Deep learning tools for the measurement of animal behavior in neuroscience

Swimming into the future: Machine learning in zebrafish behavioral research

The zebrafish (Danio rerio) has emerged as a powerful organism in behavioral neuroscience, offering invaluable insights into the neural circuits and molecular pathways underlying complex behaviors. Although the knowledge of zebrafish behavioral repertoire is expanding rapidly, fundamental questions regarding complex behaviors remain poorly explored. Recent advances in machine learning offer potential for enhancing zebrafish behavioral analysis, enabling more precise, scalable, and unbiased assessments when compared to the traditional method. Thus, machine learning automates tracking and pattern recognition, uncovering new behavioral phenotypes and streamlining analysis typically manually assessed. Here, we highlight the potential use of machine learning tools in zebrafish-based models uncovering nuanced behavioral phenotypes to accelerate discoveries in translational neurobehavioral research, addressing the challenges and ethical considerations in the field. We emphasize that associating machine learning with zebrafish behavioral research, significant advances to elucidate neural and molecular mechanisms driving complex behaviors are expected. Collectively, the progressive refinement of these methods by enabling more detailed and efficient analysis will not only enhance the utility of zebrafish in translational neuroscience, but also contribute to develop more effective models of human disorders and in the search of potential neuroprotective strategies.

An Automated AI Framework for Quantitative Measurement of Mammalian Behavior

Despite the large amount of video data captured during ethological studies of wild mammals, there is no widely accepted method available to automatically and quantitatively measure and analyze animal behavior. We developed a framework using facial recognition and deep learning to automatically track, measure, and quantify the behavior of single or multiple individuals from 10 distinct mammalian taxa, including three species of primates, three species of bovids, three species of carnivores, and one species of equid. We used spatiotemporal information based on animal skeleton models to recognize a set of distinct behaviors such as walking, feeding, grooming, and resting, and achieved an accuracy ranging from 0.82 to 0.96. Accuracies of validation videos ranged from 0.80 to 0.99. Our study offers an innovative analytical platform for the rapid and accurate evaluation of animal behavior in both captive and field settings.

Computer vision for primate behavior analysis in the wild

Advances in computer vision and increasingly widespread video-based behavioral monitoring are currently transforming how we study animal behavior. However, there is still a gap between the prospects and practical application, especially in videos from the wild. In this Perspective, we aim to present the capabilities of current methods for behavioral analysis, while at the same time highlighting unsolved computer vision problems that are relevant to the study of animal behavior. We survey state-of-the-art methods for computer vision problems relevant to the video-based study of individualized animal behavior, including object detection, multi-animal tracking, individual identification and (inter)action understanding. We then review methods for effort-efficient learning, one of the challenges from a practical perspective. In our outlook on the emerging field of computer vision for animal behavior, we argue that the field should develop approaches to unify detection, tracking, identification and (inter)action understanding in a single, video-based framework.

The MacqD deep-learning-based model for automatic detection of socially housed laboratory macaques

Despite advancements in video-based behaviour analysis and detection models for various species, existing methods are suboptimal to detect macaques in complex laboratory environments. To address this gap, we present MacqD, a modified Mask R-CNN model incorporating a SWIN transformer backbone for enhanced attention-based feature extraction. MacqD robustly detects macaques in their home-cage under challenging scenarios, including occlusions, glass reflections, and overexposure to light. To evaluate MacqD and compare its performance against pre-existing macaque detection models, we collected and analysed video frames from 20 caged rhesus macaques at Newcastle University, UK. Our results demonstrate MacqD's superiority, achieving a median F1-score of 99% for frames with a single macaque in the focal cage (surpassing the next-best model by 21%) and 90% for frames with two macaques. Generalisation tests on frames from a different set of macaques from the same animal facility yielded median F1-scores of 95% for frames with a single macaque (surpassing the next-best model by 15%) and 81% for frames with two macaques (surpassing the alternative approach by 39% ). Finally, MacqD was applied to videos of paired macaques from another facility and resulted in F1-score of 90%, reflecting its strong generalisation capacity. This study highlights MacqD's effectiveness in accurately detecting macaques across diverse settings.

AI-assisted digital video analysis reveals changes in gait among three-day event horses during competition

The value and welfare of performance horses is closely tied to locomotor behaviors, but we lack objective and quantitative measures for these characteristics, and qualitative approaches for assessing gait do not provide measures suitable for large-scale biomechanical research studies. Digital video analysis utilizing artificial intelligence-based strategies holds promise to meet the need for an economical, accurate, repeatable and objective technique for field quantification of equine locomotion. Here we describe pilot work using a consumer-level digital video camera to capture high-resolution and high-speed videos of horses moving at the trot during mandatory inspections for international-level eventing competitions. We assessed 194 horses from five different competition venues, recorded at pre-competition (first) and post-cross-country (second) inspections as a model of gait change following exertion. We labeled twenty-six keypoints on each frame with DeepLabCut and processed the resulting tracking data using MatLab to derive quantitative gait parameters. Once trained, the DeepLabCut model labeled the 388 videos in just minutes, a task that would have otherwise taken months of human effort to complete. A Generalized Linear Mixed Model (GLMM) examining seven gait parameters identified significant changes in duty factor, speed, and forelimb swing range following the completion of the cross-country phase (P ≤ 0.05). Despite some limitations, video analysis through artificial intelligence proved capable of quantifying several gait parameters quickly, efficiently, and without the need for specialized equipment, making this tool a promising option for future biomechanical research in the athletic horse.

