Improving pain assessment in mice and rats with advanced videography and computational approaches

Closed-loop neural interfaces for pain: Where do we stand?

Advances in closed-loop neural interfaces and neuromodulation have offered a potentially effective and non-addictive treatment for chronic pain. These interfaces link neural sensors with device outputs to provide temporally precise stimulation. We discuss challenges and trends of state-of-the-art neural interfaces for treating pain in animal models and human pilot trials.

High-speed imaging of evoked rodent mechanical behaviors yields variable results that are not predictive of inflammatory injury

ABSTRACT

Few analgesics identified using preclinical models have successfully translated to clinical use. These translational limitations may be due to the unidimensional nature of behavioral response measures used to assess rodent nociception. Advances in high-speed videography for pain behavior allow for objective quantification of nuanced aspects of evoked paw withdrawal responses. However, whether videography-based assessments of mechanical hypersensitivity outperform traditional measurement reproducibility is unknown. First, we determined whether high-speed videography of paw withdrawal was reproducible across experimenters. Second, we examined whether this method distinguishes behavioral responses exhibited by naive mice and mice with complete Freund's adjuvant (CFA)-induced inflammation. Twelve experimenters stimulated naive C57BL/6 mice with varying mechanical stimuli. Paw withdrawal responses were recorded with high-speed videography and scored offline by one individual. Our group was unable to replicate the original findings produced by high-speed videography analysis. Surprisingly, ∼80% of variation was not accounted for by variables previously reported to distinguish between responses to innocuous and noxious stimuli (paw height, paw velocity, and pain score), or by additional variables (experimenter, time-of-day, and animal), but rather by unidentified factors. Similar high-speed videography assessments were performed in CFA- and vehicle-treated animals, and the cumulative data failed to reveal an effect of CFA injection on withdrawal as measured by high-speed videography. This study does not support using paw height, velocity, or pain score measurements from high-speed recordings to delineate behavioral responses to innocuous and noxious stimuli. Our group encourages the continued use of traditional mechanical withdrawal assessments until additional high-speed withdrawal measures are validated in established pain models.

Current understanding of the molecular mechanisms of chemotherapy-induced peripheral neuropathy

Chemotherapy-induced peripheral neuropathy (CIPN) is the most common off-target adverse effects caused by various chemotherapeutic agents, such as cisplatin, oxaliplatin, paclitaxel, vincristine and bortezomib. CIPN is characterized by a substantial loss of primary afferent sensory axonal fibers leading to sensory disturbances in patients. An estimated of 19-85% of patients developed CIPN during the course of chemotherapy. The lack of preventive measures and limited treatment options often require a dose reduction or even early termination of life-saving chemotherapy, impacting treatment efficacy and patient survival. In this Review, we summarized the current understanding on the pathogenesis of CIPN. One prominent change induced by chemotherapeutic agents involves the disruption of neuronal cytoskeletal architecture and axonal transport dynamics largely influenced by the interference of microtubule stability in peripheral neurons. Due to an ineffective blood-nerve barrier in our peripheral nervous system, exposure to some chemotherapeutic agents causes mitochondrial swelling in peripheral nerves, which lead to the opening of mitochondrial permeability transition pore and cytochrome c release resulting in degeneration of primary afferent sensory fibers. The exacerbated nociceptive signaling and pain transmission in CIPN patients is often linked the increased neuronal excitability largely due to the elevated expression of various ion channels in the dorsal root ganglion neurons. Another important contributing factor of CIPN is the neuroinflammation caused by an increased infiltration of immune cells and production of inflammatory cytokines. In the central nervous system, chemotherapeutic agents also induce neuronal hyperexcitability in the spinal dorsal horn and anterior cingulate cortex leading to the development of central sensitization that causes CIPN. Emerging evidence suggests that the change in the composition and diversity of gut microbiota (dysbiosis) could have direct impact on the development and progression of CIPN. Collectively, all these aspects contribute to the pathogenesis of CIPN. Recent advances in RNA-sequencing offer solid platform for in silico drug screening which enable the identification of novel therapeutic agents or repurpose existing drugs to alleviate CIPN, holding immense promises for enhancing the quality of life for cancer patients who undergo chemotherapy and improve their overall treatment outcomes.

