Name: a step-by-step implementation of deepbehavior, deep learning toolbox for automated behavior analysis
Uploaded: 2020-02-06T15:00:00
Description: Watch this Scientific Journal Video about A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis at JoVE.com

We would like to thank Pingping Zhao and Peyman Golshani for providing the raw data for two-mouse social interaction tests used in the original paper2. This study was supported by NIH NS109315 and NVIDIA GPU grants (AA).

1. GPU and Python Setup
<ol>
	<li>GPU Software 
	When the computer is first setup for deep learning applications, GPU-appropriate software and drivers should be installed which can be found on the GPU&#39;s respective website. (see the Table of Materials for those used in this study).</li>
	<li>Python 2.7 Installation 
	Open a command line prompt on your machine. 
	Command line: sudo apt-get install python-pip python-dev python-virtualenv</li>
<...

<ol>
	<li>Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver, M. A., Poeppel, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuroscience+Needs+Behavior:+Correcting+a+Reductionist+Bias.">Neuroscience Needs Behavior: Correcting a Reductionist Bias.</a> Neuron. 93 (3), 480-490 (2017).</li><li>Arac, A., Zhao, P., Dobkin, B. H., Carmichael, S. T., Golshani, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DeepBehavior:+A+Deep+Learning+Toolbox+for+Automated+Analysis+of+Animal+and+Human+Behavior+Imaging+Data.">DeepBehavior: A Deep Learning Toolbox for Automated Analysis of Animal and Human Behavior Imaging Data.</a> Front Syst Neurosci. 13, 20 (2019).</li><li>Pereira, T. D., Aldarondo, D. E., Willmore, L., Kislin, M., Wang, S. S., Murthy, 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=Fast+animal+pose+estimation+using+deep+neural+networks.">Fast animal pose estimation using deep neural networks.</a> Nat Methods. 16 (1), 117-125 (2019).</li><li>Mathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., 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>Stern, U., He, R., Yang, C. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analyzing+animal+behavior+via+classifying+each+video+frame+using+convolutional+neural+networks.">Analyzing animal behavior via classifying each video frame using convolutional neural networks.</a> Sci Rep. 5, 14351 (2015).</li><li>Tinbergen, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+aims+and+methods+of+ethology.">On aims and methods of ethology.</a> Zeitschrift fu&#776;r Tierpsychologie. 20, 410-433 (1963).</li><li>LeCun, Y., Bengio, Y., Hinton, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+Learning.">Deep Learning.</a> Nature. 521 (7553), 436-444 (2015).</li><li>Zhao, Z., Zheng, P., Xu, S., Wu, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Object+Detection+With+Deep+Learning:+A+Review.">Object Detection With Deep Learning: A Review.</a> IEEE Transactions on Neural Networks and Learning Systems. , 1-21 (2019).</li><li>He, K., Zhang, X., Ren, S., Deep Sun, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Residual+Learning+for+Image+Recognition.">Residual Learning for Image Recognition.</a> arXiv. , (2015).</li><li>Krizhevsky, A., Sutskever, I., Hinton, G. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ImageNet+classification+with+deep+convolutional+neural+networks.">ImageNet classification with deep convolutional neural networks.</a> Proceedings of the 25th International Conference on Neural Information Processing Systems. 1, 1097-1105 (2012).</li><li>Szegedy, C., Wei, L., Yangqing, J., Sermanet, P., Reed, S., Anguelov, D., 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=Going+deeper+with+convolutions.">Going deeper with convolutions.</a> 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). , 7-12 (2015).</li><li>Stewart, R., Andriluka, M., Ng, A. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=End-to-End+People+Detection+in+Crowded+Scenes.">End-to-End People Detection in Crowded Scenes.</a> 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). , 27-30 (2016).</li><li>Redmon, J., Farhadi, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=YOLOv3:+An+Incremental+Improvement.">YOLOv3: An Incremental Improvement.</a> arXiv. , (2018).</li><li>Cao, Z., Simon, T., Wei, S. E., Sheikh, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Realtime+Multi-Person+2D+Pose+Estimation+using+Part+Affinity+Fields.">Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields.</a> arXiv. , (2017).</li><li>Simon, T., Joo, H., Matthews, I., Sheikh, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hand+Keypoint+Detection+in+Single+Images+using+Multiview+Bootstrapping.">Hand Keypoint Detection in Single Images using Multiview Bootstrapping.</a> arXiv. , (2017).</li><li>Wei, S. E., Ramakrishna, V., Kanade, T., Sheikh, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Convolutional+Pose+Machines.">Convolutional Pose Machines.</a> arXiv. , (2016).</li></ol>