Forestwalk: A Machine Learning Workflow Brings New Insights Into Posture and Balance in Rodent Beam Walking

The beam walk is widely used to study coordination and balance in rodents. While the task has ethological validity, the main endpoints of "foot slip counts" and "time to cross" are prone to human-rater variability and offer limited sensitivity and specificity. We asked if machine learning-based methods could reveal previously hidden, but biologically relevant, insights from the task. Marker-less pose estimation, using DeepLabCut, was deployed to label 13 anatomical key points on mice traversing the beam. Next, we automated classical endpoint detection, including foot slips, with high recall (> 90%) and precision (> 80%). Using data derived from key point tracking, a total of 395 features were engineered and a random forest classifier deployed that, together with skeletal visualizations, could test for group differences and identify determinant features. This workflow, named Forestwalk, uncovered pharmacological treatment effects in C57BL/6J mice, revealed phenotypes in transgenic mice used to study Angelman syndrome and SLC6A1-related neurodevelopmental disorder, and will facilitate a deeper understanding of how the brain controls balance in health and disease.

The dopaminergic effects of esketamine are mediated by a dual mechanism involving glutamate and opioid receptors

Esketamine represents a new class of drugs for treating mood disorders. Unlike traditional monoaminergic-based therapies, esketamine primarily targets N-methyl-D-aspartate receptors (NMDAR). However, esketamine is a complex drug with low affinity for NMDAR and can also bind to other targets, such as opioid receptors. Its precise mechanism of action for its antidepressant properties remains debated, as does its potential for misuse. A key component at the intersection of mood and reward processing is the dopaminergic system. In this study, we evaluated the effects of esketamine in locomotion, anxiety tests and operant responding and we used in vivo fiber photometry to explore the neurochemical effects of esketamine in the nucleus accumbens of mice. Our findings demonstrated multifaceted effects of esketamine on neurotransmitter dynamics. In freely behaving mice, esketamine increased locomotion and increased extracellular dopamine tone -by impairing dopamine clearance rather than promoting dopamine release- while decreasing glutamatergic activity. However, it decreased dopamine spontaneous release event frequency and impaired reward-evoked dopamine release, leading to a reduction in operant responding rates. These dopaminergic effects were partially, and conditionally, blocked by the opioid antagonist naloxone and required glutamatergic input. In summary, our study reveals a complex interaction between neurotransmitter systems, suggesting that the neurochemical effects of esketamine are both circuit- and state-dependent.

Applying deep learning and the ecological home range concept to document the spatial distribution of Atlantic salmon parr (Salmo salar L.) in experimental tanks

Measuring and monitoring fish welfare in aquaculture research relies on the use of outcome- (biotic) and input-based (e.g., abiotic) welfare indicators (WIs). Incorporating behavioural auditing into this toolbox can sometimes be challenging because sourcing quantitative data is often labour intensive and it can be a time-consuming process. Digitalization of this process via the use of computer vision and artificial intelligence can help automate and streamline the procedure, help gather continuous quantitative data and help process optimisation and assist in decision-making. The tool introduced in this study (1) adapts the DeepLabCut framework, based on computer vision and machine learning, to obtain pose estimation of Atlantic salmon parr under replicated experimental conditions, (2) quantifies the spatial distribution of the fish through a toolbox of metrics inspired by the ecological concepts home range and core area, and (3) applies it to inspect behavioural variability in and around feeding. This proof of concept study demonstrates the potential of our methodology for automating the analysis of fish behaviour in relation to home range and core area, including fish detection, spatial distribution and the variations within and between tanks. The impact of feeding on these patterns is also briefly outlined, using 5 days of experimental data as a demonstrative case study. This approach can provide stakeholders with valuable information on how the fish use their rearing environment in small-scale experimental settings and can be used for the further development of technologies for measuring and monitoring the behaviour of fish in research settings in future studies.

In vivo electrophysiology recordings and computational modeling can predict octopus arm movement

The octopus has many features that make it advantageous for revealing principles of motor circuits and control and predicting behavior. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100 ms after stimulation were predictive of the resultant movement response. Machine learning models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. For kinematic analysis, deep learning models and unsupervised dimensionality reduction identified a consistent set of features that could be used to distinguish different types of arm movements. The neural circuits and the computational models identified here generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit. This study demonstrates how real-time motor behaviors can be predicted and distinguished, contributing to the development of brain-machine interfaces. The ability to accurately model and predict complex movement patterns has broad implications for advancing technologies in robotics, neuroprosthetics, and artificial intelligence, paving the way for more sophisticated and adaptable systems.

Anxiety in aquatics: Leveraging machine learning models to predict adult zebrafish behavior

Accurate analysis of anxiety behaviors in animal models is pivotal for advancing neuroscience research and drug discovery. This study compares the potential of DeepLabCut, ZebraLab, and machine learning models to analyze anxiety-related behaviors in adult zebrafish. Using a dataset comprising video recordings of unstressed and pre-stressed zebrafish, we extracted features such as total inactivity duration/immobility, time spent at the bottom, time spent at the top and turn angles (large and small). We observed that the data obtained using DeepLabCut and ZebraLab were highly correlated. Using this data, we annotated behaviors as anxious and not anxious and trained several machine learning models, including Logistic Regression, Decision Tree, K-Nearest Neighbours (KNN), Random Forests, Naive Bayes Classifiers, and Support Vector Machines (SVMs). The effectiveness of these machine learning models was validated and tested on independent datasets. We found that some machine learning models, such as Decision Tree and Random Forests, performed excellently to differentiate between anxious and non-anxious behavior, even in the control group, where the differences between subjects were more subtle. Our findings show that upcoming technologies, such as machine learning models, are able to effectively and accurately analyze anxiety behaviors in zebrafish and provide a cost-effective method to analyze animal behavior.

