Researchers at the University of Guelph have created a low-cost automated apparatus for measuring catalepsy that increases measurement accuracy and reduces observer bias.
Catalepsy is a measure of muscular rigidity that can result from several factors including Parkinson’s disease, or pharmacological exposure to antipsychotics or cannabis. Catalepsy bar tests are widely used to measure this rigidity. The test consists of placing the arms of a rodent on a horizontal bar that has been raised off the ground and measuring the time it takes for the subject to remove themselves from this imposed posture. Traditionally, this has been measured by an experimenter with a stopwatch, or with prohibitively expensive commercial apparatus that have issues of their own. The automated bar test described here uses a 3D printed base with an Arduino operated design to make the design simple and affordable. This design sets itself apart by using extremely low-cost beam break sensors that avoid pitfalls of the traditional “complete the circuit” approach where changes in rat grip can result in false measurements. The beam break sensors to are used to determine whether the rat is on the bar or not and automatically measures and stores the time the rat takes to remove itself from the bar on an SD card for later retrieval. The device has been validated in rats; however, the bar height is adjustable so there is no reason it cannot be used in other rodents as well. This bar test thus makes catalepsy measures easy, accurate, and limits any experimenter bias due to manual measurements.
David A. Bjånes and Chet T. Moritz from the University of Washington in Seattle have developed and published their device for training rats to perform a modified center out task.
As neuroscience tools for studying rodent brains have improved in the 21st century, researchers have started to utilize increasingly complex tasks to study their behavior, sometimes adapting tasks commonly used with primates. One such task used for studying motor behavior, the center-out reaching task, has been modified for use in rodents. Bjånes and Moritz have further contributed to the adaptation of this task by creating ACRoBaT, or the Automated Center-out Rodent Behavioral Trainer. This device features two custom printed PCBs, a 3D printed housing unit, an Arduino microchip, and other commercially available parts that can be mounted outside a behavioral arena. It also provides a fully automated algorithm to train rats based on behavioral feedback fed into the device through various sensors. The authors show the effectiveness of the device with data from 18 rats across different conditions to find the optimal training procedure for this task. Information for how to build the device is available in their publication, as well as on Github.
Read the full publication here, or check out the files on GitHub!
Thanks to Jan Homolak from the Department of Pharmacology, University of Zagreb School of Medicine, Zagreb, Croatia for sharing the following about repurposing a digital kitchen scale for neuroscience research: a complete hardware and software cookbook for PASTA (Platform for Acoustic STArtle).
“As we were starving for a solution on how to obtain relevant and reliable information from a kitchen scale sometimes used in very creative ways in neuroscience research, we decided to cut the waiting and cook something ourselves. Here we introduce a complete hardware and software cookbook for PASTA, a guide on how to demolish your regular kitchen scale and use the parts to turn it into a beautiful multifunctional neurobehavioral platform. This project is still medium raw, as its the work in progress, however, we hope you will still find it well done.
PASTA comes in various flavors such as:
– complete hardware design for PASTA
– PASTA data acquisition software codes (C++/Arduino)
– PASTA Chef: An automatic experimental protocol execution Python script for data acquisition and storage
– ratPASTA (R-based Awesome Toolbox for PASTA): An R-package for PASTA data analysis and visualization
Erno Kuusela and Juho Lämsä, from the University of Oulu in Finland, have shared their design for an open source, computer controlled robotic flower system for studying bumble bee behavior.
Oh.. to be a honey bee.. collecting nectar from a robotic flower.. of open source design… splendid. As with behavioral studies from species common to neuroscience (rodents to drosophila to humans or zebrafish, etc), data collection for behavioral studies in bees can be time-consuming and sensitive to human error. Thanks to the growth in the open source movement, it’s easier than ever to develop hardware and software to automate such studies, which is what Kuusela and Lämsä have demonstrated in their publication. They developed a system of robotic flowers to study bee behavior. Their design features a control unit, based on an Arduino Mega 2560, which can collect data from and send inputs to up to 32 individual robotic flowers. Each flower contains its own servo controlled refill system. The nectar cup (in this design, a phillips screw head that can hold 1.7 uL!) is attached to servomotor’s shaft via a servo horn which, when prompted by the program, dips the cup into the flower’s individual nectar reservoir. The flower is designed in a way to capture data when an animal is feeding by the placement of IR beams that are broken when engaged on the flower’s feeding mechanism and sends data to the control unit. A covering on the system can be marked with symbols to attract bees. Custom control software is available on an open source license to be used as is, or modified to fit an experimenter’s needs. While developed and tested with bumble bees, the system can also be adapted for a number of species.
