Reproducibility in Neuroimaging Analysis: Challenges and Solutions

Rotem Botvinik-Nezer*, Tor D. Wager

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


Recent years have marked a renaissance in efforts to increase research reproducibility in psychology, neuroscience, and related fields. Reproducibility is the cornerstone of a solid foundation of fundamental research—one that will support new theories built on valid findings and technological innovation that works. The increased focus on reproducibility has made the barriers to it increasingly apparent, along with the development of new tools and practices to overcome these barriers. Here, we review challenges, solutions, and emerging best practices with a particular emphasis on neuroimaging studies. We distinguish 3 main types of reproducibility, discussing each in turn. Analytical reproducibility is the ability to reproduce findings using the same data and methods. Replicability is the ability to find an effect in new datasets, using the same or similar methods. Finally, robustness to analytical variability refers to the ability to identify a finding consistently across variation in methods. The incorporation of these tools and practices will result in more reproducible, replicable, and robust psychological and brain research and a stronger scientific foundation across fields of inquiry.

Original languageEnglish
Pages (from-to)780-788
Number of pages9
JournalBiological Psychiatry: Cognitive Neuroscience and Neuroimaging
Issue number8
StatePublished - Aug 2023
Externally publishedYes


  • Mental health
  • Neuroimaging
  • Open science
  • Replicability
  • Reproducibility
  • Robustness


Dive into the research topics of 'Reproducibility in Neuroimaging Analysis: Challenges and Solutions'. Together they form a unique fingerprint.

Cite this