Automated tracking of animal posture and movement during exploration and sensory orientation behaviors

Linking neural circuits to the mechanics of animal behavior in Drosophila larval locomotion

The motions that make up animal behavior arise from the interplay between neural circuits and the mechanical parts of the body. Therefore, in order to comprehend the operational mechanisms governing behavior, it is essential to examine not only the underlying neural network but also the mechanical characteristics of the animal's body. The locomotor system of fly larvae serves as an ideal model for pursuing this integrative approach. By virtue of diverse investigation methods encompassing connectomics analysis and quantification of locomotion kinematics, research on larval locomotion has shed light on the underlying mechanisms of animal behavior. These studies have elucidated the roles of interneurons in coordinating muscle activities within and between segments, as well as the neural circuits responsible for exploration. This review aims to provide an overview of recent research on the neuromechanics of animal locomotion in fly larvae. We also briefly review interspecific diversity in fly larval locomotion and explore the latest advancements in soft robots inspired by larval locomotion. The integrative analysis of animal behavior using fly larvae could establish a practical framework for scrutinizing the behavior of other animal species.

CATER: Combined Animal Tracking & Environment Reconstruction

Quantifying the behavior of small animals traversing long distances in complex environments is one of the most difficult tracking scenarios for computer vision. Tiny and low-contrast foreground objects have to be localized in cluttered and dynamic scenes as well as trajectories compensated for camera motion and drift in multiple lengthy recordings. We introduce CATER, a novel methodology combining an unsupervised probabilistic detection mechanism with a globally optimized environment reconstruction pipeline enabling precision behavioral quantification in natural environments. Implemented as an easy to use and highly parallelized tool, we show its application to recover fine-scale motion trajectories, registered to a high-resolution image mosaic reconstruction, of naturally foraging desert ants from unconstrained field recordings. By bridging the gap between laboratory and field experiments, we gain previously unknown insights into ant navigation with respect to motivational states, previous experience, and current environments and provide an appearance-agnostic method applicable to study the behavior of a wide range of terrestrial species under realistic conditions.

Long-Term Observation of Locomotion of Drosophila Larvae Facilitates Feasibility of Food-Choice Assays

Animal behavior is reflected by locomotor patterns. To decipher the underlying neural circuitry locomotion has to be monitored over often longer time periods. Here a simple adaptation is described to constrain movement of third instar Drosophila larvae to a defined area and use Frustrated total internal reflection based imaging method (FIM) imaging to monitor larval movements up to 1 h. It is demonstrated that the combination of FIM imaging and long analysis periods facilitates the conduction of food choice assays and provides the means to easily quantify food preferences.

Anatomy and Neural Pathways Modulating Distinct Locomotor Behaviors in Drosophila Larva

The control of movements is a fundamental feature shared by all animals. At the most basic level, simple movements are generated by coordinated neural activity and muscle contraction patterns that are controlled by the central nervous system. How behavioral responses to various sensory inputs are processed and integrated by the downstream neural network to produce flexible and adaptive behaviors remains an intense area of investigation in many laboratories. Due to recent advances in experimental techniques, many fundamental neural pathways underlying animal movements have now been elucidated. For example, while the role of motor neurons in locomotion has been studied in great detail, the roles of interneurons in animal movements in both basic and noxious environments have only recently been realized. However, the genetic and transmitter identities of many of these interneurons remains unclear. In this review, we provide an overview of the underlying circuitry and neural pathways required by Drosophila larvae to produce successful movements. By improving our understanding of locomotor circuitry in model systems such as Drosophila, we will have a better understanding of how neural circuits in organisms with different bodies and brains lead to distinct locomotion types at the organism level. The understanding of genetic and physiological components of these movements types also provides directions to understand movements in higher organisms.

An assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor

The analysis of fish behavior in response to odor stimulation is a crucial component of the general study of cross-modal sensory integration in vertebrates. In zebrafish, the centrifugal pathway runs between the olfactory bulb and the neural retina, originating at the terminalis neuron in the olfactory bulb. Any changes in the ambient odor of a fish's environment warrant a change in visual sensitivity and can trigger mating-like behavior in males due to increased GnRH signaling in the terminalis neuron. Behavioral experiments to study this phenomenon are commonly conducted in a controlled environment where a video of the fish is recorded over time before and after the application of chemicals to the water. Given the subtleties of behavioral change, trained biologists are currently required to annotate such videos as part of a study. This process of manually analyzing the videos is time-consuming, requires multiple experts to avoid human error/bias and cannot be easily crowdsourced on the Internet. Machine learning algorithms from computer vision, on the other hand, have proven to be effective for video annotation tasks because they are fast, accurate, and, if designed properly, can be less biased than humans. In this work, we propose to automate the entire process of analyzing videos of behavior changes in zebrafish by using tools from computer vision, relying on minimal expert supervision. The overall objective of this work is to create a generalized tool to predict animal behaviors from videos using state-of-the-art deep learning models, with the dual goal of advancing understanding in biology and engineering a more robust and powerful artificial information processing system for biologists.

Development of an automatic turntable-type multiple T-maze device and observation of pill bug behavior

In recent years, various animal observation instruments have been developed to support long-term measurement and analysis of animal behaviors. This study proposes an automatic observation instrument that specializes for turning behaviors of pill bugs and aims to obtain new knowledge in the field of ethology. Pill bugs strongly tend to turn in the opposite direction of a preceding turn. This alternation of turning is called turn alternation reaction. However, a repetition of turns in the same direction is called turn repetition reaction and has been considered a malfunction of turn alternation. In this research, the authors developed an automatic turntable-type multiple T-maze device and observed the turning behavior of 34 pill bugs for 6 h to investigate whether turn repetition is a malfunction. As a result, most of the pill bug movements were categorized into three groups: sub-diffusion, Brownian motion, and Lévy walk. This result suggests that pill bugs do not continue turn alternation mechanically but elicit turn repetition moderately, which results in various movement patterns. In organisms with relatively simple nervous systems such as pill bugs, stereotypical behaviors such as turn alternation have been considered mechanical reactions and variant behaviors such as turn repetition have been considered malfunctions. However, our results suggest that a moderate generation of turn repetition is involved in the generation of various movement patterns. This study is expected to provide a new perspective on the conventional view of the behaviors of simple organisms.

