Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates
TLDR
It is found that the most common software packages for fMRI analysis (SPM, FSL, AFNI) can result in false-positive rates of up to 70%.Abstract:
The most widely used task functional magnetic resonance imaging (fMRI) analyses use parametric statistical methods that depend on a variety of assumptions. In this work, we use real resting-state data and a total of 3 million random task group analyses to compute empirical familywise error rates for the fMRI software packages SPM, FSL, and AFNI, as well as a nonparametric permutation method. For a nominal familywise error rate of 5%, the parametric statistical methods are shown to be conservative for voxelwise inference and invalid for clusterwise inference. Our results suggest that the principal cause of the invalid cluster inferences is spatial autocorrelation functions that do not follow the assumed Gaussian shape. By comparison, the nonparametric permutation test is found to produce nominal results for voxelwise as well as clusterwise inference. These findings speak to the need of validating the statistical methods being used in the field of neuroimaging.read more
Citations
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DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging.
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A manifesto for reproducible science
Marcus R. Munafò,Brian A. Nosek,Brian A. Nosek,Dorothy V. M. Bishop,Katherine S. Button,Christopher D. Chambers,Nathalie Percie du Sert,Uri Simonsohn,Eric-Jan Wagenmakers,Jennifer J. Ware,John P. A. Ioannidis +10 more
TL;DR: This work argues for the adoption of measures to optimize key elements of the scientific process: methods, reporting and dissemination, reproducibility, evaluation and incentives, in the hope that this will facilitate action toward improving the transparency, reproducible and efficiency of scientific research.
Statistical Parametric Mapping The Analysis Of Functional Brain Images
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Scanning the horizon: towards transparent and reproducible neuroimaging research
Russell A. Poldrack,Chris I. Baker,Joke Durnez,Krzysztof J. Gorgolewski,Paul M. Matthews,Marcus R. Munafò,Thomas E. Nichols,Jean-Baptiste Poline,Edward Vul,Tal Yarkoni +9 more
TL;DR: How the field of functional MRI should evolve is described to produce the most meaningful and reliable answers to neuroscientific questions.
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The Human Connectome Project's neuroimaging approach
Matthew F. Glasser,Stephen M. Smith,Daniel S. Marcus,Jesper L. R. Andersson,Edward J. Auerbach,Timothy E.J. Behrens,Timothy S. Coalson,Michael P. Harms,Mark Jenkinson,Steen Moeller,Emma C. Robinson,Stamatios N. Sotiropoulos,Junqian Xu,Essa Yacoub,Kamil Ugurbil,David C. Van Essen +15 more
TL;DR: An integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP), and should accelerate progress in understanding the brain in health and disease.
References
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AFNI: software for analysis and visualization of functional magnetic resonance neuroimages
TL;DR: A package of computer programs for analysis and visualization of three-dimensional human brain functional magnetic resonance imaging (FMRI) results is described and techniques for automatically generating transformed functional data sets from manually labeled anatomical data sets are described.
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Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.
TL;DR: It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.
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Voxel-Based Morphometry—The Methods
John Ashburner,Karl J. Friston +1 more
TL;DR: In this paper, the authors describe the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with non-uniformity artifact and provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data.
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Nonparametric permutation tests for functional neuroimaging: A primer with examples
TL;DR: The standard nonparametric randomization and permutation testing ideas are developed at an accessible level, using practical examples from functional neuroimaging, and the extensions for multiple comparisons described.
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Power failure: why small sample size undermines the reliability of neuroscience
Katherine S. Button,John P. A. Ioannidis,Claire Mokrysz,Brian A. Nosek,Jonathan Flint,Emma S J Robinson,Marcus R. Munafò +6 more
TL;DR: It is shown that the average statistical power of studies in the neurosciences is very low, and the consequences include overestimates of effect size and low reproducibility of results.