An individual's choices are shaped by its experience, a fundamental property of behavior important to understanding complex processes. Learning and memory are observed across many taxa and can drive behaviors, including foraging behavior. To explore the conditions under which memory provides an advantage, we present a continuous-space, continuous-time model of animal movement that incorporates learning and memory. Using simulation models, we evaluate the benefit memory provides across several types of landscapes with variable-quality resources and compare the memory model within a nested hierarchy of simpler models (behavioral switching and random walk). We find that memory almost always leads to improved foraging success, but that this effect is most marked in landscapes containing sparse, contiguous patches of high-value resources that regenerate relatively fast and are located in an otherwise devoid landscape. In these cases, there is a large payoff for finding a resource patch, due to size, value, or locational difficulty. While memory-informed search is difficult to differentiate from other factors using solely movement data, our results suggest that disproportionate spatial use of higher value areas, higher consumption rates, and consumption variability all point to memory influencing the movement direction of animals in certain ecosystems.

Recent developments in automated tracking allow uninterrupted, high-resolution recording of animal trajectories, sometimes coupled with the identification of stereotyped changes of body pose or other behaviors of interest. Analysis and interpretation of such data represents a challenge: the timing of animal behaviors may be stochastic and modulated by kinematic variables, by the interaction with the environment or with the conspecifics within the animal group, and dependent on internal cognitive or behavioral state of the individual. Existing models for collective motion typically fail to incorporate the discrete, stochastic, and internal-state-dependent aspects of behavior, while models focusing on individual animal behavior typically ignore the spatial aspects of the problem. Here we propose a probabilistic modeling framework to address this gap. Each animal can switch stochastically between different behavioral states, with each state resulting in a possibly different law of motion through space. Switching rates for behavioral transitions can depend in a very general way, which we seek to identify from data, on the effects of the environment as well as the interaction between the animals. We represent the switching dynamics as a Generalized Linear Model and show that: (i) forward simulation of multiple interacting animals is possible using a variant of the Gillespie's Stochastic Simulation Algorithm; (ii) formulated properly, the maximum likelihood inference of switching rate functions is tractably solvable by gradient descent; (iii) model selection can be used to identify factors that modulate behavioral state switching and to appropriately adjust model complexity to data. To illustrate our framework, we apply it to two synthetic models of animal motion and to real zebrafish tracking data.

Channelrhodopsin-2 (ChR2) is widely used for rapid photodepolarization of neurons, yet, as it requires high-intensity blue light for activation, it is not suited for long-term in vivo applications, e.g. for manipulations of behavior, or photoactivation of neurons during development. We used "slow" ChR2 variants with mutations in the C128 residue, that exhibit delayed off-kinetics and increased light sensitivity in Caenorhabditis elegans. Following a 1 s light pulse, we could photodepolarize neurons and muscles for minutes (and with repeated brief stimulation, up to days) with low-intensity light. Photoactivation of ChR2(C128S) in command interneurons elicited long-lasting alterations in locomotion. Finally, we could optically induce profound changes in animal development: Long-term photoactivation of ASJ neurons, which regulate larval growth, bypassed the constitutive entry into the "dauer" larval state in daf-11 mutants. These lack a guanylyl cyclase, which possibly renders ASJ neurons hyperpolarized. Furthermore, photostimulated ASJ neurons could acutely trigger dauer-exit. Thus, slow ChR2s can be employed to long-term photoactivate behavior and to trigger alternative animal development.

Saraswatarishta (SA) is a herbo-mineral formulation consisting of 18 plants some of which are Medhyarasayanas. It has been claimed to be useful in treating central nervous system disorders.

Elevated amyloid precursor protein (APP) expression in the choroid plexus suggests an important role for extracellular APP metabolites such as sAPPα in cerebrospinal fluid. Despite widespread App brain expression, we hypothesized that specifically targeting choroid plexus expression could alter animal physiology. Through various genetic and viral approaches in the adult mouse, we show that choroid plexus APP levels significantly impact proliferation in both subventricular zone and hippocampus dentate gyrus neurogenic niches. Given the role of Aβ peptides in Alzheimer disease pathogenesis, we also tested whether favoring the production of Aβ in choroid plexus could negatively affect niche functions. After AAV5-mediated long-term expression of human mutated APP specifically in the choroid plexus of adult wild-type mice, we observe reduced niche proliferation, reduced hippocampus APP expression, behavioral defects in reversal learning, and deficits in hippocampal long-term potentiation. Our findings highlight the unique role played by the choroid plexus in regulating brain function and suggest that targeting APP in choroid plexus may provide a means to improve hippocampus function and alleviate disease-related burdens.

