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>
2. TENSORBOX
<ol>
	<li>Tensorbox Setup
	<ol>
		<li>Create Virtual Environment for Tensorbox 
		Command line: cd ~ 
		Command line: virtualenv --system-site-packages ~/tensorflow 
		NOTE: &#39;~/tensorflow&#39; is the name of the environment and is arbitrary</li>
		<li>Activate environment 
		Command line: source ~/tensorflow/bin/activate</li>
	</ol>
	</li>
	<li>Tensorbox Installation 
	We will be using GitHub to clone TensorBox from <a href="http://github.com/aarac/TensorBox" target="_blank">http://github.com/aarac/TensorBox</a> and install it on our machine as well as installing additional dependencies. 
	Command line: cd ~ 
	Command line: git clone <a href="http://github.com/aarac/TensorBox" target="_blank">http://github.com/aarac/TensorBox</a> 
	Command line: cd TensorBox 
	Command line: pip install -r requirements.txt</li>
	<li>Label Data
	<ol>
		<li>Create a folder of images of behavior 
		Open source tools such as ffmpeg are useful to accomplish converting videos to individual frames We recommend labeling at least 600 images from a wide-distribution of behavior frames for training. Put these images in a folder.</li>
		<li>Launch labeling graphical user interface 
		Command line: python make_json.py &#60;path to image folder&#62; labels.json 
		To label an image, click the top left corner of the object of interest (i.e. paw) first and then click the bottom right corner of the object of interest (Figure 4). Inspect that the bounding box captures the entire object of interest. Press &#39;undo&#39; to re-label the same image or press &#39;next&#39; to move onto the next frame.</li>
	</ol>
	</li>
	<li>Train TensorBox
	<ol>
		<li>Link training images to network hyperparameters file 
		Within the tensorbox folder, open the following folder in a text editor: 
		/TensorBox/hypes/overfeat_rezoom.json. Navigate to the attribute under data named train_idl and replace the file path from ./data/brainwash/train_boxes.json to the labels.json filepath. Save the changes to file.</li>
		<li>Begin training script 
		Command line: cd ~/TensorBox 
		Command line: python train.py --hypes hypes/overfeat_rezoom.json --gpu 0 --logdir output 
		The network will then begin training for 600,000 iterations. In the output folder, the resulting trained weights of the convolutional neural network will be generated.</li>
	</ol>
	</li>
	<li>Predict on New Images 
	For image labeling: 
	Command line: cd ~/TensorBox 
	Command line: python label_images.py --folder &#60;path to image folder&#62; --weights output/overfeat_rezoom_&#60;timestamp&#62;/save.ckpt-600000 --hypes /hypes/overfeat_rezoom.json --gpu 0 
	To get coordinates of bounding boxes: 
	Command line: cd ~/TensorBox 
	Command line: python predict_images_to_json.py --folder &#60;path to image folder&#62; --weights 
	output/overfeat_rezoom_&#60;timestamp&#62;/save.ckpt-600000 --hypes 
	/hypes/overfeat_rezoom.json --gpu 0</li>
	<li>MATLAB Post-Processing for TensorBox 
	Additional MATLAB code has been provided to extract kinematics and visualizations of the coordinates using the resulting JSON coordinate file from the model 
	Run the &#34;Process_files_3Dreaching_mouse.m&#34; script for 3D kinematic analysis of single food pellet reaching task.</li>
</ol>
3. YOLOv3
<ol>
	<li>Install YOLOv3 
	Command line: cd ~ 
	Command line: git clone cd darknet 
	For GPU usage, open &#39;Makefile&#39; and change the following lines: GPU=1; CUDNN=1. 
	Command line: make</li>
	<li>Labeling Training Data using Yolo_mark 
	Command line: cd ~ 
	Command line: git clone cd ~/Yolo_Mark 
	Command line: cmake . 
	Command line: make 
	Place the training images in ~/Yolo_mark/data/obj folder 
	Command line: chmod +x ./linux_mark.sh 
	Command line: ./linux_mark.sh 
	Label the images one by one in the graphical user interface (Figure 5). The recommended amount of images is approximately 200.</li>
	<li>Training YOLOv3
	<ol>
		<li>Setup configuration file 
		Command line: cd ~/Yolo_mark 
		Command line: scp -r ./data ~/darknet 
		Command line: cd ~/darknet/cfg 
		Command line: cp yolov3.cfg yolo-obj.cfg</li>
		<li>Modify the configuration file 
		Open the yolo-obj.cfg folder and modify the following lines: batch=64, subdivision=8, classes=(# of class to detect), and for each convolutional layer before a yolo layer change the filter=(classes+5)x3. Details on these changes can be found at <a href="https://github.com/aarac/darknet/blob/master/README.md" target="_blank">https://github.com/aarac/darknet/blob/master/README.md</a></li>
		<li>Download network weights 
		Download the network weights from <a href="https://www.dropbox.com/s/613n2hwm5ztbtuf/darknet53.conv.74?dl=0" target="_blank">https://www.dropbox.com/s/613n2hwm5ztbtuf/darknet53.conv.74?dl=0</a> 
		Place the downloaded weight file into ~/darknet/build/darknet/x64</li>
		<li>Run training algorithm 
		Command line: cd ~/darknet 
		Command line: ./darknet detector train data/obj.data cfg/yolo-obj.cfg darknet53.conv.74</li>
		<li>YOLOv3 Evaluation 
		After the training is complete based on a set number of iterations (ITERATIONNUMBER), you can view them by 
		Command line: ./darknet detector test data/obj.data cfg/yolo-obj.cfg backup/yolo-obj_ITERATIONNUMBER.weights &#60;IMAGE&#62;.jpg</li>
	</ol>
	</li>
	<li>Predict on new videos and get coordinates 
	This command can be run to obtain the coordinates of the labels in the new video: 
	Command line: ./darknet detector demo data/obj.data cfg/yolo-obj.cfg backup/yolo-obj_ITERATIONNUMBER.weights VIDEO.avi -ext_output &#60;VIDEO.avi&#62; FILENAME.txt</li>
	<li>YOLOv3 PostProcessing in MATLAB 
	Take the FILENAME.txt file to MATLAB, and run the &#34;Process_socialtest_mini.m&#34; script for two mice social interaction test. See results in Figure 2</li>
</ol>
4. OpenPose
OpenPose is ideal to track multiple body parts in a human subject. The setup and installation processes are very similar to the previous two frameworks. However, there is no training step as the network is already trained on human data.
<ol>
	<li>OpenPose Installation 
	Navigate to <a href="https://github.com/aarac/openpose" target="_blank">https://github.com/aarac/openpose</a> and follow the installation instructions.</li>
	<li>Process Video 
	./build/examples/openpose/openpose.bin --video VIDEONAME.avi --net_resolution &#34;1312x736&#34; --scale_number 4 --scale_gap 0.25 --hand --hand_scale_number 6 --hand_scale_range 0.4 --write_json JSONFOLDERNAME --write_video RESULTINGVIDEONAME.avi 
	Here the --net_resolution, --scale_number, --scale_gap, --hand_scale_number and --hand_scale_range handles can be omitted if a high precision detection is not needed (this would decrease the processing time).</li>
	<li>OpenPose Post-Processing 
	In MATLAB folder, please use &#39;process_files_human3D.m&#39; script to run the code after adding the appropriate folder containing json files from cameras 1 and 2, as well as the calibration file. This will create a &#34;cell&#34; file with all the 3D poses of the joints. It will also make a movie of the 3D skeletal view. For camera calibration, please follow the instructions at this link: <a href="http://www.vision.caltech.edu/bouguetj/calib_doc/" target="_blank">http://www.vision.caltech.edu/bouguetj/calib_doc/</a></li>
</ol>