Paired fruit flies synchronize behavior: Uncovering social interactions in Drosophila melanogaster

Social behaviors are ubiquitous and crucial to an animal's survival and success. The behaviors an animal performs in a social setting are affected by internal factors, inputs from the environment, and interactions with others. To quantify social behaviors, we need to measure both the stochastic nature of the behavior of isolated individuals and how this behavioral repertoire changes as a function of the environment and interactions between individuals. We probed the behavior of male and female fruit flies in a circular arena as individuals and within all possible pairings. By combining measurements of the animals' position in the arena with an unsupervised analysis of their behaviors, we define the effects of position in the environment and the presence of a partner on locomotion, grooming, singing, and other behaviors that make up an animal's repertoire. We find that geometric context tunes behavioral preference, pairs of animals synchronize their behavioral preferences across shared trials, and paired individuals display signatures of behavioral mimicry.

Rage Against the Machine: Advancing the study of aggression ethology via machine learning

RATIONALE

Aggression, comorbid with neuropsychiatric disorders, exhibits with diverse clinical presentations and places a significant burden on patients, caregivers, and society. This diversity is observed because aggression is a complex behavior that can be ethologically demarcated as either appetitive (rewarding) or reactive (defensive), each with its own behavioral characteristics, functionality, and neural basis that may transition from adaptive to maladaptive depending on genetic and environmental factors. There has been a recent surge in the development of preclinical animal models for studying appetitive aggression-related behaviors and identifying the neural mechanisms guiding their progression and expression. However, adoption of these procedures is often impeded by the arduous task of manually scoring complex social interactions. Manual observations are generally susceptible to observer drift, long analysis times, and poor inter-rater reliability, and are further incompatible with the sampling frequencies required of modern neuroscience methods.

OBJECTIVES

In this review, we discuss recent advances in the preclinical study of appetitive aggression in mice, paired with our perspective on the potential for machine learning techniques in producing automated, robust scoring of aggressive social behavior. We discuss critical considerations for implementing valid computer classifications within behavioral pharmacological studies.

KEY RESULTS

Open-source automated classification platforms can match or exceed the performance of human observers while removing the confounds of observer drift, bias, and inter-rater reliability. Furthermore, unsupervised approaches can identify previously uncharacterized aggression-related behavioral repertoires in model species.

DISCUSSION AND CONCLUSIONS

Advances in open-source computational approaches hold promise for overcoming current manual annotation caveats while also introducing and generalizing computational neuroethology to the greater behavioral neuroscience community. We propose that currently available open-source approaches are sufficient for overcoming the main limitations preventing wide adoption of machine learning within the context of preclinical aggression behavioral research.

Discriminating between sleep and exercise-induced fatigue using computer vision and behavioral genetics

Following prolonged swimming, Caenorhabditis elegans cycle between active swimming bouts and inactive quiescent bouts. Swimming is exercise for C. elegans and here we suggest that inactive bouts are a recovery state akin to fatigue. It is known that cGMP-dependent kinase (PKG) activity plays a conserved role in sleep, rest, and arousal. Using C. elegans EGL-4 PKG, we first validate a novel learning-based computer vision approach to automatically analyze C. elegans locomotory behavior and an edge detection program that is able to distinguish between activity and inactivity during swimming for long periods of time. We find that C. elegans EGL-4 PKG function impacts timing of exercise-induced quiescent (EIQ) bout onset, fractional quiescence, bout number, and bout duration, suggesting that previously described pathways are engaged during EIQ bouts. However, EIQ bouts are likely not sleep as animals are feeding during the majority of EIQ bouts. We find that genetic perturbation of neurons required for other C. elegans sleep states also does not alter EIQ dynamics. Additionally, we find that EIQ onset is sensitive to age and DAF-16 FOXO function. In summary, we have validated behavioral analysis software that enables a quantitative and detailed assessment of swimming behavior, including EIQ. We found novel EIQ defects in aged animals and animals with mutations in a gene involved in stress tolerance. We anticipate that further use of this software will facilitate the analysis of genes and pathways critical for fatigue and other C. elegans behaviors.

Studying neural circuits of decision-making in Drosophila larva

To study neural circuits underlying decisions, the model organism used for that purpose has to be simple enough to be able to dissect the circuitry neuron by neuron across the nervous system and in the same time complex enough to be able to perform different types of decisions. Here, I lay out the case: (1) that Drosophila larva is an advantageous model system that balances well these two requirements and (2) the insights gained from this model, assuming that circuit principles may be shared across species, can be used to advance our knowledge of neural circuit implementation of decision-making in general, including in more complex brains.

Data-driven analyses of motor impairments in animal models of neurological disorders

Behavior provides important insights into neuronal processes. For example, analysis of reaching movements can give a reliable indication of the degree of impairment in neurological disorders such as stroke, Parkinson disease, or Huntington disease. The analysis of such movement abnormalities is notoriously difficult and requires a trained evaluator. Here, we show that a deep neural network is able to score behavioral impairments with expert accuracy in rodent models of stroke. The same network was also trained to successfully score movements in a variety of other behavioral tasks. The neural network also uncovered novel movement alterations related to stroke, which had higher predictive power of stroke volume than the movement components defined by human experts. Moreover, when the regression network was trained only on categorical information (control = 0; stroke = 1), it generated predictions with intermediate values between 0 and 1 that matched the human expert scores of stroke severity. The network thus offers a new data-driven approach to automatically derive ratings of motor impairments. Altogether, this network can provide a reliable neurological assessment and can assist the design of behavioral indices to diagnose and monitor neurological disorders.