Reproducible and fully automated testing of nocifensive behavior in mice

Pain in rodents is often inferred from their withdrawal from noxious stimulation. Threshold stimulus intensity or response latency is used to quantify pain sensitivity. This usually involves applying stimuli by hand and measuring responses by eye, which limits reproducibility and throughput. We describe a device that standardizes and automates pain testing by providing computer-controlled aiming, stimulation, and response measurement. Optogenetic and thermal stimuli are applied using blue and infrared light, respectively. Precise mechanical stimulation is also demonstrated. Reflectance of red light is used to measure paw withdrawal with millisecond precision. We show that consistent stimulus delivery is crucial for resolving stimulus-dependent variations in withdrawal and for testing with sustained stimuli. Moreover, substage video reveals "spontaneous" behaviors for consideration alongside withdrawal metrics to better assess the pain experience. The entire process was automated using machine learning. RAMalgo (reproducible automated multimodal algometry) improves the standardization, comprehensiveness, and throughput of preclinical pain testing.

Pain: Behavioural expression and response in an evolutionary framework

An evolutionary perspective offers insights into the major public health problem of chronic (persistent) pain; behaviours associated with it perpetuate both pain and disability. Pain is motivating, and pain-related behaviours promote recovery by immediate active or passive defence; subsequent protection of wounds; suppression of competing responses; energy conservation; vigilance to threat; and learned avoidance of associated cues. When these persist beyond healing, as in chronic pain, they are disabling. In mammals, facial and bodily expression of pain is visible and identifiable by others, while social context, including conspecifics' responses, modulate pain. Studies of responses to pain emphasize onlooker empathy, but people with chronic pain report feeling disbelieved and stigmatized. Observers frequently discount others' pain, best understood in terms of cheater detection-alertness to free riders that underpins the capacity for prosocial behaviours. These dynamics occur both in everyday life and in clinical encounters, providing an account of the adaptiveness of pain-related behaviours.

A Deep-Learning Driven Investigation of the Circuit Basis for Reflexive Hypersensitivity to Thermal Pain

Objectively measuring animal behavior is vital to understanding the neural circuits underlying pain. Recent progress in machine vision has presented unprecedented scope in behavioral analysis. Here, we apply DeepLabCut (DLC) to dissect mouse behavior on the thermal-plate test - a commonly used paradigm to ascertain supraspinal contributions to noxious thermal sensation and pain hypersensitivity. We determine the signature characteristics of the pattern of mouse movement and posture in 3D in response to a range of temperatures from innocuous to noxious on the thermal-plate test. Next, we test how acute chemical and chronic inflammatory injuries sensitize mouse behaviors. Repeated exposure to noxious temperatures on the thermal plate can induce learning. In this study, we design a novel assay and formulate an analytical pipeline to facilitate the dissection of plasticity mechanisms in pain circuits in the brain. Last, we record and test how activating Tacr1 expressing PBN neurons (PBNTacr1) - a population responsive to sustained noxious stimuli- affects mouse behavior on the thermal plate test. Taken together, we demonstrate that by tracking a single body part of a mouse, we can reveal the behavioral signatures of mice exposed to noxious surface temperatures, report the alterations of the same when injured, and determine if a molecularly and anatomically defined pain-responsive circuit plays a role in the reflexive hypersensitivity to thermal pain.

HomeCageScan analysis reveals ongoing pain in Fabry rats

HomeCageScan (HCS) is an automated behavioral scoring system that can be used to classify and quantify rodent behaviors in the home cage. Although HCS has been used for a number of inducible models of severe pain, little has been done to test this system in clinically relevant genetic disease models associated with chronic pain such as Fabry disease. Rats with Fabry disease exhibit mechanical hypersensitivity, however, it is unclear if these rodents also exhibit ongoing non-evoked pain. Therefore, we analyzed HCS data from male and female rats with Fabry disease. Using hierarchical clustering and principal component analysis, we found both sex and genotype differences in several home cage behaviors. Additionally, we used hierarchical clustering to derive behavioral clusters in an unbiased manner. Analysis of these behavioral clusters showed that primarily female Fabry animals moved less, spent less time caring for themselves (e.g., less time spent grooming and drinking), explored less, and slept more; changes that are similar to lifestyle changes observed in patients with long lasting chronic pain. We also show that sniffing, one of the exploratory behaviors that is depressed in Fabry animals, can be partly restored with the analgesic gabapentin, suggesting that depressed sniffing may reflect ongoing pain. Therefore, this approach to HCS data analysis can be used to assess drug efficacy in Fabry disease and potentially other genetic and inducible rodent models associated with persistent pain.

