Category: Data Analysis

Updates on LocoWhisk and ART

OCTOBER 3, 2019

Dr Robyn Grant from Manchester Metropolitan University in Manchester, UK has shared her group’s most recent project called LocoWhisk, which is a hardware and software solution for measuring rodent exploratory, sensory and motor behaviours:


In describing the project, Dr Grant writes, “Previous studies from our lab have shown that that analysing whisker movements and locomotion allows us to quantify the behavioural consequences of sensory, motor and cognitive deficits in rodents. Independent whisker and feet trackers existed but there was no fully-automated, open-source software and hardware solution, that could measure both whisker movements and gait.

We developed the LocoWhisk arena and new accompanying software, that allows the automatic detection and measurement of both whisker and gait information from high-speed video footage. The arena can easily be made from low-cost materials; it is portable and incorporates both gait analysis (using a pedobarograph) and whisker movements (using high-speed video camera and infrared light source).

The software, ARTv2 is freely available and open source. ARTv2 is also fully-automated and has been developed from our previous ART software (Automated Rodent Tracker).

ARTv2 contains new whisker and foot detector algorithms. On high-speed video footage of freely moving small mammals (including rat, mouse and opossum), we have found that ARTv2 is comparable in accuracy, and in some cases significantly better, than readily available software and manual trackers.

The LocoWhisk system enables the collection of quantitative data from whisker movements and locomotion in freely behaving rodents. The software automatically records both whisker and gait information and provides added statistical tools to analyse the data. We hope the LocoWhisk system and software will serve as a solid foundation from which to support future research in whisker and gait analysis.”

For more details on the ARTv2 software, check out the github page here.

Check out the paper that describes LocoWhisk and ARTv2, which has recently been published in the Journal of Neuroscience Methods.

LocoWhisk was initially shared and developed through the NC3Rs CRACK IT website here.


SpikeGadgets

AUGUST 22, 2019

We’d like to highlight groups and companies that support an open-source framework to their software and/or hardware in behavioral neuroscience. One of these groups is SpikeGadgets, a company co-founded by Mattias Karlsson and Magnus Karlsson.


SpikeGadgets is a group of electrophysiologists and engineers who are working to develop neuroscience hardware and software tools. Their open-source software, Trodes, is a cross-platform software suite for neuroscience data acquisition and experimental control, which is made up of modules that communicate with a centralized GUI to visualize and save electrophysiological data. Trodes has a camera module and a StateScript module, which is a state-based scripting language that can be used to program behavioral tasks through using lights, levels, beam breaks, lasers, stimulation sources, audio, solenoids, etc. The camera module can be used to acquire video that can synchronize to neural recordings; the camera module can track the animal’s position in real-time or play it back after the experiment. The camera module can work with USB webcams or GigE cameras.

Paired with the Trodes software and StateScript language is the SpikeGadgets hardware that can be purchased on their website. The hardware is used for data acquisition (Main Control Unit, used for electrophysiology) and behavioral control (Environmental Control Unit).  SpikeGadgets also provides both Matlab and Python toolboxes on their site that can be used to analyze both behavioral and electrophysiological data. Trodes can be used on Windows, Linux, or Mac, and there are step-by-step instructions for how to install and use Trodes on the group’s bitbucket page.

Spikegadgets mission is “to develop the most advanced neuroscience tools on the market, while preserving ease of use and science-driven customization.”

 


For more information on SpikeGadgets or to download or purchase their software or hardware, check out their website here.

There is additional documentation on their BitBucket Wiki, with a user manual, instructions for installation, and FAQ.