Here, we provide a step-by-step guide for implementation of DeepBehavior, our recently developed deep learning based toolbox for animal and human behavior imaging data analysis2. We provide detailed explanations for each step for installation of the frameworks for each network architecture, and provide links for installation of the open-source requirements to be able to run these frameworks. We demonstrate how to install them, how to create training data, how to train the network, and how to proce...

A detailed analysis of behavior is key to understanding the brain and behavior relationships. There have been many exciting advances in methodologies for recording and manipulating neuronal populations with high temporal resolution, however, behavior analysis methods have not developed at the same rate and are limited to indirect measurements and a reductionist approach1. Recently, deep learning based methods have been developed to perform automated and detailed behavior analysis2,3,4,5. This protocol provides a step-by-step implementation guide for the DeepBehavior toolbox.
Traditional behavioral analysis methods often include manually labeling data by multiple evaluators, leading to variance in how experimenters define a behavior6. Manual labeling of the data requires time and resources that increase disproportionately to the amount of data collected. Moreover, manually labelled data reduce the behavior outcomes into categorical measurements which do not capture the richness of the behavior, and will be more subjective. Thus, the current traditional methods may be limited in capturing the details in the natural behaviors.
The DeepBehavior toolbox presents a precise, detailed, highly temporal, and automated solution using deep learning for behavioral analysis. Deep learning has quickly become accessible to all with open-source tools and packages. Convolutional neural networks (CNNs) are proven to be highly effective in object recognition and tracking tasks7,8. Using modern day CNNs and high-performance graphics-processing-units (GPUs), large image and video datasets can be processed quickly with high precision7,9,10,11. In DeepBehavior, there are three different convolutional neural net architectures, TensorBox, YOLOv3, and OpenPose2.
The first framework, Tensorbox, is a versatile framework that incorporates many different CNN architectures for object detection12. TensorBox is best suited for detecting only one object class per image. The resulting outputs are bounding boxes of the object of interest (Figure 1) and the cartesian coordinates of the bounding box.
The second CNN framework is YOLOv3, which stands for &#34;You Only Look Once&#34;13. YOLOv3 is advantageous when there are multiple objects of interest that must be tracked separately. The output of this network includes the bounding box with the associated object label class as well as the bounding box cartesian coordinates of the object in the video frame (Figure 2).
The previous two frameworks are advantageous for generalized behavioral data collected from standard laboratory experiments in animal subjects. The last CNN framework is OpenPose14,15,16 which is used for human joint pose estimation. OpenPose detects human body, hand, facial, and foot key points on images. The outputs of the framework are labeled images of the human subject as well as the coordinates of all the 25 key points in the body and 21 key points of each hand (Figure 3).
This detailed step-by-step guide for implementation of our recently developed open-source DeepBehavior toolbox employs state-of-the-art convolutional neural nets to track animal behavior (e.g. movement of a paw) or human behavior (e.g. reaching tasks). By tracking the behavior, useful kinematics can be derived from the behavior such as position, velocity, and acceleration. The protocol explains the installation of each CNN architecture, demonstrates how to create training datasets, how to train the networks, how to process new videos on the trained network, how to extract the data from the network on the new videos, and how to post-process the output data to make it useful for further analysis.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>CUDA v8.0.61</td>
			<td>NVIDIA</td>
			<td>n/a</td>
			<td>GPU Software</td>
		</tr>
		<tr>
			<td>MATLAB R2016b</td>
			<td>Mathworks</td>
			<td>n/a</td>
			<td>Matlab</td>
		</tr>
		<tr>
			<td>Python 2.7</td>
			<td>Python</td>
			<td>n/a</td>
			<td>Python Version</td>
		</tr>
		<tr>
			<td>Quadro P6000</td>
			<td>NVIDIA</td>
			<td>n/a</td>
			<td>GPU Processor</td>
		</tr>
		<tr>
			<td>Ubuntu v16.04</td>
			<td>Ubuntu</td>
			<td>n/a</td>
			<td>Operating System</td>
		</tr>
	</tbody>
</table>

