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      Standardized and reproducible measurement of decision-making in mice

      research-article
      The International Brain Laboratory , 1 , 2 , 3 , 4 , 5 , 4 , 6 , 1 , 7 , 4 , 6 , 5 , 8 , 6 , 9 , 7 , 1 , 1 , 10 , 4 , 4 , 4 , 9 , 9 , 4 , 2 , 8 , 11 , 12 , 5 , 11 , 1 , 13 , 9 , 11 , 2 , 8 , 11 , 1
      ,
      eLife
      eLife Sciences Publications, Ltd
      behavior, reproducibility, decision making, Mouse

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Progress in science requires standardized assays whose results can be readily shared, compared, and reproduced across laboratories. Reproducibility, however, has been a concern in neuroscience, particularly for measurements of mouse behavior. Here, we show that a standardized task to probe decision-making in mice produces reproducible results across multiple laboratories. We adopted a task for head-fixed mice that assays perceptual and value-based decision making, and we standardized training protocol and experimental hardware, software, and procedures. We trained 140 mice across seven laboratories in three countries, and we collected 5 million mouse choices into a publicly available database. Learning speed was variable across mice and laboratories, but once training was complete there were no significant differences in behavior across laboratories. Mice in different laboratories adopted similar reliance on visual stimuli, on past successes and failures, and on estimates of stimulus prior probability to guide their choices. These results reveal that a complex mouse behavior can be reproduced across multiple laboratories. They establish a standard for reproducible rodent behavior, and provide an unprecedented dataset and open-access tools to study decision-making in mice. More generally, they indicate a path toward achieving reproducibility in neuroscience through collaborative open-science approaches.

          eLife digest

          In science, it is of vital importance that multiple studies corroborate the same result. Researchers therefore need to know all the details of previous experiments in order to implement the procedures as exactly as possible. However, this is becoming a major problem in neuroscience, as animal studies of behavior have proven to be hard to reproduce, and most experiments are never replicated by other laboratories.

          Mice are increasingly being used to study the neural mechanisms of decision making, taking advantage of the genetic, imaging and physiological tools that are available for mouse brains. Yet, the lack of standardized behavioral assays is leading to inconsistent results between laboratories. This makes it challenging to carry out large-scale collaborations which have led to massive breakthroughs in other fields such as physics and genetics.

          To help make these studies more reproducible, the International Brain Laboratory (a collaborative research group) et al. developed a standardized approach for investigating decision making in mice that incorporates every step of the process; from the training protocol to the software used to analyze the data. In the experiment, mice were shown images with different contrast and had to indicate, using a steering wheel, whether it appeared on their right or left. The mice then received a drop of sugar water for every correction decision. When the image contrast was high, mice could rely on their vision. However, when the image contrast was very low or zero, they needed to consider the information of previous trials and choose the side that had recently appeared more frequently.

          This method was used to train 140 mice in seven laboratories from three different countries. The results showed that learning speed was different across mice and laboratories, but once training was complete the mice behaved consistently, relying on visual stimuli or experiences to guide their choices in a similar way.

          These results show that complex behaviors in mice can be reproduced across multiple laboratories, providing an unprecedented dataset and open-access tools for studying decision making. This work could serve as a foundation for other groups, paving the way to a more collaborative approach in the field of neuroscience that could help to tackle complex research challenges.

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          Most cited references88

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          Array programming with NumPy

          Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
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            Is Open Access

            The UK Biobank resource with deep phenotyping and genomic data

            The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.
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              DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

              Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                20 May 2021
                2021
                : 10
                : e63711
                Affiliations
                [1 ]Cold Spring Harbor Laboratory New YorkUnited States
                [2 ]Center for Neural Science, New York University New YorkUnited States
                [3 ]Zuckerman Institute, Columbia University New YorkUnited States
                [4 ]Champalimaud Centre for the Unknown LisbonPortugal
                [5 ]UCL Institute of Ophthalmology, University College London LondonUnited Kingdom
                [6 ]Wolfson Institute for Biomedical Research, University College London LondonUnited Kingdom
                [7 ]Department of Molecular and Cell Biology, University of California, Berkeley BerkeleyUnited States
                [8 ]Princeton Neuroscience Institute, Princeton University PrincetonUnited States
                [9 ]Sainsbury-Wellcome Centre for Neural Circuits and Behaviour, University College London LondonUnited Kingdom
                [10 ]Watson School of Biological Sciences New YorkUnited States
                [11 ]UCL Queen Square Institute of Neurology, University College London LondonUnited Kingdom
                [12 ]Sanworks LLC New YorkUnited States
                [13 ]Cognitive Psychology Unit, Leiden University LeidenNetherlands
                Harvard University United States
                Brown University United States
                Harvard University United States
                Author notes
                [†]

                David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States.

                [‡]

                Department of Neurophysiology, University of Yamanashi, Kōfu, Japan.

                Author information
                https://orcid.org/0000-0002-9650-8962
                https://orcid.org/0000-0002-5644-4124
                https://orcid.org/0000-0003-4880-7682
                https://orcid.org/0000-0002-9648-4761
                https://orcid.org/0000-0002-3205-3794
                https://orcid.org/0000-0002-3818-877X
                https://orcid.org/0000-0002-2673-8957
                https://orcid.org/0000-0001-7827-9548
                https://orcid.org/0000-0002-8929-7984
                https://orcid.org/0000-0001-7913-9109
                https://orcid.org/0000-0001-5297-3363
                https://orcid.org/0000-0001-5270-6513
                https://orcid.org/0000-0003-0548-2160
                Article
                63711
                10.7554/eLife.63711
                8137147
                34011433
                31e2364e-a93d-4ce0-bfa5-2086b9bf2c3a
                © 2021, The International Brain Laboratory et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 03 October 2020
                : 08 April 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 209558
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000893, Simons Foundation;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 216324
                Award Recipient :
                Funded by: German National Academy of Sciences Leopoldina;
                Award Recipient :
                Funded by: Marie Skłodowska-Curie Actions, European Commission;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003043, EMBO;
                Award ID: Long term fellowship
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001961, AXA Research Fund;
                Award ID: Postdoctoral fellowship
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Tools and Resources
                Neuroscience
                Custom metadata
                A standard for complex mouse behavior that can be successfully reproduced across laboratories, along with open-access data and tools to implement provide a resource for reproducibility through collaborative open-science approaches.

                Life sciences
                behavior,reproducibility,decision making,mouse
                Life sciences
                behavior, reproducibility, decision making, mouse

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