Evidence map›Paper›PMID 26704381›Full record

ArticleJournal of neural transmission (Vienna, Austria : 1996)2017

Mouse Social Interaction Test (MoST): a quantitative computer automated analysis of behavior.

Panayotis K Thanos, Christophe Restif, Joseph R O'Rourke, Chiu Yin Lam, Dimitris Metaxas

Abstract read
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In one paragraph

Article in Journal of neural transmission (Vienna, Austria : 1996), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 20% of its field
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

4 citing papers in PubMed, 16 citations in OpenAlex.

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  2. Review
  3. Review
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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

5 authors at 1 institution in 1 country.

Panayotis K ThanosBehavioral Neuropharmacology and Neuroimaging Lab, Research Institute on Addictions, University at Buffalo, Buffalo, NY, 14203, USA. pkthanos@gmail.com.
Christophe RestifCenter for Computational Biomedicine Imaging and Computation, Rutgers University, New Brunswick, NJ, 08904, USA.
Joseph R O'RourkeBehavioral Neuropharmacology and Neuroimaging Lab, Research Institute on Addictions, University at Buffalo, Buffalo, NY, 14203, USA.
Chiu Yin LamBehavioral Neuropharmacology and Neuroimaging Lab, Research Institute on Addictions, University at Buffalo, Buffalo, NY, 14203, USA.
Dimitris MetaxasCenter for Computational Biomedicine Imaging and Computation, Rutgers University, New Brunswick, NJ, 08904, USA.
Rutgers, The State University of New Jersey · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rodents are the most commonly used preclinical model of human disease assessing the mechanism(s) involved as well as the role of genetics, epigenetics, and pharmacotherapy on this disease as well as identifying vulnerability factors and risk assessment for disease critical in the development of improved treatment strategies. Unfortunately, the majority of rodent preclinical studies utilize single housed approaches where animals are either entirely housed and tested in solitary environments or group housed but tested in solitary environments. This approach, however, ignores the important contribution of social interaction and social behavior. Social interaction in rodents is found to be a major criterion for the ethological validity of rodent species-specific behavioral characteristics (Zurn et al. 2007; Analysis 2011). It is also well established that there is significant and growing number of reports, which illustrates the important role of social environment and social interaction in all diseases, with particularly significance in all neuropsychiatric diseases. Thus, it is imperative that research studies be able to add large-scale evaluations of social interaction and behavior in mice and benefit from automated tracking of behaviors and measurements by removing user bias and by quantifying aspects of behaviors that cannot be assessed by a human observer. Single mouse setups have been used routinely, but cannot be easily extended to multiple-animal studies where social behavior is key, e.g., autism, depression, anxiety, substance and non-substance addictive disorders, aggression, sexual behavior, or parenting. While recent efforts are focusing on multiple-animal tracking alone, a significant limitation remains the lack of insightful measures of social interactions. We present a novel, non-invasive single camera-based automated tracking method described as Mouse Social Test (MoST) and set of measures designed for estimating the interactions of multiple mice at the same time in the same environment interacting freely. Our results show measurement of social interactions and designed to be adaptable and applicable to most existing home cage systems used in research, and provide a greater level of detailed analysis of social behavior than previously possible. The present study describes social behaviors assessed in a home cage environment setup containing six mice that interact freely over long periods of time, and we illustrate how these measures can be interpreted and combined to classify rodent social behaviors. In addition, we illustrate how these measures can be interpreted and combined to classify and analyze comprehensively rodent behaviors involved in several neuropsychiatric diseases as well as provide opportunity for the basic research of rodent behavior previously not possible.

Indexed as

Behavior, AnimalHousing, AnimalMice, Inbred C57BLSocial BehaviorActigraphyAnimalsAutomation, LaboratoryExploratory BehaviorMaleMotor ActivityPattern Recognition, AutomatedRecognition, PsychologyAutomationsRodentsSocial interactionsTracking

Identifiers

PMID26704381
OpenAlexW2215323701

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.