Clinical Assessments of Fracture Healing and Basic Science Correlates: Is There Room for Convergence?

PURPOSE OF REVIEW

The purpose of this review is to summarize the clinical and basic science methods used to assess fracture healing and propose a framework to improve the translational possibilities.

RECENT FINDINGS

Mainstays of fracture healing assessment include clinical examination, various imaging modalities, and assessment of function. Pre-clinical studies have yielded insight into biomechanical progression as well as the genetic, molecular, and cellular processes of fracture healing. Efforts are emerging to identify early markers to predict impaired healing and possibly early intervention to alter these processes. Despite of the differences in clinical and preclinical research, opportunities exist to unify and improve the translational efforts between these arenas to develop and optimize our ability to assess and predict fracture healing, thereby improving the clinical care of these patients.

Next generation behavioral sequencing for advancing pain quantification

With symptoms such as spontaneous pain and pathologically heightened sensitivity to stimuli, chronic pain accounts for about 20% of physician visits and up to 2/3 of patients are dissatisfied with current treatments. Much of our knowledge on pain processing and analgesics has emerged from behavioral studies performed on animals presenting the same symptoms under pathological conditions. While humans can verbally describe their pain, studies on rodents have relied on behavioral assays providing non-exhaustive characterization or altering animals' original sensitivity through repetitive stimulations. The emergence of what we term "next-generation behavioral sequencing" is now permitting us to quantitatively describe behavioral features on millisecond to minutes long timescales that lie beyond easy detection with the unaided eye. Here, we summarize emerging videography and computational based behavioral approaches that have the potential to significantly improve pain research.

Machine learning and deep learning frameworks for the automated analysis of pain and opioid withdrawal behaviors

The automation of behavioral tracking and analysis in preclinical research can serve to advance the rate of research outcomes, increase experimental scalability, and challenge the scientific reproducibility crisis. Recent advances in the efficiency, accuracy, and accessibility of deep learning (DL) and machine learning (ML) frameworks are enabling this automation. As the ongoing opioid epidemic continues to worsen alongside increasing rates of chronic pain, there are ever-growing needs to understand opioid use disorders (OUDs) and identify non-opioid therapeutic options for pain. In this review, we examine how these related needs can be advanced by the development and validation of DL and ML resources for automated pain and withdrawal behavioral tracking. We aim to emphasize the utility of these tools for automated behavioral analysis, and we argue that currently developed models should be deployed to address novel questions in the fields of pain and OUD research.

The history of pain measurement in humans and animals

Pain needs to be measured in order to be studied and managed. Pain measurement strategies in both humans and non-human animals have varied widely over the years and continue to evolve. This review describes the historical development of human and animal algesiometry.

Advancements in Genomic and Behavioral Neuroscience Analysis for the Study of Normal and Pathological Brain Function

Psychiatric and neurological disorders are influenced by an undetermined number of genes and molecular pathways that may differ among afflicted individuals. Functionally testing and characterizing biological systems is essential to discovering the interrelationship among candidate genes and understanding the neurobiology of behavior. Recent advancements in genetic, genomic, and behavioral approaches are revolutionizing modern neuroscience. Although these tools are often used separately for independent experiments, combining these areas of research will provide a viable avenue for multidimensional studies on the brain. Herein we will briefly review some of the available tools that have been developed for characterizing novel cellular and animal models of human disease. A major challenge will be openly sharing resources and datasets to effectively integrate seemingly disparate types of information and how these systems impact human disorders. However, as these emerging technologies continue to be developed and adopted by the scientific community, they will bring about unprecedented opportunities in our understanding of molecular neuroscience and behavior.

