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Camera Control

February 6, 2020

The Adaptive Motor Control Lab at Harvard recently posted their project, Camera Control, a python based camera software GUI, to Github.


Camera Control is an open-source software package written by postdoctoral fellow Gary Kane that allows video to be recorded in sync with behavior. The python GUI and scripts allows investigators to record from multiple imaging source camera feeds with associated timestamps for each frame. When used in combination with a NIDAQ card, timestamps from a behavioral task can also be recorded on the falling edge of a TTL signal. This allows video analysis to be paired with physiological recording which can be beneficial in assessing behavioral results. This package requires Windows 10, Anaconda, and Git, and is compatible with Imaging Source USB3 cameras. The software package is accessible for download from the lab’s github and instructions for installation and video recording are provided.

Find more on Github.


Kane, G. & Mathis, M. (2019). Camera Control: record video and system timestamps from Imaging Source USB3 cameras. GitHub. https://zenodo.org/badge/latestdoi/200101590

MouseMove

July 18, 2019

In a 2015 Scientific Reports article, Andre Samson and colleagues shared their project MouseMove, an open-source software for quantifying movement in the open field test:


The Open Field (OF) test is a commonly used assay for monitoring exploratory behavior and locomotion in rodents. Most research groups use commercial systems for recording and analyzing behavior in the OF test, but these commercial systems can be expensive and lack flexibility. A few open-source OF systems have been developed, but are limited in the movement parameters that can be collected and analyzed. MouseMove is the first open-source software capable of providing qualitative and quantitative information on mouse locomotion in a semi-automated and high-throughput approach. With the aim of providing a freely available program for analyzing OF test data, these researchers developed a software that accurately quantifies numerous parameters of movement.

In their manuscript, Samson et al. describe the design and implementation of MouseMove. Their OF system allows for the measurement of distance, speed, and laterality with >96% accuracy. They use MouseMove as a method to analyze OF behavior of mice after experimental stroke to show reduced locomotor activity and quantify laterality deficits. The system is used in combination with the open source program ImageJ and the MTrack2 plugin to analyze pre-recorded OF test video.

The system has two downloadable components, the ImageJ macro and a separate program with the custom-built MouseMove GUI. ImageJ is used to subtract the background video from the experiment and create an image of the animals total trajectory. The MouseMove GUI then completes a detailed analysis of the movement patterns, measuring the fractional time spent stationary, the distance traveled, speed mean and various details of laterality. The results are depicted in both a visual/graphical form and as a saveable text file. In the manuscript, they provide step-wise instructions of how to use Mousemove. The authors additionally highlight the defined region-of-interest (ROI) ability of the software that makes it suitable for analysis of cognitive tests such as Novel Object Recognition. This tool offers relatively fast video-processing of motor cognitive behaviors and has many applications for the study of rodent models of brain injury/stimulation to measure altered locomotion.

 

More information on MouseMove can be found in their manuscript here.


Samson, A. L., Ju, L., Ah Kim, H., Zhang, S. R., Lee, J. A. A., Sturgeon, S. A., … Schoenwaelder, S. M. (2015). MouseMove: an open source program for semi-automated analysis of movement and cognitive testing in rodents. Scientific Reports, 5, 16171.  doi: 10.1038/srep16171

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)

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!


Actifield

March 21, 2019

Victor Wumbor-Apin Kumbol and colleagues have developed and shared Actifield, an automated open-source actimeter for rodents, in a recent HardwareX publication.


Measuring locomotor activity can be a useful readout for understanding effects of a number of experimental manipulations related to neuroscience research. Commercially available locomotor activity recording devices can be cost-prohibitive and often lack the ability to be customized to fit a specific lab’s needs. Kumbol et al. offer an open-source alternative that utilizes infrared motion detection and an arduino to record activity in a variety of chamber set ups. A full list of build materials, links to 3D-print and laser-cut files, and assembly instructions are available in their publication.

Read more from HardwareX!


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!


Dual-port Lick Detector

January 16, 2019

In the Journal of Neurophysiology, Brice Williams and colleagues have  shared their design for a novel dual-port lick detector. This device can be used for both real-time measurement and manipulation of licking behavior in head-fixed mice.


Measuring licking behavior in mice provides a valuable metric of sensory-motor processing and can be nicely paired with simultaneous neural recordings. Williams and colleagues have developed their own device for precise measuring of licking behavior as well as for manipulating this behavior in real time. To address limitations of many available lick sensors, the authors designed their device to be smaller (appropriate for mice), contactless (to diminish electric artifacts for neural recording), and precise to a submillisecond timescale. This dual-port detector can be implemented to detect directional licking behavior during sensory tasks and can be used in combination with neural recording. Further, given the submillisecond precision of this device, it can be used in a closed-loop system to perturb licking behaviors via neural inhibition. Overall, this dual-port lick detector is a cost-effective, replicable solution that can be used in a variety of applications.

Learn how to build your own here!

And be sure to check out their Github.


DeepSqueak

January 9, 2019

Kevin Coffey has shared the following about DeepSqueak, a deep learning-based system for detection and analysis of ultrasonic vocalizations, which he developed with Russell Marx.


Rodents engage in social communication through a rich repertoire of ultrasonic vocalizations (USVs). Recording and analysis of USVs can be performed noninvasively in almost any rodent behavioral model to provide rich insights into the emotional state and motor function. Despite strong evidence that USVs serve an array of communicative functions, technical and financial limitations have inhibited widespread adoption of vocalization analysis. Manual USV analysis is slow and laborious, while existing automated analysis software are vulnerable to broad spectrum noise routinely encountered in the testing environment.

To promote accessible and accurate USV research, we present “DeepSqueak”, a fully graphical MATLAB package for high-throughput USV detection, classification, and analysis. DeepSqueak applies state-of-the-art regional object detection neural networks (Faster-RCNN) to detect USVs. This dramatically reduces the false positive rate to facilitate reliable analysis in standard experimental conditions. DeepSqueak included pre-trained detection networks for mouse USVs, and 50 kHz and 22 kHz rat USVs. After detection, USVs can be clustered by k-means models or classified by trainable neural networks.

Read more in their recent publication and check out DeepSqueak on Github!