a step-by-step implementation of deepbehavior, deep learning toolbox for automated behavior analysis

Understanding behavior is the first step to truly understanding neural mechanisms in the brain that drive it. Traditional behavioral analysis methods often do not capture the richness inherent to the natural behavior. Here, we provide detailed step-by-step instructions with visualizations of our recent methodology, DeepBehavior. The DeepBehavior toolbox uses deep learning frameworks built with convolutional neural networks to rapidly process and analyze behavioral videos. This protocol demonstrates three different frameworks for single object detection, multiple object detection, and three-dimensional (3D) human joint pose tracking. These frameworks return cartesian coordinates of the object of interest for each frame of the behavior video. Data collected from the DeepBehavior toolbox contain much more detail than traditional behavior analysis methods and provides detailed insights to the behavior dynamics. DeepBehavior quantifies behavior tasks in a robust, automated, and precise way. Following the identification of behavior, post-processing code is provided to extract information and visualizations from the behavioral videos.

When the protocol is followed, the data for each network architecture should be similar to the following. For TensorBox, it outputs a bounding box around the object of interest. In our example, we used videos from a food pellet reaching task, and labeled the right paws to track their movement. As seen in Figure 1, the right paw can be detected in different positions in both the front view and side view cameras. After post-processing with camera calibration, 3...

Watch this Scientific Journal Video about A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis at JoVE.com

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

The purpose of this protocol is to utilize pre-built convolutional neural nets to automate behavior tracking and perform detailed behavior analysis. Behavior tracking can be applied to any video data or sequences of images and is generalizable to track any user-defined object.

Understanding behavior is the first step to truly understanding neural mechanisms in the brain that drive it. Traditional behavioral analysis methods ...

