Understanding in vivo modelling of depression in non-human animals: a systematic review protocol

Alexandra Bannach-Brown, Jing Liao, Gregers Wegener, Malcolm Macleod

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Abstract

The aim of this study is to systematically collect all published preclinical non-human animal literature on depression to provide an unbiased overview of existing knowledge. A systematic search will be carried out in PubMed and Embase. Studies will be included if they use non-human animal experimental model(s) to induce or mimic a depressive-like phenotype. Data that will be extracted include the model or method of induction; species and gender of the animals used; the behavioural, anatomical, electrophysiological, neurochemical or genetic outcome measure(s) used; risk of bias/quality of reporting; and any intervention(s) tested. There were no exclusion criteria based on language or date of publication. Automation techniques will be used, where appropriate, to reduce the human reviewer time. Meta-analyses will be conducted if feasible. This broad systematic review aims to gain a better understanding of the strengths and limitations of current approaches, models and outcome measures used. This study aims to provide insights into factors affecting the efficiency of model induction and the efficacy of intervention. Here, we outline the protocol for a systematic review and possible meta-analysis of the preclinical studies modelling depression-like behaviours and phenotypes in animals.
Original languageEnglish
Pages (from-to)20-27
Number of pages8
JournalEvidence-based Preclinical Medicine
Volume3
Issue number2
DOIs
Publication statusPublished - 6 Apr 2017
Externally publishedYes

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Depression
Meta-Analysis
Outcome Assessment (Health Care)
Phenotype
Automation
PubMed
Publications
Language
Animal Models

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Understanding in vivo modelling of depression in non-human animals: a systematic review protocol. / Bannach-Brown, Alexandra; Liao, Jing; Wegener, Gregers; Macleod, Malcolm.

In: Evidence-based Preclinical Medicine, Vol. 3, No. 2, 06.04.2017, p. 20-27.

Research output: Contribution to journalArticleResearchpeer-review

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