Category: Video Analysis

CAVE

In a recent article, Jennifer Tegtmeier and colleagues have shared CAVE: an open-source tool in MATLAB for combined analysis of head-mounted calcium imaging and behavior.


Calcium imaging is spreading through the neuroscience field like melted butter on hot toast. Like other imaging techniques, the data collected with calcium imaging is large and complex. CAVE (Calcium ActiVity Explorer) aims to analyze imaging data from head-mounted microscopes simultaneously with behavioral data. Tegtmeier et al. developed this software in MATLAB with a bundle of unique algorithms to specifically analyze single-photon imaging data, which can then be correlated to behavioral data. A streamlined workflow is available for novice users, with more advanced options available for advanced users. The code is available for download from GitHub.

Read more from Frontiers in Neuroscience, or check it out directly from GitHub.


idtracker.ai

February 20, 2019

Francisco Romero Ferrero and colleagues have developed idtracker.ai, an algorithm and software for tracking individuals in large collectives of unmarked animals, recently described in Nature Methods.


Tracking individual animals in large collective groups can give interesting insights to behavior, but has proven to be a challenge for analysis. With advances in artificial intelligence and tracking software, it has become increasingly easier to collect such information from video data. Ferrero et al. have developed an algorithm and tracking software that features two deep networks. The first tracks animal identification and the second tracks when animals touch or cross paths in front of one another. The software has been validated to track individuals with high accuracy in cohorts of up to 100 animals with diverse species from rodents to zebrafish to ants. This software is free, fully-documented and available online with additional jupyter notebooks for data analysis.

Check out their website with full documentation, the recent Nature Methods article, BioRXiv preprint, and a great video of idtracker.ai tracking 100 zebrafish!


Open-source platform for worm behavior

February 13, 2019

In Nature Methods, Avelino Javer and colleagues developed and shared an open-source platform for analyzing and sharing worm behavioral data.


Collecting behavioral data is important and analyzing this data is just as crucial. Sharing this data is also important because it can further our understanding of behavior and increase replicability of worm behavioral studies. This is achieved by allowing many scientists to re-analyze available data, as well as develop new methods for analysis. Javer and colleagues developed an open resource in an effort to streamline the steps involved in this process — from storing and accessing video files to creating software to read and analyze the data. This platform features: an open-access repository for storing, accessing, and filtering data; an interchange format for notating single or multi-worm behavior; and file formats written in Python for feature extraction, review, and analysis. Together, these tools serve as an accessible suite for quantitative behavior analysis that can be used by experimentalists and computational scientists alike.

 

Read more about this platform from Nature Methods! (the preprint is also available from bioRxiv!)


Head-Mounted Camera System

February 6, 2019

Arne Meyer and colleagues recently shared their design and implementation of a head-mounted camera system for capturing detailed behavior in freely moving mice.


Video monitoring of animals can give great insight to behaviors. Most video monitoring systems to collect precise behavioral data require fixed position cameras and stationary animals, which can limit observation of natural behaviors. To address this, Meyer et al. developed a system which combines a lightweight head-mounted camera and head-movement sensors to detect behaviors in mice. The system, built using commercially available and 3D printed parts, can be used to monitor a variety of subtle behaviors including eye position, whisking, and ear movements in unrestrained animals. Furthermore, this device can be mounted in combination with neural implants for recording brain activity.

Read more here!


Live Mouse Tracker

December 5, 2018

In a recent preprint, Fabrice de Chaumont and colleagues share Live Mouse Tracker, a real-time behavioral analysis system for groups of mice.


Monitoring social interactions of mice is an important aspect to understand pre-clinical models of various psychiatric disorders, however, gathering data on social behaviors can be time-consuming and often limited to a few subjects at a time. With advances in computer vision, machine learning, and individual identification methods, gathering social behavior data from many mice is now easier. de Chaumont and colleagues have developed Live Mouse Tracker which allows for behavior tracking for up to 4 mice at a time with RFID sensors. The use of infrared/depth RGBD cameras allow for tracking of animal shape and posture. This tracking system automatically labels behaviors on an individual, dyadic, and group level. Live Mouse Tracker can be used to assess complex social behavioral differences between mice.

Learn more on BioRXiv, or check out the Live Mouse Tracker website!


KineMouse Wheel

October 10, 2018

On Hackaday, Richard Warren of the Sawtell Lab at Columbia University has shared his design for KineMouse Wheel, a light-weight running wheel for head-fixed locomotion that allows for 3D positioning of mice with a single camera.


Locomotive behavior is a common behavioral readout used in neuroscience research, and running wheels are a great tool for assessing motor function in head-fixed mice. KineMouse Wheel takes this tool a step further. Constructed out of light-weight, transparent polycarbonate with an angled mirror mounted inside, this innovative device allows for a single camera to capture two views of locomotion simultaneously. When combined with DeepLabCut, a deep-learning tracking software, head-fixed mice locomotion can be captured in three dimensions allowing for a more complete assessment of motor behavior. This wheel can also be further customized to fit the needs of a lab by using different materials for the build. More details about the KineMouse Wheel are available at hackaday.io, in addition to a full list of parts and build instructions.

Read more about KineMouse Wheel on Hackaday,

and check out other awesome open-source tools on the OpenBehavior Hackaday list!


 

OpenBehavior Feedback Survey

We are looking for your feedback to understand how we can better serve the community! We’re also interested to know if/how you’ve implemented some of the open-source tools from our site in your own research.

We would greatly appreciate it if you could fill out a short survey (~5 minutes to complete) about your experiences with OpenBehavior.

https://american.co1.qualtrics.com/jfe/form/SV_0BqSEKvXWtMagqp

Thanks!

