Thanks to Rajyashree Sen&#160;for insightful conversations. Thanks tothe McKnight Foundation Neurobiology of Disease Award (RH), NIH 1DP2MH126377-01 (RH), the Roy J. Carver Charitable Trust (RH), NINDS T32NS007124 (MJ), Ramon D. Buckley Graduate Student Award (MJ), and VA-ORD (RR&#38;D) MERIT 1 I01 RX003523-0 (LS).

NOTE: All animals utilized in these experiments were handled according to protocols approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Iowa.
1. Prepare equipment for data collection
<ol>
	<li>Ensure the availability of all necessary equipment: ensure that the recommended hardware for running DLC has at least 8 GB of memory. See the Table of Materials for information related to hardware and software. 
	NOTE: Data can be collected in any format but must be converted to a format readable by DLC before analysis. The most common formats are AVI and MP4.</li>
	<li>Configure at least one camera so that one eye of an animal can be detected. If both eyes are visible, perform additional filtering, as it may cause interference in tracking. See section 10 for an example of such filtering for the data provided here.</li>
	<li>Install DLC using the package found at Deeplabcut.github.io/DeepLabCut/docs/installation.</li>
	<li>In the camera setup, include a single camera at a side angle (~90&#176;) to the mouse. To follow this example, sample at 10 Hz, with the mice restrained but free to access their full range of head movements relative to the body. Keep between 2 and 4 inches from the camera to the animal.</li>
</ol>
2. Setting up DLC
<ol>
	<li>After installing DLC, create the environment to work out of. To do this, navigate to the folder where the DLC software was downloaded using the change directory with the following command. 
	cd folder_name 
	NOTE: This will be where the DEEPLABCUT.yaml file is located.</li>
	<li>Run the first command to create the environment and enable it by typing the second command. 
	conda env create -f DEEPLABCUT.yaml 
	conda activate Deeplabcut 
	NOTE: Ensure the environment is activated before each use of DLC. 
	After activating the environment, open the graphical user interface (GUI) with the following command and begin creating the model. 
	python -m deeplabcut</li>
</ol>
3. Create the model
<ol>
	<li>After the GUI is opened, begin creating a model by clicking on Create New Project at the bottom.</li>
	<li>Name the project something meaningful and unique to identify it later and enter a name as experimenter. Check the Location section to see where the project will be saved.</li>
	<li>Select Browse folders&#160;and find the videos to train the model. Select Copy videos to project folder if the videos are not to be moved from their original directory.</li>
	<li>Select Create to generate a new project on the computer. 
	NOTE: The videos must cover the full range of the behavior you will observe (i.e., squint, non-squint, and all behaviors in between). The model will only be able to recognize behavior similar to that in the training data, and if some components of the behavior are missing, the model may have trouble recognizing it.</li>
</ol>
4. Configure the settings
NOTE: This is where details like what points to track, how many frames to extract from each training video, default labeling dot size, and variables relating to how the model will train can be defined.
<ol>
	<li>After creating the model, edit the configuration settings by selecting Edit config.yaml. Select edit to open the configuration settings file to specify key settings relating to the model.</li>
	<li>Modify bodyparts to include all parts of the eye to track, then modify numframes2pick to the number of frames needed per training video to get 400 total frames. Lastly, change dotsize to six so that the default size when labeling is small enough to be accurately placed around the edges of the eye.</li>
</ol>
5. Extract training frames
<ol>
	<li>Following configuration, navigate to the Extract Frames tab at the top of the GUI and select Extract Frames at the bottom right of the page.</li>
	<li>Monitor progress using the loading bar at the bottom of the GUI.</li>
</ol>
6. Label training frames
<ol>
	<li>Navigate to the Label Frames tab in the GUI and select Label Frames. Find the new window that shows folders for each of the selected training videos. Select the first folder, and a new labeling GUI will open.</li>
	<li>Label the points defined during configuration for every frame of the selected video. After all frames are labeled, save them and repeat the process for the next video.</li>
	<li>For adequate labeling of squint, use two points as close to the largest peak of the eye (center) as possible and indicate the up/down positions for each point. Approximate squint as the average of these two lengths. 
	NOTE: When labeling, DLC does not automatically save progress. Periodic saving is recommended to avoid loss of labeled data.</li>
</ol>
7. Create a training dataset
<ol>
	<li>After manually labeling, navigate to the Train network tab and select Train network to prompt the software to start training the model.</li>
	<li>Monitor progress in the command window.</li>
</ol>
8. Evaluate the network
<ol>
	<li>After network training is&#160;complete, navigate to the Evaluate&#160;network tab and select Evaluate network. Wait&#160;for a few moments until the blue loading circle&#160;disappears indicating it has finished self-evaluating and the model is ready for use.</li>
</ol>
9. Analyze data/generate labeled videos
<ol>
	<li>To analyze videos, navigate to the Analyze videos tab. Select Add more videos and select the videos to be analyzed.</li>
	<li>Select Save result(s) as csv if a csv output of the data is sufficient.</li>
	<li>When the videos have all been acquired, select Analyze videos at the bottom to begin analysis of the videos. 
	NOTE: This step must be completed before generating labeled videos in step 9.5</li>
	<li>After the videos have been analyzed, navigate to the Create videos tab and select the analyzed videos.</li>
	<li>Select Create videos and the software will begin generating labeled videos that represent the data shown in the corresponding .csv.</li>
</ol>
10. Process final data
<ol>
	<li>Apply the macros found at&#160;https://research-git.uiowa.edu/rainbo-hultman/facial-grimace-dlc&#160;to convert raw data into the format used for this analysis (i.e., Euclidian distance).</li>
	<li>Import and apply macros labeled Step1 and Step 2 to the csv to filter out all suboptimal data points and convert the data to an averaged Euclidean distance for the centermost points at the top and bottom of the eye.</li>
	<li>Run macro called Step3 to mark each point as a 0 no squint and 1 squint based on the threshold value in the script, which is set at 75 pixels. 
	NOTE: The parameters for these macros may require adjustment depending on the experimental setup (see discussion). The threshold for squint and the automatic filter for the maximum value of the eye are parameters that may be changed depending on the size of the animal and distance from the camera. You might also adjust the values used for removing suboptimal points depending on how selectively the data have to be filtered.</li>
</ol>

