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Systematic reviews and meta-analyses have been increasingly recognized for their potential value in pre-clinical research, but their applications have not been extensively explored in behavioral neuroscience. In this work, we studied protein synthesis inhibition, a classic intervention used to disrupt fear learning, reconsolidation, and extinction in rodents, to explore how meta-analyses can identify potential moderators of its effect. We initially performed separate meta-analyses for different injection sites and target sessions to evaluate the effect of the intervention in various scenarios. Heterogeneity was further investigated by multilevel meta-regression models aggregating various sites, with article or research group as additional levels. We detected robust effects of protein synthesis inhibitors on training and reconsolidation, but not on extinction, possibly due to the lower number of studies on the latter. Our analyses identified some well-established moderators, such as intervention timing and reexposure duration. However, other factors proposed as boundary conditions for reconsolidation, such as memory age and training strength, were not associated with effect size. While our results point to the value of meta-analyses in consolidating findings from the literature, we believe that associations suggested by data synthesis should ideally be verified by well-powered, rigorous confirmatory experiments.

Conversations about open science have reached the mainstream, yet many open science practices such as data sharing remain uncommon. Our efforts towards openness therefore need to increase in scale and aim for a more ambitious target. We need an ecosystem not only where research outputs are openly shared but also in which transparency permeates the research process from the start and lends itself to more rigorous and collaborative research. To support this vision, this Essay provides an overview of a selection of open science initiatives from the past 2 decades, focusing on methods transparency, scholarly communication, team science, and research culture, and speculates about what the future of open science could look like. It then draws on these examples to provide recommendations for how funders, institutions, journals, regulators, and other stakeholders can create an environment that is ripe for improvement.

Remembering is not a static process: When retrieved, a memory can be destabilized and become prone to modifications. This phenomenon has been demonstrated in a number of brain regions, but the neuronal mechanisms that rule memory destabilization and its boundary conditions remain elusive. Using two distinct computational models that combine Hebbian plasticity and synaptic downscaling, we show that homeostatic plasticity can function as a destabilization mechanism, accounting for behavioral results of protein synthesis inhibition upon reactivation with different re-exposure times. Furthermore, by performing systematic reviews, we identify a series of overlapping molecular mechanisms between memory destabilization and synaptic downscaling, although direct experimental links between both phenomena remain scarce. In light of these results, we propose a theoretical framework where memory destabilization can emerge as an epiphenomenon of homeostatic adaptations prompted by memory retrieval.

Meta-analytic methods are powerful resources to summarize the existing evidence concerning a given research question and are widely used in many academic fields. Meta-analyzes can also be used to study sources of heterogeneity and bias among results, which should be considered to avoid inaccuracies. Many of these sources can be related to study authorship, as both methodological heterogeneity and researcher bias may lead to deviations in results between different research groups. In this work, we describe a method to objectively attribute study authorship within a given meta-analysis to different research groups by using graph cluster analysis of collaboration networks. We then provide empirical examples of how the research group of origin can impact effect size in distinct types of meta-analyzes, demonstrating how non-independence between within-group results can bias effect size estimates if uncorrected. Finally, we show that multilevel random-effects models using research group as a level of analysis can be a simple tool for correcting for authorship dependence in results.

Introduction: Previous studies about the replicability of clinical research based on the published literature have suggested that highly cited articles are often contradicted or found to have inflated effects. Nevertheless, there are no recent updates of such efforts, and this situation may have changed over time. Methods: We searched the Web of Science database for articles studying medical interventions with more than 2000 citations, published between 2004 and 2018 in high-impact medical journals. We then searched for replications of these studies in PubMed using the PICO (Population, Intervention, Comparator and Outcome) framework. Replication success was evaluated by the presence of a statistically significant effect in the same direction and by overlap of the replication's effect size confidence interval (CIs) with that of the original study. Evidence of effect size inflation and potential predictors of replicability were also analyzed. Results: A total of 89 eligible studies, of which 24 had valid replications (17 meta-analyses and 7 primary studies) were found. Of these, 21 (88%) had effect sizes with overlapping CIs. Of 15 highly cited studies with a statistically significant difference in the primary outcome, 13 (87%) had a significant effect in the replication as well. When both criteria were considered together, the replicability rate in our sample was of 20 out of 24 (83%). There was no evidence of systematic inflation in these highly cited studies, with a mean effect size ratio of 1.03 [95% CI (0.88, 1.21)] between initial and subsequent effects. Due to the small number of contradicted results, our analysis had low statistical power to detect predictors of replicability. Conclusion: Although most studies did not have eligible replications, the replicability rate of highly cited clinical studies in our sample was higher than in previous estimates, with little evidence of systematic effect size inflation. This estimate is based on a very select sample of studies and may not be generalizable to clinical research in general. [ABSTRACT FROM AUTHOR]

Retrieval of an associative memory can lead to different phenomena. Brief reexposure sessions tend to trigger reconsolidation, whereas more extended ones trigger extinction. In appetitive and fear cued Pavlovian memories, an intermediate "null point" period has been observed where neither process seems to be engaged. Here we investigated whether this phenomenon extends to contextual fear memory. Adult rats were subjected to a contextual fear conditioning paradigm, reexposed to the context 2 d later for 3, 5, 10, 20, or 30 min, with immediate injections of MK-801 or saline following reexposure, and tested on the following day. We observed a significant effect of MK-801 with the 3- and 30-min sessions, impairing reconsolidation and extinction, respectively. However, it did not have significant effects with 5-, 10-, or 20-min sessions, even though freezing decreased from reexposure to test. Further analyses indicated that this is not likely to be due to a variable transition point at the population level. In conclusion, the results show that in contextual fear memories there is a genuine "null point" between the parameters that induce reconsolidation and extinction, as defined by the effects of MK-801, although NMDA receptor-independent decreases in freezing can still occur in these conditions.