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

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	<li>Disease, G. B. D., Injury, I., Prevalence, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=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.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cgrp+as+a+neuropeptide+in+migraine:+Lessons+from+mice.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cgrp+in+animal+models+of+migraine.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Calcitonin+gene-related+peptide+triggers+migraine-like+attacks+in+patients+with+migraine+with+aura.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Induction+of+migraine-like+photophobic+behavior+in+mice+by+both+peripheral+and+central+cgrp+mechanisms.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Peripherally+administered+cgrp+induces+spontaneous+pain+in+mice:+Implications+for+migraine.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prevention+of+stress-+or+nitric+oxide+donor-induced+medication+overuse+headache+by+a+calcitonin+gene-related+peptide+antibody+in+rodents.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=No-induced+migraine+attack:+Strong+increase+in+plasma+calcitonin+gene-related+peptide+(cgrp)+concentration+and+negative+correlation+with+platelet+serotonin+release.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advances+in+cgrp+monoclonal+antibodies+as+migraine+therapy:+A+narrative+review.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+controlled+trial+of+erenumab+for+episodic+migraine.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+development+and+use+of+facial+grimace+scales+for+pain+measurement+in+animals.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methods+used+and+application+of+the+mouse+grimace+scale+in+biomedical+research+10+years+on:+A+scoping+review.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Coding+of+facial+expressions+of+pain+in+the+laboratory+mouse.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+detection+of+squint+as+a+sensitive+assay+of+sex-dependent+calcitonin+gene-related+peptide+and+amylin-induced+pain+in+mice.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+deep+neural+network+to+assess+spontaneous+pain+from+mouse+facial+expressions.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deeplabcut:+Markerless+pose+estimation+of+user-defined+body+parts+with+deep+learning.">Deeplabcut: Markerless pose estimation of user-defined body parts with deep learning.</a> Nat Neurosci. 21 (9), 1281-1289 (2018).</li><li>Wattiez, A. 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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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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&#160;</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&#39;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

Research

JoVE Journal

Behavior

552 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 Procedure to Observe Context-induced Renewal of Pavlovian-conditioned Alcohol-seeking Behavior in Rats

Environmental contexts in which drugs of abuse are consumed can trigger craving, a subjective Pavlovian-conditioned response that can facilitate drug-seeking behavior and prompt relapse in abstinent drug users. We have developed a procedure to study the behavioral and neural processes that mediate the impact of context on alcohol-seeking behavior in rats. Following acclimation to the taste and pharmacological effects of 15% ethanol in the home cage, male Long-Evans rats receive Pavlovian discrimination training (PDT) in conditioning chambers. In each daily (Mon-Fri) PDT session, 16 trials each of two different 10 sec auditory conditioned stimuli occur. During one stimulus, the CS+, 0.2 ml of 15% ethanol is delivered into a fluid port for oral consumption. The second stimulus, the CS-, is not paired with ethanol. Across sessions, entries into the fluid port during the CS+ increase, whereas entries during the CS- stabilize at a lower level, indicating that a predictive association between the CS+ and ethanol is acquired. During PDT each chamber is equipped with a specific configuration of visual, olfactory and tactile contextual stimuli. Following PDT, extinction training is conducted in the same chamber that is now equipped with a different configuration of contextual stimuli. The CS+ and CS- are presented as before, but ethanol is withheld, which causes a gradual decline in port entries during the CS+. At test, rats are placed back into the PDT context and presented with the CS+ and CS- as before, but without ethanol. This manipulation triggers a robust and selective increase in the number of port entries made during the alcohol predictive CS+, with no change in responding during the CS-. This effect, referred to as context-induced renewal, illustrates the powerful capacity of contexts associated with alcohol consumption to stimulate alcohol-seeking behavior in response to Pavlovian alcohol cues.

A procedure to study the capacity of an alcohol associated environmental context to trigger the renewal of alcohol-seeking behavior in rats is described.

Environmental contexts in which drugs of abuse are consumed can trigger craving, a subjective Pavlovian-conditioned response that can facilitate ...

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 ...