Here we developed an open-source Python-based library called Python rodent Analysis and Tracking (PyRAT). Our library analyzes tracking data to classify distinct behaviors, estimate traveled distance, speed and area occupancy. To classify and cluster behaviors, we used two unsupervised algorithms: hierarchical agglomerative clustering and t-distributed stochastic neighbor embedding (t-SNE). Finally, we built algorithms that associate the detected behaviors with synchronized neural data and facilitate the visualization of this association in the pixel space. PyRAT is fully available on GitHub: https://github.com/pyratlib/pyrat.

Integrating trees into pastures, a practice known as silvopasture, may benefit livestock in the summertime through the provision of shade. The purpose of this project was to compare the behavioral patterns of sheep grazing in silvopastures and open pastures. Black walnut (Juglans nigra L.) and honeylocust (Gleditisia triacanothose L.) based silvopasture systems were compared with open pastures in a randomized complete block design with three blocks over two summers. Behavior measures were recorded within a replicate within a week, and these measures were taken sequentially within three experimental periods. Ewe lambs (n = 3) within each experimental unit were equipped with a wideband audio-recording device to detect prehension events. Time-lapse cameras documented sheep behavior every 60 s. In the silvopastures, the lambs spent over 90% of daylight hours within shade from trees. Lambs in silvopastures spent more time lying down than animals in the open pastures (P ≤ 0.01), while lambs in the open pastures spent more than 2 h longer each day standing (P < 0.0001). Lambs in the black walnut silvopastures spent more time grazing (488 ± 14 min · d-1) than lambs in the honeylocust silvopastures (438 ± 14 min · d-1; P = 0.0493) and lambs in the open pastures (417 ± 14 min · d-1; P = 0.0026). There was no difference in grazing time for lambs in the latter two systems (P = 0.5597). Spectral analysis of the imagery revealed that the lambs in the black walnut silvopastures grazed more frequently than the lambs in the other systems for both years. The acoustic analysis, though limited by recorder durability to 47 complete recordings, revealed no difference in total bites taken per day (P ≥ 0.7222) or in the morning (P ≥ 0.2069), afternoon (P ≥ 0.5816), and evening periods (P ≥ 0.9337). Silvopastures provide an opportunity to improve lamb comfort in the summer.

Animal behavior usually has a hierarchical structure and dynamics. Therefore, to understand how the neural system coordinates with behaviors, neuroscientists need a quantitative description of the hierarchical dynamics of different behaviors. However, the recent end-to-end machine-learning-based methods for behavior analysis mostly focus on recognizing behavioral identities on a static timescale or based on limited observations. These approaches usually lose rich dynamic information on cross-scale behaviors. Here, inspired by the natural structure of animal behaviors, we address this challenge by proposing a parallel and multi-layered framework to learn the hierarchical dynamics and generate an objective metric to map the behavior into the feature space. In addition, we characterize the animal 3D kinematics with our low-cost and efficient multi-view 3D animal motion-capture system. Finally, we demonstrate that this framework can monitor spontaneous behavior and automatically identify the behavioral phenotypes of the transgenic animal disease model. The extensive experiment results suggest that our framework has a wide range of applications, including animal disease model phenotyping and the relationships modeling between the neural circuits and behavior.

Rationale: The endocannabinoid system is known to be involved in learning, memory, emotional processing and regulation of personality patterns. Here we assessed the endocannabinoid profile in the brains of mice with strong characteristics of social dominance and submissiveness. Methods: A lipidomics approach was employed to assess the endocannabinoidome in the brains of Dominant (Dom) and Submissive (Sub) mice. The endocannabinoid showing the greatest difference in concentration in the brain between the groups, docosatetraenoyl ethanolamine (DEA), was synthesized, and its effects on the physiological and behavioral responses of Dom and Sub mice were evaluated. mRNA expression of the endocannabinoid receptors and enzymes involved in PUFA biosynthesis was assessed using qRT-PCR. Results: Targeted LC/MS analysis revealed that long-chain polyunsaturated ethanolamides including arachidonoyl ethanolamide (AEA), DEA, docosatrienoyl ethanolamide (DTEA), eicosatrienoyl ethanolamide (ETEA), eicosapentaenoyl ethanolamide (EPEA) and docosahexaenoyl ethanolamide (DHEA) were higher in the Sub compared with the Dom mice. Untargeted LC/MS analysis showed that the parent fatty acids, docosatetraenoic (DA) and eicosapentaenoic (EPA), were higher in Sub vs. Dom. Gene expression analysis revealed increased mRNA expression of genes encoding the desaturase FADS2 and the elongase ELOVL5 in Sub mice compared with Dom mice. Acute DEA administration at the dose of 15 mg/kg produced antinociceptive and locomotion-inducing effects in Sub mice, but not in Dom mice. Subchronic treatment with DEA at the dose of 5 mg/kg augmented dominant behavior in wild-type ICR and Dom mice but not in Sub mice. Conclusion: This study suggests that the endocannabinoid system may play a role in the regulation of dominance and submissiveness, functional elements of social behavior and personality. While currently we have only scratched the surface, understanding the role of the endocannabinoid system in personality may help in revealing the mechanisms underlying the etiopathology of psychiatric disorders.