Measuring Mouse Somatosensory Reflexive Behaviors with High-speed Videography, Statistical Modeling, and Machine Learning

Objectively measuring and interpreting an animal's sensory experience remains a challenging task. This is particularly true when using preclinical rodent models to study pain mechanisms and screen for potential new pain treatment reagents. How to determine their pain states in a precise and unbiased manner is a hurdle that the field will need to overcome. Here, we describe our efforts to measure mouse somatosensory reflexive behaviors with greatly improved precision by high-speed video imaging. We describe how coupling sub-second ethograms of reflexive behaviors with a statistical reduction method and supervised machine learning can be used to create a more objective quantitative mouse "pain scale." Our goal is to provide the readers with a protocol of how to integrate some of the new tools described here with currently used mechanical somatosensory assays, while discussing the advantages and limitations of this new approach.

Chronic paternal morphine exposure increases sensitivity to morphine-derived pain relief in male progeny

Parental history of opioid exposure is seldom considered when prescribing opioids for pain relief. To explore whether parental opioid exposure may affect sensitivity to morphine in offspring, we developed a "rat pain scale" with high-speed imaging, machine learning, and mathematical modeling in a multigenerational model of paternal morphine self-administration. We find that the most commonly used tool to measure mechanical sensitivity in rodents, the von Frey hair, is not painful in rats during baseline conditions. We also find that male progeny of morphine-treated sires had no baseline changes in mechanical pain sensitivity but were more sensitive to the pain-relieving effects of morphine. Using RNA sequencing across pain-relevant brain regions, we identify gene expression changes within the regulator of G protein signaling family of proteins that may underlie this multigenerational phenotype. Together, this rat pain scale revealed that paternal opioid exposure increases sensitivity to morphine's pain-relieving effects in male offspring.

Innovations and advances in modelling and measuring pain in animals

Best practices in preclinical algesiometry (pain behaviour testing) have shifted over the past decade as a result of technological advancements, the continued dearth of translational progress and the emphasis that funding institutions and journals have placed on rigour and reproducibility. Here we describe the changing trends in research methods by analysing the methods reported in preclinical pain publications from the past 40 years, with a focus on the last 5 years. We also discuss how the status quo may be hampering translational success. This discussion is centred on four fundamental decisions that apply to every pain behaviour experiment: choice of subject (model organism), choice of assay (pain-inducing injury), laboratory environment and choice of outcome measures. Finally, we discuss how human tissues, which are increasingly accessible, can be used to validate the translatability of targets and mechanisms identified in animal pain models.

Mouse Models of Osteoarthritis: A Summary of Models and Outcomes Assessment

Osteoarthritis (OA) is a multidimensional health problem and a common chronic disease. It has a substantial impact onpatient quality of life and is a common cause of pain and mobility issues in older adults. The functional limitations, lack of curative treatments, and cost to society all demonstrate the need for translational and clinical research. The use of OA models in mice is important for achieving a better understanding of the disease. Models with clinical relevance are needed to achieve 2 main goals: to assess the impact of the OA disease (pain and function) and to study the efficacy of potential treatments. However, few OA models include practical strategies for functional assessment of the mice. OA signs in mice incorporate complex interrelations between pain and dysfunction. The current review provides a comprehensive compilation of mousemodels of OA and animal evaluations that include static and dynamic clinical assessment of the mice, merging evaluationof pain and function by using automatic and noninvasive techniques. These new techniques allow simultaneous recordingof spontaneous activity from thousands of home cages and also monitor environment conditions. Technologies such as videographyand computational approaches can also be used to improve pain assessment in rodents but these new tools must first be validated experimentally. An example of a new tool is the digital ventilated cage, which is an automated home-cage monitor that records spontaneous activity in the cages.

Brain circuits for pain and its treatment

[Figure: see text].