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Human pain models are useful in the assessing the analgesic effect of drugs, providing information about a drug&#39;s pharmacology and identify potentially suitable therapeutic populations. The need to use a comprehensive battery of pain models is highlighted by studies whereby only a single pain model, thought to relate to the clinical situation, demonstrates lack of efficacy. No single experimental model can mimic the complex nature of clinical pain. The integrated, multi-modal pain task&#160;battery presented here encompasses the electrical stimulation task, pressure stimulation task, cold pressor task, the UVB inflammatory model which includes a thermal task and a paradigm for inhibitory conditioned pain modulation. These human pain models have been tested for predicative validity and reliability both in their own right and in combination, and can be used repeatedly, quickly, in short succession, with minimum burden for the subject and with a modest quantity of equipment. This allows a drug to be fully characterized and profiled for analgesic effect which is especially useful for drugs with a novel or untested mechanism of action.

Human pain models are valuable tools used to assess the analgesic potential of novel compounds and predict their clinical efficacy, especially when used in an integrated manner. Although implementation of these models is complex, with proper execution, the pain models described in this protocol can provide predictive and reliable results.

Human pain models are useful in the assessing the analgesic effect of drugs, providing information about a drug&#39;s pharmacology and identify ...

The Knob Supination Task: A Semi-automated Method for Assessing Forelimb Function in Rats

Tasks that accurately measure dexterity in animal models are critical to understand hand function. Current rat behavioral tasks that measure dexterity largely use video analysis of reaching or food manipulation. While these tasks are easy to implement and are robust across disease models, they are subjective and laborious for the experimenter. Automating traditional tasks or creating new automated tasks can make the tasks more efficient, objective, and quantitative. Since rats are less dexterous than primates, central nervous system (CNS) injury produces more subtle deficits in dexterity, however, supination is highly affected in rodents and crucial to hand function in primates. Therefore, we designed a semi-automated task that measures forelimb supination in rats. Rats are trained to reach and grasp a knob-shaped manipulandum and turn the manipulandum in supination to receive a reward. Rats can acquire the skill within 20 &plusmn; 5 days. While the early part of training is highly supervised, much of the training is done without direct supervision. The task reliably and reproducibly captures subtle deficits after injury and shows functional recovery that accurately reflects clinical recovery curves. Analysis of data is performed by specialized software through a graphical user interface that is designed to be intuitive. We also give solutions to common problems encountered during training, and show that minor corrections to behavior early in training produce reliable acquisition of supination. Thus, the knob supination task provides efficient and quantitative evaluation of a critical movement for dexterity in rats.

This manuscript describes a semi-automated task that quantifies supination in rats. Rats reach, grasp, and supinate a spherical manipulandum. The rat is rewarded with a pellet if the turn angle exceeds a criterion set by the user. This task increases throughput, sensitivity to injury, and objectivity compared to traditional tasks.

Tasks that accurately measure dexterity in animal models are critical to understand hand function. Current rat behavioral tasks that measure dexterity ...

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in  Free Moving Rodents

Many neurological and psychiatric patients demonstrate difficulties and/or deficits in decision making. Rodent models are helpful to produce a deeper understanding of the neurobiological causes underlying the decision-making problems. A cost-benefit based T-maze task is used for measuring decision making in which rodents choose between a high reward arm (HRA) and a low reward arm (LRA). There are two paradigms of the T-maze decision-making task, one in which the cost is a time delay and the other in which it is physical effort. Both paradigms require a tedious and labor-intensive management of experimental animals, multiple doors, pellet reward, and arm choice recordings. In the current work, we invented an apparatus based on traditional T-maze with full automation for pellet delivery, door management and choice recordings. This automated setup can be used for the evaluation of both delay- and effort-based decision making in rodents. With the protocol described here, our lab investigated the decision-making phenotypes of multiple genetically modified mice. In the representative data, we showed that the mice with ablated medial habenular showed aversions of both delay and effort and tended to choose the immediate and effortless reward. This protocol helps to decrease the variability caused by experimenter intervention and to enhance experiment efficiency. In addition, chronic silicon probe or microelectrode recording, fiber-optic imaging and/or manipulation of neural activity can be easily applied during the decision-making task using the setup described here.

