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Scanning the horizon: towards transparent and reproducible neuroimaging research

Nature Reviews Neuroscience volume 18, pages 115126 (2017) | Download Citation


Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions.

Key points

  • 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.

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R.A.P., J.D., J.-B.P. and K.J.G. are supported by the Laura and John Arnold Foundation. J.D. has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 706561. M.R.M. is supported by the Medical Research Council (MRC) (MC UU 12013/6) and is a member of the UK Centre for Tobacco and Alcohol Studies, a UK Clinical Research Council Public Health Research Centre of Excellence. Funding from the British Heart Foundation, Cancer Research UK, the Economic and Social Research Council, the MRC and the National Institute for Health Research, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. C.I.B. is supported by the Intramural Research Program of the US National Institutes of Health (NIH)–National Institute of Mental Health (NIMH) (ZIA-MH002909). T.Y. is supported by the NIMH (R01MH096906). P.M.M. acknowledges personal support from the Edmond J. Safra Foundation and Lily Safra and research support from the MRC, the Imperial College Healthcare Trust Biomedical Research Centre and the Imperial Engineering and Physical Sciences Research Council Mathematics in Healthcare Centre. T.E.N. is supported by the Wellcome Trust (100309/Z/12/Z), NIH–National Institute of Neurological Disorders and Stroke (R01NS075066) and NIH–National Institute of Biomedical Imaging and Bioengineering (NIBIB) (R01EB015611). J.-B.P. is supported by the NIBIB (P41EB019936) and by NIH–National Institute on Drug Abuse (U24DA038653). Data were provided (in part) by the Human Connectome Project, WU-Minn Consortium (principal investigators: D. Van Essen and K. Ugurbil; 1U54MH091657), which is funded by the 16 Institutes and Centers of the NIH that support the NIH Blueprint for Neuroscience Research, and by the McDonnell Center for Systems Neuroscience at Washington University. The authors thank J. Wexler for performing annotation of Neurosynth data, S. David for providing sample-size data, and R. Cox and P. Taylor for helpful comments on a draft of the manuscript.

Author information


  1. Department of Psychology and Stanford Center for Reproducible Neuroscience, Stanford University, Stanford, California 94305, USA.

    • Russell A. Poldrack
    • , Joke Durnez
    •  & Krzysztof J. Gorgolewski
  2. Laboratory of Brain and Cognition, National Institute of Mental Health, US National Institutes of Health, Maryland 20892, USA.

    • Chris I. Baker
  3. Institut National de Recherche en Informatique et en Automatique (INRIA) Parietal, Neurospin, Building 145, CEA Saclay, 91191 Gif sur Yvette, France.

    • Joke Durnez
  4. Division of Brain Sciences, Department of Medicine, Imperial College Hammersmith Hospital, London W12 0NN, UK.

    • Paul M. Matthews
  5. Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1BN, UK.

    • Marcus R. Munafò
  6. UK Centre for Tobacco and Alcohol Studies, School of Experimental Psychology, University of Bristol, Bristol BS8 1TU, UK.

    • Marcus R. Munafò
  7. Department of Statistics and WMG, University of Warwick, Coventry CV4 7AL, UK.

    • Thomas E. Nichols
  8. Helen Wills Neuroscience Institute, Henry H. Wheeler Jr. Brain Imaging Center, University of California, 132 Barker Hall 210S, Berkeley, California 94720-3192, USA.

    • Jean-Baptiste Poline
  9. Department of Psychology, University of California, San Diego, San Diego, California 92093, USA.

    • Edward Vul
  10. Department of Psychology, University of Texas at Austin, Austin, Texas 78712, USA.

    • Tal Yarkoni


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Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Russell A. Poldrack.

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    Supplementary information S1 (figure)

    A depiction of the data from Figure 1 showing all data points.


Linear mixed-effects analysis

An analysis in which some measured independent variables are treated as randomly sampled from the population, in contrast to a traditional fixed-effects analysis, in which all predictors are treated as fixed and known.

Familywise error

(FWE). The probability of at least one false positive among multiple statistical tests.

Random field theory

The theory describing the behaviour of geometric points on a random topological space.

Euler characteristic

A topological measure that is used to describe the set of thresholded voxels in the context of random field theory.

False discovery rate

(FDR). The expected proportion of false positives among all significant findings when performing multiple statistical tests.

Functional localizer

An independent scan that is used to identify regions on the basis of their functional response; for example, for the responses of face-responsive regions to faces.

Bayesian methods

An approach to statistical analysis focusing on updating beliefs via probability distributions and symmetrically comparing candidate models.

Mass univariate testing

An approach to the analysis of multivariate data in which the same model is fit to each element of the observed data (for example, each voxel).

Permutation tests

Also known as randomization tests. Approaches for testing statistical significance by comparing to a null distribution that is obtained by rearranging the labels of the observed data.

'Not invented here' philosophy

The philosophy that any solution to a problem that was developed by someone else is necessarily inferior and must be re-engineered from scratch.


The operation by which a function is applied to the sampled data to obtain estimates of the data at positions where data have not been sampled.

Software container

A self-contained software tool that encompasses all of the necessary software and dependencies to run a particular program.

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