<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. 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The authors have nothing to disclose.

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 ...

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

Research

JoVE Journal

Behavior

9.3K Views.  David Geffen School of Medicine, University of California, Los Angeles. 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.

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

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 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 ...

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 ...

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)

The Drosophila model has been invaluable for the study of neurological function and for understanding the molecular and cellular mechanisms that underlie neurodegeneration. While fly techniques for the manipulation and study of neuronal subsets have grown increasingly sophisticated, the richness of the resultant behavioral phenotypes has not been captured at a similar detail. To be able to study subtle fly leg movements for comparison amongst mutants requires the ability to automatically measure and quantify high-speed and rapid leg movements. Hence, we developed a machine-learning algorithm for automated leg claw tracking in freely walking flies, Feature Learning-based Limb segmentation and Tracking (FLLIT). Unlike most deep learning methods, FLLIT is fully automated and generates its own training sets without a need for user annotation, using morphological parameters built into the learning algorithm. This article describes an in depth protocol for carrying out gait analysis using FLLIT. It details the procedures for camera setup, arena construction, video recording, leg segmentation and leg claw tracking. It also gives an overview of the data produced by FLLIT, which includes raw tracked body and leg positions in every video frame, 20 gait parameters, 5 plots and a tracked video. To demonstrate the use of FLLIT, we quantify relevant diseased gait parameters in a fly model of Spinocerebellar ataxia 3.

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

We describe detailed protocols for using FLLIT, a fully automated machine learning method for leg claw movement tracking in freely moving Drosophila melanogaster and other insects. These protocols can be used to quantitatively measure subtle walking gait movements in wild type flies, mutant flies and fly models of neurodegeneration.

The Drosophila model has been invaluable for the study of neurological function and for understanding the molecular and cellular mechanisms that underlie ...

The Participant-Reported Implementation Update and Score (PRIUS): A Novel Method for Capturing Implementation-Related Data Over Time

"Implementation" of new initiatives in healthcare settings typically encompasses two distinct components: a "clinical intervention" plus accompanying "implementation strategies" that support putting the clinical intervention into day-to-day practice. A novel clinical intervention, for example, might consist of a new medication, a new protocol, a new device, or a new program. As clinical interventions are not self-implementing, however, they nearly always require effective implementation strategies in order to succeed. Implementation strategies set out to engage healthcare providers, staff and patients in ways that increase the likelihood of the new initiative being successfully adopted, a process that often involves behavior change and new ways of thinking by participants. One of the challenges in studying implementation is that it can be difficult to collect data about the status and progress of implementation, including participants' own perspectives and experiences concerning implementation to date. This protocol describes a novel method for collecting and analyzing data related to ongoing implementation called the Participant-Reported Implementation Update and Score, or PRIUS. The PRIUS method allows for the efficient and systematic capture of qualitative and quantitative data that can provide a detailed and nuanced account of implementation over time and from multiple viewpoints. This longitudinal method can enable researchers, as well as implementation leaders and organizational stakeholders, to monitor implementation progress more closely, conduct formative evaluation, identify improvement opportunities, and gauge the effect of any implementation changes on a rolling basis.

The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time

This protocol describes a novel method for collecting and analyzing data related to ongoing implementation called the Participant-Reported Implementation Update and Score (PRIUS). The PRIUS method allows for the efficient and systematic capture of data over time and from multiple viewpoints in healthcare settings.

"Implementation" of new initiatives in healthcare settings typically encompasses two distinct components: a "clinical intervention" plus accompanying ...