A modular machine learning tool for holistic and fine-grained behavioral analysis

Artificial intelligence techniques offer promising avenues for exploring human body features from videos, yet no freely accessible tool has reliably provided holistic and fine-grained behavioral analyses to date. To address this, we developed a machine learning tool based on a two-level approach: a first lower-level processing using computer vision for extracting fine-grained and comprehensive behavioral features such as skeleton or facial points, gaze, and action units; a second level of machine learning classification coupled with explainability providing modularity, to determine which behavioral features are triggered by specific environments. To validate our tool, we filmed 16 participants across six conditions, varying according to the presence of a person ("Pers"), a sound ("Snd"), or silence ("Rest"), and according to emotional levels using self-referential ("Self") and control ("Ctrl") stimuli. We demonstrated the effectiveness of our approach by extracting and correcting behavior from videos using two computer vision software (OpenPose and OpenFace) and by training two algorithms (XGBoost and long short-term memory [LSTM]) to differentiate between experimental conditions. High classification rates were achieved for "Pers" conditions versus "Snd" or "Rest" (AUC = 0.8-0.9), with explainability revealing actions units and gaze as key features. Additionally, moderate classification rates were attained for "Snd" versus "Rest" (AUC = 0.7), attributed to action units, limbs and head points, as well as for "Self" versus "Ctrl" (AUC = 0.7-0.8), due to facial points. These findings were consistent with a more conventional hypothesis-driven approach. Overall, our study suggests that our tool is well suited for holistic and fine-grained behavioral analysis and offers modularity for extension into more complex naturalistic environments.

SAUSI: an integrative assay for measuring social aversion and motivation

Social aversion is a key feature of numerous mental health disorders such as Social Anxiety and Autism Spectrum Disorders. Nevertheless, the biobehavioral mechanisms underlying social aversion remain poorly understood. Progress in understanding the etiology of social aversion has been hindered by the lack of comprehensive tools to assess social aversion in model systems. Here, we created a new behavioral task - Selective Access to Unrestricted Social Interaction (SAUSI), which integrates elements of social motivation, hesitancy, decision-making, and free interaction to enable the wholistic assessment of social aversion in mice. Using this novel assay, we found that social isolation-induced social aversion in mice is largely driven by increases in social fear and social motivation. Deep learning analyses revealed a unique behavioral footprint underlying the socially aversive state produced by isolation, demonstrating the compatibility of modern computational approaches with SAUSI. Social aversion was further assessed using traditional assays - including the 3-chamber sociability assay and the resident intruder assay - which were sufficient to reveal fragments of a social aversion phenotype, including changes to either social motivation or social interaction, but which failed to provide a wholistic assessment of social aversion. Critically, these assays were not sufficient to reveal key components of social aversion, including social freezing and social hesitancy behaviors. Lastly, we demonstrated that SAUSI is generalizable, as it can be used to assess social aversion induced by non-social stressors, such as foot shock. Our findings debut a novel task for the behavioral toolbox - one which overcomes limitations of previous assays, allowing for both social choice as well as free interaction, and offers a new approach for assessing social aversion in rodents.

SARTAB, a scalable system for automated real-time behavior detection based on animal tracking and Region Of Interest analysis: validation on fish courtship behavior

Methods from Machine Learning (ML) and Computer Vision (CV) have proven powerful tools for quickly and accurately analyzing behavioral recordings. The computational complexity of these techniques, however, often precludes applications that require real-time analysis: for example, experiments where a stimulus must be applied in response to a particular behavior or samples must be collected soon after the behavior occurs. Here, we describe SARTAB (Scalable Automated Real-Time Analysis of Behavior), a system that achieves automated real-time behavior detection by continuously monitoring animal positions relative to behaviorally relevant Regions Of Interest (ROIs). We then show how we used this system to detect infrequent courtship behaviors in Pseudotropheus demasoni (a species of Lake Malawi African cichlid fish) to collect neural tissue samples from actively behaving individuals for multiomic profiling at single nucleus resolution. Within this experimental context, we achieve high ROI and animal detection accuracies (mAP@[.5 : .95] of 0.969 and 0.718, respectively) and 100% classification accuracy on a set of 32 manually selected behavioral clips. SARTAB is unique in that all analysis runs on low-cost, edge-deployed hardware, making it a highly scalable and energy-efficient solution for real-time experimental feedback. Although our solution was developed specifically to study cichlid courtship behavior, the intrinsic flexibility of neural network analysis ensures that our approach can be adapted to novel species, behaviors, and environments.

Acquiring musculoskeletal skills with curriculum-based reinforcement learning

Efficient musculoskeletal simulators and powerful learning algorithms provide computational tools to tackle the grand challenge of understanding biological motor control. Our winning solution for the inaugural NeurIPS MyoChallenge leverages an approach mirroring human skill learning. Using a novel curriculum learning approach, we trained a recurrent neural network to control a realistic model of the human hand with 39 muscles to rotate two Baoding balls in the palm of the hand. In agreement with data from human subjects, the policy uncovers a small number of kinematic synergies, even though it is not explicitly biased toward low-dimensional solutions. However, selectively inactivating parts of the control signal, we found that more dimensions contribute to the task performance than suggested by traditional synergy analysis. Overall, our work illustrates the emerging possibilities at the interface of musculoskeletal physics engines, reinforcement learning, and neuroscience to advance our understanding of biological motor control.

Building egocentric models of local space from retinal input

Determining the location of objects relative to ourselves is essential for interacting with the world. Neural activity in the retina is used to form a vision-independent model of the local spatial environment relative to the body. For example, when an animal navigates through a forest, it rapidly shifts its gaze to identify the position of important objects, such as a tree obstructing its path. This seemingly trivial behavior belies a sophisticated neural computation. Visual information entering the brain in a retinocentric reference frame must be transformed into an egocentric reference frame to guide motor planning and action. This, in turn, allows the animal to extract the location of the tree and plan a path around it. In this review, we explore the anatomical, physiological, and computational implementation of retinocentric-to-egocentric reference frame transformations - a research area undergoing rapid progress stimulated by an ever-expanding molecular, physiological, and computational toolbox for probing neural circuits. We begin by summarizing evidence for retinocentric and egocentric reference frames in the brains of diverse organisms, from insects to primates. Next, we cover how distance estimation contributes to creating a three-dimensional representation of local space. We then review proposed implementations of reference frame transformations across different biological and artificial neural networks. Finally, we discuss how an internal egocentric model of the environment is maintained independently of the sensory inputs from which it is derived. By comparing findings across a variety of nervous systems and behaviors, we aim to inspire new avenues for investigating the neural basis of reference frame transformation, a canonical computation critical for modeling the external environment and guiding goal-directed behavior.