Read more about specifics of this system in Kuusela & Lämsä (2016). The circuit diagrams, parts list, and control software and source code are available in the paper’s supplemental information.
Richard Warren, a graduate student in the Sawtell lab at Columbia University, recently shared his new open-source project called SignalBuddy:
SignalBuddy is an easy-to-make, easy-to-use signal generator for scientific applications. Making friends is hard, but making SignalBuddy is easy. All you need is an Arduino Uno! SignalBuddy replaces more complicated and (much) more expensive signal generators in laboratory settings where one millisecond resolution is sufficient. SignalBuddy generates digital or true analog signals (sine waves, step functions, and pulse trains), can be controlled with an intuitive serial monitor interface, and looks fabulous in an optional 3D printed enclosure.
To get SignalBuddy working, all you need to do is install the SignalBuddy.ino Arduino code provided on their github, and follow the step-by-step instructions on github to get the Arduino programmed up for your specific experimental needs. SignalBuddy can be used for numerous lab purposes, including creating pulse trains for optogenetic light stimulation, microstimulation, electrophysiology, or for programming up stimuli for behavioral paradigms.
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.
In the journal HardwareX, Jinook Oh and colleagues share their design for OpenFeeder, an automatic feeder for animal experiments.
Automatic delivery of precisely measured food amounts is important when studying reward and feeding behavior. Commercially available devices are often designed with specific species and food types in mind, limiting the ways that they can be used. This open-source automatic feeding design can easily be customized for food types from seeds to pellets to fit the needs of any species. OpenFeeder integrates plexiglass tubes, Arduino Uno, a motor driver, and piezo sensor to reliably deliver accurate amounts of food, and can also be built using 3D printed parts.
In a 2014 PLoS ONE article, Shaun R. Patel and colleagues share their design for PriED, an easy to assemble modular micro-drive system for acute primate neurophysiology.
Electrode micro-drives are a great tool that allow for independent positioning of multiple electrodes in primate neurophysiology, however, commercially available micro-drives are often expensive. Printed Electronic Device (PriED) is designed to advance existing micro-drive technology while staying inexpensive and requiring minimal skill and effort to assemble. The device combines 3D printed parts and affordable, commercially available steel and brass components which can then be controlled manually, or automatically with the addition of an optional motor. Using 3D printing technology researchers have the flexibility to be able to modify part designs and create custom solutions to specific recording needs. A public repository of drive designs has been made available where researchers can download PriED components to print for assembly. Additionally, researchers can upload modified designs with annotations for others to use. PriED is an innovative, inexpensive, and user friendly micro-drive solution for flexible multi-site cortical and subcortical recordings in non-human primates.
Alexxai Kravitz has generously shared the following regarding FED, part 2:
The Feeding Experimentation Device (FED) is a free, open-source system for measuring food intake in rodents. FED uses an Arduino processor, a stepper motor, an infrared beam detector, and an SD card to record time-stamps of 20mg pellets eaten by singly housed rodents. FED is powered by a battery, which allows it to be placed in colony caging or within other experimental equipment. The battery lasts ~5 days on a charge, providing uninterrupted feeding records over this duration. The electronics for building each FED cost around $150USD, and the 3D printed parts cost between $20 and $400, depending on access to 3D printers and desired print quality.
The Kravitz lab has published a large update of their Feeding Experimentation Device (FED) to their Github site, including updated 3D design files that print more easily and updates to the code to dispense pellets more reliably.
The Kelly Tan research group at the University of Basel, Switzerland investigates the neural correlates of motor behavior, focusing on the role of the basal ganglia in controlling various aspects of motor actions. To aid in their investigation, the group has developed an open-source nose-poke system utilizing an Arduino microcontroller, several low-cost electronic components, and a PVC behavioral arena. These researchers have shared the following information about the project:
Operant behavioral tasks for animals have long been used to probe the function of multiple brain regions. The recent development of tools and techniques has opened the door to refine the answer to these same questions with a much higher degree of specificity and accuracy, both in biological and spatial-temporal domains. A variety of systems designed to test operant behavior are now commercially available, but have prohibitive costs. Here, we provide a low-cost alternative to a nose poke system for mice. Adapting a freely available sketch for ARDUINO boards, in combination with an in-house built PVC box and inexpensive electronic material we constructed a four-port nose poke system that detects and counts port entries.
We provide a low cost alternative to commercially available nose poke system.
Our custom made apparatus is open source and TTL compatible.
We validate our system with optogenetic self-stimulation of dopamine neurons in mice.
The Kelly Tan research group provides further documentation for this device, including SketchUp design files, Arduino source code, and a full bill of materials, as supplementary data in their 2016 paper.