving zebrafish, we use unsupervised multivariate time-series analysis (B-SOiD machine learning software). When applied to single individuals, this method reveals a best-fit for 36 to 50 clusters in contrast 86 clusters for data pooled from all 12 animals. The centroids of each cluster obtained over 14,000 sequential frames recorded for a single fish represent an apriori classification into relatively stable "target body postures" and inter-pose "transitional postures" that lead to and away from a target pose. We use multidimensional scaling of mean parameter values for each cluster to map cluster-centroids within two dimensions of postural space. From a post-priori visual analysis, we condense neighboring postural variants into 15 superclusters or core body configurations. We develop a nomenclature specifying the anteroposterior level/s (upper, mid and lower) and degree of bending. Our results suggest that constraining bends to mainly three levels in adult zebrafish preempts the neck, fore- and hindlimb design for maneuverability in land vertebrates.

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

Keypoint tracking algorithms have revolutionized the analysis of animal behavior, enabling investigators to flexibly quantify behavioral dynamics from conventional video recordings obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into the modules out of which behavior is organized. This challenge is particularly acute because keypoint data is susceptible to high frequency jitter that clustering algorithms can mistake for transitions between behavioral modules. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ("syllables") from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to effectively identify syllables whose boundaries correspond to natural sub-second discontinuities inherent to mouse behavior. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior, and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq therefore renders behavioral syllables and grammar accessible to the many researchers who use standard video to capture animal behavior.

Artificial Neural Network (ANN)-Based Pattern Recognition Approach Illustrates a Biphasic Behavioral Effect of Ethanol in Zebrafish: A High-Throughput Method for Animal Locomotor Analysis

Variations in stress responses between individuals are linked to factors ranging from stress coping styles to the sensitivity of neurotransmitter systems. Many anxiolytic compounds can increase stressor engagement through the modulation of neurotransmitter systems and are used to investigate stress response mechanisms. The effect of such modulation may vary in time depending on concentration or environment, but those effects are hard to dissect because of the slow transition. We investigated the temporal effect of ethanol and found that ethanol-treated individual zebrafish larvae showed altered behavior that is different between drug concentrations and decreases with time. We used an artificial neural network approach with a time-dependent method for analyzing long (90 min) experiments on zebrafish larvae and found that individuals from the 0.5% group begin to show locomotor activity corresponding to the control group starting from the 60th minute. The locomotor activity of individuals from the 2% group after the 80th minute is classified as the activity of individuals from the 1.5% group. Our method shows three clusters of different concentrations in comparison with two clusters, which were obtained with the usage of a statistical approach for analyzing just the speed of fish movements. In addition, we show that such changes are not explained by basic behavior statistics such as speed and are caused by shifts in locomotion patterns.

Comparative profiling of cellular gait on adhesive micropatterns defines statistical patterns of activity that underlie native and cancerous cell dynamics

Cell dynamics are powered by patterns of activity, but it is not straightforward to quantify these patterns or compare them across different environmental conditions or cell-types. Here we digitize the long-term shape fluctuations of metazoan cells grown on micropatterned fibronectin islands to define and extract statistical features of cell dynamics without the need for genetic modification or fluorescence imaging. These shape fluctuations generate single-cell morphological signals that can be decomposed into two major components: a continuous, slow-timescale meandering of morphology about an average steady-state shape; and short-lived "events" of rapid morphology change that sporadically occur throughout the timecourse. By developing statistical metrics for each of these components, we used thousands of hours of single-cell data to quantitatively define how each axis of cell dynamics was impacted by environmental conditions or cell-type. We found the size and spatial complexity of the micropattern island modulated the statistics of morphological events-lifetime, frequency, and orientation-but not its baseline shape fluctuations. Extending this approach to profile a panel of triple negative breast cancer cell-lines, we found that different cell-types could be distinguished from one another along specific and unique statistical axes of their behavior. Our results suggest that micropatterned substrates provide a generalizable method to build statistical profiles of cell dynamics to classify and compare emergent cell behaviors.

Rapid automated 3-D pose estimation of larval zebrafish using a physical model-trained neural network

Quantitative ethology requires an accurate estimation of an organism's postural dynamics in three dimensions plus time. Technological progress over the last decade has made animal pose estimation in challenging scenarios possible with unprecedented detail. Here, we present (i) a fast automated method to record and track the pose of individual larval zebrafish in a 3-D environment, applicable when accurate human labeling is not possible; (ii) a rich annotated dataset of 3-D larval poses for ethologists and the general zebrafish and machine learning community; and (iii) a technique to generate realistic, annotated larval images in different behavioral contexts. Using a three-camera system calibrated with refraction correction, we record diverse larval swims under free swimming conditions and in response to acoustic and optical stimuli. We then employ a convolutional neural network to estimate 3-D larval poses from video images. The network is trained against a set of synthetic larval images rendered using a 3-D physical model of larvae. This 3-D model samples from a distribution of realistic larval poses that we estimate a priori using a template-based pose estimation of a small number of swim bouts. Our network model, trained without any human annotation, performs larval pose estimation three orders of magnitude faster and with accuracy comparable to the template-based approach, capturing detailed kinematics of 3-D larval swims. It also applies accurately to other datasets collected under different imaging conditions and containing behavioral contexts not included in our training.