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

<ol><li>Disease, G. B. D., Injury, I., Prevalence, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Global%2C+regional%2C+and+national+incidence%2C+prevalence%2C+and+years+lived+with+disability+for+354+diseases+and+injuries+for+195+countries+and+territories%2C+1990-2017%3A+A+systematic+analysis+for+the+global+burden+of+disease+study+2017%5BTitle%5D)+AND+%22Lancet%22%5BJournal%5D)">Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: A systematic analysis for the global burden of disease study 2017.</a> Lancet. 392 (10159), 1789-1858 (2018).</li>
<li>Russo, A. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cgrp+as+a+neuropeptide+in+migraine%3A+Lessons+from+mice%5BTitle%5D)+AND+%22Br+J+Clin+Pharmacol%22%5BJournal%5D)">Cgrp as a neuropeptide in migraine: Lessons from mice.</a> Br J Clin Pharmacol. 80 (3), 403-414 (2015).</li>
<li>Wattiez, A. S., Wang, M., Russo, A. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cgrp+in+animal+models+of+migraine%5BTitle%5D)+AND+%22Handb+Exp+Pharmacol%22%5BJournal%5D)">Cgrp in animal models of migraine.</a> Handb Exp Pharmacol. 255, 85-107 (2019).</li>
<li>Hansen, J. M., Hauge, A. W., Olesen, J., Ashina, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Calcitonin+gene-related+peptide+triggers+migraine-like+attacks+in+patients+with+migraine+with+aura%5BTitle%5D)+AND+%22Cephalalgia%22%5BJournal%5D)">Calcitonin gene-related peptide triggers migraine-like attacks in patients with migraine with aura.</a> Cephalalgia. 30 (10), 1179-1186 (2010).</li>
<li>Mason, B. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Induction+of+migraine-like+photophobic+behavior+in+mice+by+both+peripheral+and+central+cgrp+mechanisms%5BTitle%5D)+AND+%22J+Neurosci%22%5BJournal%5D)">Induction of migraine-like photophobic behavior in mice by both peripheral and central cgrp mechanisms.</a> J Neurosci. 37 (1), 204-216 (2017).</li>
<li>Rea, B. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Peripherally+administered+cgrp+induces+spontaneous+pain+in+mice%3A+Implications+for+migraine%5BTitle%5D)+AND+%22Pain%22%5BJournal%5D)">Peripherally administered cgrp induces spontaneous pain in mice: Implications for migraine.</a> Pain. 159 (11), 2306-2317 (2018).</li>
<li>Kopruszinski, C. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Prevention+of+stress-+or+nitric+oxide+donor-induced+medication+overuse+headache+by+a+calcitonin+gene-related+peptide+antibody+in+rodents%5BTitle%5D)+AND+%22Cephalalgia%22%5BJournal%5D)">Prevention of stress- or nitric oxide donor-induced medication overuse headache by a calcitonin gene-related peptide antibody in rodents.</a> Cephalalgia. 37 (6), 560-570 (2017).</li>
<li>Juhasz, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((No-induced+migraine+attack%3A+Strong+increase+in+plasma+calcitonin+gene-related+peptide+%28cgrp%29+concentration+and+negative+correlation+with+platelet+serotonin+release%5BTitle%5D)+AND+%22Pain%22%5BJournal%5D)">No-induced migraine attack: Strong increase in plasma calcitonin gene-related peptide (cgrp) concentration and negative correlation with platelet serotonin release.</a> Pain. 106 (3), 461-470 (2003).</li>