Performing a detailed behavior analysis is crucial to understanding brain behavior relationship. One of the best ways to evaluate behavior is through careful observations. However, quantifying the observed behavior is time consuming and challenging.Classical methods of behavior analysis are not easily quantifiable and are inherently subjective. Recent developments in deep learning, a branch of machine learning and artificial intelligence fields, provide opportunities for automated and objective quantification of images and videos. Here we present our recently developed methods utilizing deep neural networks to perform detailed behavior analysis in rodents and humans.The main advantage of this technique are its flexibility and applicability to any imaging data for behavior analysis. The DeepBehavior Toolbox supports single object identification, multi object detection, and human pose tracking. We also provide the post processing code in MATLAB for more in-depth kinematic analysis methods.Begin by setting up Tensor Box. Activate the environment, then use GitHub to clone Tensor Box and install it on the machine and on additional dependencies. Next, launch the labeling graphical user interface and label at least 600 images from a wide distribution of behavior frames.To label an image click the top left corner of the object of interest and then the bottom right corner. Then make sure that the bounding box captures the entire object. Click next to move to the next frame.To link the training images to a network hyper parameters file, open overfeat_rezoom. json in a text editor and replace the file path under train_idl to labels.json. Then add the same file path under test-idl and save the changes.Initiate the training script which will begin training for 600, 000 iterations and generate the resulting trained weights of the convolutional neural network in the output folder. Then perform prediction on new images, and view the outputs of the network as labeled images and as bounding box coordinates. Install YOLOv3.Then label the training data with Yolo_mark by placing the images in the Yolo_mark-data-obga folder and labeling them one by one in the graphical user interface. Label approximately 200 images. Next, set up the configuration file.To modify the configuration file open the YOLO-obj. cfg folder. Modify the batch, subdivision, and classes lines.Then change the filter for each convolution layer before a YOLO layer. Download the network weights and place them into the darknet-build. x64 folder.Run the training algorithm, and once it is complete view the iterations. To track multiple body parts in a human subject, install OpenPose then use it to process the desired video. The capabilities of the DeepBehavior Toolbox were demonstrated on videos of mice performing a food pellet reaching task.Their right paws were labeled and movement was tracked with front and side view cameras. After post processing with camera calibration, 3D trajectories of the reach were obtained. The outputs of YOLOv3 are multiple bounding boxes because multiple objects can be tracked.The bounding boxes are around the objects of interest which can be parts of the body. In OpenPose, the network detected the joint positions and after post processing with camera calibration, a 3D model of the subject was created. One critical step not covered in this protocol is ensuring that your device has the appropriate Python versions and dependencies as well as a GPU configured device before beginning.After successfully obtaining the track behavior from the network additional post processing can be done to further analyze the kinematics and patterns of the behavior. Why the DeepBehavior Toolbox is applicable for diagnostic approaches in disease models of rodents and human subjects is not direct therapeutic benefit. Use of these techniques as a diagnostic or prognostic tool is under active research within our laboratory.This technique is being used to investigate the neural mechanisms of skilled motor behavior in rodents as well as being used in clinical studies to evaluate motor recovery in patients with neurological diseases.

a-step-step-implementation-deepbehavior-deep-learning-toolbox-for

Research

JoVE Journal

Behavior

Contextual and Cued Fear Conditioning Test Using a Video Analyzing System in Mice

The contextual and cued fear conditioning test is one of the behavioral tests that assesses the ability of mice to learn and remember an association between environmental cues and aversive experiences. In this test, mice are placed into a conditioning chamber and are given parings of a conditioned stimulus (an auditory cue) and an aversive unconditioned stimulus (an electric footshock). After a delay time, the mice are exposed to the same conditioning chamber and a differently shaped chamber with presentation of the auditory cue. Freezing behavior during the test is measured as an index of fear memory. To analyze the behavior automatically, we have developed a video analyzing system using the ImageFZ application software program, which is available as a free download at http://www.mouse-phenotype.org/. Here, to show the details of our protocol, we demonstrate our procedure for the contextual and cued fear conditioning test in C57BL/6J mice using the ImageFZ system. In addition, we validated our protocol and the video analyzing system performance by comparing freezing time measured by the ImageFZ system or a photobeam-based computer measurement system with that scored by a human observer. As shown in our representative results, the data obtained by ImageFZ were similar to those analyzed by a human observer, indicating that the behavioral analysis using the ImageFZ system is highly reliable. The present movie article provides detailed information regarding the test procedures and will promote understanding of the experimental situation.

This article presents a protocol for a contextual and cued fear conditioning test using a video analyzing system to assess fear learning and memory in mice.

The contextual and cued fear conditioning test is one of the behavioral tests that assesses the ability of mice to learn and remember an association ...