Efficacy of administered mesenchymal stem cells in the initiation and co-ordination of repair processes by resident disc cells in an ovine (Ovis aries) large destabilizing lesion model of experimental disc degeneration

BACKGROUND

Forty percent of low back pain cases are due to intervertebral disc degeneration (IVDD), with mesenchymal stem cells (MSCs) a reported treatment. We utilized an ovine IVDD model and intradiscal heterologous MSCs to determine therapeutic efficacy at different stages of IVDD.

METHODOLOGY

Three nonoperated control (NOC) sheep were used for MSC isolation. In 36 sheep, 6 × 20 mm annular lesions were made at three spinal levels using customized blades/scalpel handles, and IVDD was allowed to develop for 4 weeks in the Early (EA) and late Acute (LA) groups, or 12 weeks in the chronic (EST) group. Lesion IVDs received injections of 10 × 106 MSCs or PBS, and after 8 (EA), 22 (LA) or 14 (EST) weeks recuperation the sheep were sacrificed. Longitudinal lateral radiographs were used to determine disc heights. IVD glycosaminoglycan (GAG) and hydroxyproline contents were quantified using established methods. An Instron materials testing machine and customized jigs analyzed IVD (range of motion, neutral zone [NZ] and stiffness) in flexion/extension, lateral bending and axial rotation. qRTPCR gene profiles of key anabolic and catabolic matrix molecules were undertaken. Toluidine blue and hematoxylin and eosin stained IVD sections were histopathologically scoring by two blinded observers.

RESULTS

IVDD significantly reduced disc heights. MSC treatment restored 95% to 100% of disc height, maximally improved NZ and stiffness in flexion/extension and lateral bending in the EST group, restoring GAG levels. With IVDD qRTPCR demonstrated elevated catabolic gene expression (MMP2/3/9/13, ADAMTS4/5) in the PBS IVDs and expession normalization in MSC-treated IVDs. Histopathology degeneracy scores were close to levels of NOC IVDs in MSC IVDs but IVDD developed in PBS injected IVDs.

DISCUSSION

Administered MSCs produced recovery in degenerate IVDs, restored disc height, composition, biomechanical properties, down regulated MMPs and fibrosis, strongly supporting the efficacy of MSCs for disc repair.

DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.

High-resolution behavioral mapping of electric fishes in Amazonian habitats

The study of animal behavior has been revolutionized by sophisticated methodologies that identify and track individuals in video recordings. Video recording of behavior, however, is challenging for many species and habitats including fishes that live in turbid water. Here we present a methodology for identifying and localizing weakly electric fishes on the centimeter scale with subsecond temporal resolution based solely on the electric signals generated by each individual. These signals are recorded with a grid of electrodes and analyzed using a two-part algorithm that identifies the signals from each individual fish and then estimates the position and orientation of each fish using Bayesian inference. Interestingly, because this system involves eavesdropping on electrocommunication signals, it permits monitoring of complex social and physical interactions in the wild. This approach has potential for large-scale non-invasive monitoring of aquatic habitats in the Amazon basin and other tropical freshwater systems.

3-D motion capture for long-term tracking of spontaneous locomotor behaviors and circadian sleep/wake rhythms in mouse

BACKGROUND

Locomotor activity provides an index of an animal's behavioral state. Here, we report a reliable and cost-effective method that allows long-term (days to months) simultaneous tracking of locomotion in mouse cohorts (here consisting of 24 animals).

NEW METHOD

The technique is based on a motion capture system used mainly for human movement study. A reflective marker was placed on the head of each mouse using a surgical procedure and labeled animals were returned to their individual home cages. Camera-recorded data of marker displacement resulting from locomotor movements were then analyzed with custom built software. To avoid any data loss, data files were saved every hour and automatically concatenated. Long-term recordings (up to 3 months) with high spatial (<1mm) and temporal (up to 100Hz) resolution of animal movements were obtained.

RESULTS

The system was validated by analyzing the spontaneous activity of mice from post-natal day 30-90. Daily motor activity increased up to 70days in correspondence with maturational changes in locomotor performance. The recorded actigrams also permitted analysis of circadian and ultradian rhythms in cohort sleep/wake behavior.

COMPARISON WITH EXISTING METHOD(S)

In contrast to traditional session-based experimental approaches, our technique allows locomotor activity to be recorded with minimal experimenter manipulation, thereby minimizing animal stress.

CONCLUSIONS

Our method enables the continuous long-term (up to several months) monitoring of tens of animals, generating manageable amounts of data at minimal costs without requiring individual dedicated devices. The actigraphic data collected allows circadian and ultradian analysis of sleep/wake behaviors to be performed.

The speed-curvature power law of movements: a reappraisal

Several types of curvilinear movements obey approximately the so called 2/3 power law, according to which the angular speed varies proportionally to the 2/3 power of the curvature. The origin of the law is debated but it is generally thought to depend on physiological mechanisms. However, a recent paper (Marken and Shaffer, Exp Brain Res 88:685-690, 2017) claims that this power law is simply a statistical artifact, being a mathematical consequence of the way speed and curvature are calculated. Here we reject this hypothesis by showing that the speed-curvature power law of biological movements is non-trivial. First, we confirm that the power exponent varies with the shape of human drawing movements and with environmental factors. Second, we report experimental data from Drosophila larvae demonstrating that the power law does not depend on how curvature is calculated. Third, we prove that the law can be violated by means of several mathematical and physical examples. Finally, we discuss biological constraints that may underlie speed-curvature power laws discovered in empirical studies.