<li>Aditya, S., Rattan, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Advances+in+cgrp+monoclonal+antibodies+as+migraine+therapy%3A+A+narrative+review%5BTitle%5D)+AND+%22Saudi+J+Med+Med+Sci%22%5BJournal%5D)">Advances in cgrp monoclonal antibodies as migraine therapy: A narrative review.</a> Saudi J Med Med Sci. 11 (1), 11-18 (2023).</li>
<li>Goadsby, P. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+controlled+trial+of+erenumab+for+episodic+migraine%5BTitle%5D)+AND+%22N+Engl+J+Med%22%5BJournal%5D)">A controlled trial of erenumab for episodic migraine.</a> N Engl J Med. 377 (22), 2123-2132 (2017).</li>
<li>Mogil, J. S., Pang, D. S. J., Silva Dutra, G. G., Chambers, C. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+development+and+use+of+facial+grimace+scales+for+pain+measurement+in+animals%5BTitle%5D)+AND+%22Neurosci+Biobehav+Rev%22%5BJournal%5D)">The development and use of facial grimace scales for pain measurement in animals.</a> Neurosci Biobehav Rev. 116, 480-493 (2020).</li>
<li>Whittaker, A. L., Liu, Y., Barker, T. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Methods+used+and+application+of+the+mouse+grimace+scale+in+biomedical+research+10+years+on%3A+A+scoping+review%5BTitle%5D)+AND+%22Animals+%28Basel%29%22%5BJournal%5D)">Methods used and application of the mouse grimace scale in biomedical research 10 years on: A scoping review.</a> Animals (Basel). 11 (3), 673(2021).</li>
<li>Langford, D. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Coding+of+facial+expressions+of+pain+in+the+laboratory+mouse%5BTitle%5D)+AND+%22Nat+Methods%22%5BJournal%5D)">Coding of facial expressions of pain in the laboratory mouse.</a> Nat Methods. 7 (6), 447-449 (2010).</li>
<li>Rea, B. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Automated+detection+of+squint+as+a+sensitive+assay+of+sex-dependent+calcitonin+gene-related+peptide+and+amylin-induced+pain+in+mice%5BTitle%5D)+AND+%22Pain%22%5BJournal%5D)">Automated detection of squint as a sensitive assay of sex-dependent calcitonin gene-related peptide and amylin-induced pain in mice.</a> Pain. 163 (8), 1511-1519 (2022).</li>
<li>Tuttle, A. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+deep+neural+network+to+assess+spontaneous+pain+from+mouse+facial+expressions%5BTitle%5D)+AND+%22Mol+Pain%22%5BJournal%5D)">A deep neural network to assess spontaneous pain from mouse facial expressions.</a> Mol Pain. 14, 1744806918763658(2018).</li>
<li>Mathis, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Deeplabcut%3A+Markerless+pose+estimation+of+user-defined+body+parts+with+deep+learning%5BTitle%5D)+AND+%22Nat+Neurosci%22%5BJournal%5D)">Deeplabcut: Markerless pose estimation of user-defined body parts with deep learning.</a> Nat Neurosci. 21 (9), 1281-1289 (2018).</li>
<li>Wattiez, A. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Different+forms+of+traumatic+brain+injuries+cause+different+tactile+hypersensitivity+profiles%5BTitle%5D)+AND+%22Pain%22%5BJournal%5D)">Different forms of traumatic brain injuries cause different tactile hypersensitivity profiles.</a> Pain. 162 (4), 1163-1175 (2021).</li>
</ol>

