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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
  • Applications publications
    Other information
    Keywords
    functional, HBP supported, nonparametric, permutation test, statistical
    Platforms
    MATLAB
    IATR listing last updated
    20 Mar 2006