Ethoscopes: An open platform for high-throughput ethomics

Here, we present the use of ethoscopes, which are machines for high-throughput analysis of behavior in Drosophila and other animals. Ethoscopes provide a software and hardware solution that is reproducible and easily scalable. They perform, in real-time, tracking and profiling of behavior by using a supervised machine learning algorithm, are able to deliver behaviorally triggered stimuli to flies in a feedback-loop mode, and are highly customizable and open source. Ethoscopes can be built easily by using 3D printing technology and rely on Raspberry Pi microcomputers and Arduino boards to provide affordable and flexible hardware. All software and construction specifications are available at http://lab.gilest.ro/ethoscope.

Species-specific modulation of food-search behavior by respiration and chemosensation in Drosophila larvae

Animals explore their environment to encounter suitable food resources. Despite its vital importance, this behavior puts individuals at risk by consuming limited internal energy during locomotion. We have developed a novel assay to investigate how food-search behavior is organized in Drosophila melanogaster larvae dwelling in hydrogels mimicking their natural habitat. We define three main behavioral modes: resting at the gel's surface, digging while feeding near the surface, and apneic dives. In unstimulated conditions, larvae spend most of their time digging. By contrast, deep and long exploratory dives are promoted by olfactory stimulations. Hypoxia and chemical repellents impair diving. We report remarkable differences in the dig-and-dive behavior of D. melanogaster and the fruit-pest D. suzukii. The present paradigm offers an opportunity to study how sensory and physiological cues are integrated to balance the limitations of dwelling in imperfect environmental conditions and the risks associated with searching for potentially more favorable conditions.

Anatomy and behavioral function of serotonin receptors in Drosophila melanogaster larvae

The biogenic amine serotonin (5-HT) is an important neuroactive molecule in the central nervous system of the majority of animal phyla. 5-HT binds to specific G protein-coupled and ligand-gated ion receptors to regulate particular aspects of animal behavior. In Drosophila, as in many other insects this includes the regulation of locomotion and feeding. Due to its genetic amenability and neuronal simplicity the Drosophila larva has turned into a useful model for studying the anatomical and molecular basis of chemosensory behaviors. This is particularly true for the olfactory system, which is mostly described down to the synaptic level over the first three orders of neuronal information processing. Here we focus on the 5-HT receptor system of the Drosophila larva. In a bipartite approach consisting of anatomical and behavioral experiments we describe the distribution and the implications of individual 5-HT receptors on naïve and acquired chemosensory behaviors. Our data suggest that 5-HT_1A, 5-HT_1B, and 5-HT₇ are dispensable for larval naïve olfactory and gustatory choice behaviors as well as for appetitive and aversive associative olfactory learning and memory. In contrast, we show that 5-HT/5-HT_2A signaling throughout development, but not as an acute neuronal function, affects associative olfactory learning and memory using high salt concentration as a negative unconditioned stimulus. These findings describe for the first time an involvement of 5-HT signaling in learning and memory in Drosophila larvae. In the longer run these results may uncover developmental, 5-HT dependent principles related to reinforcement processing possibly shared with adult Drosophila and other insects.

Supervised and Unsupervised Learning Technology in the Study of Rodent Behavior

Quantifying behavior is a challenge for scientists studying neuroscience, ethology, psychology, pathology, etc. Until now, behavior was mostly considered as qualitative descriptions of postures or labor intensive counting of bouts of individual movements. Many prominent behavioral scientists conducted studies describing postures of mice and rats, depicting step by step eating, grooming, courting, and other behaviors. Automated video assessment technologies permit scientists to quantify daily behavioral patterns/routines, social interactions, and postural changes in an unbiased manner. Here, we extensively reviewed published research on the topic of the structural blocks of behavior and proposed a structure of behavior based on the latest publications. We discuss the importance of defining a clear structure of behavior to allow professionals to write viable algorithms. We presented a discussion of technologies that are used in automated video assessment of behavior in mice and rats. We considered advantages and limitations of supervised and unsupervised learning. We presented the latest scientific discoveries that were made using automated video assessment. In conclusion, we proposed that the automated quantitative approach to evaluating animal behavior is the future of understanding the effect of brain signaling, pathologies, genetic content, and environment on behavior.

Turns with multiple and single head cast mediate Drosophila larval light avoidance

Drosophila larvae exhibit klinotaxis when placed in a gradient of temperature, chemicals, or light. The larva samples environmental stimuli by casting its head from side to side. By comparing the results of two consecutive samples, it decides the direction of movement, appearing as a turn proceeded by one or more head casts. Here by analyzing larval behavior in a light-spot-based phototaxis assay, we showed that, in addition to turns with a single cast (1-cast), turns with multiple head casts (n-cast) helped to improve the success of light avoidance. Upon entering the light spot, the probability of escape from light after the first head cast was only ~30%. As the number of head casts increased, the chance of successful light avoidance increased and the overall chance of escaping from light increased to >70%. The amplitudes of first head casts that failed in light avoidance were significantly smaller in n-cast turns than those in 1-cast events, indicating that n-cast turns might be planned before completion of the first head cast. In n-casts, the amplitude of the second head cast was generally larger than that of the first head cast, suggesting that larvae tried harder in later attempts to improve the efficacy of light avoidance. We propose that both 1-cast turns and n-cast turns contribute to successful larval light avoidance, and both can be initiated at the first head cast.