Drugs prescribed for Phelan-McDermid syndrome differentially impact sensory behaviors in shank3 zebrafish models

Background: Altered sensory processing is a pervasive symptom in individuals with Autism Spectrum Disorders (ASD); people with Phelan McDermid syndrome (PMS), in particular, show reduced responses to sensory stimuli. PMS is caused by deletions of the terminal end of chromosome 22 or point mutations in Shank3. People with PMS can present with an array of symptoms including ASD, epilepsy, gastrointestinal distress, and reduced responses to sensory stimuli. People with PMS are often medicated to manage behaviors like aggression and/or self-harm and/or epilepsy, and it remains unclear how these medications might impact perception/sensory processing. Here we test this using zebrafish mutant shank3ab PMS models that likewise show reduced sensory responses in a visual motor response (VMR) assay, in which increased locomotion is triggered by light to dark transitions. Methods: We screened three medications, risperidone, lithium chloride (LiCl), and carbamazepine (CBZ), prescribed to people with PMS and one drug, 2-methyl-6-(phenylethynyl) pyridine (MPEP) tested in rodent models of PMS, for their effects on a sensory-induced behavior in two zebrafish PMS models with frameshift mutations in either the N- or C- termini. To test how pharmacological treatments affect the VMR, we exposed larvae to selected drugs for 24 hours and then quantified their locomotion during four ten-minute cycles of lights on-to-off stimuli. Results: We found that risperidone normalized the VMR in shank3 models. LiCl and CBZ had no effect on the VMR in any of the three genotypes. MPEP reduced the VMR in wildtype (WT) to levels seen in shank3 models but caused no changes in either shank3 model. Finally, shank3 mutants showed resistance to the seizure-inducing drug pentylenetetrazol (PTZ), at a dosage that results in hyperactive swimming in WT zebrafish. Conclusions: Our work shows that the effects of drugs on sensory processing are varied in ways that can be highly genotype- and drug-dependent.

The canonical HPA axis facilitates and maintains light adaptive behavior

The vertebrate stress response (SR) is mediated by the hypothalamic-pituitary-adrenal (HPA) axis and contributes to generating context appropriate physiological and behavioral changes. Although the HPA axis plays vital roles both in stressful and basal conditions, research has focused on the response under stress. To understand broader roles of the HPA axis in a changing environment, we characterized an adaptive behavior of larval zebrafish during ambient illumination changes. The glucocorticoid receptor (nr3c1) was necessary to maintain basal locomotor activity in light and darkness. The HPA axis was required to adapt to light more efficiently but became dispensable when longer illumination was provided. Light adaptation was more efficient in dimmer light and did not require the mineralocorticoid receptor (nr3c2). Our findings show that the HPA axis contributes to the SR at various stages, facilitating the phasic response and maintaining an adapted basal state, and that certain adaptations occur without HPA axis activity.

Zebrafish larvae use stimulus intensity and contrast to estimate distance to prey

The ability to determine the distance to objects is an important feature of most visual systems, but little is known about the neuronal mechanisms for distance estimation. Larval zebrafish execute different visual behaviors depending on distance; at medium distances, they converge their eyes and approach, but when the prey is close enough, they execute a strike and suck the prey into their mouths. To study distance estimation, we developed a head-fixed strike assay. We found that we could evoke strike behavior in head-fixed larvae and quantify head movements to classify the behavior as a strike. Strikes were dependent on distance to prey, allowing us to use them to study distance estimation. Light intensity is rapidly attenuated as it travels through water, so we hypothesized that larvae could use intensity as a distance cue. We found that increasing stimulus intensity could cause larvae to strike at prey that would normally be out of range, and decreasing the intensity could lower the strike rate even for very proximal stimuli. In addition, stimulus contrast is a key parameter, and this could allow larvae to estimate distance over the range of natural illumination. Finally, we presented prey in the binocular vs. monocular visual field and found that monocular prey did evoke strikes, although the binocular input produced more. These results suggest that strike behavior is optimally evoked by bright UV dots in the binocular zone with minimal UV background light and provide a foundation to study the neuronal mechanisms of distance estimation.