This article introduces an automated T-maze apparatus that we invented, and a protocol based on this apparatus for analyzing delay-based decision making and effort-based decision making in free moving rodents.

Many neurological and psychiatric patients demonstrate difficulties and/or deficits in decision making. Rodent models are helpful to produce a deeper ...

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage

Multiple factors&#8212;such as aging and genes&#8212;are frequently associated with cognitive decline. Genetically modified mouse models of cognitive decline, such as Alzheimer&#39;s disease (AD), have become a promising tool to elucidate the underlying mechanisms and promote the therapeutic advances. An important step is the validation and characterization of expected behavioral abnormality in the models, in the case of AD, cognitive decline. The long-term behavioral investigations of laboratory animals to study the effect of aging demand substantial efforts from researchers. The IntelliCage system is a high-throughput and cost-effective test battery for mice that eliminates the need for daily human handling. Here, we describe how the system is utilized in the long-term phenotyping of a genetic Alzheimer&#39;s disease model, specifically focusing on the cognitive functions. The experiment employs repeated battery of tests that assess spatial learning and executive functions. This cost-effective age-dependent phenotyping allows us to identify the transient and/or permanent effects of genes on various cognitive aspects.

This paper describes a protocol for cognitive assessments for genetic models of the Alzheimer&#39;s disease using the IntelliCage system, which is a high throughput automated behavioral monitoring system with operant conditioning.

Multiple factors&#8212;such as aging and genes&#8212;are frequently associated with cognitive decline. Genetically modified mouse models of cognitive ...

Measuring Active and Passive Tameness Separately in Mice

Domesticated animals such as dogs and laboratory mice show a high level of tameness, which is important for humans to handle them easily. Tameness has two behavioral components: a reluctance to avoid humans (passive tameness) and a motivation to approach humans (active tameness). To quantify these components in mice, we previously developed behavioral tests for active tameness, passive tameness, and the willingness to stay on a human hand, each designed to be completed within 3 min. The data obtained were used for selective breeding, with a large number of mice analyzed per generation. The active tameness test measures the movement of the mouse toward a human hand and the contact it engages in. The passive tameness test measures the duration of time that a mouse tolerates human touch. In the stay-on-hand test, a mouse is placed on a human hand and touched slowly using the thumb of that hand; the duration of time that the animal remains on the hand is measured. Here, we describe the test set-up and apparatus, explain the procedures, and discuss the appropriate data analysis. Finally, we explain how to interpret the results.

Tameness in animals includes a reduction of avoidance responses to humans (passive tameness) and an increase in active approaches to humans (active tameness). Here, we describe detailed protocols for three behavioral tests (active tameness, passive tameness, and stay-on-hand tests) to separately measure the active and passive tameness of mice.

Domesticated animals such as dogs and laboratory mice show a high level of tameness, which is important for humans to handle them easily. Tameness has two ...

An Automated Method to Determine the Performance of Drosophila in Response to Temperature Changes in Space and Time

Temperature is a ubiquitous environmental factor that affects how species distribute and behave. Different species of Drosophila fruit flies have specific responses to changing temperatures according to their physiological tolerance and adaptability. Drosophila flies also possess a temperature sensing system that has become fundamental to understanding the neural basis of temperature processing in ectotherms. We present here a temperature-controlled arena that permits fast and precise temperature changes with temporal and spatial control to explore the response of individual flies to changing temperatures. Individual flies are placed in the arena and exposed to pre-programmed temperature challenges, such as uniform gradual increases in temperature to determine reaction norms or spatially distributed temperatures at the same time to determine preferences. Individuals are automatically tracked, allowing the quantification of speed or location preference. This method can be used to rapidly quantify the response over a large range of temperatures to determine temperature performance curves in Drosophila or other insects of similar size. In addition, it can be used for genetic studies to quantify temperature preferences and reactions of mutants or wild-type flies. This method can help uncover the basis of thermal speciation and adaptation, as well as the neural mechanisms behind temperature processing.