The Ol1mpiad: concordance of behavioural faculties of stage 1 and stage 3 Drosophila larvae

Mapping brain function to brain structure is a fundamental task for neuroscience. For such an endeavour, the Drosophila larva is simple enough to be tractable, yet complex enough to be interesting. It features about 10,000 neurons and is capable of various taxes, kineses and Pavlovian conditioning. All its neurons are currently being mapped into a light-microscopical atlas, and Gal4 strains are being generated to experimentally access neurons one at a time. In addition, an electron microscopic reconstruction of its nervous system seems within reach. Notably, this electron microscope-based connectome is being drafted for a stage 1 larva – because stage 1 larvae are much smaller than stage 3 larvae. However, most behaviour analyses have been performed for stage 3 larvae because their larger size makes them easier to handle and observe. It is therefore warranted to either redo the electron microscopic reconstruction for a stage 3 larva or to survey the behavioural faculties of stage 1 larvae. We provide the latter. In a community-based approach we called the Ol1mpiad, we probed stage 1 Drosophila larvae for free locomotion, feeding, responsiveness to substrate vibration, gentle and nociceptive touch, burrowing, olfactory preference and thermotaxis, light avoidance, gustatory choice of various tastants plus odour–taste associative learning, as well as light/dark–electric shock associative learning. Quantitatively, stage 1 larvae show lower scores in most tasks, arguably because of their smaller size and lower speed. Qualitatively, however, stage 1 larvae perform strikingly similar to stage 3 larvae in almost all cases. These results bolster confidence in mapping brain structure and behaviour across developmental stages.

OptiMouse: a comprehensive open source program for reliable detection and analysis of mouse body and nose positions

BACKGROUND

Accurate determination of mouse positions from video data is crucial for various types of behavioral analyses. While detection of body positions is straightforward, the correct identification of nose positions, usually more informative, is far more challenging. The difficulty is largely due to variability in mouse postures across frames.

RESULTS

Here, we present OptiMouse, an extensively documented open-source MATLAB program providing comprehensive semiautomatic analysis of mouse position data. The emphasis in OptiMouse is placed on minimizing errors in position detection. This is achieved by allowing application of multiple detection algorithms to each video, including custom user-defined algorithms, by selection of the optimal algorithm for each frame, and by correction when needed using interpolation or manual specification of positions.

CONCLUSIONS

At a basic level, OptiMouse is a simple and comprehensive solution for analysis of position data. At an advanced level, it provides an open-source and expandable environment for a detailed analysis of mouse position data.

FIMTrack: An open source tracking and locomotion analysis software for small animals

Imaging and analyzing the locomotion behavior of small animals such as Drosophila larvae or C. elegans worms has become an integral subject of biological research. In the past we have introduced FIM, a novel imaging system feasible to extract high contrast images. This system in combination with the associated tracking software FIMTrack is already used by many groups all over the world. However, so far there has not been an in-depth discussion of the technical aspects. Here we elaborate on the implementation details of FIMTrack and give an in-depth explanation of the used algorithms. Among others, the software offers several tracking strategies to cover a wide range of different model organisms, locomotion types, and camera properties. Furthermore, the software facilitates stimuli-based analysis in combination with built-in manual tracking and correction functionalities. All features are integrated in an easy-to-use graphical user interface. To demonstrate the potential of FIMTrack we provide an evaluation of its accuracy using manually labeled data. The source code is available under the GNU GPLv3 at https://github.com/i-git/FIMTrack and pre-compiled binaries for Windows and Mac are available at http://fim.uni-muenster.de.

Neural Circuits Underlying Fly Larval Locomotion

Locomotion is a complex motor behavior that may be expressed in different ways using a variety of strategies depending upon species and pathological or environmental conditions. Quadrupedal or bipedal walking, running, swimming, flying and gliding constitute some of the locomotor modes enabling the body, in all cases, to move from one place to another. Despite these apparent differences in modes of locomotion, both vertebrate and invertebrate species share, at least in part, comparable neural control mechanisms for locomotor rhythm and pattern generation and modulation. Significant advances have been made in recent years in studies of the genetic aspects of these control systems. Findings made specifically using Drosophila (fruit fly) models and preparations have contributed to further understanding of the key role of genes in locomotion. This review focuses on some of the main findings made in larval fruit flies while briefly summarizing the basic advantages of using this powerful animal model for studying the neural locomotor system.

Optimization of wrMTrck to monitor Drosophila larval locomotor activity

An efficient and low-cost method of examining larval movement in Drosophila melanogaster is needed to study how mutations and/or alterations in the muscular, neural, and olfactory systems affect locomotor behavior. Here, we describe the implementation of wrMTrck, a freely available ImageJ plugin originally developed for examining multiple behavioral parameters in the nematode C. elegans. Our optimized method is rapid, reproducible and does not require automated microscope setups or the purchase of proprietary software. To demonstrate the utility of this method, we analyzed the velocity and crawling paths of two Drosophila mutants that affect muscle structure and/or function. Additionally, we show that this approach is useful for tracking the behavior of adult insects, including Tribolium castaneum and Drosophila melanogaster.

The speed-curvature power law in Drosophila larval locomotion

We report the discovery that the locomotor trajectories of Drosophila larvae follow the power-law relationship between speed and curvature previously found in the movements of human and non-human primates. Using high-resolution behavioural tracking in controlled but naturalistic sensory environments, we tested the law in maggots tracing different trajectory types, from reaching-like movements to scribbles. For most but not all flies, we found that the law holds robustly, with an exponent close to three-quarters rather than to the usual two-thirds found in almost all human situations, suggesting dynamic effects adding on purely kinematic constraints. There are different hypotheses for the origin of the law in primates, one invoking cortical computations, another viscoelastic muscle properties coupled with central pattern generators. Our findings are consistent with the latter view and demonstrate that the law is possible in animals with nervous systems orders of magnitude simpler than in primates. Scaling laws might exist because natural selection favours processes that remain behaviourally efficient across a wide range of neural and body architectures in distantly related species.

