The Future Is Open: Open-Source Tools for Behavioral Neuroscience Research

White, Samantha R.; Amarante, Linda M.; Kravitz, Alexxai V.; Laubach, Mark

doi:10.1523/eneuro.0223-19.2019

Cited by 46 publications

(44 citation statements)

References 20 publications

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“…Open-source platforms such as www.openbehavior. com are important drivers for these rapid advancements [58]. For example, user-friendly software interfaces that promise more wide-spread implementation of state-of-the-art algorithms have started to emerge faster than the peer-review process can handle them [29,59].…”

Section: Supervised Machine Learningmentioning

confidence: 99%

Big behavior: challenges and opportunities in a new era of deep behavior profiling

Ziegler

Sturman

Bohacek

2020

Neuropsychopharmacol.

111

100

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The assessment of rodent behavior forms a cornerstone of preclinical assessment in neuroscience research. Nonetheless, the true and almost limitless potential of behavioral analysis has been inaccessible to scientists until very recently. Now, in the age of machine vision and deep learning, it is possible to extract and quantify almost infinite numbers of behavioral variables, to break behaviors down into subcategories and even into small behavioral units, syllables or motifs. However, the rapidly growing field of behavioral neuroethology is experiencing birthing pains. The community has not yet consolidated its methods, and new algorithms transfer poorly between labs. Benchmarking experiments as well as the large, well-annotated behavior datasets required are missing. Meanwhile, big data problems have started arising and we currently lack platforms for sharing large datasets—akin to sequencing repositories in genomics. Additionally, the average behavioral research lab does not have access to the latest tools to extract and analyze behavior, as their implementation requires advanced computational skills. Even so, the field is brimming with excitement and boundless opportunity. This review aims to highlight the potential of recent developments in the field of behavioral analysis, whilst trying to guide a consensus on practical issues concerning data collection and data sharing.

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Section: Supervised Machine Learningmentioning

confidence: 99%

Big behavior: challenges and opportunities in a new era of deep behavior profiling

Ziegler

Sturman

Bohacek

2020

Neuropsychopharmacol.

111

100

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“…The potential of open-source tools in biological research has gathered attention as of late 42 , and it is our hope that by making the code and design files for PiDose open-source and freely accessible, others will adapt and improve it as necessary for their use. Although PiDose does require some wiring and basic tools to assemble, the skills required are straightforward and can be quickly learned.…”

Section: Discussionmentioning

confidence: 99%

PiDose: an open-source system for accurate and automated oral drug administration to group-housed mice

Woodard

Nasrallah

Samiei

et al. 2020

Sci Rep

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Drug treatment studies in laboratory mice typically employ manual administration methods such as injection or gavage, which can be time-consuming to perform over long periods and cause substantial stress in animals. These stress responses may mask or enhance treatment effects, increasing the risk of false positive or negative results and decreasing reliability. to address the lack of an automated method for drug treatment in group-housed mice, we have developed PiDose, a home-cage attached device that weighs individual animals and administers a daily dosage of drug solution based on each animal's bodyweight through their drinking water. Group housed mice are identified through the use of RFID tagging and receive both regular water and drug solution drops by licking at a spout within the PiDose module. This system allows animals to be treated over long periods (weeks to months) in a fully automated fashion, with high accuracy and minimal experimenter interaction. PiDose is low-cost and fully open-source and should prove useful for researchers in both translational and basic research. Biological research often involves treating experimental rodents with compounds over extended periods. A variety of routes of administration are used in these studies, with the goal to optimize delivery of the agent while reducing the potential for injury and procedure-associated stress. Parenteral administration via subcutaneous or intraperitoneal injection is often used due to the high bioavailability of injected drugs; however, repeated restraint and injection causes stress and puts the animal at risk of physical complications 1,2,3. These stress responses are particularly undesirable in behavioural studies, as chronic stress affects a variety of behaviours and may mask treatment affects and increase the risk of Type I/II errors 4,5. An alternative to injection is oral administration, which is often useful in a pre-clinical context as oral drug treatment is the most common and convenient route of administration in humans. Unfortunately, oral gavage presents the same problems as injection regarding treatment stress and the potential for injury 1,6. To avoid this, several studies have provided methods for the voluntary feeding of drugs to animals in a palatable form (e.g. sucrose water, peanut butter) 7,8,9,10. This avoids some of the side effects associated with injection and gavage, but is time-consuming for chronic experiments and involves extensive experimenter interaction, which in itself may be enough to increase animal stress 11. To circumvent the need for manual administration, other studies have mixed the drug with the animal's drinking water 12,13,14,15. However, this method typically estimates drug dosage based on the average bodyweight and water consumption for all mice in a cage. This relies on the assumption that mice are drinking an amount of water that is directly proportional to their bodyweight, for which there is not clear support. To avoid this caveat, animals can be single-housed, or double-housed with a divider....

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“…While scaling up manual training involves more human resources, increasing the number of mice trained in ATS (two mice per systems in parallel in the present implementation) only requires increasing the number of ATS setups. Since these systems are affordable and open source 32 , ATS provides a modular, readily scalable solution for mouse training. Future iterations may make use of RFID chips and decoders, thus allowing training multiple mice within the same ATS 12,33 .…”

Section: Discussionmentioning

confidence: 99%

Rapid learning of the 5-choice serial reaction time task in an automated rodent training system

Birtalan

Bánhidi

et al. 2020

Preprint

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11Experiments aiming to understand sensory-motor systems, cognition and behavior often require animals 12 trained to perform complex tasks. Traditional training protocols require lab personnel to move the animals 13 between home cages and training chambers, to start and end training sessions, and in some cases, to 14 hand-control each training trial. Human labor not only limits the amount of training per day, but also 15 introduces several sources of variability and may increase animal stress. Here we present an automated 16 training system for the 5-choice serial reaction time task (5CSRTT), a classic rodent task often used to test 17 sensory detection, sustained attention and impulsivity. We found that fully automated training without 18 human intervention greatly increased the speed and efficiency of learning, and decreased stress as 19 measured by corticosterone levels. Introducing training breaks did not cancel these beneficial effects of 20 automated training, and mice readily generalized across training systems when transferred from 21 automated to manual protocols. Additionally, we validated our automated training system with mice 22 implanted with wireless optogenetic stimulators, expanding the breadth of experimental needs our 23 system may fulfill. Our automated 5CSRTT system can serve as a prototype for fully automated behavioral 24 training, with methods and principles transferrable to a range of rodent tasks. 25 A few automated training systems have been developed for rodent behavioral tasks 8-15 , including 5-choice 40 serial reaction time task (5CSRTT) 16,17 , in order to standardize the training and reduce the effects of human 41 factors and other random variables. While these systems provide means for large capacity automated 42 training of rodents, most of them are customized to train a specific task variant, and/or contain expensive, 43proprietary components. For these reasons, automated behavioral training of the 5CSRTT task has not yet 44 become widespread. Here we developed an affordable, open source, high-throughput automated training 45 system for mice and demonstrate its use on an automated protocol of the widely used 5CSRTT assay 18-21 . 46 We show that use of this Automated Training System (ATS) allows faster training of mice, and that 47

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The Future Is Open: Open-Source Tools for Behavioral Neuroscience Research

Cited by 46 publications

References 20 publications

Big behavior: challenges and opportunities in a new era of deep behavior profiling

Big behavior: challenges and opportunities in a new era of deep behavior profiling

PiDose: an open-source system for accurate and automated oral drug administration to group-housed mice

Rapid learning of the 5-choice serial reaction time task in an automated rodent training system

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