Automated phenotyping of postoperative delirium-like behaviour in mice reveals the therapeutic efficacy of dexmedetomidine

Postoperative delirium (POD) is a complicated and harmful clinical syndrome. Traditional behaviour analysis mostly focuses on static parameters. However, animal behaviour is a bottom-up and hierarchical organizational structure composed of time-varying posture dynamics. Spontaneous and task-driven behaviours are used to conduct comprehensive profiling of behavioural data of various aspects of model animals. A machine-learning based method is used to assess the effect of dexmedetomidine. Fourteen statistically different spontaneous behaviours are used to distinguish the non-POD group from the POD group. In the task-driven behaviour, the non-POD group has greater deep versus shallow investigation preference, with no significant preference in the POD group. Hyperactive and hypoactive subtypes can be distinguished through pose evaluation. Dexmedetomidine at a dose of 25 μg kg-1 reduces the severity and incidence of POD. Here we propose a multi-scaled clustering analysis framework that includes pose, behaviour and action sequence evaluation. This may represent the hierarchical dynamics of delirium-like behaviours.

Edge Computing in Nature: Minimal pre-processing of multi-muscle ensembles of spindle signals improves discriminability of limb movements

Multiple proprioceptive signals, like those from muscle spindles, are thought to enable robust estimates of body configuration. Yet, it remains unknown whether spindle signals suffice to discriminate limb movements. Here, a simulated 4-musculotendon, 2-joint planar limb model produced repeated cycles of five end-point trajectories in forward and reverse directions, which generated spindle Ia and II afferent signals (proprioceptors for velocity and length, respectively) from each musculotendon. We find that cross-correlation of the 8D time series of raw firing rates (four Ia, four II) cannot discriminate among most movement pairs (∼ 29% accuracy). However, projecting these signals onto their 1st and 2nd principal components greatly improves discriminability of movement pairs (82% accuracy). We conclude that high-dimensional ensembles of muscle proprioceptors can discriminate among limb movements-but only after dimensionality reduction. This may explain the pre-processing of some afferent signals before arriving at the somatosensory cortex, such as processing of cutaneous signals at the cat's cuneate nucleus.

SAMPL is a high-throughput solution to study unconstrained vertical behavior in small animals

Balance and movement are impaired in many neurological disorders. Recent advances in behavioral monitoring provide unprecedented access to posture and locomotor kinematics but without the throughput and scalability necessary to screen candidate genes/potential therapeutics. Here, we present a scalable apparatus to measure posture and locomotion (SAMPL). SAMPL includes extensible hardware and open-source software with real-time processing and can acquire data from D. melanogaster, C. elegans, and D. rerio as they move vertically. Using SAMPL, we define how zebrafish balance as they navigate vertically and discover small but systematic variations among kinematic parameters between genetic backgrounds. We demonstrate SAMPL's ability to resolve differences in posture and navigation as a function of effect size and data gathered, providing key data for screens. SAMPL is therefore both a tool to model balance and locomotor disorders and an exemplar of how to scale apparatus to support screens.

Optimizing assays of zebrafish larvae swimming performance for drug discovery

INTRODUCTION

Zebrafish larvae are one of the few vertebrates amenable to large-scale drug discovery screens. Larval swimming behavior is often used as an outcome variable and many fields of study have developed assays for evaluating swimming performance. An unintended consequence of this wide interest is that details related to assay methodology and interpretation become scattered across the literature. The aim of this review is to consolidate this information, particularly as it relates to high-throughput approaches.

AREAS COVERED

The authors describe larval swimming behaviors as this forms the basis for understanding their experimentally evoked swimming or spontaneous activity. Next, they detail how swimming activity can serve as an outcome variable, particularly in the multi-well formats used in large-scale screening studies. They also highlight biological and technical factors that can impact the sensitivity and variability of these measurements.

EXPERT OPINION

Careful attention to animal husbandry, experimental design, data acquisition, and interpretation of results can improve screen outcomes by maximizing swimming activity while minimizing intra- and inter-larval variability. The development of more sensitive, quantitative methods of assessing swimming performance that can be incorporated into high-throughput workflows will be important in order to take full advantage of the zebrafish model.

Whole-brain imaging of freely-moving zebrafish

One of the holy grails of neuroscience is to record the activity of every neuron in the brain while an animal moves freely and performs complex behavioral tasks. While important steps forward have been taken recently in large-scale neural recording in rodent models, single neuron resolution across the entire mammalian brain remains elusive. In contrast the larval zebrafish offers great promise in this regard. Zebrafish are a vertebrate model with substantial homology to the mammalian brain, but their transparency allows whole-brain recordings of genetically-encoded fluorescent indicators at single-neuron resolution using optical microscopy techniques. Furthermore zebrafish begin to show a complex repertoire of natural behavior from an early age, including hunting small, fast-moving prey using visual cues. Until recently work to address the neural bases of these behaviors mostly relied on assays where the fish was immobilized under the microscope objective, and stimuli such as prey were presented virtually. However significant progress has recently been made in developing brain imaging techniques for zebrafish which are not immobilized. Here we discuss recent advances, focusing particularly on techniques based on light-field microscopy. We also draw attention to several important outstanding issues which remain to be addressed to increase the ecological validity of the results obtained.