An Automated Method to Determine the Performance of Drosophila in Response to Temperature Changes in Space and Time

Here we present a protocol to automatically determine the locomotor performance of Drosophila at changing temperatures using a programmable temperature-controlled arena that produces fast and accurate temperature changes in time and space.

Temperature is a ubiquitous environmental factor that affects how species distribute and behave. Different species of Drosophila fruit flies have specific ...

Simultaneous Recordings of Cortical Local Field Potentials and Electrocorticograms in Response to Nociceptive Laser Stimuli from Freely Moving Rats

Electrocortical responses, elicited by laser heat pulses that selectively activate nociceptive free nerve endings, are widely used in many animal and human studies to investigate the cortical processing of nociceptive information. These laser-evoked brain potentials (LEPs) consist of several transient responses that are time-locked to the onset of laser stimuli. However, the functional properties of the LEP responses are still largely unknown, due to the lack of a sampling technique that can simultaneously record neural activities at the surface of the cortex (i.e., electrocorticogram [ECoG] and scalp electroencephalogram [scalp EEG]) and inside the brain (i.e., local field potential [LFP]). To address this issue, we present here an animal protocol using freely moving rats. This protocol is composed of three main procedures: (1) animal preparation and surgical procedures, (2) a simultaneous recording of ECoG and LFP in response to nociceptive laser stimuli, and (3) data analysis and feature extraction. Specifically, with the help of a 3D-printed protective shell, both ECoG and LFP electrodes implanted on the rat&#39;s skull were securely held together. During data collection, laser pulses were delivered on the rat&#39;s forepaws through gaps in the bottom of the chamber when the animal was in spontaneous stillness. Ongoing white noise was played to avoid the activation of the auditory system by the laser-generated ultrasounds. As a consequence, only nociceptive responses were selectively recorded. Using the standard analytical procedures (e.g., band-pass filtering, epoch extraction, and baseline correction) to extract stimulus-related brain responses, we obtained results showing that LEPs with a high signal-to-noise ratio were simultaneously recorded from ECoG and LFP electrodes. This methodology makes the simultaneous recording of ECoG and LFP activities possible, which provides a bridge of electrocortical signals at the mesoscopic and macroscopic levels, thereby facilitating the investigation of nociceptive information processing in the brain.

We developed a technique that simultaneously records both electrocorticography and local field potentials in response to nociceptive laser stimuli from freely moving rats. This technique helps establish a direct relationship of electrocortical signals at the mesoscopic and macroscopic levels, which facilitates the investigation of nociceptive information processing in the brain.

Electrocortical responses, elicited by laser heat pulses that selectively activate nociceptive free nerve endings, are widely used in many animal and ...

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Exploring the neurobiological mechanisms of social behavior requires behavioral tests that can be applied to animal models in an unbiased and observer-independent manner. Since the beginning of the millennium, the three-chamber test has been widely used as a standard paradigm to evaluate sociability (social preference) and social novelty preference in small rodents. However, this test suffers from multiple limitations, including its dependence on spatial navigation and negligence of behavioral dynamics. Presented and validated here is a novel experimental system that offers an alternative to the three-chamber test, while also solving some of its caveats. The system requires a simple and affordable experimental apparatus and publicly available open-source analysis system, which automatically measures and analyzes multiple behavioral parameters at individual and population levels. It allows detailed analysis of the behavioral dynamics of small rodents during any social discrimination test. We demonstrate the efficiency of the system in analyzing the dynamics of social behavior during the social preference and social novelty preference tests as performed by adult male mice and rats. Moreover, we validate the ability of the system to reveal modified dynamics of social behavior in rodents following manipulations such as whisker trimming. Thus, the system allows for rigorous investigation of social behavior and dynamics in small rodent models and supports more accurate comparisons between strains, conditions, and treatments.

Described here is a novel automated experimental system that offers an alternative to the three-chamber test and also solves several caveats. This system supplies multiple behavioral parameters that enable rigorous analysis of small rodent behavioral dynamics during the social preference and social novelty preference tests.