A contextual fear conditioning paradigm in head-fixed mice exploring virtual reality

Contextual fear conditioning is a classical laboratory task that tests associative memory formation and recall. Techniques such as multi-photon microscopy and holographic stimulation offer tremendous opportunities to understand the neural underpinnings of these memories. However, these techniques generally require animals to be head-fixed. There are few paradigms that test contextual fear conditioning in head-fixed mice, and none where the behavioral outcome following fear conditioning is freezing, the most common measure of fear in freely moving animals. To address this gap, we developed a contextual fear conditioning paradigm in head-fixed mice using virtual reality (VR) environments. We designed an apparatus to deliver tail shocks (unconditioned stimulus, US) while mice navigated a VR environment (conditioned stimulus, CS). The acquisition of contextual fear was tested when the mice were reintroduced to the shock-paired VR environment the following day. We tested three different variations of this paradigm and, in all of them, observed an increased conditioned fear response characterized by increased freezing behavior. This was especially prominent during the first trial in the shock-paired VR environment, compared to a neutral environment where the mice received no shocks. Our results demonstrate that head-fixed mice can be fear conditioned in VR, discriminate between a feared and neutral VR context, and display freezing as a conditioned response, similar to freely behaving animals. Furthermore, using a two-photon microscope, we imaged from large populations of hippocampal CA1 neurons before, during, and following contextual fear conditioning. Our findings reconfirmed those from the literature on freely moving animals, showing that CA1 place cells undergo remapping and show narrower place fields following fear conditioning. Our approach offers new opportunities to study the neural mechanisms underlying the formation, recall, and extinction of contextual fear memories. As the head-fixed preparation is compatible with multi-photon microscopy and holographic stimulation, it enables long-term tracking and manipulation of cells throughout distinct memory stages and provides subcellular resolution for investigating axonal, dendritic, and synaptic dynamics in real-time.

From animal models to human individuality: Integrative approaches to the study of brain plasticity

Plasticity allows organisms to form lasting adaptive changes in neural structures in response to interactions with the environment. It serves both species-general functions and individualized skill acquisition. To better understand human plasticity, we need to strengthen the dialogue between human research and animal models. Therefore, we propose to (1) enhance the interpretability of macroscopic methods used in human research by complementing molecular and fine-structural measures used in animals with such macroscopic methods, preferably applied to the same animals, to create macroscopic metrics common to both examined species; (2) launch dedicated cross-species research programs, using either well-controlled experimental paradigms, such as motor skill acquisition, or more naturalistic environments, where individuals of either species are observed in their habitats; and (3) develop conceptual and computational models linking molecular and fine-structural events to phenomena accessible by macroscopic methods. In concert, these three component strategies can foster new insights into the nature of plastic change.

Characterizing the structure of mouse behavior using Motion Sequencing

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

Historical insights at scale: A corpus-wide machine learning analysis of early modern astronomic tables

Understanding the evolution and dissemination of human knowledge over time faces challenges due to the abundance of historical materials and limited specialist resources. However, the digitization of historical archives presents an opportunity for AI-supported analysis. This study advances historical analysis by using an atomization-recomposition method that relies on unsupervised machine learning and explainable AI techniques. Focusing on the "Sacrobosco Collection," consisting of 359 early modern printed editions of astronomy textbooks from European universities (1472-1650), totaling 76,000 pages, our analysis uncovers temporal and geographic patterns in knowledge transformation. We highlight the relevant role of astronomy textbooks in shaping a unified mathematical culture, driven by competition among educational institutions and market dynamics. This approach deepens our understanding by grounding insights in historical context, integrating with traditional methodologies. Case studies illustrate how communities embraced scientific advancements, reshaping astronomic and geographical views and exploring scientific roots amidst a changing world.

The big mixup: Neural representation during natural modes of primate visual behavior

The primate brain has evolved specialized visual capacities to navigate complex physical and social environments. Researchers studying cortical circuits underlying these capacities have traditionally favored the use of simplified tasks and brief stimulus presentations in order to isolate cognitive variables with tight experimental control. As a result, operational theories about visual brain function have come to emphasize feature detection, hierarchical stimulus encoding, top-down task modulation, and functional segregation in distinct cortical areas. Recently, however, experimental paradigms combining natural behavior with electrophysiological recordings have begun to offer a distinctly different portrait of how the brain takes in and analyzes its visual surroundings. The present article reviews recent work in this area, highlighting some of the more surprising findings in domains of social vision and spatial navigation along with shifts in thinking that have begun to emanate from this approach.

Single unit electrophysiology recordings and computational modeling can predict octopus arm movement

The octopus simplified nervous system holds the potential to reveal principles of motor circuits and improve brain-machine interface devices through computational modeling with machine learning and statistical analysis. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100ms after stimulation were predictive of the resultant movement response. Computational models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. Deep learning models and unsupervised dimension reduction identified a consistent set of features that could be used to distinguish different types of arm movements. These models generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit.

Unsupervised decomposition of natural monkey behavior into a sequence of motion motifs

Nonhuman primates (NHPs) exhibit complex and diverse behavior that typifies advanced cognitive function and social communication, but quantitative and systematical measure of this natural nonverbal processing has been a technical challenge. Specifically, a method is required to automatically segment time series of behavior into elemental motion motifs, much like finding meaningful words in character strings. Here, we propose a solution called SyntacticMotionParser (SMP), a general-purpose unsupervised behavior parsing algorithm using a nonparametric Bayesian model. Using three-dimensional posture-tracking data from NHPs, SMP automatically outputs an optimized sequence of latent motion motifs classified into the most likely number of states. When applied to behavioral datasets from common marmosets and rhesus monkeys, SMP outperformed conventional posture-clustering models and detected a set of behavioral ethograms from publicly available data. SMP also quantified and visualized the behavioral effects of chemogenetic neural manipulations. SMP thus has the potential to dramatically improve our understanding of natural NHP behavior in a variety of contexts.

