RESUMO
This scoping review aimed to systematically identify and summarize data related to subiculum involvement in learning and memory behavioral tasks in rats and mice. Following a systematic strategy based on PICO and PRISMA guidelines, we searched five indexed databases (PubMed, Web of Science, EMBASE, Scopus, and PsycInfo) using a standardized search strategy to identify peer-reviewed articles published in English (pre-registration: osf.io/hm5ea). We identified 31 articles investigating the role of the subiculum in spatial, working, and recognition memories (n = 11), memories related to addiction models (n = 9), aversive memories (n = 7), and memories related to appetitive learning (n = 5). We highlight a dissociation in the dorsoventral axis of the subiculum with many studies exploring the ventral subiculum (n = 21) but only a few exploring the dorsal one (n = 10). We also observe the necessity of more data including mice, female animals, genetic tools, and better statistical approaches for replication purposes and research refinement. These findings provide a broad framework of the subiculum involvement in learning and memory, showing essential questions that can be explored by further studies.
Assuntos
Hipocampo , Aprendizagem , Ratos , Camundongos , Feminino , AnimaisRESUMO
In neuroscience research, longitudinal data are often analysed using analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) for repeated measures (rmANOVA/rmMANOVA). However, these analyses have special requirements: The variances of the differences between all possible pairs of within-subject conditions (i.e., levels of the independent variable) must be equal. They are also limited to fixed repeated time intervals and are sensitive to missing data. In contrast, other models, such as the generalized estimating equations (GEE) and the generalized linear mixed models (GLMM), suggest another way to think about the data and the studied phenomenon. Instead of forcing the data into the ANOVAs assumptions, it is possible to design a flexible/personalized model according to the nature of the dependent variable. We discuss some advantages of GEE and GLMM as alternatives to rmANOVA and rmMANOVA in neuroscience research, including the possibility of using different distributions for the parameters of the dependent variable, a better approach for different time length points, and better adjustment to missing data. We illustrate these advantages by showing a comparison between rmANOVA and GEE in a real example and providing the data and a tutorial code to reproduce these analyses in R. We conclude that GEE and GLMM may provide more reliable results when compared to rmANOVA and rmMANOVA in neuroscience research, especially in small sample sizes with unbalanced longitudinal designs with or without missing data.