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Name

SnPM - Statistical Nonparametric Mapping

Byline

Department of Biostatistics, University of Michigan

Description

Statistical nonParametric Mapping (SnPM) is a toolbox for Statistical Parametric Mapping (SPM) provides an extensible framework for voxel level non-parametric permutation/randomisation tests of functional Neuroimaging experiments with independent observations. The SnPM toolbox provides an alternative to the Statistics section of SPM. SnPM uses the General Linear Model to construct pseudo t-statistic images, which are then assessed for significance using a standard non-parametric multiple comparisons procedure based on randomisation/permutation testing. It is most suitable for single subject PET/SPECT analyses, or designs with low degrees of freedom available for variance estimation. In these situations the freedom to use weighted locally pooled variance estimates, or variance smoothing, makes the non-parametric approach considerably more powerful than conventional parametric approaches, as are implemented in SPM. Further, the non-parametric approach is always valid, given only minimal assumptions.

NIfTI-1 support

SnPM is a toolbox for SPM, and as such depends on SPM routines for file image reading and writing. The current version (March 2006) SnPM2 and SnPM3 are toobloxes for SPM2, and hence do not support NIfTI-1. We expect to port SnPM to SPM5 before the end of the year (Dec 2006), at which time we will have NIfTI support.

Creator

Andrew Holmes, Thomas Nichols, and others.

Contact

snpm-authors@umich.edu

WWW

http://www.sph.umich.edu/ni-stat/SnPM/

Availability

Publically available.

How to get

Please see http://www.sph.umich.edu/ni-stat/SnPM/#distrib . After completing a short registration for you can download the software.

Current version

SnPM2; SnPM3b.

Current version release date

October 7, 2005

Open source

Yes

License

GPL.

Available free of charge

Yes

Requirements

Requires Matlab 6.5 or 7 and SPM2.