Deep learning based decoding of single local field potential events

How is information processed in the cerebral cortex? In most cases, recorded brain activity is averaged over many (stimulus) repetitions, which erases the fine-structure of the neural signal. However, the brain is obviously a single-trial processor. Thus, we here demonstrate that an unsupervised machine learning approach can be used to extract meaningful information from electro-physiological recordings on a single-trial basis. We use an auto-encoder network to reduce the dimensions of single local field potential (LFP) events to create interpretable clusters of different neural activity patterns. Strikingly, certain LFP shapes correspond to latency differences in different recording channels. Hence, LFP shapes can be used to determine the direction of information flux in the cerebral cortex. Furthermore, after clustering, we decoded the cluster centroids to reverse-engineer the underlying prototypical LFP event shapes. To evaluate our approach, we applied it to both extra-cellular neural recordings in rodents, and intra-cranial EEG recordings in humans. Finally, we find that single channel LFP event shapes during spontaneous activity sample from the realm of possible stimulus evoked event shapes. A finding which so far has only been demonstrated for multi-channel population coding.

A Markovian dynamics for Caenorhabditis elegans behavior across scales

How do we capture the breadth of behavior in animal movement, from rapid body twitches to aging? Using high-resolution videos of the nematode worm Caenorhabditis elegans, we show that a single dynamics connects posture-scale fluctuations with trajectory diffusion and longer-lived behavioral states. We take short posture sequences as an instantaneous behavioral measure, fixing the sequence length for maximal prediction. Within the space of posture sequences, we construct a fine-scale, maximum entropy partition so that transitions among microstates define a high-fidelity Markov model, which we also use as a means of principled coarse-graining. We translate these dynamics into movement using resistive force theory, capturing the statistical properties of foraging trajectories. Predictive across scales, we leverage the longest-lived eigenvectors of the inferred Markov chain to perform a top-down subdivision of the worm's foraging behavior, revealing both "runs-and-pirouettes" as well as previously uncharacterized finer-scale behaviors. We use our model to investigate the relevance of these fine-scale behaviors for foraging success, recovering a trade-off between local and global search strategies.

Muscle Synergy Analysis as a Tool for Assessing the Effectiveness of Gait Rehabilitation Therapies: A Methodological Review and Perspective

According to the modular hypothesis for the control of movement, muscles are recruited in synergies, which capture muscle coordination in space, time, or both. In the last two decades, muscle synergy analysis has become a well-established framework in the motor control field and for the characterization of motor impairments in neurological patients. Altered modular control during a locomotion task has been often proposed as a potential quantitative metric for characterizing pathological conditions. Therefore, the purpose of this systematic review is to analyze the recent literature that used a muscle synergy analysis of neurological patients' locomotion as an indicator of motor rehabilitation therapy effectiveness, encompassing the key methodological elements to date. Searches for the relevant literature were made in Web of Science, PubMed, and Scopus. Most of the 15 full-text articles which were retrieved and included in this review identified an effect of the rehabilitation intervention on muscle synergies. However, the used experimental and methodological approaches varied across studies. Despite the scarcity of studies that investigated the effect of rehabilitation on muscle synergies, this review supports the utility of muscle synergies as a marker of the effectiveness of rehabilitative therapy and highlights the challenges and open issues that future works need to address to introduce the muscle synergies in the clinical practice and decisional process.

LarvaTagger: manual and automatic tagging of Drosophila larval behaviour

MOTIVATION

As more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of the archetypal actions of a larva are regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for Drosophila larval behaviour must be retrained to learn new representations from new data. However, existing tools cannot transfer knowledge from large amounts of previously accumulated data. We introduce LarvaTagger, a piece of software that combines a pre-trained deep neural network, providing a continuous latent representation of larva actions for stereotypical behaviour identification, with a graphical user interface to manually tag the behaviour and train new automatic taggers with the updated ground truth.

RESULTS

We reproduced results from an automatic tagger with high accuracy, and we demonstrated that pre-training on large databases accelerates the training of a new tagger, achieving similar prediction accuracy using less data.

AVAILABILITY AND IMPLEMENTATION

All the code is free and open source. Docker images are also available. See gitlab.pasteur.fr/nyx/LarvaTagger.jl.

SuperAnimal pretrained pose estimation models for behavioral analysis

Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, reliable inference of poses currently requires domain knowledge and manual labeling effort to build supervised models. We present SuperAnimal, a method to develop unified foundation models that can be used on over 45 species, without additional manual labels. These models show excellent performance across six pose estimation benchmarks. We demonstrate how to fine-tune the models (if needed) on differently labeled data and provide tooling for unsupervised video adaptation to boost performance and decrease jitter across frames. If fine-tuned, SuperAnimal models are 10-100× more data efficient than prior transfer-learning-based approaches. We illustrate the utility of our models in behavioral classification and kinematic analysis. Collectively, we present a data-efficient solution for animal pose estimation.

An implantable device for wireless monitoring of diverse physio-behavioral characteristics in freely behaving small animals and interacting groups

Comprehensive, continuous quantitative monitoring of intricately orchestrated physiological processes and behavioral states in living organisms can yield essential data for elucidating the function of neural circuits under healthy and diseased conditions, for defining the effects of potential drugs and treatments, and for tracking disease progression and recovery. Here, we report a wireless, battery-free implantable device and a set of associated algorithms that enable continuous, multiparametric physio-behavioral monitoring in freely behaving small animals and interacting groups. Through advanced analytics approaches applied to mechano-acoustic signals of diverse body processes, the device yields heart rate, respiratory rate, physical activity, temperature, and behavioral states. Demonstrations in pharmacological, locomotor, and acute and social stress tests and in optogenetic studies offer unique insights into the coordination of physio-behavioral characteristics associated with healthy and perturbed states. This technology has broad utility in neuroscience, physiology, behavior, and other areas that rely on studies of freely moving, small animal models.