Scalable Apparatus to Measure Posture and Locomotion (SAMPL): a high-throughput solution to study unconstrained vertical behavior in small animals

Balance and movement are impaired in a wide variety of neurological disorders. Recent advances in behavioral monitoring provide unprecedented access to posture and locomotor kinematics, but without the throughput and scalability necessary to screen candidate genes / potential therapeutics. We present a powerful solution: a Scalable Apparatus to Measure Posture and Locomotion (SAMPL). SAMPL includes extensible imaging hardware and low-cost open-source acquisition software with real-time processing. We first demonstrate that SAMPL's hardware and acquisition software can acquire data from from D. melanogaster, C. elegans, and D. rerio as they move vertically. Next, we leverage SAMPL's throughput to rapidly (two weeks) gather a new zebrafish dataset. We use SAMPL's analysis and visualization tools to replicate and extend our current understanding of how zebrafish balance as they navigate through a vertical environment. Next, we discover (1) that key kinematic parameters vary systematically with genetic background, and (2) that such background variation is small relative to the changes that accompany early development. Finally, we simulate SAMPL's ability to resolve differences in posture or vertical navigation as a function of affect size and data gathered -- key data for screens. Taken together, our apparatus, data, and analysis provide a powerful solution for labs using small animals to investigate balance and locomotor disorders at scale. More broadly, SAMPL is both an adaptable resource for labs looking process videographic measures of behavior in real-time, and an exemplar of how to scale hardware to enable the throughput necessary for screening.

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.

Development and optimization of an effective method for evaluating habituation learning behavior in larval zebrafish

BACKGROUND

Habituation learning is a simple and conserved behavior in all organisms which could be induced by repeated stimuli. However, no standard and universal methods for training and evaluating the habituation learning behavior in larval zebrafish were available.

NEW METHOD

This study aims to establish effective training and detection protocols for habituation learning behavior in larval zebrafish by using the ViewPoint system. For this purpose, the detection threshold of velocity-a parameter for distinguishing the escape reaction and the spontaneous motion, the detection sensitivity-a parameter for determining the size of the identified object, the number of stimuli, and the age of larvae were optimized to obtain the best performance.

RESULTS

In this study, the optimized parameters were as follows: the detection threshold of velocity at 13, the luminous intensity at 8 %, the detection sensitivity at 32, the number of stimuli at 150, and the age of larvae at 6 dpf. Furthermore, we validated the utility of the established protocol by showing a consistent memory impairment induced by cycloheximide (CHX).

COMPARISON WITH EXISTING METHOD

A similar method was reported previously. However, the equipment used in those assays, including the hardware and software, were neither standard nor universal, which might impede the extensive application of the habituation learning assays. Here, we developed an alternative method for studying the habituation learning behavior in larval zebrafish using the ViewPoint system.

CONCLUSIONS

Our study provided an alternative method for studying the habituation learning behavior in larval zebrafish.

Leveraging big data to uncover the eco-evolutionary factors shaping behavioural development

Mapping the eco-evolutionary factors shaping the development of animals' behavioural phenotypes remains a great challenge. Recent advances in 'big behavioural data' research-the high-resolution tracking of individuals and the harnessing of that data with powerful analytical tools-have vastly improved our ability to measure and model developing behavioural phenotypes. Applied to the study of behavioural ontogeny, the unfolding of whole behavioural repertoires can be mapped in unprecedented detail with relative ease. This overcomes long-standing experimental bottlenecks and heralds a surge of studies that more finely define and explore behavioural-experiential trajectories across development. In this review, we first provide a brief guide to state-of-the-art approaches that allow the collection and analysis of high-resolution behavioural data across development. We then outline how such approaches can be used to address key issues regarding the ecological and evolutionary factors shaping behavioural development: developmental feedbacks between behaviour and underlying states, early life effects and behavioural transitions, and information integration across development.

From perception to behavior: The neural circuits underlying prey hunting in larval zebrafish

A key challenge for neural systems is to extract relevant information from the environment and make appropriate behavioral responses. The larval zebrafish offers an exciting opportunity for studying these sensing processes and sensory-motor transformations. Prey hunting is an instinctual behavior of zebrafish that requires the brain to extract and combine different attributes of the sensory input and form appropriate motor outputs. Due to its small size and transparency the larval zebrafish brain allows optical recording of whole-brain activity to reveal the neural mechanisms involved in prey hunting and capture. In this review we discuss how the larval zebrafish brain processes visual information to identify and locate prey, the neural circuits governing the generation of motor commands in response to prey, how hunting behavior can be modulated by internal states and experience, and some outstanding questions for the field.

Social behavioral profiling by unsupervised deep learning reveals a stimulative effect of dopamine D3 agonists on zebrafish sociality

It has been a major challenge to systematically evaluate and compare how pharmacological perturbations influence social behavioral outcomes. Although some pharmacological agents are known to alter social behavior, precise description and quantification of such effects have proven difficult. We developed a scalable social behavioral assay for zebrafish named ZeChat based on unsupervised deep learning to characterize sociality at high resolution. High-dimensional and dynamic social behavioral phenotypes are automatically classified using this method. By screening a neuroactive compound library, we found that different classes of chemicals evoke distinct patterns of social behavioral fingerprints. By examining these patterns, we discovered that dopamine D3 agonists possess a social stimulative effect on zebrafish. The D3 agonists pramipexole, piribedil, and 7-hydroxy-DPAT-HBr rescued social deficits in a valproic-acid-induced zebrafish autism model. The ZeChat platform provides a promising approach for dissecting the pharmacology of social behavior and discovering novel social-modulatory compounds.

