From behavior to circuit modeling of light-seeking navigation in zebrafish larvae

Adaptive robustness through incoherent signaling mechanisms in a regenerative brain

Animal behavior emerges from collective dynamics of neurons, making it vulnerable to damage. Paradoxically, many organisms exhibit a remarkable ability to maintain significant behavior even after large-scale neural injury. Molecular underpinnings of this extreme robustness remain largely unknown. Here, we develop a quantitative pipeline to measure long-lasting latent states in planarian flatworm behaviors during whole-brain regeneration. By combining >20,000 animal trials with neural network modeling, we show that long-range volumetric peptidergic signals allow the planarian to rapidly restore coarse behavior output after large perturbations to the nervous system, while slow restoration of small-molecule neuromodulator functions refines precision. This relies on the different time and length scales of neuropeptide and small-molecule transmission to generate incoherent patterns of neural activity that competitively regulate behavior. Controlling behavior through opposing communication mechanisms creates a more robust system than either alone and may serve as a generalizable approach for constructing robust neural networks.

A brainstem circuit for gravity-guided vertical navigation

The sensation of gravity anchors our perception of the environment and is crucial for navigation. However, the neural circuits that transform gravity into commands for navigation are undefined. We first determined that larval zebrafish (Danio rerio) navigate vertically by maintaining a consistent heading across a series of upward climb or downward dive bouts. Gravity-blind mutant fish swim with more variable heading and excessive veering, leading to inefficient vertical navigation. After targeted photoablation of ascending vestibular neurons and spinal projecting midbrain neurons, but not vestibulospinal neurons, vertical navigation was impaired. These data define a sensorimotor circuit that uses evolutionarily-conserved brainstem architecture to transform gravitational signals into persistent heading for vertical navigation. The work lays a foundation to understand how vestibular inputs allow animals to move efficiently through their environment.

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.

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.

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.

Adaptive robustness through incoherent signaling mechanisms in a regenerative brain

Animal behavior emerges from collective dynamics of interconnected neurons, making it vulnerable to connectome damage. Paradoxically, many organisms maintain significant behavioral output after large-scale neural injury. Molecular underpinnings of this extreme robustness remain largely unknown. Here, we develop a quantitative behavioral analysis pipeline to measure previously uncharacterized long-lasting latent memory states in planarian flatworms during whole-brain regeneration. By combining >20,000 animal trials with neural population dynamic modeling, we show that long-range volumetric peptidergic signals allow the planarian to rapidly reestablish latent states and restore coarse behavior after large structural perturbations to the nervous system, while small-molecule neuromodulators gradually refine the precision. The different time and length scales of neuropeptide and small-molecule transmission generate incoherent patterns of neural activity which competitively regulate behavior and memory. Controlling behavior through opposing communication mechanisms creates a more robust system than either alone and may serve as a generic approach to construct robust neural networks.

Evaluation of drug seeking behavior on nicotine conditioned place preference in zebrafish

Seeking of drugs is commonly evaluated in a specific environment for assessing drug preference. However, cognitive strategies involved in drug seeking are mostly unknown. To assess the strength of environmental cues that can be associated with nicotine in the zebrafish brain reward circuitry, we have designed herein a modified conditioned place preference (CPP) paradigm. This task was devised to identify salient environmental cues relevant for strong nicotine-environment association and drug seeking induction. During test sessions, background colors of the CPP tank chambers were shifted and preference for colors associated to nicotine was assessed. We have compared several tank designs and different compartment colors. Our findings indicated that zebrafish seeking behavior was strongly dependent on compartment color shades. Combination of red and yellow environments, which were preferred and avoided compartments, respectively, was the most effective design presenting the highest CPP-score. Interestingly, animals that stayed for longer periods in the environment conditioned to nicotine during a first testing interval were also able to follow the background color shade conditioned to nicotine to the other compartment immediately after background colors were relocated between compartments. During a second testing period, zebrafish also stayed for longer periods in the colored compartment paired to nicotine during conditioning. These findings suggest that under salient environmental conditions, zebrafish voluntarily followed a shifting visual cue previously associated with nicotine delivery. Furthermore, our findings indicate that zebrafish exhibit spatial associative learning and memory, which generates a repertoire of conspicuous locomotor behaviors induced by nicotine preference in the CPP task.

A scalable assay for chemical preference of small freshwater fish

Sensing the chemical world is of primary importance for aquatic organisms, and small freshwater fish are increasingly used in toxicology, ethology, and neuroscience by virtue of their ease of manipulation, tissue imaging amenability, and genetic tractability. However, precise behavioral analyses are generally challenging to perform due to the lack of knowledge of what chemical the fish are exposed to at any given moment. Here we developed a behavioral assay and a specific infrared dye to probe the preference of young zebrafish for virtually any compound. We found that the innate aversion of zebrafish to citric acid is not mediated by modulation of the swim but rather by immediate avoidance reactions when the product is sensed and that the preference of juvenile zebrafish for ATP changes from repulsion to attraction during successive exposures. We propose an information-based behavioral model for which an exploration index emerges as a relevant behavioral descriptor, complementary to the standard preference index. Our setup features a high versatility in protocols and is automatic and scalable, which paves the way for high-throughput preference compound screening at different ages.