Measuring the replicability of our own research

In the study of transgenic mouse models of neurodevelopmental and neurodegenerative disorders, we use batteries of tests to measure deficits in behaviour and from the results of these tests, we make inferences about the mental states of the mice that we interpret as deficits in "learning", "memory", "anxiety", "depression", etc. This paper discusses the problems of determining whether a particular transgenic mouse is a valid mouse model of disease X, the problem of background strains, and the question of whether our behavioural tests are measuring what we say they are. The problem of the reliability of results is then discussed: are they replicable between labs and can we replicate our results in our own lab? This involves the study of intra- and inter- experimenter reliability. The variables that influence replicability and the importance of conducting a complete behavioural phenotype: sensory, motor, cognitive and social emotional behaviour are discussed. Then the thorny question of failure to replicate is examined: Is it a curse or a blessing? Finally, the role of failure in research and what it tells us about our research paradigms is examined.

Ethological computational psychiatry: Challenges and opportunities

Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over short timescales (e.g., trials, hours), many mental health conditions involve complex interactions of mood and cognition that are non-stationary across behavioral contexts and evolve over extended timescales. Here, we discuss opportunities, challenges, and possible future directions in computational psychiatry to quantify non-stationary continuously monitored behaviors. We suggest that this exploratory effort may contribute to a more precision-based approach to treating mental disorders and facilitate a more robust reverse translation across animal species. We conclude with ethical considerations for any field that aims to bridge artificial intelligence and patient monitoring.

Examining a punishment-related brain circuit with miniature fluorescence microscopes and deep learning

In humans experiencing substance use disorder (SUD), abstinence from drug use is often motivated by a desire to avoid some undesirable consequence of further use: health effects, legal ramifications, etc. This process can be experimentally modeled in rodents by training and subsequently punishing an operant response in a context-induced reinstatement procedure. Understanding the biobehavioral mechanisms underlying punishment learning is critical to understanding both abstinence and relapse in individuals with SUD. To date, most investigations into the neural mechanisms of context-induced reinstatement following punishment have utilized discrete loss-of-function manipulations that do not capture ongoing changes in neural circuitry related to punishment-induced behavior change. Here, we describe a two-pronged approach to analyzing the biobehavioral mechanisms of punishment learning using miniature fluorescence microscopes and deep learning algorithms. We review recent advancements in both techniques and consider a target neural circuit.

Visual detection of seizures in mice using supervised machine learning

Seizures are caused by abnormally synchronous brain activity that can result in changes in muscle tone, such as twitching, stiffness, limpness, or rhythmic jerking. These behavioral manifestations are clear on visual inspection and the most widely used seizure scoring systems in preclinical models, such as the Racine scale in rodents, use these behavioral patterns in semiquantitative seizure intensity scores. However, visual inspection is time-consuming, low-throughput, and partially subjective, and there is a need for rigorously quantitative approaches that are scalable. In this study, we used supervised machine learning approaches to develop automated classifiers to predict seizure severity directly from noninvasive video data. Using the PTZ-induced seizure model in mice, we trained video-only classifiers to predict ictal events, combined these events to predict an univariate seizure intensity for a recording session, as well as time-varying seizure intensity scores. Our results show, for the first time, that seizure events and overall intensity can be rigorously quantified directly from overhead video of mice in a standard open field using supervised approaches. These results enable high-throughput, noninvasive, and standardized seizure scoring for downstream applications such as neurogenetics and therapeutic discovery.

STPoseNet: A real-time spatiotemporal network model for robust mouse pose estimation

Animal behavior analysis plays a crucial role in contemporary neuroscience research. However, the performance of the frame-by-frame approach may degrade in scenarios with occlusions or motion blur. In this study, we propose a spatiotemporal network model based on YOLOv8 to enhance the accuracy of key-point detection in mouse behavioral experimental videos. This model integrates a time-domain tracking strategy comprising two components: the first part utilizes key-point detection results from the previous frame to detect potential target locations in the subsequent frame; the second part employs Kalman filtering to analyze key-point changes prior to detection, allowing for the estimation of missing key-points. In the comparison of pose estimation results between our approach, YOLOv8, DeepLabCut and SLEAP on videos of three mouse behavioral experiments, our approach demonstrated significantly superior performance. This suggests that our method offers a new and effective means of accurately tracking and estimating pose in mice through spatiotemporal processing.

An efficient visual servo tracker for herd monitoring by UAV

It is a challenging and meaningful task to carry out UAV-based livestock monitoring in high-altitude (more than 4500 m on average) and cold regions (annual average - 4 °C) on the Qinghai Tibet Plateau. The purpose of artificial intelligence (AI) is to execute automated tasks and to solve practical problems in actual applications by combining the software technology with the hardware carrier to create integrated advanced devices. Only in this way, the maximum value of AI could be realized. In this paper, a real-time tracking system with dynamic target tracking ability is proposed. It is developed based on the tracking-by-detection architecture using YOLOv7 and Deep SORT algorithms for target detection and tracking, respectively. In response to the problems encountered in the tracking process of complex and dense scenes, our work (1) Uses optical flow to compensate the Kalman filter, to solve the problem of mismatch between the target bounding box predicted by the Kalman filter (KF) and the input when the target detection in the current frame is complex, thereby improving the prediction accuracy; (2) Using a low confidence trajectory filtering method to reduce false positive trajectories generated by Deep SORT, thereby mitigating the impact of unreliable detection on target tracking. (3) A visual servo controller has been designed for the Unmanned Aerial Vehicle (UAV) to reduce the impact of rapid movement on tracking and ensure that the target is always within the field of view of the UAV camera, thereby achieving automatic tracking tasks. Finally, the system was tested using Tibetan yaks on the Qinghai Tibet Plateau as tracking targets, and the results showed that the system has real-time multi tracking ability and ideal visual servo effect in complex and dense scenes.