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device (ALDM) Test Systems

Fagot &#38; Paleressompoulle1 and Fagot &#38; Bonte2 have published an automated learning device (ALDM) for the study of cognitive abilities of monkeys maintained in semi-free ranging conditions. Data accumulated during the last five years have consistently demonstrated the efficiency of this protocol to investigate individual/physical cognition in monkeys, and have further shown that this procedure reduces stress level during animal testing3. This paper demonstrates that networks of ALDM can also be used to investigate different facets of social cognition and in-group expressed behaviors in monkeys, and describes three illustrative protocols developed for that purpose. The first study demonstrates how ethological assessments of social behavior and computerized assessments of cognitive performance could be integrated to investigate the effects of socially exhibited moods on the cognitive performance of individuals. The second study shows that batteries of ALDM running in parallel can provide unique information on the influence of the presence of others on task performance. Finally, the last study shows that networks of ALDM test units can also be used to study issues related to social transmission and cultural evolution. Combined together, these three studies demonstrate clearly that ALDM testing is a highly promising experimental tool for bridging the gap in the animal literature between research on individual cognition and research on social cognition.

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Fagot &#38; Paleressompoulle1 have published an automated learning device (ALDM) aimed at testing individual cognitive abilities in semi-free ranging monkeys. The main goal of our protocol is to use a network of ALDM test units to study social cognition in non-human primates.

Fagot &#38; Paleressompoulle1 and Fagot &#38; Bonte2 have published an automated learning device (ALDM) for the study of cognitive abilities of monkeys ...

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning process. Here a method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process. This discovery-learning task uses an infant activated mobile that rotates and plays music based on specified leg action of infants. Supine infants activate the mobile by moving their feet vertically across a virtual threshold. This paradigm is unique in that as infants independently discover that their leg actions activate the mobile, the infants&#8217; leg movements are tracked using a motion capture system allowing for the quantification of the learning process. Specifically, learning is quantified in terms of the duration of mobile activation, the position variance of the end effectors (feet) that activate the mobile, changes in hip-knee coordination patterns, and changes in hip and knee muscle torque. This information describes infant exploration and exploitation at the interplay of person and environmental constraints that support task-specific action. Subsequent research using this method can investigate how specific impairments of different populations of infants at risk for movement disorders influence the discovery-learning process for task-specific action.

A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process.

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning ...

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

This protocol describes how to temporarily and remotely silence neuronal activity in discrete brain regions while animals are engaged in learning and memory tasks. The approach combines pharmacogenetics (Designer-Receptors-Exclusively-Activated-by-Designer-Drugs) with a behavioral paradigm (sensory preconditioning) that is designed to distinguish between different forms of learning. Specifically, viral-mediated delivery is used to express a genetically modified inhibitory G-protein coupled receptor (the Designer Receptor) into a discrete brain region in the rodent. Three weeks later, when designer receptor expression levels are high, a pharmacological agent (the Designer Drug) is administered systemically 30 min prior to a specific behavioral session. The drug has affinity for the designer receptor and thus results in inhibition of neurons that express the designer receptor, but is otherwise biologically inert. The brain region remains silenced for 2-5 hr (depending on the dose and route of administration). Upon completion of the behavioral paradigm, brain tissue is assessed for correct placement and receptor expression. This approach is particularly useful for determining the contribution of individual brain regions to specific components of behavior and can be used across any number of behavioral paradigms.

This protocol describes how to temporarily and remotely silence neuronal activity in discrete brain regions while rats are engaged in learning and memory tasks. The approach combines pharmacogenetics (Designer-Receptors-Exclusively-Activated-by-Designer-Drugs) with a behavioral paradigm (sensory preconditioning) that is designed to distinguish between different components of learning.

This protocol describes how to temporarily and remotely silence neuronal activity in discrete brain regions while animals are engaged in learning and ...

A General Method for Evaluating Deep Brain Stimulation Effects on Intravenous Methamphetamine Self-Administration

Substance use disorders, particularly to methamphetamine, are devastating, relapsing diseases that disproportionally affect young people. There is a need for novel, effective and practical treatment strategies that are validated in animal models. Neuromodulation, including deep brain stimulation (DBS) therapy, refers to the use of electricity to influence pathological neuronal activity and has shown promise for psychiatric disorders, including drug dependence. DBS in clinical practice involves the continuous delivery of stimulation into brain structures using an implantable pacemaker-like system that is programmed externally by a physician to alleviate symptoms. This treatment will be limited in methamphetamine users due to challenging psychosocial situations. Electrical treatments that can be delivered intermittently, non-invasively and remotely from the drug-use setting will be more realistic. This article describes the delivery of intracranial electrical stimulation that is temporally and spatially separate from the drug-use environment for the treatment of IV methamphetamine dependence. Methamphetamine dependence is rapidly developed in rodents using an operant paradigm of intravenous (IV) self-administration that incorporates a period of extended access to drug and demonstrates both escalation of use and high motivation to obtain drug.