BDE-99 Disrupts the Photoreceptor Patterning of Zebrafish Larvae via Transcription Factor six7

Proper visual function is essential for collecting environmental information and supporting the decision-making in the central nervous system and is therefore tightly associated with wildlife survival and human health. Polybrominated diphenyl ethers (PBDEs) were reported to impair zebrafish vision development, and thyroid hormone (TH) signaling was suspected as the main contributor. In this study, a pentabrominated PBDE, BDE-99, was chosen to further explore the action mechanism of PBDEs on the disruption of zebrafish color vision. The results showed that BDE-99 could impair multiple photoreceptors in the retina and disturb the behavior guided by the color vision of zebrafish larvae at 120 h post-fertilization. Although the resulting alteration in photoreceptor patterning highly resembled the effects of 3,3',5-triiodo-l-thyroine, introducing the antagonist for TH receptors was unable to fully recover the alteration, which suggested the involvement of other potential regulatory factors. By modulating the expression of six7, a key inducer of middle-wavelength opsins, we demonstrated that six7, not THs, dominated the photoreceptor patterning in the disruption of BDE-99. Our work promoted the understanding of the regulatory role of six7 in the process of photoreceptor patterning and proposed a novel mechanism for the visual toxicity of PBDEs.

Thermal modulation of Zebrafish exploratory statistics reveals constraints on individual behavioral variability

BACKGROUND

Variability is a hallmark of animal behavior. It contributes to survival by endowing individuals and populations with the capacity to adapt to ever-changing environmental conditions. Intra-individual variability is thought to reflect both endogenous and exogenous modulations of the neural dynamics of the central nervous system. However, how variability is internally regulated and modulated by external cues remains elusive. Here, we address this question by analyzing the statistics of spontaneous exploration of freely swimming zebrafish larvae and by probing how these locomotor patterns are impacted when changing the water temperatures within an ethologically relevant range.

RESULTS

We show that, for this simple animal model, five short-term kinematic parameters - interbout interval, turn amplitude, travelled distance, turn probability, and orientational flipping rate - together control the long-term exploratory dynamics. We establish that the bath temperature consistently impacts the means of these parameters, but leave their pairwise covariance unchanged. These results indicate that the temperature merely controls the sampling statistics within a well-defined kinematic space delineated by this robust statistical structure. At a given temperature, individual animals explore the behavioral space over a timescale of tens of minutes, suggestive of a slow internal state modulation that could be externally biased through the bath temperature. By combining these various observations into a minimal stochastic model of navigation, we show that this thermal modulation of locomotor kinematics results in a thermophobic behavior, complementing direct gradient-sensing mechanisms.

CONCLUSIONS

This study establishes the existence of a well-defined locomotor space accessible to zebrafish larvae during spontaneous exploration, and quantifies self-generated modulation of locomotor patterns. Intra-individual variability reflects a slow diffusive-like probing of this space by the animal. The bath temperature in turn restricts the sampling statistics to sub-regions, endowing the animal with basic thermophobicity. This study suggests that in zebrafish, as well as in other ectothermic animals, ambient temperature could be used to efficiently manipulate internal states in a simple and ethological way.

Unsupervised quantification of naturalistic animal behaviors for gaining insight into the brain

Neural computation has evolved to optimize the behaviors that enable our survival. Although much previous work in neuroscience has focused on constrained task behaviors, recent advances in computer vision are fueling a trend toward the study of naturalistic behaviors. Automated tracking of fine-scale behaviors is generating rich datasets for animal models including rodents, fruit flies, zebrafish, and worms. However, extracting meaning from these large and complex data often requires sophisticated computational techniques. Here we review the latest methods and modeling approaches providing new insights into the brain from behavior. We focus on unsupervised methods for identifying stereotyped behaviors and for resolving details of the structure and dynamics of behavioral sequences.

The Effects of Early Life Stress on the Brain and Behaviour: Insights From Zebrafish Models

The early life period represents a window of increased vulnerability to stress, during which exposure can lead to long-lasting effects on brain structure and function. This stress-induced developmental programming may contribute to the behavioural changes observed in mental illness. In recent decades, rodent studies have significantly advanced our understanding of how early life stress (ELS) affects brain development and behaviour. These studies reveal that ELS has long-term consequences on the brain such as impairment of adult hippocampal neurogenesis, altering learning and memory. Despite such advances, several key questions remain inadequately answered, including a comprehensive overview of brain regions and molecular pathways that are altered by ELS and how ELS-induced molecular changes ultimately lead to behavioural changes in adulthood. The zebrafish represents a novel ELS model, with the potential to contribute to answering some of these questions. The zebrafish offers some important advantages such as the ability to non-invasively modulate stress hormone levels in a whole animal and to visualise whole brain activity in freely behaving animals. This review discusses the current status of the zebrafish ELS field and its potential as a new ELS model.