A Critical Review of Zebrafish Neurological Disease Models-1. The Premise: Neuroanatomical, Cellular and Genetic Homology and Experimental Tractability

The last decade has seen a dramatic rise in the number of genes linked to neurological disorders, necessitating new models to explore underlying mechanisms and to test potential therapies. Over a similar period, many laboratories adopted zebrafish as a tractable model for studying brain development, defining neural circuits and performing chemical screens. Here we discuss strengths and limitations of using the zebrafish system to model neurological disorders. The underlying premise for many disease models is the high degree of homology between human and zebrafish genes, coupled with the conserved vertebrate Bauplan and repertoire of neurochemical signaling molecules. Yet, we caution that important evolutionary divergences often limit the extent to which human symptoms can be modeled meaningfully in zebrafish. We outline advances in genetic technologies that allow human mutations to be reproduced faithfully in zebrafish. Together with methods that visualize the development and function of neuronal pathways at the single cell level, there is now an unprecedented opportunity to understand how disease-associated genetic changes disrupt neural circuits, a level of analysis that is ideally suited to uncovering pathogenic changes in human brain disorders.

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.

Gigapixel imaging with a novel multi-camera array microscope

The dynamics of living organisms are organized across many spatial scales. However, current cost-effective imaging systems can measure only a subset of these scales at once. We have created a scalable multi-camera array microscope (MCAM) that enables comprehensive high-resolution recording from multiple spatial scales simultaneously, ranging from structures that approach the cellular scale to large-group behavioral dynamics. By collecting data from up to 96 cameras, we computationally generate gigapixel-scale images and movies with a field of view over hundreds of square centimeters at an optical resolution of 18 µm. This allows us to observe the behavior and fine anatomical features of numerous freely moving model organisms on multiple spatial scales, including larval zebrafish, fruit flies, nematodes, carpenter ants, and slime mold. Further, the MCAM architecture allows stereoscopic tracking of the z-position of organisms using the overlapping field of view from adjacent cameras. Overall, by removing the bottlenecks imposed by single-camera image acquisition systems, the MCAM provides a powerful platform for investigating detailed biological features and behavioral processes of small model organisms across a wide range of spatial scales.

Optic flow in the natural habitats of zebrafish supports spatial biases in visual self-motion estimation

Animals benefit from knowing if and how they are moving. Across the animal kingdom, sensory information in the form of optic flow over the visual field is used to estimate self-motion. However, different species exhibit strong spatial biases in how they use optic flow. Here, we show computationally that noisy natural environments favor visual systems that extract spatially biased samples of optic flow when estimating self-motion. The performance associated with these biases, however, depends on interactions between the environment and the animal's brain and behavior. Using the larval zebrafish as a model, we recorded natural optic flow associated with swimming trajectories in the animal's habitat with an omnidirectional camera mounted on a mechanical arm. An analysis of these flow fields suggests that lateral regions of the lower visual field are most informative about swimming speed. This pattern is consistent with the recent findings that zebrafish optomotor responses are preferentially driven by optic flow in the lateral lower visual field, which we extend with behavioral results from a high-resolution spherical arena. Spatial biases in optic-flow sampling are likely pervasive because they are an effective strategy for determining self-motion in noisy natural environments.

An analysis pipeline to compare explorative locomotion across fish species

Recently, we introduced a powerful approach that leverages differences in swimming behaviors of two closely related fish species to identify previously unreported locomotion-related neuronal correlates. Here, we present this analysis approach applicable for any species of fish to compare their short and long timescale swimming kinematics. We describe steps for data collection and cleaning, followed by the calculation of short timescale kinematics using half tail beats and the analysis of long timescale kinematics using mean square displacement and heading decorrelation. For complete details on the use and execution of this protocol, please refer to Rajan et al. (2022).¹.

Quantification of functional recovery in a larval zebrafish model of spinal cord injury

Human spinal cord injury (SCI) is characterized by permanent loss of damaged axons, resulting in chronic disability. In contrast, zebrafish can regenerate axonal projections following central nervous system injury and re-establish synaptic contacts with distant targets; elucidation of the underlying molecular events is an important goal with translational potential for improving outcomes in SCI patients. We generated transgenic zebrafish with GFP-labeled axons and transected their spinal cords at 10 days post-fertilization. Intravital confocal microscopy revealed robust axonal regeneration following the procedure, with abundant axons bridging the transection site by 48 h post-injury. In order to analyze neurological function in this model, we developed and validated new open-source software to measure zebrafish lateral trunk curvature during propulsive and turning movements at high temporal resolution. Immediately following spinal cord transection, axial movements were dramatically decreased caudal to the lesion site, but preserved rostral to the injury, suggesting the induction of motor paralysis below the transection level. Over the subsequent 96 h, the magnitude of movements caudal to the lesion recovered to baseline, but the rate of change of truncal curvature did not fully recover, suggesting incomplete restoration of caudal strength over this time course. Quantification of both morphological and functional recovery following SCI will be important for the analysis of axonal regeneration and downstream events necessary for restoration of motor function. An extensive array of genetic and pharmacological interventions can be deployed in the larval zebrafish model to investigate the underlying molecular mechanisms.