Optogenetic stimulation of cortico-subthalamic projections is sufficient to ameliorate bradykinesia in 6-ohda lesioned mice

Electrical deep brain stimulation (DBS) of the subthalamic nucleus (STN) is effective for ameliorating the motor symptoms of Parkinson's disease (PD) including bradykinesia. The STN receives its main excitatory input from cortex; however, the contribution of cortico-subthalamic projection neurons to the effects of DBS remains unclear. To isolate the consequences of stimulating layer 5 primary motor cortex (M1) projections to the STN, we used a dual virus transfection technique to selectively express opsins in these neurons in mice made parkinsonian by unilateral nigrostriatal 6-OHDA lesioning. AAVs containing WGA-Cre constructs were injected in the STN to retrogradely place Cre in STN afferents, while AAVs containing Cre-dependent ultrafast hChR2(E123T/T159C)-EYFP opsin constructs were injected in M1 layer 5, producing specific opsin expression in M1-STN projections. Under unstimulated conditions, lesioned mice showed bradykinesia and hypokinesia (decreased movement), along with electrophysiological changes similar to those observed in PD patients. Specifically, low frequency power (theta, alpha, low beta) was increased and gamma power was decreased, while M1/STN coherence and STN phase-amplitude-coupling (PAC) were increased. Optogenetic stimulation (100-130Hz) of STN afferents in these mice ameliorated bradykinesia and hypokinesia and brought the neural dynamics closer to the non-parkinsonian state by reducing theta and alpha and increasing gamma power in M1, decreasing STN PAC, and reducing theta band coherence. Histological examination of the EYFP expression revealed that, in addition to orthodromic and antidromic effects, stimulation of cortico-subthalamic neurons may cause wide-spread increased glutamatergic activity due to collaterals that project to areas of the thalamus and other brain regions.

Quantifying and predicting Drosophila larvae crawling phenotypes

The fruit fly Drosophila melanogaster is a widely used model for cell biology, development, disease, and neuroscience. The fly's power as a genetic model for disease and neuroscience can be augmented by a quantitative description of its behavior. Here we show that we can accurately account for the complex and unique crawling patterns exhibited by individual Drosophila larvae using a small set of four parameters obtained from the trajectories of a few crawling larvae. The values of these parameters change for larvae from different genetic mutants, as we demonstrate for fly models of Alzheimer's disease and the Fragile X syndrome, allowing applications such as genetic or drug screens. Using the quantitative model of larval crawling developed here we use the mutant-specific parameters to robustly simulate larval crawling, which allows estimating the feasibility of laborious experimental assays and aids in their design.

A Kinect-based system for automatic recording of some pigeon behaviors

Contact switches and touch screens are the state of the art for recording pigeons' pecking behavior. Recording other behavior, however, requires a different sensor for each behavior, and some behaviors cannot easily be recorded. We present a flexible and inexpensive image-based approach to detecting and counting pigeon behaviors that is based on the Kinect sensor from Microsoft. Although the system is as easy to set up and use as the standard approaches, it is more flexible because it can record behaviors in addition to key pecking. In this article, we show how both the fast, fine motion of key pecking and the gross body activity of feeding can be measured. Five pigeons were trained to peck at a lighted contact switch, a pigeon key, to obtain food reward. The timing of the pecks and the food reward signals were recorded in a log file using standard equipment. The Kinect-based system, called BehaviorWatch, also measured the pecking and feeding behavior and generated a different log file. For key pecking, BehaviorWatch had an average sensitivity of 95% and a precision of 91%, which were very similar to the pecking measurements from the standard equipment. For detecting feeding activity, BehaviorWatch had a sensitivity of 95% and a precision of 97%. These results allow us to demonstrate that an advantage of the Kinect-based approach is that it can also be reliably used to measure activity other than key pecking.

Generative rules of Drosophila locomotor behavior as a candidate homology across phyla

The discovery of shared behavioral processes across phyla is a significant step in the establishment of a comparative study of behavior. We use immobility as an origin and reference for the measurement of fly locomotor behavior; speed, walking direction and trunk orientation as the degrees of freedom shaping this behavior; and cocaine as the parameter inducing progressive transitions in and out of immobility. We characterize and quantify the generative rules that shape Drosophila locomotor behavior, bringing about a gradual buildup of kinematic degrees of freedom during the transition from immobility to normal behavior, and the opposite narrowing down into immobility. Transitions into immobility unfold via sequential enhancement and then elimination of translation, curvature and finally rotation. Transitions out of immobility unfold by progressive addition of these degrees of freedom in the opposite order. The same generative rules have been found in vertebrate locomotor behavior in several contexts (pharmacological manipulations, ontogeny, social interactions) involving transitions in-and-out of immobility. Recent claims for deep homology between arthropod central complex and vertebrate basal ganglia provide an opportunity to examine whether the rules we report also share common descent. Our approach prompts the discovery of behavioral homologies, contributing to the elusive problem of behavioral evolution.

FIM$^{2c\;}$: Multicolor, Multipurpose Imaging System to Manipulate and Analyze Animal Behavior

In vivo whole-body imaging of small animals plays an important role for biomedical studies. In particular, animals like the fruit fly Drosophila melanogaster or the nematode Caenorhabditis elegans are popular model organisms for preclinical research since they offer sophisticated genetic tool-kits. Recording these translucent animals with high contrast in a large arena is however not trivial. Furthermore, fluorescent proteins are widely used to mark cells in vivo and report their functions. This paper introduces a novel optical imaging technique called FIM^2c enabling simultaneous detection of the animals posture and movement as well as fluorescent markers like green fluorescent protein (GFP). FIM^2c utilizes frustrated total internal reflection of two distinct wavelengths and captures both, reflected and emitted light. The resultant two-color high-contrast images are superb compared to other imaging systems for larvae or worms. This multipurpose method enables a large variety of different experimental approaches. For example, FIM^2c can be used to image GFP positive cells/tissues/animals and supports the integration of fluorescent tracers into multitarget tracking paradigms. Moreover, optogenetic tools can be applied in large-scale behavioral analysis to manipulate and study neuronal functions. To demonstrate the benefit of our system, we use FIM^2c to resolve colliding larvae in a high-throughput approach, which was impossible given the existing tools. Finally, we present a comprehensive database including images and locomotion features of more than 1300 resolved collisions available for the community. In conclusion, FIM^2c is a versatile tool for advanced imaging and locomotion analysis for a variety of different model organisms.