We have no conflicts of interest to disclose. The views in this paper are not representative of the VA or The United States Government.

This protocol provides an easily accessible in-depth method for using machine-learning-based tools that can differentiate squint at near-human accuracy while maintaining the same (or better) temporal resolution of prior approaches. Primarily, it makes evaluation of automated squint more readily available to a wider audience. Our new method for evaluating automated squint has several improvements compared to previous models. First, it provides a more robust metric than ASM by utilizing fewer points that actually contribut...

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.

<table><tbody><tr><td>CUDA toolkit 11.8</td><td></td><td></td><td></td></tr><tr><td>cuDNN SDK 8.6.0</td><td></td><td></td><td></td></tr><tr><td>Intel computers with Windows 11, 13th gen&amp;nbsp;</td><td></td><td></td><td></td></tr><tr><td>LabFaceX 2D Eyelid Tracker Add-on Module for a Free Roaming Mouse:</td><td>FaceX LLC</td><td>NA</td><td>Any camera that can record an animal&#039;s eye is sufficient, but this is our eye tracking hardware.</td></tr><tr><td>NVIDIA GPU driver that is version 450.80.02 or higher</td><td></td><td></td><td></td></tr><tr><td>NVIDIA RTX A5500, 24 GB DDR6</td><td>NVIDIA</td><td>[490-BHXV]</td><td>Any GPU that meets the minimum requirements specified for your version of DLC, currently 8 GB, is sufficient. We used NVIDIA GeForce RTX 3080 Ti GPU</td></tr><tr><td>Python 3.9-3.11</td><td></td><td></td><td></td></tr><tr><td>TensorFlow version 2.10</td><td></td><td></td><td></td></tr></tbody></table>

an automated squint method for time-syncing behavior and brain dynamics in mouse pain studies

Spontaneous pain has been challenging to track in real time and quantify in a way that prevents human bias. This is especially true for metrics of head pain, as in disorders such as migraine. Eye squint has emerged as a continuous variable metric that can be measured over time and is effective for predicting pain states in such assays. This paper provides a protocol for the use of DeepLabCut (DLC) to automate and quantify eye squint (Euclidean distance between eyelids) in restrained mice with freely rotating head motions. This protocol enables unbiased quantification of eye squint to be paired with and compared directly against mechanistic measures such as neurophysiology. We provide an assessment of AI training parameters necessary for achieving success as defined by discriminating squint and non-squint periods. We demonstrate an ability to reliably track and differentiate squint in a CGRP-induced migraine-like phenotype at a sub second resolution.

Here, we provide a method for the reliable detection of squint at high temporal resolution using DeepLabCut. We optimized training parameters, and we provide an evaluation of this method&#39;s strengths and weaknesses (Figure 1).
After training our models, we verified that they were able to correctly estimate the top and bottom points of the eyelid (Figure 2), which serve as the coordinate points for the Euclidean distance measure. Eu...

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

This protocol provides a method for tracking automated eye squint in rodents over time in a manner compatible with time-locking to neurophysiological measures. This protocol is expected to be useful to researchers studying mechanisms of pain disorders such as migraine.

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

Spontaneous pain has been challenging to track in real time and quantify in a way that prevents human bias. This is especially true for metrics of head ...

an-automated-squint-method-for-time-syncing-behavior-brain-dynamics

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Behavior

733 Views.  University of Iowa. This protocol provides a method for tracking automated eye squint in rodents over time in a manner compatible with time-locking to neurophysiological measures. This protocol is expected to be useful to researchers studying mechanisms of pain disorders such as migraine.