Technical publications

Statistical Issues in functional Brain Mapping Holmes AP (1994) Doctor of Philosophy Thesis, University of Glasgow, December 1994. Non-Parametric Analysis of Statistic Images From Functional Mapping Experiments Holmes AP, Blair RC, Watson JDG, Ford I (1996) Journal of Cerebral Blood Flow and Metabolism 16:7-22 Nonparametric Analysis of PET functional Neuroimaging Experiments: A Primer Nichols TE, Holmes AP (2001) Human Brain Mapping, 15:1-25. Holmes & Watson, on ``Sherlock\'\' We reply to Halber et al.\'s ``Performance of a Randomization Test for Single-Subject ¹⁵ O-Water PET Activation Studies\'\' published in the Journal of Cerebral Blood Flow and Metabolism 171033-1039. Halber et al assert that our non-parametric approach (their implementation of which they dub `Sherlock\') is less powerful than a ``standard\'\' analysis. This conclusion is at variance with our findings, which we consider is simply due to the fact that the ``standard analysis\'\' they compare to does not strongly control experimentwise Type~I error. Randomization Tests Edgington ES (1980) Marcel Dekker, New York & Basel Permutation tests: A practical guide to resampling methods for testing hypotheses Good P (1994) Springer-Verlag, New York: Holmes AP (1994)
Doctor of Philosophy Thesis, University of Glasgow, December 1994.
Non-Parametric Analysis of Statistic Images From Functional Mapping Experiments Holmes AP, Blair RC, Watson JDG, Ford I (1996) Journal of Cerebral Blood Flow and Metabolism 16:7-22 Nonparametric Analysis of PET functional Neuroimaging Experiments: A Primer Nichols TE, Holmes AP (2001) Human Brain Mapping, 15:1-25. Holmes & Watson, on ``Sherlock\'\' We reply to Halber et al.\'s ``Performance of a Randomization Test for Single-Subject ¹⁵ O-Water PET Activation Studies\'\' published in the Journal of Cerebral Blood Flow and Metabolism 171033-1039. Halber et al assert that our non-parametric approach (their implementation of which they dub `Sherlock\') is less powerful than a ``standard\'\' analysis. This conclusion is at variance with our findings, which we consider is simply due to the fact that the ``standard analysis\'\' they compare to does not strongly control experimentwise Type~I error. Randomization Tests Edgington ES (1980) Marcel Dekker, New York & Basel Permutation tests: A practical guide to resampling methods for testing hypotheses Good P (1994) Springer-Verlag, New York: Holmes AP, Blair RC, Watson JDG, Ford I (1996)
Journal of Cerebral Blood Flow and Metabolism 16:7-22
Nonparametric Analysis of PET functional Neuroimaging Experiments: A Primer Nichols TE, Holmes AP (2001) Human Brain Mapping, 15:1-25. Holmes & Watson, on ``Sherlock\'\' We reply to Halber et al.\'s ``Performance of a Randomization Test for Single-Subject ¹⁵ O-Water PET Activation Studies\'\' published in the Journal of Cerebral Blood Flow and Metabolism 171033-1039. Halber et al assert that our non-parametric approach (their implementation of which they dub `Sherlock\') is less powerful than a ``standard\'\' analysis. This conclusion is at variance with our findings, which we consider is simply due to the fact that the ``standard analysis\'\' they compare to does not strongly control experimentwise Type~I error. Randomization Tests Edgington ES (1980) Marcel Dekker, New York & Basel Permutation tests: A practical guide to resampling methods for testing hypotheses Good P (1994) Springer-Verlag, New York: Nichols TE, Holmes AP (2001)
Human Brain Mapping, 15:1-25.
Holmes & Watson, on ``Sherlock\'\' We reply to Halber et al.\'s ``Performance of a Randomization Test for Single-Subject ¹⁵ O-Water PET Activation Studies\'\' published in the Journal of Cerebral Blood Flow and Metabolism 171033-1039. Halber et al assert that our non-parametric approach (their implementation of which they dub `Sherlock\') is less powerful than a ``standard\'\' analysis. This conclusion is at variance with our findings, which we consider is simply due to the fact that the ``standard analysis\'\' they compare to does not strongly control experimentwise Type~I error. Randomization Tests Edgington ES (1980) Marcel Dekker, New York & Basel Permutation tests: A practical guide to resampling methods for testing hypotheses Good P (1994) Springer-Verlag, New York: We reply to Halber et al.\'s ``Performance of a Randomization Test for Single-Subject ¹⁵ O-Water PET Activation Studies\'\' published in the Journal of Cerebral Blood Flow and Metabolism 171033-1039.
Halber et al assert that our non-parametric approach (their implementation of which they dub `Sherlock\') is less powerful than a ``standard\'\' analysis. This conclusion is at variance with our findings, which we consider is simply due to the fact that the ``standard analysis\'\' they compare to does not strongly control experimentwise Type~I error.
Randomization Tests
Edgington ES (1980)
Marcel Dekker, New York & Basel
Permutation tests: A practical guide to resampling methods for testing hypotheses
Good P (1994)
Springer-Verlag, New York; Halber et al assert that our non-parametric approach (their implementation of which they dub `Sherlock\') is less powerful than a ``standard\'\' analysis. This conclusion is at variance with our findings, which we consider is simply due to the fact that the ``standard analysis\'\' they compare to does not strongly control experimentwise Type~I error.
Randomization Tests
Edgington ES (1980)
Marcel Dekker, New York & Basel
Permutation tests: A practical guide to resampling methods for testing hypotheses
Good P (1994)
Springer-Verlag, New York
Randomization Tests Edgington ES (1980) Marcel Dekker, New York & Basel Permutation tests: A practical guide to resampling methods for testing hypotheses Good P (1994) Springer-Verlag, New York: Edgington ES (1980)
Marcel Dekker, New York & Basel
Permutation tests: A practical guide to resampling methods for testing hypotheses Good P (1994) Springer-Verlag, New York: Good P (1994)
Springer-Verlag, New York

Applications publications

Other information

Keywords

functional, HBP supported, nonparametric, permutation test, statistical

Platforms

MATLAB

IATR listing last updated

20 Mar 2006

Internet Analysis Tools Registry

supported by The Human Brain Project

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