This article describes the delivery of intracranial electrical stimulation that is temporally and spatially separate from the drug-use environment for the treatment of IV methamphetamine dependence.

Substance use disorders, particularly to methamphetamine, are devastating, relapsing diseases that disproportionally affect young people.  There is a need ...

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis (AFEA) software. Additionally, we demonstrate a method for extracting relevant emotional data output and plotting the emotional response of a population over a specified time frame. By time pairing each participant's treatment response to a control stimulus (baseline), the overall emotional response over time and across multiple participants can be quantified. AFEA is a prospective analytical tool for assessing unbiased response to food and beverages. At present, most research has mainly focused on beverages. Methodologies and analyses have not yet been standardized for the application of AFEA to beverages and foods; however, a consistent standard methodology is needed. Optimizing video capture procedures and resulting video quality aids in a successful collection of emotional response to foods. Furthermore, the methodology of data analysis is novel for extracting the pertinent data relevant to the emotional response. The combinations of video capture optimization and data analysis will aid in standardizing the protocol for automated facial expression analysis and interpretation of emotional response data.

A protocol for capturing and statistically analyzing emotional response of a population to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis software is described.

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial ...

Automated Analysis of a Nematode Population-based Chemosensory Preference Assay

The nematode, Caenorhabditis elegans' compact nervous system of only 302 neurons underlies a diverse repertoire of behaviors. To facilitate the dissection of the neural circuits underlying these behaviors, the development of robust and reproducible behavioral assays is necessary. Previous C. elegans behavioral studies have used variations of a "drop test", a "chemotaxis assay", and a "retention assay" to investigate the response of C. elegans to soluble compounds. The method described in this article seeks to combine the complementary strengths of the three aforementioned assays. Briefly, a small circle in the middle of each assay plate is divided into four quadrants with the control and experimental solutions alternately placed. After the addition of the worms, the assay plates are loaded into a behavior chamber where microscope cameras record the worms' encounters with the treated regions. Automated video analysis is then performed and a preference index (PI) value for each video is generated. The video acquisition and automated analysis features of this method minimizes the experimenter's involvement and any associated errors. Furthermore, minute amounts of the experimental compound are used per assay and the behavior chamber's multi-camera setup increases experimental throughput. This method is particularly useful for conducting behavioral screens of genetic mutants and novel chemical compounds. However, this method is not appropriate for studying stimulus gradient navigation due to the close proximity of the control and experimental solution regions. It should also not be used when only a small population of worms is available. While suitable for assaying responses only to soluble compounds in its current form, this method can be easily modified to accommodate multimodal sensory interaction and optogenetic studies. This method can also be adapted to assay the chemosensory responses of other nematode species.

We present a behavior recording setup and protocol that enables automated analysis of the nematode, Caenorhabditis elegans' preference for soluble compounds in a population-based assay. This article describes the construction of a behavior chamber, the behavioral assay protocol, and video analysis software usage.

The nematode, Caenorhabditis elegans' compact nervous system of only 302 neurons underlies a diverse repertoire of behaviors. To facilitate the dissection ...