Evidence for socially influenced and potentially actively coordinated cooperation by bumblebees

Cooperation is common in animals, yet the specific mechanisms driving collaborative behaviour in different species remain unclear. We investigated the proximate mechanisms underlying the cooperative behaviour of bumblebees in two different tasks, where bees had to simultaneously push a block in an arena or a door at the end of a tunnel for access to reward. In both tasks, when their partner's entry into the arena/tunnel was delayed, bees took longer to first push the block/door compared with control bees that learned to push alone. In the tunnel task, just before gaining access to reward, bees were more likely to face towards their partner than expected by chance or compared with controls. These results show that bumblebees' cooperative behaviour is not simply a by-product of individual efforts but is socially influenced. We discuss how bees' turning behaviours, e.g. turning around before first reaching the door when their partner was delayed and turning back towards the door in response to seeing their partner heading towards the door, suggest the potential for active coordination. However, because these behaviours could also be interpreted as combined responses to social and secondary reinforcement cues, future studies are needed to help clarify whether bumblebees truly use active coordination.

Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools

Contemporary pose estimation methods enable precise measurements of behavior via supervised deep learning with hand-labeled video frames. Although effective in many cases, the supervised approach requires extensive labeling and often produces outputs that are unreliable for downstream analyses. Here, we introduce "Lightning Pose," an efficient pose estimation package with three algorithmic contributions. First, in addition to training on a few labeled video frames, we use many unlabeled videos and penalize the network whenever its predictions violate motion continuity, multiple-view geometry, and posture plausibility (semi-supervised learning). Second, we introduce a network architecture that resolves occlusions by predicting pose on any given frame using surrounding unlabeled frames. Third, we refine the pose predictions post-hoc by combining ensembling and Kalman smoothing. Together, these components render pose trajectories more accurate and scientifically usable. We release a cloud application that allows users to label data, train networks, and predict new videos directly from the browser.

A-SOiD, an active-learning platform for expert-guided, data-efficient discovery of behavior

To identify and extract naturalistic behavior, two methods have become popular: supervised and unsupervised. Each approach carries its own strengths and weaknesses (for example, user bias, training cost, complexity and action discovery), which the user must consider in their decision. Here, an active-learning platform, A-SOiD, blends these strengths, and in doing so, overcomes several of their inherent drawbacks. A-SOiD iteratively learns user-defined groups with a fraction of the usual training data, while attaining expansive classification through directed unsupervised classification. In socially interacting mice, A-SOiD outperformed standard methods despite requiring 85% less training data. Additionally, it isolated ethologically distinct mouse interactions via unsupervised classification. We observed similar performance and efficiency using nonhuman primate and human three-dimensional pose data. In both cases, the transparency in A-SOiD's cluster definitions revealed the defining features of the supervised classification through a game-theoretic approach. To facilitate use, A-SOiD comes as an intuitive, open-source interface for efficient segmentation of user-defined behaviors and discovered sub-actions.

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

Introduction

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

Methods

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

Results

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

Discussion

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

The Emerging Science of Interacting Minds

For over a century, psychology has focused on uncovering mental processes of a single individual. However, humans rarely navigate the world in isolation. The most important determinants of successful development, mental health, and our individual traits and preferences arise from interacting with other individuals. Social interaction underpins who we are, how we think, and how we behave. Here we discuss the key methodological challenges that have limited progress in establishing a robust science of how minds interact and the new tools that are beginning to overcome these challenges. A deep understanding of the human mind requires studying the context within which it originates and exists: social interaction.

MouseVUER: video based open-source system for laboratory mouse home-cage monitoring

Video monitoring of mice in the home-cage reveals behavior profiles without the disruptions caused by specialized test setups and makes it possible to quantify changes in behavior patterns continually over long time frames. Several commercial home-cage monitoring systems are available with varying costs and capabilities; however there are currently no open-source systems for home-cage monitoring. We present an open-source system for top-down video monitoring of research mice in a slightly modified home-cage. The system is designed for integration with Allentown NexGen ventilated racks and allows unobstructed view of up to three mice, but can also be operated outside the rack. The system has an easy to duplicate and assemble home-cage design along with a video acquisition solution. The system utilizes a depth video camera, and we demonstrate the robustness of depth video for home-cage mice monitoring. For researchers without access to Allentown NexGen ventilated racks, we provide designs and assembly instructions for a standalone non-ventilated rack solution that holds three systems for more compact and efficient housing. We make all the design files, along with detailed assembly and installation instructions, available on the project webpage ( https://github.com/NIH-CIT-OIR-SPIS/MouseVUER ).

Capturing wild animal welfare: a physiological perspective

Affective states, such as emotions, are presumably widespread across the animal kingdom because of the adaptive advantages they are supposed to confer. However, the study of the affective states of animals has thus far been largely restricted to enhancing the welfare of animals managed by humans in non-natural contexts. Given the diversity of wild animals and the variable conditions they can experience, extending studies on animal affective states to the natural conditions that most animals experience will allow us to broaden and deepen our general understanding of animal welfare. Yet, this same diversity makes examining animal welfare in the wild highly challenging. There is therefore a need for unifying theoretical frameworks and methodological approaches that can guide researchers keen to engage in this promising research area. The aim of this article is to help advance this important research area by highlighting the central relationship between physiology and animal welfare and rectify its apparent oversight, as revealed by the current scientific literature on wild animals. Moreover, this article emphasises the advantages of including physiological markers to assess animal welfare in the wild (e.g. objectivity, comparability, condition range, temporality), as well as their concomitant limitations (e.g. only access to peripheral physiological markers with complex relationships with affective states). Best-practice recommendations (e.g. replication and multifactorial approaches) are also provided to allow physiological markers to be used most effectively and appropriately when assessing the welfare of animals in their natural habitat. This review seeks to provide the foundation for a new and distinct research area with a vast theoretical and applied potential: wild animal welfare physiology.