Incipient Social Groups: An Analysis via In-Vivo Behavioral Tracking

Social psychology is fundamentally the study of individuals in groups, yet there remain basic unanswered questions about group formation, structure, and change. We argue that the problem is methodological. Until recently, there was no way to track who was interacting with whom with anything approximating valid resolution and scale. In the current study we describe a new method that applies recent advances in image-based tracking to study incipient group formation and evolution with experimental precision and control. In this method, which we term “in vivo behavioral tracking,” we track individuals’ movements with a high definition video camera mounted atop a large field laboratory. We report results of an initial study that quantifies the composition, structure, and size of the incipient groups. We also apply in-vivo spatial tracking to study participants’ tendency to cooperate as a function of their embeddedness in those crowds. We find that participants form groups of seven on average, are more likely to approach others of similar attractiveness and (to a lesser extent) gender, and that participants’ gender and attractiveness are both associated with their proximity to the spatial center of groups (such that women and attractive individuals are more likely than men and unattractive individuals to end up in the center of their groups). Furthermore, participants’ proximity to others early in the study predicted the effort they exerted in a subsequent cooperative task, suggesting that submergence in a crowd may predict social loafing. We conclude that in vivo behavioral tracking is a uniquely powerful new tool for answering longstanding, fundamental questions about group dynamics.

Mitochondrial targeting of XJB-5-131 attenuates or improves pathophysiology in HdhQ150 animals with well-developed disease phenotypes

Oxidative damage to mitochondria (MT) is a major mechanism for aging and neurodegeneration. We have developed a novel synthetic antioxidant, XJB-5-131, which directly targets MT, the primary site and primary target of oxidative damage. XJB-5-131 prevents the onset of motor decline in an HdhQ(150/150) mouse model for Huntington's disease (HD) if treatment starts early. Here, we report that XJB-5-131 attenuates or reverses disease progression if treatment occurs after disease onset. In animals with well-developed pathology, XJB-5-131 promotes weight gain, prevents neuronal death, reduces oxidative damage in neurons, suppresses the decline of motor performance or improves it, and reduces a graying phenotype in treated HdhQ(150/150) animals relative to matched littermate controls. XJB-5-131 holds promise as a clinical candidate for the treatment of HD.

Mapping Sub-Second Structure in Mouse Behavior

Complex animal behaviors are likely built from simpler modules, but their systematic identification in mammals remains a significant challenge. Here we use depth imaging to show that 3D mouse pose dynamics are structured at the sub-second timescale. Computational modeling of these fast dynamics effectively describes mouse behavior as a series of reused and stereotyped modules with defined transition probabilities. We demonstrate this combined 3D imaging and machine learning method can be used to unmask potential strategies employed by the brain to adapt to the environment, to capture both predicted and previously hidden phenotypes caused by genetic or neural manipulations, and to systematically expose the global structure of behavior within an experiment. This work reveals that mouse body language is built from identifiable components and is organized in a predictable fashion; deciphering this language establishes an objective framework for characterizing the influence of environmental cues, genes and neural activity on behavior.

Analyzing animal behavior via classifying each video frame using convolutional neural networks

High-throughput analysis of animal behavior requires software to analyze videos. Such software analyzes each frame individually, detecting animals’ body parts. But the image analysis rarely attempts to recognize “behavioral states”—e.g., actions or facial expressions—directly from the image instead of using the detected body parts. Here, we show that convolutional neural networks (CNNs)—a machine learning approach that recently became the leading technique for object recognition, human pose estimation, and human action recognition—were able to recognize directly from images whether Drosophila were “on” (standing or walking) or “off” (not in physical contact with) egg-laying substrates for each frame of our videos. We used multiple nets and image transformations to optimize accuracy for our classification task, achieving a surprisingly low error rate of just 0.072%. Classifying one of our 8 h videos took less than 3 h using a fast GPU. The approach enabled uncovering a novel egg-laying-induced behavior modification in Drosophila. Furthermore, it should be readily applicable to other behavior analysis tasks.

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning

A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body "pose" of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics.

Imaging fictive locomotor patterns in larval Drosophila

We have established a preparation in larval Drosophila to monitor fictive locomotion simultaneously across abdominal and thoracic segments of the isolated CNS with genetically encoded Ca²⁺ indicators. The Ca²⁺ signals closely followed spiking activity measured electrophysiologically in nerve roots. Three motor patterns are analyzed. Two comprise waves of Ca²⁺ signals that progress along the longitudinal body axis in a posterior-to-anterior or anterior-to-posterior direction. These waves had statistically indistinguishable intersegmental phase delays compared with segmental contractions during forward and backward crawling behavior, despite being ∼10 times slower. During these waves, motor neurons of the dorsal longitudinal and transverse muscles were active in the same order as the muscle groups are recruited during crawling behavior. A third fictive motor pattern exhibits a left-right asymmetry across segments and bears similarities with turning behavior in intact larvae, occurring equally frequently and involving asymmetry in the same segments. Ablation of the segments in which forward and backward waves of Ca²⁺ signals were normally initiated did not eliminate production of Ca²⁺ waves. When the brain and subesophageal ganglion (SOG) were removed, the remaining ganglia retained the ability to produce both forward and backward waves of motor activity, although the speed and frequency of waves changed. Bilateral asymmetry of activity was reduced when the brain was removed and abolished when the SOG was removed. This work paves the way to studying the neural and genetic underpinnings of segmentally coordinated motor pattern generation in Drosophila with imaging techniques.