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

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Rodents have been traditionally used as a standard animal model in laboratory experiments involving a myriad of sensory, cognitive, and motor tasks. Higher cognitive functions that require precise control over sensorimotor responses such as decision-making and attentional modulation, however, are typically assessed in nonhuman primates. Despite the richness of primate behavior that allows multiple variants of these functions to be studied, the rodent model remains an attractive, cost-effective alternative to primate models. Furthermore, the ability to fully automate operant conditioning in rodents adds unique advantages over the labor intensive training of nonhuman primates while studying a broad range of these complex functions.
Here, we introduce a protocol for operantly conditioning rats on performing working memory tasks. During critical epochs of the task, the protocol ensures that the animal's overt movement is minimized by requiring the animal to 'fixate' until a Go cue is delivered, akin to nonhuman primate experimental design. A simple two alternative forced choice task is implemented to demonstrate the performance. We discuss the application of this paradigm to other tasks.

A fully automated protocol for rodent operant conditioning is proposed. The protocol relies on precise temporal control of behavioral events to investigate the extent to which this control influences neural activity underlying sensorimotor integration and cognitive control experiments.

Rodents have been traditionally used as a standard animal model in laboratory experiments involving a myriad of sensory, cognitive, and motor tasks. ...

Flat-floored Air-lifted Platform: A New Method for Combining Behavior with Microscopy or Electrophysiology on Awake Freely Moving Rodents

It is widely acknowledged that the use of general anesthetics can undermine the relevance of electrophysiological or microscopical data obtained from a living animal&#8217;s brain. Moreover, the lengthy recovery from anesthesia limits the frequency of repeated recording/imaging episodes in longitudinal studies. Hence, new methods that would allow stable recordings from non-anesthetized behaving mice are expected to advance the fields of cellular and cognitive neurosciences. Existing solutions range from mere physical restraint to more sophisticated approaches, such as linear and spherical treadmills used in combination with computer-generated virtual reality. Here, a novel method is described where a head-fixed mouse can move around an air-lifted mobile homecage and explore its environment under stress-free conditions. This method allows researchers to perform behavioral tests (e.g., learning, habituation or novel object recognition) simultaneously with two-photon microscopic imaging and/or patch-clamp recordings, all combined in a single experiment. This video-article describes the use of the awake animal head fixation device (mobile homecage), demonstrates the procedures of animal habituation, and exemplifies a number of possible applications of the method.

This method creates a tangible, familiar environment for the mouse to navigate and explore during microscopic imaging or single-cell electrophysiological recordings, which require firm fixation of the animal&#8217;s head.

It is widely acknowledged that the use of general anesthetics can undermine the relevance of electrophysiological or microscopical data obtained from a ...

Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach

Mouse is the preferred model organism for testing drugs designed to increase sociability. We present a method to quantify mouse sociability in which the test mouse is placed in a standardized apparatus and relevant behaviors are assessed in three different sessions (called session I, II, and III).
The apparatus has three compartments (see Figure 1), the left and right compartments contain an inverted cup which can house a mouse (called &#34;stimulus mouse&#34;).
In session I, the test mouse is placed in the cage and its mobility is characterized by the number of transitions made between compartments. In session II, a stimulus mouse is placed under one of the inverted cups and the sociability of the test mouse is quantified by the amounts of time it spends near the cup containing the enclosed stimulus mouse vs. the empty inverted cup. In session III, the inverted cups are removed and both mice interact freely. The sociability of the test mouse in session III is quantified by the number of social approaches it makes toward the stimulus mouse and by the number of times it avoids a social approach by the stimulus mouse.
The automated evaluation of the movie detects the nose of the test mouse, which allows the determination of all described sociability measures in session I and II (in session III, approaches are identified automatically but classified manually). To find the nose, the image of an empty cage is digitally subtracted from each frame of the movie and the resulting image is binarized to identify the mouse pixels. The mouse tail is automatically removed and the two most distant points of the remaining mouse are determined; these are close to nose and base of tail. By analyzing the motion of the mouse and using continuity arguments, the nose is identified.
<img alt="Figure 1" src="/files/ftp_upload/52508/52508fig1.jpg" /> 
Figure 1. Assessment of Sociability During 3 sessions. Session I (top): Acclimation of test mouse to the cage. Session II (middle): Test mouse moving freely in the cage while the stimulus mouse is enclosed in an inverted cup. Session III (bottom): Both test mouse and stimulus mouse are allowed to move freely and interact with each other.