EthoWatcher: a tool for behavioral and video-tracking analysis in laboratory animals

September 26, 2018

In Computers in Biology and Medicine, Carlos Fernando Crispin Jr. and colleagues share their software EthoWatcher: a computational tool that supports video-tracking, detailed ethography, and extraction of kinematic variables from video files of laboratory animals.


The freely available EthoWatcher software has two modules: a tracking module and an ethography module. The tracking module permits the controlled separation of the target from its background, the extraction of image attributes used to calculate distances traveled, orientation, length, area and a path graph of the target. The ethography module allows recording of catalog-based behaviors from video files, the environment, or frame-by-frame. The output reports latency, frequency, and duration of each behavior as well as the sequence of events in a time-segmented format fixed by the user. EthoWatcher was validated conducting tests on the detection of the known behavioral effects of drugs and on kinematic measurements.

Read more in their paper or download the software from the EthoWatcher webpage!


Junior, C. F., Pederiva, C. N., Bose, R. C., Garcia, V. A., Lino-De-Oliveira, C., & Marino-Neto, J. (2012). ETHOWATCHER: Validation of a tool for behavioral and video-tracking analysis in laboratory animals. Computers in Biology and Medicine,42(2), 257-264. doi:10.1016/j.compbiomed.2011.12.002

Argus

September 5, 2018

In a recent Behavior Research Methods article, Soaleha Shams and colleagues share Argus, a data extraction and analysis tool built in the open-source R language for tracking zebrafish behavior.


Based on a formerly developed custom-software for zebrafish behavior tracking, Argus was developed with behavioral researchers in mind. It includes a  new, user-friendly, and efficient graphical user interface and offers simplicity and flexibility in measuring complex zebrafish behavior through customizable parameters set by the researcher. The program is validated against two commercially available programs for zebrafish behavior analysis, and measures up in its ability to track speed, freezing, erratic movement, and interindividual distance. In summary, Argus is shown to be a novel, cost- effective, and customizable method for the analysis and quantification of both single and socially interacting zebrafish.

Read more here!


Q&A with Dr. Mackenzie Mathis on her experience with developing DeepLabCut

August 22, 2018

Dr. Mackenzie Mathis, Principal Investigator of the Adaptive Motor Control Lab (Rowland Institute at Harvard University), has shared the following responses to a short Q&A about the inspiration behind, development of and sharing of DeepLabCut — a toolbox for animal tracking using deep-learning.


What inspired you and your colleagues to create this toolbox as opposed to using previously developed commercial software?

Alexander Mathis and I both worked on behaviors where we wanted to track particular features, and they proved to be unreliably tracked with the methods we tried. Specifically, Alexander has an odor-guided navigation task that he works on in the lab of Prof. Venkatesh Murthy at Harvard, where the mice are placed in a very large “endless” paper trail and he inkjet prints odors for them to follow to get rewards (chocolate milk). The position of the snout is very important to measure accurately, so background subtraction or other heuristics didn’t work when the nose crossed the trail and when the droplet was right in front of the snout. I worked on a skilled joystick behavior for mice, and I wanted to track joints accurately and non-invasively – a challenging problem for little hands. So, we teamed up with Prof. Matthias Bethge at the University of Tuebingen, to work on a new approach. He suggested we start looking into the rapidly advancing human pose estimation literature, and we looked at several before deciding to seriously benchmark DeeperCut, a top performing algorithm in the large MPII dataset. Those authors did something very clever, namely, they used a deep neural network (ResNet) that was pre-trained on a large image set called ImageNet. This gives the ResNet a chance to learn natural scene statistics first. Remarkably, we found that we could use only a few frames to very accurately track the snout in the odor-guided navigation task, so we next tried videos from my joystick task, and to flex DeepLabCut’s muscles, we teamed up with Kevin Cury (who, like myself was an alumni of Prof. Nao Uchida’s group) to track fruit flies in the 3D chamber. After all this benchmarking, we built a toolbox that implements a complete pipeline to extract and label frames, train and evaluate the deep neural nets, as well as analyze new experimental videos.  We call this toolbox DeepLabCut, as a nod to DeeperCut.

What was the motivation for immediately sharing your work as an open source tool, thus making it accessible to the broader neuroscience community?

Some of the options we first tried to track with were very expensive commercial systems, and they failed quite badly. On the other hand, deep learning has revolutionized computer vision in the last few years, so we were eager to try some new approaches to solve the problem. So, in addition to being advocates of open science, we really wanted to make a toolbox that someone with minimal to no coding experience could, absolutely for free, track whatever they wanted.

We also know peer review can be slow, so as soon as we had the toolbox in place, we wrote up the arxiv paper and released the code base immediately. Honestly, it has been one of my most rewarding papers – the feedback from our peers, and seeing what people have used the code for, has been a very rewarding experience. This was my first preprint, and especially for methods manuscripts, I now cannot imagine another way to share our future work too.

How do you think open source tools, such as yours, will continue to impact the progress of scientific research?

Open source code and preprints have been the norm in some fields for decades (such as math and physics), and I am really excited to see it come of age in biology and neuroscience. I am excited to see how tools will continue to improve as the community gets behind them, just as we could build on DeeperCut, which was open source. Also, at least in my experience, many individuals write their own code, which leads to a lot of duplicated efforts. Moreover, datasets are becoming increasingly more complicated and code to work with such data need to be robust shared. My expectation is that open source code will become the norm in the future, which can only help science become more robust.

Even before formal publication this week (see Nature Neuroscience), we estimate that about 100 labs are actively using DeepLabCut, so releasing the code before publication, we hope,  has really allowed for rapid progress to be made. We were also very happy that The Atlantic could highlight some of the early adopters, as it’s one thing to say you made something, but it’s another to hear others saying it is actually ‘something.’


DeepLabCut provides an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. Read more on the website, or in Nature Neuroscience.