Trichoplax adhaerens is a flat, millimeter-sized marine animal that adheres to surfaces and grazes on algae. Trichoplax displays a repertoire of different feeding behaviors despite the apparent absence of a true nervous system with electrical or chemical synapses. It glides along surfaces to find food, propelled by beating cilia on cells at its ventral surface, and pauses during feeding by arresting ciliary beating. We found that when endomorphin-like peptides are applied to an animal, ciliary beating is arrested, mimicking natural feeding pauses. Antibodies against these neuropeptides label cells that express the neurosecretory proteins and voltage-gated calcium channels implicated in regulated secretion. These cells are embedded in the ventral epithelium, where they comprise only 4% of the total, and are concentrated around the edge of the animal. Each bears a cilium likely to be chemosensory and used to detect algae. Trichoplax pausing during feeding or spontaneously in the absence of food often induce their neighbors to pause as well, even neighbors not in direct contact. Pausing behavior propagates from animal to animal across distances much greater than the signal that diffuses from just one animal, so we presume that the peptides secreted from one animal elicit secretion from nearby animals. Signal amplification by peptide-induced peptide secretion explains how a small number of sensory secretory cells lacking processes and synapses can evoke a wave of peptide secretion across the entire animal to globally arrest ciliary beating and allow pausing during feeding.

The aim of this study was to analyze the behavioral characteristics and success of adoption for previously hoarded cats.

Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are time-intensive (e.g., rest), time & distance-intensive (e.g., area restricted search), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST's ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST's ability to discriminate between behavior states relative to other classical movement metrics. We then temporally sub-sample albatross track data to illustrate RST's response to less resolved data. Finally, we evaluate RST's performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology.

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.

Highly expressed in the mammalian brain and widely distributed across the genome, MeCP2 is a key player in recognizing modified DNA and interpreting the epigenetic information encoded in different DNA methylation/hydroxymethylation patterns. Alterations in sequence or copy number of the X-linked human MECP2 gene cause either Rett syndrome (RTT) or MECP2 duplication syndrome. Alterations in MECP2 levels have also been identified in patients with autism. To fully understand the significant role of MECP2 in regulating the development and function of the nervous system, it is important to study all aspects of MeCP2 function. Stimulus-induced MeCP2 phosphorylation has been shown to influence the proliferation and differentiation of neural progenitor cells, synaptic scaling, excitatory synaptogenesis, and animal behavior. However, all of the previous functional evidence is from studying phospho-dead mutations. In addition, the relationship between phosphorylation events at multiple sites on the MeCP2 protein is not well understood. Here, we report the generation of a phospho-mimic knockin Mecp2 mouse line. At the synaptic and behavioral levels, the phospho-mimic Mecp2 mice show phenotypes opposite to those observed in phospho-dead mutation at the same phosphorylation site. Moreover, we report opposite phenotypes between phospho-mutants of two sites on the MeCP2 protein. Our new data further confirm the functional significance of specific MeCP2 phosphorylation event and support the opposing regulatory role between different MeCP2 phosphorylation events.

Chrysin (5,7-dihydroxyflavone), a nutraceutical flavonoid present in diverse plants, has a backbone structure shared with the flavone backbone, with additional hydroxyl groups that confers its antioxidant properties and effects at the GABAA receptor complex. However, whether these effects are due to the hydroxyl groups is unknown. Here we report the effects of chrysin or the flavone backbone (1 mg/kg) in rats subjected to the elevated plus-maze and the locomotor activity test, as well as in the zebrafish evaluated in light/dark model. Chrysin, but not flavone, increased entries and time in the open arms of the elevated plus-maze, as well as time on white compartment of the light/dark model in zebrafish. These effects were comparable to diazepam, and were devoid of motor effects in both tests, as well as in the locomotor activity test. On the other hand, flavone decreased risk assessment in the light/dark test but increased rearing in the locomotor activity test in rats, suggesting effects threat information gathering; important species differences suggest new avenues of research. It is suggested that the specific effects of chrysin in relation to flavone include more of a mechanism of action in which in addition to its action at the GABAA/benzodiazepine receptor complex also could be involved its free radical scavenging abilities, which require specific research. Preprint: https://doi.org/10.1101/575514; Data and scripts:https://github.com/lanec-unifesspa/chrysin.