The evolutionary trajectory of drosophilid walking

Neural circuits must both execute the behavioral repertoire of individuals and account for behavioral variation across species. Understanding how this variation emerges over evolutionary time requires large-scale phylogenetic comparisons of behavioral repertoires. Here, we describe the evolution of walking in fruit flies by capturing high-resolution, unconstrained movement from 13 species and 15 strains of drosophilids. We find that walking can be captured in a universal behavior space, the structure of which is evolutionarily conserved. However, the occurrence of and transitions between specific movements have evolved rapidly, resulting in repeated convergent evolution in the temporal structure of locomotion. Moreover, a meta-analysis demonstrates that many behaviors evolve more rapidly than other traits. Thus, the architecture and physiology of locomotor circuits can execute precise individual movements in one species and simultaneously support rapid evolutionary changes in the temporal ordering of these modular elements across clades.

Visual recognition of social signals by a tectothalamic neural circuit

Social affiliation emerges from individual-level behavioural rules that are driven by conspecific signals^1-5. Long-distance attraction and short-distance repulsion, for example, are rules that jointly set a preferred interanimal distance in swarms^6-8. However, little is known about their perceptual mechanisms and executive neural circuits³. Here we trace the neuronal response to self-like biological motion^9,10, a visual trigger for affiliation in developing zebrafish^2,11. Unbiased activity mapping and targeted volumetric two-photon calcium imaging revealed 21 activity hotspots distributed throughout the brain as well as clustered biological-motion-tuned neurons in a multimodal, socially activated nucleus of the dorsal thalamus. Individual dorsal thalamus neurons encode local acceleration of visual stimuli mimicking typical fish kinetics but are insensitive to global or continuous motion. Electron microscopic reconstruction of dorsal thalamus neurons revealed synaptic input from the optic tectum and projections into hypothalamic areas with conserved social function^12-14. Ablation of the optic tectum or dorsal thalamus selectively disrupted social attraction without affecting short-distance repulsion. This tectothalamic pathway thus serves visual recognition of conspecifics, and dissociates neuronal control of attraction from repulsion during social affiliation, revealing a circuit underpinning collective behaviour.

Neural mechanisms underlying the temporal organization of naturalistic animal behavior

Naturalistic animal behavior exhibits a strikingly complex organization in the temporal domain, with variability arising from at least three sources: hierarchical, contextual, and stochastic. What neural mechanisms and computational principles underlie such intricate temporal features? In this review, we provide a critical assessment of the existing behavioral and neurophysiological evidence for these sources of temporal variability in naturalistic behavior. Recent research converges on an emergent mechanistic theory of temporal variability based on attractor neural networks and metastable dynamics, arising via coordinated interactions between mesoscopic neural circuits. We highlight the crucial role played by structural heterogeneities as well as noise from mesoscopic feedback loops in regulating flexible behavior. We assess the shortcomings and missing links in the current theoretical and experimental literature and propose new directions of investigation to fill these gaps.

Zebrafish behavior as a gateway to nervous system assembly and plasticity

Nervous system assembly relies on a diversity of cellular processes ranging from dramatic tissue reorganization to local, subcellular changes all driven by precise molecular programs. Combined, these processes culminate in an animal's ability to plan and execute behaviors. Animal behavior can, therefore, serve as a functional readout of nervous system development. Benefitting from an expansive and growing set of molecular and imaging tools paired with an ever-growing number of assays of diverse behaviors, the zebrafish system has emerged as an outstanding platform at the intersection of nervous system assembly, plasticity and behavior. Here, we summarize recent advancements in the field, including how developing neural circuits are refined to shape complex behaviors and plasticity.

Voltage imaging identifies spinal circuits that modulate locomotor adaptation in zebrafish

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

Evolutionary divergence of locomotion in two related vertebrate species

Locomotion exists in diverse forms in nature; however, little is known about how closely related species with similar neuronal circuitry can evolve different navigational strategies to explore their environments. Here, we investigate this question by comparing divergent swimming pattern in larval Danionella cerebrum (DC) and zebrafish (ZF). We show that DC displays long continuous swimming events when compared with the short burst-and-glide swimming in ZF. We reveal that mesencephalic locomotion maintenance neurons in the midbrain are sufficient to cause this increased swimming. Moreover, we propose that the availability of dissolved oxygen and timing of swim bladder inflation drive the observed differences in the swim pattern. Our findings uncover the neural substrate underlying the evolutionary divergence of locomotion and its adaptation to their environmental constraints.