Check out their entire list of collaborators, contributors, and developers here.

optoPAD

June 27, 2019

Carlos Ribeiro’s lab at Champalimaud recently published their new project called optoPAD in eLife:


Both the analysis of behavior and of neural activity need to be time-precise in order to make any correlation or comparison to each other. The analysis of behavior can be done through many methods (as seen by many featured projects on this site!). The Ribeiro lab has previously published their work on flyPAD (Itskov et al., 2014), which is a system for automated analysis of feeding behavior in Drosophila with high temporal precision. However, in attempts to manipulate specific feeding behaviors, the group wanted to go one step further to manipulate neural activity during feeding, and needed a method to do so that would be precise enough to compare with behavior.

In their new manuscript, Moreira et al. describe the design and implementation of a high-throughput system of closed-loop optogenetic manipulation of neurons in Drosophila during feeding behavior. Named optoPAD, the system allows for specific perturbation of specific groups of neurons. They use optoPAD as a method to induce appetitive and aversive effects on feeding through activating or inhibiting gustatory neurons in a closed-loop manner. OptoPAD is a combination of the previous flyPAD system with an additional method for stimulating LEDs for optogenetic perturbation. They also used their system combined with Bonsai, a current open-source framework for behavioral analysis.

The system first uses flyPAD to measure the interaction of the fly with the food given in an experiment. Then, Bonsai detects when the fly interacts with a food electrode, then sending a signal to a microcontroller which will turn on an LED for optogenetic perturbation of neurons in the fly. The authors additionally highlight the flexibility and expandability of the optoPAD system. They detail how flyPAD, once published and then implemented in an optogenetics framework by their group, had been successfully adapted by another group, which is a great example of the benefit of open-source sharing of projects.

 

Details on the hardware and software can be found at the Ribeiro lab Github. More details on flyPAD, the original project, can be found on their github as well.

Information on FlyPAD can also be found on the FlyPAD website and in the FlyPAD paper .


Moreira, J. M., Itskov, P. M., Goldschmidt, D., Steck, K., Walker, S. J., & Ribeiro, C. (2019). optoPAD: a closed-loop optogenetics system to study the circuit basis of feeding behaviors. eLife, doi: 10.7554/eLife.43924

ezTrack

June 13, 2019

Zach Pennington from Denise Cai’s lab at Mt. Sinai recently posted a preprint describing their latest open-source project called ezTrack:


ezTrack is an open-source, platform independent set of behavior analysis pipelines using interactive Python (iPython/Jupyter Notebook) that researchers with no prior programming experience can use. ezTrack is a sigh of relief for researchers with little to no computer programming experience. Behavioral tracking analysis shouldn’t be limited to those with extensive programming knowledge, and ezTrack is a nice alternative to currently available software that may require a bit more programming experience. The manuscript and Jupyter notebooks are written in the style of a tutorial, and is meant to provide straightforward instructions to the user on implementing ezTrack. ezTrack is unique from other recent video analysis toolboxes in that this method does not use deep learning algorithms and thus does not require training sets for transfer learning.

ezTrack can be used to analyze rodent behavior videos of a single animal in different settings, and the authors provide examples of positional analysis across several tasks (place-preference, water-maze, open-field, elevated plus maze, light-dark boxes, etc), as well as analysis of freezing behavior. ezTrack can provide frame-by-frame data output in .csv files, and users can crop the frames of the video to get rid of any issue with cables from optogenetic or electrophysiology experiments. ezTrack can take on multiple different video formats, such as mpg1, wav, avi, and more.

Aside from the benefit of being open-source, there are several major advantages of ezTrack. Notably, the tool is user-friendly in that it is accessible to researchers with little to no programming background. The user does not need to make many adjustments to parameters of the toolbox, and the data can processed into interactive visualizations and is easily extractable in .csv files. ezTrack is both operating system and hardware independent and can be used across multiple platforms. Utilizing ipython/Jupyter Notebook allows researchers to easily replicate their analyses as well.

Check out their GitHub with more details on how to use ezTrack: https://github.com/denisecailab/ezTrack


Pennington, Z. T., Dong, Z., Bowler, R., Feng, Y., Vetere, L. M., Shuman, T., & Cai, D. J. (2019). ezTrack: An open-source video analysis pipeline for the investigation of animal behavior. BioRxiv, 592592. 