Stress-Enhanced Fear Learning, a Robust Rodent Model of Post-Traumatic Stress Disorder

Fear behaviors are important for survival, but disproportionately high levels of fear can increase the vulnerability for developing psychiatric disorders such as post-traumatic stress disorder (PTSD). To understand the biological mechanisms of fear dysregulation in PTSD, it is important to start with a valid animal model of the disorder. This protocol describes the methodology required to conduct stress-enhanced fear learning (SEFL) experiments, a preclinical model of PTSD, in both rats and mice. SEFL was developed to recapitulate critical aspects of PTSD, including long-term sensitization of fear learning caused by an acute stressor. SEFL uses aspects of Pavlovian fear conditioning but produces a distinct and robust sensitized fear response far greater than normal conditional fear responses. The trauma procedure involves placing a rodent in a conditioning chamber and administering 15 unsignaled shocks randomly distributed over 90 minutes (for rat experiments; for mouse experiments, 10 unsignaled shocks randomly distributed over 60 minutes are used). On day 2, rodents are placed in a novel conditioning context where they receive a single shock; then, on day 3 they are placed back in the same context as on day 2 and tested for changes in freezing levels. Rodents that previously received the trauma display enhanced levels of freezing on the test day compared to those that received no shocks on the first day. Thus, with this model, a single highly stressful experience (the trauma) produces extreme fear of the stimuli associated with the traumatic event.

Here we describe the detailed methodology required to conduct stress-enhanced fear learning (SEFL) experiments, a preclinical model of post-traumatic stress disorder, in both rats and mice. The model utilizes aspects of Pavlovian fear conditioning and freezing as an index of enhanced fear in rodents.

Fear behaviors are important for survival, but disproportionately high levels of fear can increase the vulnerability for developing psychiatric disorders ...

Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Gait analysis is used to quantify changes in motor function in many rodent models of disease. Despite the importance of assessing gait and motor function in many areas of research, the available commercial options have several limitations such as high cost and lack of accessible, open code. To address these issues, we developed PrAnCER, Paw-Print Analysis of Contrast-Enhanced Recordings, for automated quantification of gait. The contrast-enhanced recordings are produced by using a translucent floor that obscures objects not in contact with the surface, effectively isolating the rat&#8217;s paw prints as it walks. Using these videos, our simple software program reliably measures a variety of spatiotemporal gait parameters. To demonstrate that PrAnCER can accurately detect changes in motor function, we employed a haloperidol model of Parkinson&#8217;s disease (PD). We tested rats at two doses of haloperidol: high dose (0.30 mg/kg) and low dose (0.15 mg/kg). Haloperidol significantly increased stance duration and hind paw contact area in the low dose condition, as might be expected in a PD model. In the high dose condition, we found a similar increase in contact area but also an unexpected increase in stride length. With further research, we found that this increased stride length is consistent with the bracing-escape phenomenon commonly observed at higher doses of haloperidol. Thus, PrAnCER was able to detect both expected and unexpected changes in rodent gait patterns. Additionally, we confirmed that PrAnCER is consistent and accurate when compared with manual scoring of gait parameters.

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

We describe a novel gait analysis system, Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER), an open-access automated system for the quantification of gait characteristics in rats that utilizes a novel semitransparent floor to automatically quantify gait. This system was validated using the haloperidol model of Parkinson&#8217;s Disease.

Gait analysis is used to quantify changes in motor function in many rodent models of disease. Despite the importance of assessing gait and motor function ...

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency sensors, such as eye-trackers, electrodermal activity wristbands, EEG bands, and motion sensors provide observations at the millisecond level. This level of precision allows researchers to collect large datasets on social interactions. In this paper, I discuss how multiple eye-trackers can capture a fundamental construct in social interactions, joint visual attention (JVA). JVA has been studied by developmental psychologists to understand how children acquire language, learning scientists to understand how small groups of learners work together, and social scientists to understand interactions in small teams. This paper describes a methodology for capturing JVA in colocated settings using mobile eye-trackers. It presents some empirical results and discusses implications of capturing microobservations to understand social interactions.

Using multimodal sensors is a promising way to understand the role of social interactions in educational settings. This paper describes a methodology for capturing joint visual attention from colocated dyads using mobile eye-trackers.

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency ...