tracking pain states in mice: automating eye squint measurement with deeplabcut

Real-Time Mouse Eye Squint Detection for Migraine Pain Tracking Using DeepLabCut

Migraine is one of the most prevalent brain disorders worldwide, affecting more than one billion people1. Preclinical mouse models of migraine have emerged as an informative way to study the mechanisms of migraine as these studies can be more easily controlled than human studies, thus enabling causal study of migraine-related behavior2. Such models have demonstrated a strong and repeatable phenotypic response to migraine-inducing compounds, such as calcitonin-gene-related peptide (CGRP). The need for robust measurements of migraine-relevant behaviors in rodent models persists, especially those that may be coupled with mechanistic metrics such as imaging and electrophysiological approaches.
Migraine-like brain states have been phenotypically characterized by the presence of light aversion, paw allodynia, facial hyperalgesia to noxious stimuli, and facial grimace3. Such behaviors are measured by total time spent in light (light aversion) and paw or facial touch sensitivity thresholds (paw allodynia and facial hyperalgesia) and are restricted to a single readout over large periods of time (minutes or longer). Migraine-like behaviors can be elicited in animals by dosing with migraine-inducing compounds such as CGRP, mimicking symptoms experienced by human patients with migraine3 (i.e., demonstrating face validity). Such compounds also produce migraine symptoms when administered in humans, demonstrating the construct&#160;validity of these models4. Studies in which behavioral phenotypes were attenuated pharmacologically have led to discoveries related to the treatment of migraine and provide further substantiation of these models (i.e., demonstrating predictive validity)5,6.
For example, a monoclonal anti-CGRP antibody (ALD405) was shown to reduce light-aversive behavior5 and facial grimace in mice6 treated with CGRP, and other studies have demonstrated that CGRP antagonist drugs reduce nitrous oxide-induced migraine-like behaviors in animals7,8. Recent clinical trials have shown success in treating migraine by blocking CGRP9,10 leading to multiple FDA-approved drugs targeting CGRP or its receptor. Preclinical assessment of migraine-related phenotypes has led to breakthroughs in clinical findings and is, therefore, essential to understanding some of the more complex aspects of migraine that are difficult to directly test in humans.
Despite numerous advantages, experiments using these rodent behavioral readouts of migraine are often restricted in their time point sampling abilities and can be subjective and prone to human experimental error. Many behavioral assays are limited in the ability to capture activity at finer temporal resolutions, often making it difficult to capture more dynamic elements that occur at a sub-second timescale, such as at the level of brain activity. It has proven difficult to quantify the more spontaneous, naturally occurring elements of behavior over time at a meaningful temporal resolution for studying neurophysiological mechanisms. Creating a way to identify migraine-like activity at faster timescales would allow for externally validating migraine-like brain states. This, in turn, could be synchronized with brain activity to create more robust brain activity profiles of migraine.
One such migraine-related phenotype, facial grimace, is utilized across various contexts as a measurement of pain in animals that can be measured instantaneously and tracked over time11. Facial grimace is often used as an indicator of spontaneous pain based on the idea that humans (especially non-verbal humans) and other mammalian species display natural changes in facial expression when experiencing pain11. Studies measuring facial grimace as an indication of pain in mice in the last decade have utilized scales such as the Mouse Grimace Scale (MGS) to standardize the characterization of pain in rodents12. The facial expression variables of the MGS include orbital tightening (squint), nose bulge, cheek bulge, ear position, and whisker change. Even though the MGS has been shown to reliably characterize pain in animals13, it is notoriously subjective and relies on accurate scoring, which can vary across experimenters. Additionally, the MGS is limited in that it utilizes a non-continuous scale and lacks the temporal resolution needed to track naturally occurring behavior across time.
One way to combat this is by objectively quantifying a consistent facial feature. Squint is the most consistently trackable facial feature6. Squint accounts for the majority of the total variability in the data when accounting for all of the MGS variables (squint, nose bulge, cheek bulge, ear position, and whisker change)6. Because squint contributes most to the overall score obtained using the MGS and reliably tracks response to CGRP6,14, it is the most reliable way to track spontaneous pain in migraine mouse models. This makes squint a quantifiable non-homeostatic behavior induced by CGRP. Several labs have used facial expression features, including squint, to represent potential spontaneous pain associated with migraine6,15.
Several challenges have remained regarding carrying out automated squints in a way that can be coupled with mechanistic studies of migraine. For example, it has been difficult to reliably track squint without relying on a fixed position that must be calibrated in the same manner across sessions. Another challenge is the ability to carry out this type of analysis on a continuous scale instead of discrete scales like the MGS. To mitigate these challenges, we aimed to integrate machine learning, in the form of DeepLabCut (DLC), into our data analysis pipeline. DLC is a pose estimation machine learning model developed by Mathis and colleagues and has been applied to a wide range of behaviors16. Using their pose estimation software, we were able to train models that could accurately predict points on a mouse eye at near-human accuracy. This solves the issues of repetitive manual scoring while also drastically increasing temporal resolution. Further, by creating these models, we have made a repeatable means to score squint and estimate migraine-like brain activity over larger experimental groups. Here, we present the development and validation of this method for tracking squint behaviors in a way that can be time-locked to other mechanistic measurements such as neurophysiology. The overarching goal is to catalyze mechanistic studies requiring time-locked squint behaviors in rodent models.

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

An Automated Squint Method for Time-syncing Behavior and Brain Dynamics in Mouse Pain Studies

Our lab investigates the electrical networks behind complex brain disorders, focusing on how brain activity is coordinated during various states. That's why we developed this analysis approach for automating squint data, which better facilitates peri-neurophysiological recordings with squint behaviors related to migraine. Within migraine studies, a new area of research is beginning to open up, as rodent models of migraine are being used more to study the underlying neural circuit substrates in migraine, and identifying more central mechanisms contributing to migraine like states.Our protocol addresses the research gap of objectively quantifying pain like responses in a way that can be paired with mechanistic measures such as neurophysiology. Our method presents a widely accessible way to simultaneously quantify spontaneous pain responses, and underlying electrical brain network activity at a sub second resolution. A major goal or hope of this work, is that it will catalyze the use of measuring brain activity in real-time, during migraine experiments in animal models.We're studying how brain wide electrical network activity changes over time during migraine like episodes. Our goals are both to observe how migraine activity evolves across the brain over time, and to try and develop brain-based markers of migraine activity for better testing therapeutics and rodent models.

Tracking Pain States in Mice: Automating Eye Squint Measurement with DeepLabCut

To begin, arrange the equipment and install the software for the DeepLabCut or DLC procedure. To create the environment, navigate to the folder where the DLC software was downloaded. Use the change directory command, cd folder name.Run the first command, conda env create f DEEPLABCUT.yaml. Then, type conda activate Deeplabcut to enable the environment. Then, open the graphical user interface using python m deeplabcut.After the interface opens, click Create New Project at the bottom of the interface. Name the project for easy identification later. Enter a name for the experimenter and check the location section to verify where the project will be saved.Select Browse Folders to locate the videos for training the model, and choose Copy videos to project folder if the videos should remain in their original directory. Click Create to generate a new project. After creating the model, select Edit config.yaml, followed by Edit to open the configuration settings file. Modify the body parts to include all parts of the eye for tracking. Adjust number of frames to pick to obtain 400 total frames for training video.Change dot size to six to ensure that the default label size is small enough for accurate placement around the edges of the eye. Following configuration, navigate to the Extract Frames tab of the graphical user interface and select Extract Frames at the bottom. Navigate to the Label Frames tab and select Label Frames.In the new window, find folders for each of the selected training videos and choose the first folder to open a new labeling interface. Label the points defined during configuration for each frame of the selected video. After labeling all frames, save the labels and repeat the process for the next video.For accurate labeling of squint, use two points near the largest peak of the eye. To create a training dataset, navigate to the Train Network tab and initiate train network. Once the network training is complete, navigate to and select Evaluate Network.To analyze videos, navigate to the Analyze Videos tab and select Add more videos to choose the videos. Select Save results as CSV if A CSV output of the data is sufficient. Once all videos are selected, click Analyze Videos to start the analysis process.Finally, apply the macros to convert the raw data into the format required for the analysis of Euclidean distance. The model accurately detected both non-squint and squint instances, marking the top and bottom eyelid points to compute Euclidean distances. Root mean square error values between manually-labeled and model-labeled points showed minimal variability after 300 frames, and the average likelihood values for correct point detection exceeded 0.95 when using 400 frames.The confusion matrix showed a positive predictive value of 96.96%and a negative predictive value of 99.66%for squint detection.

tracking-pain-states-mice-automating-eye-squint-measurement-with

Research

JoVE Journal

Behavior

20 조회수. 시작하려면 장비를 정렬하고 DeepLabCut 또는 DLC 절차용 소프트웨어를 설치하십시오. 환경을 만들려면 DLC 소프트웨어가 다운로드된 폴더로 이동합니다. 디렉토리 변경 명령, cd 폴더 이름을 사용합니다.첫 번째 명령인 conda env create f DEEPLABCUT.yaml을 실행합니다. 그런 다음 conda activate Deeplabcut을 입력하여 환경을 활성화합니다. 그런 다음 python m deeplabc...