Long Timescales, Individual Differences, and Scale Invariance in Animal Behavior

The explosion of data on animal behavior in more natural contexts highlights the fact that these behaviors exhibit correlations across many timescales. However, there are major challenges in analyzing these data: records of behavior in single animals have fewer independent samples than one might expect. In pooling data from multiple animals, individual differences can mimic long-ranged temporal correlations; conversely, long-ranged correlations can lead to an overestimate of individual differences. We suggest an analysis scheme that addresses these problems directly, apply this approach to data on the spontaneous behavior of walking flies, and find evidence for scale-invariant correlations over nearly three decades in time, from seconds to one hour. Three different measures of correlation are consistent with a single underlying scaling field of dimension Δ=0.180±0.005.

Automated procedure to detect subtle motor alterations in the balance beam test in a mouse model of early Parkinson's disease

Parkinson's disease (PD) is the most common motor neurodegenerative disorder, characterised by aggregated α-synuclein (α-syn) constituting Lewy bodies. We aimed to investigate temporal changes in motor impairments in a PD mouse model induced by overexpression of α-syn with the conventional manual analysis of the balance beam test and a novel approach using machine learning algorithms to automate behavioural analysis. We combined automated animal tracking using markerless pose estimation in DeepLabCut, with automated behavioural classification in Simple Behavior Analysis. Our automated procedure was able to detect subtle motor deficits in mouse performances in the balance beam test that the manual analysis approach could not assess. The automated model revealed time-course significant differences for the "walking" behaviour in the mean interval between each behavioural bout, the median event bout duration and the classifier probability of occurrence in male PD mice, even though no statistically significant loss of tyrosine hydroxylase in the nigrostriatal system was found in either sex. These findings are valuable for early detection of motor impairment in early PD animal models. We provide a user-friendly, step-by-step guide for automated assessment of mouse performances in the balance beam test, which aims to be replicable without any significant computational and programming knowledge.

Learning algorithms estimate pose and detect motor anomalies in flies exposed to minimal doses of a toxicant

Pesticide exposure, even at low doses, can have detrimental effects on ecosystems. This study aimed at validating the use of machine learning for recognizing motor anomalies, produced by minimal insecticide exposure on a model insect species. The Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae), was exposed to food contaminated with low concentrations of Carlina acaulis essential oil (EO). A deep learning approach enabled fly pose estimation on video recordings in a custom-built arena. Five machine learning algorithms were trained on handcrafted features, extracted from the predicted pose, to distinguish treated individuals. Random Forest and K-Nearest Neighbor algorithms best performed, with an area under the receiver operating characteristic (ROC) curve of 0.75 and 0.73, respectively. Both algorithms achieved an accuracy of 0.71. Results show the machine learning potential for detecting sublethal effects arising from insecticide exposure on fly motor behavior, which could also affect other organisms and environmental health.

OpenApePose, a database of annotated ape photographs for pose estimation

Because of their close relationship with humans, non-human apes (chimpanzees, bonobos, gorillas, orangutans, and gibbons, including siamangs) are of great scientific interest. The goal of understanding their complex behavior would be greatly advanced by the ability to perform video-based pose tracking. Tracking, however, requires high-quality annotated datasets of ape photographs. Here we present OpenApePose, a new public dataset of 71,868 photographs, annotated with 16 body landmarks of six ape species in naturalistic contexts. We show that a standard deep net (HRNet-W48) trained on ape photos can reliably track out-of-sample ape photos better than networks trained on monkeys (specifically, the OpenMonkeyPose dataset) and on humans (COCO) can. This trained network can track apes almost as well as the other networks can track their respective taxa, and models trained without one of the six ape species can track the held-out species better than the monkey and human models can. Ultimately, the results of our analyses highlight the importance of large, specialized databases for animal tracking systems and confirm the utility of our new ape database.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

Naturally Inspired Molecules for Neuropathic Pain Inhibition-Effect of Mirogabalin and Cebranopadol on Mechanical and Thermal Nociceptive Threshold in Mice

BACKGROUND

Neuropathic pain is drug-resistant to available analgesics and therefore novel treatment options for this debilitating clinical condition are urgently needed. Recently, two drug candidates, namely mirogabalin and cebranopadol have become a subject of interest because of their potential utility as analgesics for chronic pain treatment. However, they have not been investigated thoroughly in some types of neuropathic pain, both in humans and experimental animals.

METHODS

This study used the von Frey test, the hot plate test and the two-plate thermal place preference test supported by image analysis and machine learning to assess the effect of intraperitoneal mirogabalin and subcutaneous cebranopadol on mechanical and thermal nociceptive threshold in mouse models of neuropathic pain induced by streptozotocin, paclitaxel and oxaliplatin.

RESULTS

Mirogabalin and cebranopadol effectively attenuated tactile allodynia in models of neuropathic pain induced by streptozotocin and paclitaxel. Cebranopadol was more effective than mirogabalin in this respect. Both drugs also elevated the heat nociceptive threshold in mice. In the oxaliplatin model, cebranopadol and mirogabalin reduced cold-exacerbated pain.

CONCLUSIONS

Since mirogabalin and cebranopadol are effective in animal models of neuropathic pain, they seem to be promising novel therapies for various types of neuropathic pain in patients, in particular those who are resistant to available analgesics.

Emotions and courtship help bonded pairs cooperate, but emotional agents are vulnerable to deceit

Coordinated pair bonds are common in birds and also occur in many other taxa. How do animals solve the social dilemmas they face in coordinating with a partner? We developed an evolutionary model to explore this question, based on observations that a) neuroendocrine feedback provides emotional bookkeeping which is thought to play a key role in vertebrate social bonds and b) these bonds are developed and maintained via courtship interactions that include low-stakes social dilemmas. Using agent-based simulation, we found that emotional bookkeeping and courtship sustained cooperation in the iterated prisoner's dilemma in noisy environments, especially when combined. However, when deceitful defection was possible at low cost, courtship often increased cooperation, whereas emotional bookkeeping decreased it.