This protocol describes a method to quantify mouse sociability. Mice are videotaped as they move and interact in a special cage. Movie processing allows for the automated quantification of sociability with excellent accuracy and reliability.

Mouse is the preferred model organism for testing drugs designed to increase sociability. We present a method to quantify mouse sociability in which the ...

Whisker-signaled Eyeblink Classical Conditioning in Head-fixed Mice

Eyeblink conditioning is a common paradigm for investigating the neural mechanisms underlying learning and memory. To better utilize the extensive repertoire of scientific techniques available to study learning and memory at the cellular level, it is ideal to have a stable cranial platform. Because mice do not readily tolerate restraint, they are usually trained while moving about freely in a chamber. Conditioned stimulus (CS) and unconditioned stimulus (US) information are delivered and eyeblink responses recorded via a tether connected to the mouse's head. In the head-fixed apparatus presented here, mice are allowed to run as they desire while their heads are secured to facilitate experimentation. Reliable conditioning of the eyeblink response is obtained with this training apparatus, which allows for the delivery of whisker stimulation as the CS, a periorbital electrical shock as the US, and analysis of electromyographic (EMG) activity from the eyelid to detect blink responses.

The preparation presented here for whisker-signaled eyeblink conditioning in head-fixed mice precisely stimulates specific whiskers while allowing mice to ambulate on a cylindrical treadmill. A whisker stimulation conditioned stimulus (CS) paired with a periorbital shock unconditioned stimulus (US) results in reliable associative learning on this apparatus.

Eyeblink conditioning is a common paradigm for investigating the neural mechanisms underlying learning and memory. To better utilize the extensive ...

Optogenetic Manipulation of Neuronal Activity to Modulate Behavior in Freely Moving Mice

Optogenetic modulation of neuronal circuits in freely moving mice affects acute and long-term behavior. This method is able to perform manipulations of single neurons and region-specific transmitter release, up to whole neuronal circuitries in the central nervous system, and allows the direct measurement of behavioral outcomes. Neurons express optogenetic tools via an injection of viral vectors carrying the DNA of choice, such as Channelrhodopsin2 (ChR2). Light is brought into specific brain regions via chronic optical implants that terminate directly above the target region. After two weeks of recovery and proper tool-expression, mice can be repeatedly used for behavioral tests with optogenetic stimulation of the neurons of interest.
Optogenetic modulation has a high temporal and spatial resolution that can be accomplished with high cell specificity, compared to the commonly used methods such as chemical or electrical stimulation. The light does not harm neuronal tissue and can therefore be used for long-term experiments as well as for multiple behavioral experiments in one mouse. The possibilities of optogenetic tools are nearly unlimited and enable the activation or silencing of whole neurons, or even the manipulation of a specific receptor type by light.
The results of such behavioral experiments with integrated optogenetic stimulation directly visualizes changes in behavior caused by the manipulation. The behavior of the same animal without light stimulation as a baseline is a good control for induced changes. This allows a detailed overview of neuronal types or neurotransmitter systems involved in specific behaviors, such as anxiety. The plasticity of neuronal networks can also be investigated in great detail through long-term stimulation or behavioral observations after optical stimulation. Optogenetics will help to enlighten neuronal signaling in several kinds of neurological diseases.

With optogenetic manipulation of specific neuronal populations or brain regions, behavior can be modified with high temporal and spatial resolution in freely moving animals. By using different optogenetic tools in combination with chronically implanted optical fibers, a variety of neuronal modulations and behavioral testing can be performed.

Optogenetic modulation of neuronal circuits in freely moving mice affects acute and long-term behavior. This method is able to perform manipulations of ...

