Behavioral manifestations in rodent models of autism spectrum disorder: protocol for a systematic review and network meta-analysis

Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition associated with severe social communication, interaction, and sensory processing impairments. Efforts to understand its etiology and pathophysiology are crucial for improving treatment and prevention measures. Preclinical models of ASD are essential for investigating the biological mechanisms and should present translatability potential. We aim to evaluate the consistency of the most commonly used rodent models of ASD in displaying autistic-like behavior through a systematic review and meta-analysis. Methods: This review will focus on the most frequently used autism models, surveying studies of six genetic (Ube3a, Pten, Nlgn3, Shank3, Mecp2, and Fmr1), three chemically induced (valproic acid ( VPA), lipopolysaccharide (LPS), and polyinosinic:polycytidylic acid (poly(I:C))), and one inbred model (BTBR T+ Itpr3tf/J mouse strain). Two independent reviewers will screen the records. Data extraction of behavioral outcomes and risk of bias evaluation will be performed. We will conduct a meta-analysis whenever at least five studies investigate the same model and behavioral outcome. We will also explore the heterogeneity and publication bias. Network meta-analyses are planned to compare different models. Discussion: By shortening the gap between animal behavior and human endophenotypes or specific clinical symptoms, we expect to help researchers on which rodent models are adequate for research of specific behavioral manifestations of autism, which potentially require a combination of them depending on the research interest.

Automated summary

This study aims to evaluate the consistency of commonly used rodent models of autism spectrum disorder (ASD) in displaying autistic-like behavior through a systematic review and meta-analysis. The review focuses on genetic models, chemically induced models, and an inbred model, with data extraction, bias evaluation, and meta-analysis planned.