Scanning the horizon: towards transparent and reproducible neuroimaging research

Abstract

There is growing concern about the reproducibility of scientific research, and neuroimaging research suffers from many features that are thought to lead to high levels of false results. Statistical power of neuroimaging studies has increased over time but remains relatively low, especially for group comparison studies. An analysis of effect sizes in the Human Connectome Project demonstrates that most functional MRI studies are not sufficiently powered to find reasonable effect sizes. Neuroimaging analysis has a high degree of flexibility in analysis methods, which can lead to inflated false-positive rates unless controlled for. Pre-registration of analysis plans and clear delineation of hypothesis-driven and exploratory research are potential solutions to this problem. The use of appropriate corrections for multiple tests has increased, but some common methods can have highly inflated false-positive rates. The use of non-parametric methods is encouraged to provide accurate correction for multiple tests. Software errors have the potential to lead to incorrect or irreproducible results. The adoption of improved software engineering methods and software testing strategies can help to reduce such problems. Reproducibility will be improved through greater transparency in methods reporting and through increased sharing of data and code.