Monocular Visual Deprivation and Ocular Dominance Plasticity Measurement in the Mouse Primary Visual Cortex

Monocular visual deprivation is an excellent experimental paradigm to induce primary visual cortical response plasticity. In general, the response of the cortex to the contralateral eye to a stimulus is much stronger than the response of the ipsilateral eye in the binocular segment of the mouse primary visual cortex (V1). During the mammalian critical period, suturing the contralateral eye will result in a rapid loss of responsiveness of V1 cells to contralateral eye stimulation. With the continuing development of transgenic technologies, more and more studies are using transgenic mice as experimental models to examine the effects of specific genes on ocular dominance (OD) plasticity. In this study, we introduce detailed protocols for monocular visual deprivation and calculate the change in OD plasticity in mouse V1. After monocular deprivation (MD) for 4 days during the critical period, the orientation tuning curves of each neuron are measured, and the tuning curves of layer four neurons in V1 are compared between stimulation of the ipsilateral and contralateral eyes. The contralateral bias index (CBI) can be calculated using each cell&#39;s ocular OD score to indicate the degree of OD plasticity. This experimental technique is important for studying the neural mechanisms of OD plasticity during the critical period and for surveying the roles of specific genes in neural development. The major limitation is that the acute study cannot investigate the change in neural plasticity of the same mouse at a different time.

Here, we present detailed protocols for monocular visual deprivation and ocular dominance plasticity analysis, which are important methods for studying the neural mechanisms of visual plasticity during the critical period and the effects of specific genes on visual development.

Monocular visual deprivation is an excellent experimental paradigm to induce primary visual cortical response plasticity. In general, the response of the ...

Developmental Biology

Dural Stimulation and Periorbital von Frey Testing in Mice As a Preclinical Model of Headache

The cranial meninges, comprised of the dura mater, arachnoid, and pia mater, are thought to primarily serve structural functions for the nervous system. For example, they protect the brain from the skull and anchor/organize the vascular and neuronal supply of the cortex. However, the meninges are also implicated in nervous system disorders such as migraine, where the pain experienced during a migraine is attributed to local sterile inflammation and subsequent activation of local nociceptive afferents. Of the layers in the meninges, the dura mater is of particular interest in the pathophysiology of migraines. It is highly vascularized, harbors local nociceptive neurons, and is home to a diverse array of resident cells such as immune cells. Subtle changes in the local meningeal microenvironment may lead to activation and sensitization of dural perivascular nociceptors, thus leading to migraine pain. Studies have sought to address how dural afferents become activated/sensitized by using either in vivo electrophysiology, imaging techniques, or behavioral models, but these commonly require very invasive surgeries. This protocol presents a method for comparatively non-invasive application of compounds on the dura mater in mice and a suitable method for measuring headache-like tactile sensitivity using periorbital von Frey testing following dural stimulation. This method maintains the integrity of the dura and skull and reduces confounding effects from invasive techniques by injecting substances through a 0.65 mm modified cannula at the junction of unfused sagittal and lambdoid sutures. This preclinical model will allow researchers to investigate a wide range of dural stimuli and their role in the pathological progression of migraine, such as nociceptor activation, immune cell activation, vascular changes, and pain behaviors, all while maintaining injury-free conditions to the skull and meninges.

The most notable symptom of migraine is severe head pain, and it is hypothesized that this is mediated by sensory neurons innervating the meninges. Here, we present a method to locally apply substances to the dura in a minimally invasive manner while using facial hypersensitivity as an output.

The cranial meninges, comprised of the dura mater, arachnoid, and pia mater, are thought to primarily serve structural functions for the nervous system.  ...

Neuroscience

Mechanical Conflict-Avoidance Assay to Measure Pain Behavior in Mice

Pain comprises of both sensory (nociceptive) and affective (unpleasant) dimensions. In preclinical models, pain has traditionally been assessed using reflexive tests that allow inferences regarding pain's nociceptive component but provide little information about the affective or motivational component of pain. Developing tests that capture these components of pain are therefore translationally important. Hence, researchers need to use non-reflexive behavioral assays to study pain perception at that level. Mechanical conflict-avoidance (MCA) is an established voluntary non-reflexive behavior assay, for studying motivational responses to a noxious mechanical stimulus in a 3 chamber paradigm. A change in a mouse's location preference, when faced with competing noxious stimuli, is used to infer the perceived unpleasantness of bright light versus tactile stimulation of the paws. This protocol outlines a modified version of the MCA assay which pain researchers can use to understand affective-motivational responses in a variety of mouse pain models. Though not specifically described here, our example MCA data use the intraplantar complete Freund's adjuvant (CFA), spared nerve injury (SNI), and a fracture/casting model as pain models to illustrate the MCA procedure.

