Rigor or Mortis: Best Practices for Preclinical Research in Neuroscience

Abstract

Numerous recent reports document a lack of reproducibility of preclinical studies, raising concerns about potential lack of rigor. Examples of lack of rigor have been extensively documented and proposals for practices to improve rigor are appearing. Here, we discuss some of the details and implications of previously proposed best practices and consider some new ones, focusing on preclinical studies relevant to human neurological and psychiatric disorders.

Toward more predictive genetic mouse models of Alzheimer's disease

“…While criticisms have often focused on those that are unable to reproduce the initial discovery, it is just as likely that the originally published datasets were not necessarily produced under the optimal conditions or with the appropriate controls that deem it reproducible in the first place; notwithstanding the lack of critical details published in the methods sections [75–79]. Poorly conducted behavioral studies often performed by minimally experienced scientists with limited training in behavioral pharmacology, driven by pressures to provide functionally translational and relevant endpoints for molecular findings often leads to rushed experiments without proper controls and optimized testing conditions [80].…”

Standardization of the experimental autoimmune myasthenia gravis (EAMG) model by immunization of rats with Torpedo californica acetylcholine receptors — Recommendations for methods and experimental designs

“…The power analysis dictates the minimal number of animals necessary to register a statistical effect. Careful calculations of the “n” required in a study will ensure that the outcome result reflects the significance between the groups and not the lack of power to justify the result (Steward and Balice-Gordon, 2014). …”

Clinical Trials in a Dish: The Potential of Pluripotent Stem Cells to Develop Therapies for Neurodegenerative Diseases

“…These settings generate vast amounts of data, and computerized data management, analysis, and statistical programs are necessary to gain full value and detect phenotypic differences (116, 117). Careful experimental design starts with consideration of the end-point sensitivity, the effect size, the desired level of confidence, and the rationale behind sample size determination (118). Assay quality can be determined by the Z′ factor, which is a statistical measure that combines assay robustness with the variability of signal.…”