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Nat Rev Neurosci. 2017 Feb;18(2):115-126. doi: 10.1038/nrn.2016.167. Epub 2017 Jan 5.

Scanning the horizon: towards transparent and reproducible neuroimaging research.

Author information

1
Department of Psychology and Stanford Center for Reproducible Neuroscience, Stanford University, Stanford, California 94305, USA.
2
Laboratory of Brain and Cognition, National Institute of Mental Health, US National Institutes of Health, Maryland 20892, USA.
3
Institut National de Recherche en Informatique et en Automatique (INRIA) Parietal, Neurospin, Building 145, CEA Saclay, 91191 Gif sur Yvette, France.
4
Division of Brain Sciences, Department of Medicine, Imperial College Hammersmith Hospital, London W12 0NN, UK.
5
Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 1BN, UK.
6
UK Centre for Tobacco and Alcohol Studies, School of Experimental Psychology, University of Bristol, Bristol BS8 1TU, UK.
7
Department of Statistics and WMG, University of Warwick, Coventry CV4 7AL, UK.
8
Helen Wills Neuroscience Institute, Henry H. Wheeler Jr. Brain Imaging Center, University of California, 132 Barker Hall 210S, Berkeley, California 94720-3192, USA.
9
Department of Psychology, University of California, San Diego, San Diego, California 92093, USA.
10
Department of Psychology, University of Texas at Austin, Austin, Texas 78712, USA.

Abstract

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.

PMID:
28053326
PMCID:
PMC6910649
DOI:
10.1038/nrn.2016.167
[Indexed for MEDLINE]
Free PMC Article

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