The mechanical conflict-avoidance assay is used as a non-reflexive readout of pain sensitivity in mice which can be used to better understand affective-motivational responses in a variety of mouse pain models.

Pain comprises of both sensory (nociceptive) and affective (unpleasant) dimensions. In preclinical models, pain has traditionally been assessed using ...

Simultaneous Imaging of Microglial Dynamics and Neuronal Activity in Awake Mice

Since brain functions are under the continuous influence of the signals derived from peripheral tissues, it is critical to elucidate how glial cells in the brain sense various biological conditions in the periphery and transmit the signals to neurons. Microglia, immune cells in the brain, are involved in synaptic development and plasticity. Therefore, the contribution of microglia to neural circuit construction in response to the internal state of the body should be tested critically by intravital imaging of the relationship between microglial dynamics and neuronal activity.
Here, we describe a technique for the simultaneous imaging of microglial dynamics and neuronal activity in awake mice. Adeno-associated virus encoding R-CaMP, a gene-encoded calcium indicator of red fluorescence protein, was injected into layer 2/3 of the primary visual cortex in CX3CR1-EGFP transgenic mice expressing EGFP in microglia. After viral injection, a cranial window was installed onto the brain surface of the injected region. In vivo two-photon imaging in awake mice 4 weeks after the surgery demonstrated that neural activity and microglial dynamics could be recorded simultaneously at the sub-second temporal resolution. This technique can uncover the coordination between microglial dynamics and neuronal activity, with the former responding to peripheral immunological states and the latter encoding the internal brain states.

Here, we describe a protocol combining adeno-associated virus injection with cranial window implantation for simultaneous imaging of microglial dynamics and neuronal activity in awake mice.

Since brain functions are under the continuous influence of the signals derived from peripheral tissues, it is critical to elucidate how glial cells in ...

Evaluating Optokinetic Reflex Responses in Mice: A New High-Precision Protocol

Quantification of Visual Feature Selectivity of the Optokinetic Reflex in Mice

The optokinetic reflex (OKR) is an essential innate eye movement that is triggered by the global motion of the visual environment and serves to stabilize retinal images. Due to its importance and robustness, the OKR has been used to study visual-motor learning and to evaluate the visual functions of mice with different genetic backgrounds, ages, and drug treatments. Here, we introduce a procedure for evaluating OKR responses of head-fixed mice with high accuracy. Head fixation can rule out the contribution of vestibular stimulation on eye movements, making it possible to measure eye movements triggered only by visual motion. The OKR is elicited by a virtual drum system, in which a vertical grating presented on three computer monitors drifts horizontally in an oscillatory manner or unidirectionally at a constant velocity. With this virtual reality system, we can systematically change visual parameters like spatial frequency, temporal/oscillation frequency, contrast, luminance, and the direction of gratings, and quantify tuning curves of visual feature selectivity. High-speed infrared video-oculography ensures accurate measurement of the trajectory of eye movements. The eyes of individual mice are calibrated to provide opportunities to compare the OKRs between animals of different ages, genders, and genetic backgrounds. The quantitative power of this technique allows it to detect changes in the OKR when this behavior plastically adapts due to aging, sensory experience, or motor learning; thus, it makes this technique a valuable addition to the repertoire of tools used to investigate the plasticity of ocular behaviors.

Author Spotlight: An Accurate and Quantitative Approach to Study Visual Feature Selectivity of the Optokinetic Reflex in Mice 

Here, we describe a standard protocol for quantifying the optokinetic reflex. It combines virtual drum stimulation and video-oculography, and thus allows precise evaluation of the feature selectivity of the behavior and its adaptive plasticity.

The optokinetic reflex (OKR) is an essential innate eye movement that is triggered by the global motion of the visual environment and serves to stabilize ...