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Unified Behavioral Scoring for Preclinical Models

Preclinical mental health research relies upon animal models, and whilst many encouraging advances are being made, reproducibility and translational relevance may be limited by sub-optimal testing or model choices. Animal behaviors are complex and test batteries should be designed to include their m...

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Autores principales: Harrison, David J., Creeth, Hugo D. J., Tyson, Hannah R., Boque-Sastre, Raquel, Isles, Anthony R., Palme, Rupert, Touma, Chadi, John, Rosalind M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7154181/
https://www.ncbi.nlm.nih.gov/pubmed/32317926
http://dx.doi.org/10.3389/fnins.2020.00313
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author Harrison, David J.
Creeth, Hugo D. J.
Tyson, Hannah R.
Boque-Sastre, Raquel
Isles, Anthony R.
Palme, Rupert
Touma, Chadi
John, Rosalind M.
author_facet Harrison, David J.
Creeth, Hugo D. J.
Tyson, Hannah R.
Boque-Sastre, Raquel
Isles, Anthony R.
Palme, Rupert
Touma, Chadi
John, Rosalind M.
author_sort Harrison, David J.
collection PubMed
description Preclinical mental health research relies upon animal models, and whilst many encouraging advances are being made, reproducibility and translational relevance may be limited by sub-optimal testing or model choices. Animal behaviors are complex and test batteries should be designed to include their multifaceted nature. However, multiple behavioral testing is often avoided due to cost, availability or statistical rigor. Additionally, despite the disparity in the incidence of mental health problems between the sexes, a move toward reducing animal numbers could be a deterrent to including both male and female animals. The current study introduces a unified scoring system for specific behavioral traits with the aim of maximizing the use of all data generated whilst reducing the incidence of statistical errors. Female and male mice from two common background strains were tested on behavior batteries designed to probe multiple aspects of anxiety-related and social behavioral traits. Results for every outcome measure were normalized to generate scores for each test and combined to give each mouse a single unified score for each behavioral trait. The unified behavioral scores revealed clear differences in the anxiety and stress-related, and sociability traits of mice. Principle component analysis of data demonstrated significant clustering of animals into their experimental groups. In contrast, individual tests returned an ambiguous mixture of non-significant trends and significant effects for various outcome measures. Utilizing a range of behavioral measures and combining all outcome measure data to produce unified scores provides a useful tool for detecting subtle behavioral traits in preclinical models.
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spelling pubmed-71541812020-04-21 Unified Behavioral Scoring for Preclinical Models Harrison, David J. Creeth, Hugo D. J. Tyson, Hannah R. Boque-Sastre, Raquel Isles, Anthony R. Palme, Rupert Touma, Chadi John, Rosalind M. Front Neurosci Neuroscience Preclinical mental health research relies upon animal models, and whilst many encouraging advances are being made, reproducibility and translational relevance may be limited by sub-optimal testing or model choices. Animal behaviors are complex and test batteries should be designed to include their multifaceted nature. However, multiple behavioral testing is often avoided due to cost, availability or statistical rigor. Additionally, despite the disparity in the incidence of mental health problems between the sexes, a move toward reducing animal numbers could be a deterrent to including both male and female animals. The current study introduces a unified scoring system for specific behavioral traits with the aim of maximizing the use of all data generated whilst reducing the incidence of statistical errors. Female and male mice from two common background strains were tested on behavior batteries designed to probe multiple aspects of anxiety-related and social behavioral traits. Results for every outcome measure were normalized to generate scores for each test and combined to give each mouse a single unified score for each behavioral trait. The unified behavioral scores revealed clear differences in the anxiety and stress-related, and sociability traits of mice. Principle component analysis of data demonstrated significant clustering of animals into their experimental groups. In contrast, individual tests returned an ambiguous mixture of non-significant trends and significant effects for various outcome measures. Utilizing a range of behavioral measures and combining all outcome measure data to produce unified scores provides a useful tool for detecting subtle behavioral traits in preclinical models. Frontiers Media S.A. 2020-04-07 /pmc/articles/PMC7154181/ /pubmed/32317926 http://dx.doi.org/10.3389/fnins.2020.00313 Text en Copyright © 2020 Harrison, Creeth, Tyson, Boque-Sastre, Isles, Palme, Touma and John. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Harrison, David J.
Creeth, Hugo D. J.
Tyson, Hannah R.
Boque-Sastre, Raquel
Isles, Anthony R.
Palme, Rupert
Touma, Chadi
John, Rosalind M.
Unified Behavioral Scoring for Preclinical Models
title Unified Behavioral Scoring for Preclinical Models
title_full Unified Behavioral Scoring for Preclinical Models
title_fullStr Unified Behavioral Scoring for Preclinical Models
title_full_unstemmed Unified Behavioral Scoring for Preclinical Models
title_short Unified Behavioral Scoring for Preclinical Models
title_sort unified behavioral scoring for preclinical models
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7154181/
https://www.ncbi.nlm.nih.gov/pubmed/32317926
http://dx.doi.org/10.3389/fnins.2020.00313
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