ArticleInternational journal of biomedical imaging2011
Fast random permutation tests enable objective evaluation of methods for single-subject FMRI analysis.
Article in International journal of biomedical imaging, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Generation of a whole-brain hemodynamic response function and sex-specific differences in cerebral processing of mechano-sensation in mice detected by BOLD fMRI.Frontiers in neuroscience · 2023Article
- Cortical Surface-Informed Volumetric Spatial Smoothing of fMRI Data via Graph Signal Processing.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021Article
- Diffusion-informed spatial smoothing of fMRI data in white matter using spectral graph filters.NeuroImage · 2021Article
- Permutation inference for canonical correlation analysis.NeuroImage · 2020Article
- Pattern of Cerebellar Atrophy in Friedreich's Ataxia-Using the SUIT Template.Cerebellum (London, England) · 2019Article
- Accelerating permutation testing in voxel-wise analysis through subspace tracking: A new plugin for SnPM.NeuroImage · 2017Article
- Connectomics and new approaches for analyzing human brain functional connectivity.GigaScience · 2015Review
- BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs.Frontiers in neuroinformatics · 2014Article
- Harnessing graphics processing units for improved neuroimaging statistics.Cognitive, affective & behavioral neuroscience · 2013Article
- Multivoxel pattern analysis for FMRI data: a review.Computational and mathematical methods in medicine · 2012Review
- True 4D Image Denoising on the GPU.International journal of biomedical imaging · 2011Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Parametric statistical methods, such as Z-, t-, and F-values, are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. With nonparametric statistical methods, the two limitations described above can be overcome. The major drawback of non-parametric methods is the computational burden with processing times ranging from hours to days, which so far have made them impractical for routine use in single-subject fMRI analysis. In this work, it is shown how the computational power of cost-efficient graphics processing units (GPUs) can be used to speed up random permutation tests. A test with 10000 permutations takes less than a minute, making statistical analysis of advanced detection methods in fMRI practically feasible. To exemplify the permutation-based approach, brain activity maps generated by the general linear model (GLM) and canonical correlation analysis (CCA) are compared at the same significance level.
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Registered trials
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