Evidence map›Paper›PMID 29773907›Full record

ArticleNature protocols2018

An approach to monitoring home-cage behavior in mice that facilitates data sharing.

Edoardo Balzani, Matteo Falappa, Fuat Balci, Valter Tucci

Abstract read
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In one paragraph

Article in Nature protocols, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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  15. Selective Knockout of theFrontiers in behavioral neuroscience · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Edoardo BalzaniDepartment of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genova, Italy.
Matteo FalappaDepartment of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genova, Italy.
Fuat BalciDepartment of Psychology, Koç University, Istanbul, Turkey.
Valter TucciDepartment of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genova, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genetically modified mice are used as models for a variety of human behavioral conditions. However, behavioral phenotyping can be a major bottleneck in mouse genetics because many of the classic protocols are too long and/or are vulnerable to unaccountable sources of variance, leading to inconsistent results between centers. We developed a home-cage approach using a Chora feeder that is controlled by-and sends data to-software. In this approach, mice are tested in the standard cages in which they are held for husbandry, which removes confounding variables such as the stress induced by out-of-cage testing. This system increases the throughput of data gathering from individual animals and facilitates data mining by offering new opportunities for multimodal data comparisons. In this protocol, we use a simple work-for-food testing strategy as an example application, but the approach can be adapted for other experiments looking at, e.g., attention, decision-making or memory. The spontaneous behavioral activity of mice in performing the behavioral task can be monitored 24 h a day for several days, providing an integrated assessment of the circadian profiles of different behaviors. We developed a Python-based open-source analytical platform (Phenopy) that is accessible to scientists with no programming background and can be used to design and control such experiments, as well as to collect and share data. This approach is suitable for large-scale studies involving multiple laboratories.

Indexed as

Behavior, AnimalData CollectionInformation DisseminationAnimalsMiceMonitoring, PhysiologicSoftware

Identifiers

PMID29773907

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.