Best Practices in Data Analysis and Sharing in Neuroimaging using MRI

By Thomas E. Nichols, Samir Das, Simon B. Eickhoff, Alan C. Evans, T. Glatard, Michael Hanke, Nikolaus Kriegeskorte, Michael P. Milham, Russell A. Poldrack, Jean-Baptiste Poline and 5 more. bioRxiv, 2016

486 citations recorded by the source corpus.

Neuroimaging enables rich noninvasive measurements of human brain activity, but translating such data into neuroscientific insights and clinical applications requires complex analyses and collaboration among a diverse array of researchers. The open science movement is reshaping scientific culture and addressing the challenges of transparency and reproducibility of research. To advance open science in neuroimaging the Organization for Human Brain Mapping created the Committee on Best Practice in Data Analysis and Sharing (COBIDAS), charged with creating a report that collects best practice recommendations from experts and the entire brain imaging community. The purpose of this work is to elaborate the principles of open and reproducible research for neuroimaging using Magnetic Resonance Imaging (MRI), and then distill these principles to specific research practices. Many elements of a study are so varied that practice cannot be prescribed, but for these areas we detail the information that must be reported to fully understand and potentially replicate a study. For other elements of a study, like statistical modelling where specific poor practices can be identified, and the emerging areas of data sharing and reproducibility, we detail both good practice and reporting standards. For each of seven areas of a study we provide tabular listing of over 100 items to help plan, execute, report and share research in the most transparent fashion. Whether for individual scientists, or for editors and reviewers, we hope these guidelines serve as a benchmark, to raise the standards of practice and reporting in neuroimaging using MRI.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/720400bf69c1af50795d7ec1b58e95c682d217aa

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Citations in the corpus

Mind The Hype: A Critical Evaluation and Prescriptive Agenda for Research on Mindfulness and Meditation (2017, unknown): “We encourage contemplative neuroscientists to follow best practices in neuroimaging methods generally (cf. Nichols et al., 2017), but also to consider and accommodate unique issues that may arise while collecting brain data from meditating populations.”
Reproducibility vs. Replicability: A Brief History of a Confused Terminology (2018, background): “Nichols et al. (2017) suggest best practices for neuroimaging based on a detailed discussion of different levels of reproducibility and replicability.”
Ten simple rules for predictive modeling of individual differences in neuroimaging (2019, background): “While the need to collect two or more independent datasets has historically limited the use of external validation, the increasing availability of high-throughput neuroimaging samples and the emerging norm of data- and model-sharing in psychology and neuroscience are making external validation a rou”
Sample size evolution in neuroimaging research: an evaluation of highly-cited studies (1990-2012) and of latest practices (2017-2018) in high-impact journals (2019, unknown): “In order to increase reporting standards, the Organization for Human Brain Mapping’s 2015-16 Committee on Best Practices in Data Analysis and Sharing (COBIDAS) has formulated several reporting guidelines (Nichols et al., 2016; Nichols et al., 2017; for a summary see Box 4 in Poldrack et al., 2017).”
Influences on the Test–Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility (2017, background): “As standards for the acquisition and analysis of functional connectivity data continue to be developed (Nichols et al. 2017), these results provide important considerations for establishing best practices in the field.”
The UNC/UMN Baby Connectome Project (BCP): An Overview of the Study Design and Protocol Development (2018, unknown): “To mitigate the aforementioned limitations, the primary objectives of the UNC/UMN Baby Connectome Project, a joint effort between investigators at the University of North Carolina – Chapel Hill and the University of Minnesota, are to: 1) collect HCP-inspired neuroimaging data from 500 children acros”