A New Look at Infant Problem-Solving: Using DeepLabCut to Investigate Exploratory Problem-Solving Approaches

When confronted with novel problems, problem-solvers must decide whether to copy a modeled solution or to explore their own unique solutions. While past work has established that infants can learn to solve problems both through their own exploration and through imitation, little work has explored the factors that influence which of these approaches infants select to solve a given problem. Moreover, past work has treated imitation and exploration as qualitatively distinct, although these two possibilities may exist along a continuum. Here, we apply a program novel to developmental psychology (DeepLabCut) to archival data (Lucca et al., 2020) to investigate the influence of the effort and success of an adult's modeled solution, and infants' firsthand experience with failure, on infants' imitative versus exploratory problem-solving approaches. Our results reveal that tendencies toward exploration are relatively immune to the information from the adult model, but that exploration generally increased in response to firsthand experience with failure. In addition, we found that increases in maximum force and decreases in trying time were associated with greater exploration, and that exploration subsequently predicted problem-solving success on a new iteration of the task. Thus, our results demonstrate that infants increase exploration in response to failure and that exploration may operate in a larger motivational framework with force, trying time, and expectations of task success.

B-SOiD, an open-source unsupervised algorithm for identification and fast prediction of behaviors

Studying naturalistic animal behavior remains a difficult objective. Recent machine learning advances have enabled limb localization; however, extracting behaviors requires ascertaining the spatiotemporal patterns of these positions. To provide a link from poses to actions and their kinematics, we developed B-SOiD - an open-source, unsupervised algorithm that identifies behavior without user bias. By training a machine classifier on pose pattern statistics clustered using new methods, our approach achieves greatly improved processing speed and the ability to generalize across subjects or labs. Using a frameshift alignment paradigm, B-SOiD overcomes previous temporal resolution barriers. Using only a single, off-the-shelf camera, B-SOiD provides categories of sub-action for trained behaviors and kinematic measures of individual limb trajectories in any animal model. These behavioral and kinematic measures are difficult but critical to obtain, particularly in the study of rodent and other models of pain, OCD, and movement disorders.

Sex differences in neuroimmune and glial mechanisms of pain

ABSTRACT

Pain is the primary motivation for seeking medical care. Although pain may subside as inflammation resolves or an injury heals, it is increasingly evident that persistency of the pain state can occur with significant regularity. Chronic pain requires aggressive management to minimize its physiological consequences and diminish its impact on quality of life. Although opioids commonly are prescribed for intractable pain, concerns regarding reduced efficacy, as well as risks of tolerance and dependence, misuse, diversion, and overdose mortality rates limit their utility. Advances in development of nonopioid interventions hinge on our appreciation of underlying mechanisms of pain hypersensitivity. For instance, the contributory role of immunity and the associated presence of autoimmune syndromes has become of particular interest. Males and females exhibit fundamental differences in innate and adaptive immune responses, some of which are present throughout life, whereas others manifest with reproductive maturation. In general, the incidence of chronic pain conditions, particularly those with likely autoimmune covariates, is significantly higher in women. Accordingly, evidence is now accruing in support of neuroimmune interactions driving sex differences in the development and maintenance of pain hypersensitivity and chronicity. This review highlights known sexual dimorphisms of neuroimmune signaling in pain states modeled in rodents, which may yield potential high-value sex-specific targets to inform future analgesic drug discovery efforts.

Sensory Symphonies: How Excitatory Spinal Cord Modules Orchestrate Behavior

In this issue of Neuron, Gatto et al. (2021) demonstrate that tactile reflexes are driven by excitatory modules defined by location, while Peirs et al. (2021) show that the circuits implicated in the conversion of touch to pain are defined by the nature of the injury.

A machine-vision approach for automated pain measurement at millisecond timescales

Objective and automatic measurement of pain in mice remains a barrier for discovery in neuroscience. Here, we capture paw kinematics during pain behavior in mice with high-speed videography and automated paw tracking with machine and deep learning approaches. Our statistical software platform, PAWS (Pain Assessment at Withdrawal Speeds), uses a univariate projection of paw position over time to automatically quantify seven behavioral features that are combined into a single, univariate pain score. Automated paw tracking combined with PAWS reveals a behaviorally divergent mouse strain that displays hypersensitivity to mechanical stimuli. To demonstrate the efficacy of PAWS for detecting spinally versus centrally mediated behavioral responses, we chemogenetically activated nociceptive neurons in the amygdala, which further separated the pain-related behavioral features and the resulting pain score. Taken together, this automated pain quantification approach will increase objectivity in collecting rigorous behavioral data, and it is compatible with other neural circuit dissection tools for determining the mouse pain state.