Automated classification of self-grooming in mice

May 16, 2019

In the Journal of Neuroscience Methods, Bastijn van den Boom and colleagues have shared their ‘how-to’ instructions for implementing behavioral classification with JAABA, featuring bonsai and motr!


In honor of our 100th post on OpenBehavior, we wanted to feature a project that exemplifies how multiple open-source projects can be implemented to address a common theme in behavioral neuroscience: tracking and classifying complex behaviors! The protocol from Van den Boom et al.  implements JAABA, an open-source machine learning based behavior detection system; motr, an open-source mouse trajectory tracking software; and bonsai, an open-source system capable of streaming and recording video. Together they use these tools to process videos of mice performing grooming behaviors in a variety of behavioral setups.

They then compare multiple tools for analyzing grooming behavior sequences in both wild-type and genetic knockout mice with a tendency to over groom. The JAABA trained classifier outperforms the commercially available behavior analysis software and more closely aligns with manual analysis of behavior by expert observers. This offers a novel, cost-effective and easy to use method for assessing grooming behavior in mice comparable to that of an expert observer, with the efficient advantage of being automatic. How to instructions for how to train your own JAABA classifier can be found in their paper!

Read more in their publication here!


AutonoMouse

May 10, 2019

In a recently published article (Erskine et al., 2019), The Schaefer lab at the Francis Crick Institute introduced their new open-source project called AutonoMouse.


AutonoMouse is a fully automated, high-throughput system for self-initiated conditioning and behavior tracking in mice. Many aspects of behavior can be analyzed through having rodents perform in operant conditioning tasks. However, in operant experiments, many variables can potentially alter or confound results (experimenter presence, picking up and handling animals, altered physiological states through water restriction, and the issue that rodents often need to be individually housed to keep track of their individual performances). This was the main motivation for the authors to investigate a way to completely automate operant conditioning. The authors developed AutonoMouse as a fully automated system that can track large numbers (over 25) of socially-housed mice through implanted RFID chips on mice. With the RFID trackers and other analyses, the behavior of mice can be tracked as they train and are subsequently tested on (or self-initiate testing in) an odor discrimination task over months with thousands of trials performed every day. The novelty in this study is the fully automated nature or the entire system (training, experiments, water delivery, weighing the animals are all automated) and the ability to keep mice socially-housed 24/7, all while still training them and tracking their performance in an olfactory operant conditioning task. The modular set-up makes it possible for AutonoMouse to be used to study many other sensory modalities, such as visual stimuli or in decision-making tasks. The authors provide a components list, layouts, construction drawings, and step-by-step instructions for the construction and use of AutonoMouse in their publication and on their project’s github.


For more details, check out this youtube clip interview with Andreas Schaefer, PI on the project.

 

The github for the project’s control software is located here: https://github.com/RoboDoig/autonomouse-control and for the project’s design and hardware instructions is here: https://github.com/RoboDoig/autonomouse-design. The schedule generation program is located here: https://github.com/RoboDoig/schedule-generator


Phenopy

April 17, 2019

In a recent Nature Protocol’s article, Edoardo Balzani and colleagues from Valter Tucci’s lab have developed and shared Phenopy, a Python-based open-source analytical platform for behavioral phenotyping.


Behavioral phenotyping of mice using classic methods can be a long process and is susceptible to high variability, leading to inconsistent results. To reduce variance and speed up to process of behavioral analysis, Balzani et al. developed Phenopy, an open-source software for recording and analyzing behavioral data for phenotyping. The software allows for recording components of a behavioral task in combination with electrophysiology data. It is capable of performing online analysis as well as analysis of recorded data on a large scale, all within a user-friendly interface. Information about the software is available in their publication, available from Nature Protocols.*

Check out the full article from Nature Protocols!


(*alternatively available on ResearchGate)

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!)