Category: Behavior Tracking

Teensy-based Interface

Michael Romano and colleagues from the Han Lab at Boston University recently published their project using a Teensy microcontroller to control an sCMOS camera in behavioral experiments to obtain high temporal precision:


Teensy microcontrollers are becoming increasingly more popular and widespread in the neuroscience community. One benefit of using a Teensy is its ease of programming for those with little programming experience, as it uses Arduino/C++ language. An additional benefit of using a Teensy microcontroller is that it can take in and send out time-precise signals. Romano et al. developed a flexible Teensy 3.2-based interface for data acquisition and delivery of analog and digital signals during a rodent locomotion tracking experiment and in a trace eye blink conditioning experiment. The group shows how the interface can be paired with optical calcium imaging as well. The setup integrates a sCMOS camera with behavioral experiments, and the interface is rather user-friendly.

The Teensy interface ensures that the data is temporally precise, and the Teensy interface can also deliver digital signals with microsecond precision to capture images from a paired sCMOS camera. Calcium imaging can be performed during the eye blink conditioning experiment. This was done through pulses send to the camera to capture calcium activity in the hippocampus at 20 Hz from the Teensy. Additionally, the group shows that the Teensy interface can also generate analog sound waveforms to drive speakers for the eye blink experiment. The study shows how an inexpensive piece of lab equipment, like a simple Teensy microcontroller, can be utilized to drive multiple aspects of a neuroscience experiment, and provides inspiration for future experiments to utilize microcontrollers to control behavioral experiments.

 

For more details on the project, check out the project’s GitHub here.

 

Romano, M., Bucklin, M., Gritton, H., Mehrotra, D., Kessel, R., & Han, X. (2019). A Teensy microcontroller-based interface for optical imaging camera control during behavioral experiments. Journal of Neuroscience Methods, 320, 107-115.

 

optoPAD

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

DeepBehavior

Ahmet Arac from Peyman Golshani’s lab at UCLA recently developed DeepBehavior, a deep-learning toolbox with post processing methods for video analysis of behavior:


Recently, there has been a major push for more fine-grained and detailed behavioral analysis in the field of neuroscience. While there are methods for taking high-speed quality video to track behavior, the data still needs to be processed and analyzed. DeepBehavior is a deep learning toolbox that automates this process, as its main purpose is to analyze and track behavior in rodents and humans.

The authors provide three different convolutional neural network models (TensorBox, YOLOv3, and OpenPose) which were chosen for their ease of use, and the user can decide which model to implement based on what experiment or what kind of data they aim to collect and analyze. The article provides methods and tips on how to train neural networks with this type of data, and gives methods for post-processing of image data.

In the manuscript, the authors give examples of utilizing DeepBehavior in five behavioral tasks in both animals and humans. For rodents, they use a food pellet reaching task, a three-chamber test, and social interaction of two mice. In humans, they use a reaching task and a supination / pronation task. They provide 3D kinematic analysis in all tasks, and show that the transfer learning approach accelerates network training when images from the behavior videos are used. A major benefit of this tool is that it can be modified and generalized across behaviors, tasks, and species. Additionally, DeepBehavior uses several different neural network architectures, and uniquely provides post-processing methods for 3D kinematic analysis, which separates it from previously published toolboxes for video behavioral analysis. Finally, the authors emphasize the potential for using this toolbox in a clinical setting with analyzing human motor function.

 

For more details, take a look at their project’s Github.

All three models used in the paper also have their own Github: TensorBox, YOLOv3, and openpose.


Arac, A., Zhao, P., Dobkin, B. H., Carmichael, S. T., & Golshani, P. (2019). DeepBehavior: A deep learning toolbox for automated analysis of animal and human behavior imaging data. Frontiers in systems neuroscience, 13.

 

ezTrack

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. 

Low Cost Open Source Eye Tracking

May 30, 2019

On Hackaday, John Evans and colleagues have shared a design and build for an open-source eye-tracking system for human research.


We’ve wanted to expand our coverage of behavioral tools to include those used in human research. To get this rolling, we’d like to highlight a project for eye tracking that might be helpful to many labs, especially if you don’t have a grant to collect pilot data. Check out Low Cost Open Source Eye Tracking. It uses open-source code, available from GitHub, and a pair of cheap USB cameras.

Check out the details on Hackaday.io and GitHub!


Evans, J. (2018). Low Cost Open Source Eye Tracking. Retrieved from https://hackaday.io/project/153293-low-cost-open-source-eye-tracking

Automated classification of self-grooming in mice

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

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


Stytra

Vilim Štih has shared their new project from the Portugues lab called Stytra, which was recently published in PLOS Computational Biology (Štih, Petrucco et al., 2019):


“Stytra is a flexible open-source software package written in Python and designed to cover all the general requirements involved in larval zebrafish behavioral experiments. It provides timed stimulus presentation, interfacing with external devices and simultaneous real-time tracking of behavioral parameters such as position, orientation, tail and eye motion in both freely-swimming and head-restrained preparations. Stytra logs all recorded quantities, metadata, and code version in standardized formats to allow full provenance tracking, from data acquisition through analysis to publication. The package is modular and expandable for different experimental protocols and setups. Current releases can be found at https://github.com/portugueslab/stytra. We also provide complete documentation with examples for extending the package to new stimuli and hardware, as well as a schema and parts list for behavioral setups. We showcase Stytra by reproducing previously published behavioral protocols in both head-restrained and freely-swimming larvae. We also demonstrate the use of the software in the context of a calcium imaging experiment, where it interfaces with other acquisition devices. Our aims are to enable more laboratories to easily implement behavioral experiments, as well as to provide a platform for sharing stimulus protocols that permits easy reproduction of experiments and straightforward validation. Finally, we demonstrate how Stytra can serve as a platform to design behavioral experiments involving tracking or visual stimulation with other animals and provide an example integration with the DeepLabCut neural network-based tracking method.”

Check out the paper, the enhanced version with the documentation, at www.portugueslab.com/stytra or the pdf at PLOS Computational Biology

 


 

 

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)

Telemetry System for Recording EEG

March 29, 2019

In a 2011 Journal of Neuroscience Methods article, Pishan Chang and colleagues shared their design for an open-source, novel telemetry system for recording EEG in small animals.


EEG monitoring in freely-behaving small animals is a useful technique for observing natural fluctuations in neural activity over time. Monitoring frequencies above 80 Hz continuously over a period of weeks can be a challenge. Chang et al. have shared their design for a system that combines an implantable telemetric sensor, radio-frequency transmission, and an open-source data acquisition software to collect EEG data over a span of up to 8 weeks. Various modifications to the system  have increased the longevity of the device and reduced transmission noise to provide continuous and reliable data. Schematics of the device, transmission system, and validation results in a population of epileptic rodents are available in their publication.

 

Learn more from the Journal of Neuroscience Methods!