The identification of animal behavior in video is a critical but time-consuming task in many areas of research. Here, we introduce DeepAction, a deep learning-based toolbox for automatically annotating animal behavior in video. Our approach uses features extracted from raw video frames by a pretrained convolutional neural network to train a recurrent neural network classifier. We evaluate the classifier on two benchmark rodent datasets and one octopus dataset. We show that it achieves high accuracy, requires little training data, and surpasses both human agreement and most comparable existing methods. We also create a confidence score for classifier output, and show that our method provides an accurate estimate of classifier performance and reduces the time required by human annotators to review and correct automatically-produced annotations. We release our system and accompanying annotation interface as an open-source MATLAB toolbox.

Glia in the central nervous system engulf neuron fragments to remodel synapses and recycle photoreceptor outer segments. Whether glia passively clear shed neuronal debris or actively prune neuron fragments is unknown. How pruning of single-neuron endings impacts animal behavior is also unclear. Here, we report our discovery of glia-directed neuron pruning in Caenorhabditis elegans. Adult C. elegans AMsh glia engulf sensory endings of the AFD thermosensory neuron by repurposing components of the conserved apoptotic corpse phagocytosis machinery. The phosphatidylserine (PS) flippase TAT-1/ATP8A functions with glial PS-receptor PSR-1/PSR and PAT-2/α-integrin to initiate engulfment. This activates glial CED-10/Rac1 GTPase through the ternary GEF complex of CED-2/CrkII, CED-5/DOCK180, CED-12/ELMO. Execution of phagocytosis uses the actin-remodeler WSP-1/nWASp. This process dynamically tracks AFD activity and is regulated by temperature, the AFD sensory input. Importantly, glial CED-10 levels regulate engulfment rates downstream of neuron activity, and engulfment-defective mutants exhibit altered AFD-ending shape and thermosensory behavior. Our findings reveal a molecular pathway underlying glia-dependent engulfment in a peripheral sense-organ and demonstrate that glia actively engulf neuron fragments, with profound consequences on neuron shape and animal sensory behavior.

The Flinders Sensitive Line (FSL) model is considered a genetic animal model of depression. Among other characteristics, FSL rats express stress-induced anhedonia and an abnormal dopaminergic system. Our hypothesis was that FSL rats would show abnormal maternal behaviors, especially reduced motivation to reach and care for pups and reduced licking and non-nutritive contact, based on their anhedonic characteristics. Mother-infant interactions were assessed by time limited observations in FSL and Sprague-Dawley (SD) controls. In study 1, differences were found in consummatory behaviors: FSL dams compared to SD dams showed less licking and significant decrease in non-nutritive contact from the first to the third postpartum weeks. In addition, shorter duration of nursing postures was seen in FSL compared to SD dams in the first week postpartum, and this difference was significantly increased by the third week postpartum. In study 2, after exposure to acute swim stress, differences emerged in appetitive behaviors: latencies to reach and care for pups were longer in FSL dams compared to controls, suggesting a stress-induced motivational deficit in FSL dams. Possible explanations, especially regarding the FSL dams' reward system are discussed.

Studies over the past two decades have demonstrated that astrocytes are tightly associated with neurons and play pivotal roles in neural circuit development, operation, and adaptation in health and disease. Nevertheless, precisely how astrocytes integrate diverse neuronal signals, modulate neural circuit structure and function at multiple temporal and spatial scales, and influence animal behavior or disease through aberrant excitation and molecular output remains unclear. This Perspective discusses how new and state-of-the-art approaches, including fluorescence indicators, opto- and chemogenetic actuators, genetic targeting tools, quantitative behavioral assays, and computational methods, might help resolve these longstanding questions. It also addresses complicating factors in interpreting astrocytes' role in neural circuit regulation and animal behavior, such as their heterogeneity, metabolism, and inter-glial communication. Research on these questions should provide a deeper mechanistic understanding of astrocyte-neuron assemblies' role in neural circuit function, complex behaviors, and disease.

Despite evidence for rat tickling's animal welfare benefits, the technique is rarely implemented in part because of a lack of training. This study's purpose was to determine the efficacy of online-only or online + hands-on training programs on key outcomes for rat tickling in comparison to a waitlist control condition. After completing a baseline survey, laboratory animal personnel currently working with rats in the United States were semi-randomized to receive online-only training (n = 30), online + hands-on training (n = 34), or waitlist control (n = 32). Participants received further surveys directly after training and 2 months later. Data were analyzed using general linear mixed models. At the 2-month follow-up compared to baseline, both training groups reported increased implementation, self-efficacy, knowledge, and familiarity of rat tickling while only the online + hands-on training participants reported increased control beliefs (while the waitlist group stayed the same). At the 2-month follow-up compared to the waitlist, hands-on training participants reported increased self-efficacy and familiarity with rat tickling. Overall, findings show that both online-only and online + hands-on training can improve key outcomes for rat tickling. Although online + hands-on training is slightly more effective, the interactive online-only training has the potential to improve widescale implementation of a welfare-enhancing technique.