Long-duration animal tracking in difficult lighting conditions

High-throughput analysis of animal behavior requires software to analyze videos. Such software typically depends on the experiments' being performed in good lighting conditions, but this ideal is difficult or impossible to achieve for certain classes of experiments. Here, we describe techniques that allow long-duration positional tracking in difficult lighting conditions with strong shadows or recurring "on"/"off" changes in lighting. The latter condition will likely become increasingly common, e.g., for Drosophila due to the advent of red-shifted channel rhodopsins. The techniques enabled tracking with good accuracy in three types of experiments with difficult lighting conditions in our lab. Our technique handling shadows relies on single-animal tracking and on shadows' and flies' being accurately distinguishable by distance to the center of the arena (or a similar geometric rule); the other techniques should be broadly applicable. We implemented the techniques as extensions of the widely-used tracking software Ctrax; however, they are relatively simple, not specific to Drosophila, and could be added to other trackers as well.

Methods for studying the zebrafish brain: past, present and future

The zebrafish (Danio rerio) is one of the most promising new model organisms. The increasing popularity of this amazing small vertebrate is evident from the exponentially growing numbers of research articles, funded projects and new discoveries associated with the use of zebrafish for studying development, brain function, human diseases and screening for new drugs. Thanks to the development of novel technologies, the range of zebrafish research is constantly expanding with new tools synergistically enhancing traditional techniques. In this review we will highlight the past and present techniques which have made, and continue to make, zebrafish an attractive model organism for various fields of biology, with a specific focus on neuroscience.

Chemosensory apparatus of Drosophila larvae

Many insects, including Drosophila melanogaster, have a rich repertoire of olfactory behavior. Combination of robust behavioral assays, physiological and molecular tools render D. melanogaster as highly suitable system for olfactory studies. The small number of neurons in the olfactory system of fruit flies, especially the number of sensory neurons in the larval stage, makes the exploration of sensory coding at all stages of its nervous system a potentially tractable goal, which is not possible in the foreseeable future in any mammalian preparation. Advances in physiological recordings, olfactory signaling and detailed analysis of behavior, can place larvae in a position to ask previously unanswerable questions.

Toward a science of computational ethology

The new field of “Computational Ethology” is made possible by advances in technology, mathematics, and engineering that allow scientists to automate the measurement and the analysis of animal behavior. We explore the opportunities and long-term directions of research in this area.

Temperature sensation in Drosophila

Animals use thermosensory systems to achieve optimal temperatures for growth and reproduction and to avoid damaging extremes. Thermoregulation is particularly challenging for small animals like the fruit fly Drosophila melanogaster, whose body temperature rapidly changes in response to environmental temperature fluctuation. Recent work has uncovered some of the key molecules mediating fly thermosensation, including the Transient Receptor Potential (TRP) channels TRPA1 and Painless, and the Gustatory Receptor Gr28b, an unanticipated thermosensory regulator normally associated with a different sensory modality. There is also evidence the Drosophila phototransduction cascade may have some role in thermosensory responses. Together, the fly's diverse thermosensory molecules act in an array of functionally distinct thermosensory neurons to drive a suite of complex, and often exceptionally thermosensitive, behaviors.

Bursting activity of substantia nigra pars reticulata neurons in mouse parkinsonism in awake and anesthetized states

Electrophysiological changes in basal ganglia neurons are hypothesized to underlie motor dysfunction in Parkinson's disease (PD). Previous results in head-restrained MPTP-treated non-human primates have suggested that increased bursting within the basal ganglia and related thalamic and cortical areas may be a hallmark of pathophysiological activity. In this study, we investigated whether there is increased bursting in substantia nigra pars reticulata (SNpr) output neurons in anesthetized and awake, head-restrained unilaterally lesioned 6-OHDA mice when compared to control mice. Confirming previous studies, we show that there are significant changes in the firing rate and pattern in SNpr neuron activity under urethane anesthesia. The regular firing pattern of control urethane-anesthetized SNpr neurons was not present in the 6-OHDA-lesioned group, as the latter neurons instead became phase locked with cortical slow wave activity (SWA). Next, we examined whether such robust electrophysiological changes between groups carried over to the awake state. SNpr neurons from both groups fired at much higher frequencies in the awake state than in the anesthetized state and surprisingly showed only modest changes between awake control and 6-OHDA groups. While there were no differences in firing rate between groups in the awake state, an increase in the coefficient of variation (CV) was observed in the 6-OHDA group. Contrary to the bursting hypothesis, this increased CV was not due to changes in bursting but was instead due to a mild increase in pausing. Together, these results suggest that differences in SNpr activity between control and 6-OHDA lesioned mice may be strongly influenced by changes in network activity during different arousal and behavioral states.

Quantifying subtle locomotion phenotypes of Drosophila larvae using internal structures based on FIM images

Quantitative analysis of behavioral traits requires precise image acquisition and sophisticated image analysis to detect subtle locomotion phenotypes. In the past, we have established Frustrated Total Internal Reflection (FTIR) to improve the measurability of small animals like insects. This FTIR-based Imaging Method (FIM) results in an excellent foreground/background contrast and even internal organs and other structures are visible without any complicated imaging or labeling techniques. For example, the trachea and muscle organizations are detectable in FIM images. Here these morphological details are incorporated into phenotyping by performing cluster analysis using histogram-based statistics for the first time. We demonstrate that FIM enables the precise quantification of locomotion features namely rolling behavior or muscle contractions. Both were impossible to quantify automatically before. This approach extends the range of FIM applications by enabling advanced automatic phenotyping for particular locomotion patterns.

Automated image-based tracking and its application in ecology

The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers.

A guide to study Drosophila muscle biology

The development and molecular composition of muscle tissue is evolutionarily conserved. Drosophila is a powerful in vivo model system to investigate muscle morphogenesis and function. Here, we provide a short and comprehensive overview of the important developmental steps to build Drosophila body muscle in embryos, larvae and pupae. We describe key methods, including muscle histology, live imaging and genetics, to study these steps at various developmental stages and include simple behavioural assays to assess muscle function in larvae and adults. We list valuable antibodies and fly strains that can be used for these different methods. This overview should guide the reader to choose the best marker or the appropriate method to obtain high quality muscle morphogenesis data in Drosophila.