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks

Humans learn movements naturally, but it takes a lot of time and training to achieve expert performance in motor skills. In this review, we show how modern technologies can support people in learning new motor skills. First, we introduce important concepts in motor control, motor learning and motor skill learning. We also give an overview about the rapid expansion of machine learning algorithms and sensor technologies for human motion analysis. The integration between motor learning principles, machine learning algorithms and recent sensor technologies has the potential to develop AI-guided assistance systems for motor skill training. We give our perspective on this integration of different fields to transition from motor learning research in laboratory settings to real world environments and real world motor tasks and propose a stepwise approach to facilitate this transition.

Fish personality affects their exposure to microplastics

Microplastics (MPs) have been found in nearly half of the fish samples collected from the field worldwide. There are feeding behavior differences caused by animal personality among fish individuals. However, how personality affects the exposure of fish to microplastics is unclear. In this study, adult zebrafish were used as a model to study the effects of fish personality (boldness) on MPs exposure. Experiment showed that zebrafish captured MPs actively when MPs were added to the system but spit part of the MPs out after capturing. Bold zebrafish had higher feeding activities, captured MPs more frequently and swallowed more MPs than shy zebrafish. Zebrafish ingested brine shrimp preferentially when both brine shrimp and MPs were provided at the same time, and less MPs were swallowed in co-ingestion situation. Spearman correlation analysis indicated that there is a significantly positive correlation between boldness and feeding MPs frequency. Results of this work demonstrate that zebrafish mistake MPs as food and capture them actively but can discriminate MPs as inedible substances after ingestion and spit part of the MPs out. Bold zebrafish have a higher MP exposure burden than shy zebrafish due to their difference in feeding behaviors. The influence of fish personality needs to be considered for future toxicity studies and risk assessment of fish exposed to MPs.

A lexical approach for identifying behavioural action sequences

Animals display characteristic behavioural patterns when performing a task, such as the spiraling of a soaring bird or the surge-and-cast of a male moth searching for a female. Identifying such recurring sequences occurring rarely in noisy behavioural data is key to understanding the behavioural response to a distributed stimulus in unrestrained animals. Existing models seek to describe the dynamics of behaviour or segment individual locomotor episodes rather than to identify the rare and transient sequences of locomotor episodes that make up the behavioural response. To fill this gap, we develop a lexical, hierarchical model of behaviour. We designed an unsupervised algorithm called "BASS" to efficiently identify and segment recurring behavioural action sequences transiently occurring in long behavioural recordings. When applied to navigating larval zebrafish, BASS extracts a dictionary of remarkably long, non-Markovian sequences consisting of repeats and mixtures of slow forward and turn bouts. Applied to a novel chemotaxis assay, BASS uncovers chemotactic strategies deployed by zebrafish to avoid aversive cues consisting of sequences of fast large-angle turns and burst swims. In a simulated dataset of soaring gliders climbing thermals, BASS finds the spiraling patterns characteristic of soaring behaviour. In both cases, BASS succeeds in identifying rare action sequences in the behaviour deployed by freely moving animals. BASS can be easily incorporated into the pipelines of existing behavioural analyses across diverse species, and even more broadly used as a generic algorithm for pattern recognition in low-dimensional sequential data.

Precise visuomotor transformations underlying collective behavior in larval zebrafish

Complex schooling behaviors result from local interactions among individuals. Yet, how sensory signals from neighbors are analyzed in the visuomotor stream of animals is poorly understood. Here, we studied aggregation behavior in larval zebrafish and found that over development larvae transition from overdispersed groups to tight shoals. Using a virtual reality assay, we characterized the algorithms fish use to transform visual inputs from neighbors into movement decisions. We found that young larvae turn away from virtual neighbors by integrating and averaging retina-wide visual occupancy within each eye, and by using a winner-take-all strategy for binocular integration. As fish mature, their responses expand to include attraction to virtual neighbors, which is based on similar algorithms of visual integration. Using model simulations, we show that the observed algorithms accurately predict group structure over development. These findings allow us to make testable predictions regarding the neuronal circuits underlying collective behavior in zebrafish.