Algorithms underlying flexible phototaxis in larval zebrafish

To thrive, organisms must maintain physiological and environmental variables in suitable ranges. Given that these variables undergo constant fluctuations over varying time scales, how do biological control systems maintain control over these values? We explored this question in the context of phototactic behavior in larval zebrafish. We demonstrate that larval zebrafish use phototaxis to maintain environmental luminance at a set point, that the value of this set point fluctuates on a time scale of seconds when environmental luminance changes, and that it is determined by calculating the mean input across both sides of the visual field. These results expand on previous studies of flexible phototaxis in larval zebrafish; they suggest that larval zebrafish exert homeostatic control over the luminance of their surroundings, and that feedback from the surroundings drives allostatic changes to the luminance set point. As such, we describe a novel behavioral algorithm with which larval zebrafish exert control over a sensory variable.

Whole-brain interactions underlying zebrafish behavior

Detailed quantification of neural dynamics across the entire brain will be the key to genuinely understanding perception and behavior. With the recent developments in microscopy and biosensor engineering, the zebrafish has made a grand entrance in neuroscience as its small size and optical transparency enable imaging access to its entire brain at cellular and even subcellular resolution. However, until recently many neurobiological insights were largely correlational or provided little mechanistic insight into the brain-wide population dynamics generated by diverse types of neurons. Now with increasingly sophisticated behavioral, imaging, and causal intervention paradigms, zebrafish are revealing how entire vertebrate brains function. Here we review recent research that fulfills promises made by the early wave of technical advances. These studies reveal new features of brain-wide neural processing and the importance of integrative investigation and computational modelling. Moreover, we outline the future tools necessary for solving broader brain-scale circuit problems.

PiVR: An affordable and versatile closed-loop platform to study unrestrained sensorimotor behavior

Tools enabling closed-loop experiments are crucial to delineate causal relationships between the activity of genetically labeled neurons and specific behaviors. We developed the Raspberry Pi Virtual Reality (PiVR) system to conduct closed-loop optogenetic stimulation of neural functions in unrestrained animals. PiVR is an experimental platform that operates at high temporal resolution (70 Hz) with low latencies (<30 milliseconds), while being affordable ( Collapse

Key Words Collapse

MESH Headings

• Animals
• Behavior, Animal/physiology
• Chemotaxis
• Drosophila melanogaster/physiology
• Larva/physiology
• Light
• Locomotion
• Male
• Neurons/physiology
• Odorants
• Optogenetics
• Sensation/physiology
• Sensorimotor Cortex/physiology
• Software
• Taste/physiology
• Virtual Reality
• Zebrafish

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Grants

• R01 NS113048 NINDS NIH HHS
• R25 GM067110 NIGMS NIH HHS
• National Science Foundation
• National Institute of Health
• Gordon and Betty Moore Foundation

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Affiliation(s)

David Tadres Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, California, United States of America Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, California, United States of America Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland

Matthieu Louis Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, California, United States of America Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, California, United States of America Department of Physics, University of California, Santa Barbara, Santa Barbara, California, United States of America

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From behavior to circuit modeling of light-seeking navigation in zebrafish larvae

Bridging brain-scale circuit dynamics and organism-scale behavior is a central challenge in neuroscience. It requires the concurrent development of minimal behavioral and neural circuit models that can quantitatively capture basic sensorimotor operations. Here, we focus on light-seeking navigation in zebrafish larvae. Using a virtual reality assay, we first characterize how motor and visual stimulation sequences govern the selection of discrete swim-bout events that subserve the fish navigation in the presence of a distant light source. These mechanisms are combined into a comprehensive Markov-chain model of navigation that quantitatively predicts the stationary distribution of the fish’s body orientation under any given illumination profile. We then map this behavioral description onto a neuronal model of the ARTR, a small neural circuit involved in the orientation-selection of swim bouts. We demonstrate that this visually-biased decision-making circuit can capture the statistics of both spontaneous and contrast-driven navigation.

All animals with the ability to move use sensory signals to help them navigate towards areas that seem better than their current location. Such areas might contain desirable things like food and mates, or they might allow an animal to escape from threats such as predators. But how the brain gives rise to this navigation behavior is unclear.

Karpenko et al. have now obtained insights into the underlying mechanism by studying a behavior in zebrafish larvae called phototaxis. Phototaxis is the tendency to move in response to light. The advantage of using zebrafish larvae to study this behavior is that their brains are small and semi-transparent. This makes it possible to record the activity of almost every neuron. As a result, an individual’s brain activity can be mapped on to their behavior more precisely than in most other species.

To probe how visual cues influence fish behavior, Karpenko et al. exposed individual fish to a carefully controlled virtual light source and then tracked their movements with a camera. The fish used two strategies to move towards the light. They selected their next movement based partly on the difference in the amount of light reaching each of their eyes, and partly on the change in overall brightness with each swim movement. Karpenko et al. used this information to build a numerical model of fish phototaxis, and to show how a simple brain circuit could generate this behavior.

Species whose brains differ in size and structure may nevertheless develop similar strategies to perform similar tasks. By quantifying a generic behavior in a simple animal model, this study could provide insights into comparable behaviors in other species. In addition, the study suggests a simple mechanism for how animals select actions on the basis of sensory signals, which may also be relevant to other species and other tasks.