Mate Choice, Sex Roles and Sexual Cognition: Neuronal Prerequisites Supporting Cognitive Mate Choice

Across taxa, mate choice is a highly selective process involving both intra- and intersexual selection processes aiming to pass on one’s genes, making mate choice a pivotal tool of sexual selection. Individuals adapt mate choice behavior dynamically in response to environmental and social changes. These changes are perceived sensorily and integrated on a neuronal level, which ultimately leads to an adequate behavioral response. Along with perception and prior to an appropriate behavioral response, the choosing sex has (1) to recognize and discriminate between the prospective mates and (2) to be able to assess and compare their performance in order to make an informed decision. To do so, cognitive processes allow for the simultaneous processing of multiple information from the (in-) animate environment as well as from a variety of both sexual and social (but non-sexual) conspecific cues. Although many behavioral aspects of cognition on one side and of mate choice displays on the other are well understood, the interplay of neuronal mechanisms governing both determinants, i.e., governing cognitive mate choice have been described only vaguely. This review aimed to throw a spotlight on neuronal prerequisites, networks and processes supporting the interaction between mate choice, sex roles and sexual cognition, hence, supporting cognitive mate choice. How does neuronal activity differ between males and females regarding social cognition? Does sex or the respective sex role within the prevailing mating system mirror at a neuronal level? How does cognitive competence affect mate choice? Conversely, how does mate choice affect the cognitive abilities of both sexes? Benefitting from studies using different neuroanatomical techniques such as neuronal activity markers, differential coexpression or candidate gene analyses, modulatory effects of neurotransmitters and hormones, or imaging techniques such as fMRI, there is ample evidence pointing to a reflection of sex and the respective sex role at the neuronal level, at least in individual brain regions. Moreover, this review aims to summarize evidence for cognitive abilities influencing mate choice and vice versa. At the same time, new questions arise centering the complex relationship between neurobiology, cognition and mate choice, which we will perhaps be able to answer with new experimental techniques.

CNS Hypomyelination Disrupts Axonal Conduction and Behavior in Larval Zebrafish

Myelination is essential for central nervous system (CNS) formation, health and function. As a model organism, larval zebrafish have been extensively employed to investigate the molecular and cellular basis of CNS myelination, because of their genetic tractability and suitability for non-invasive live cell imaging. However, it has not been assessed to what extent CNS myelination affects neural circuit function in zebrafish larvae, prohibiting the integration of molecular and cellular analyses of myelination with concomitant network maturation. To test whether larval zebrafish might serve as a suitable platform with which to study the effects of CNS myelination and its dysregulation on circuit function, we generated zebrafish myelin regulatory factor (myrf) mutants with CNS-specific hypomyelination and investigated how this affected their axonal conduction properties and behavior. We found that myrf mutant larvae exhibited increased latency to perform startle responses following defined acoustic stimuli. Furthermore, we found that hypomyelinated animals often selected an impaired response to acoustic stimuli, exhibiting a bias toward reorientation behavior instead of the stimulus-appropriate startle response. To begin to study how myelination affected the underlying circuitry, we established electrophysiological protocols to assess various conduction properties along single axons. We found that the hypomyelinated myrf mutants exhibited reduced action potential conduction velocity and an impaired ability to sustain high-frequency action potential firing. This study indicates that larval zebrafish can be used to bridge molecular and cellular investigation of CNS myelination with multiscale assessment of neural circuit function.SIGNIFICANCE STATEMENT Myelination of CNS axons is essential for their health and function, and it is now clear that myelination is a dynamic life-long process subject to modulation by neuronal activity. However, it remains unclear precisely how changes to myelination affects animal behavior and underlying action potential conduction along axons in intact neural circuits. In recent years, zebrafish have been employed to study cellular and molecular mechanisms of myelination, because of their relatively simple, optically transparent, experimentally tractable vertebrate nervous system. Here we find that changes to myelination alter the behavior of young zebrafish and action potential conduction along individual axons, providing a platform to integrate molecular, cellular, and circuit level analyses of myelination using this model.

Contributions of Luminance and Motion to Visual Escape and Habituation in Larval Zebrafish

Animals from insects to humans perform visual escape behavior in response to looming stimuli, and these responses habituate if looms are presented repeatedly without consequence. While the basic visual processing and motor pathways involved in this behavior have been described, many of the nuances of predator perception and sensorimotor gating have not. Here, we have performed both behavioral analyses and brain-wide cellular-resolution calcium imaging in larval zebrafish while presenting them with visual loom stimuli or stimuli that selectively deliver either the movement or the dimming properties of full loom stimuli. Behaviorally, we find that, while responses to repeated loom stimuli habituate, no such habituation occurs when repeated movement stimuli (in the absence of luminance changes) are presented. Dim stimuli seldom elicit escape responses, and therefore cannot habituate. Neither repeated movement stimuli nor repeated dimming stimuli habituate the responses to subsequent full loom stimuli, suggesting that full looms are required for habituation. Our calcium imaging reveals that motion-sensitive neurons are abundant in the brain, that dim-sensitive neurons are present but more rare, and that neurons responsive to both stimuli (and to full loom stimuli) are concentrated in the tectum. Neurons selective to full loom stimuli (but not to movement or dimming) were not evident. Finally, we explored whether movement- or dim-sensitive neurons have characteristic response profiles during habituation to full looms. Such functional links between baseline responsiveness and habituation rate could suggest a specific role in the brain-wide habituation network, but no such relationships were found in our data. Overall, our results suggest that, while both movement- and dim-sensitive neurons contribute to predator escape behavior, neither plays a specific role in brain-wide visual habituation networks or in behavioral habituation.