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SnPM - Statistical NonParametric Mapping - A toolbox for SPM



Statistical nonParametric Mapping

A toolbox for SPM

Developed by Tom Nichols and others
URL reference:

Quick links! Download SnPM via the Registration page, then see the Distribution page for installation details.


The Statistical nonParametric Mapping toolbox provides an extensible framework for non-parametric permutation/randomisation tests using the General Linear Model and pseudo t-statistics for independent observations.

Suggestion for citing SnPM
Citation of the SnPM software can be made with reference to this URL; please also note the version (i.e. SnPM13) and the date that you last checked for updates. Concepts implemented in the SnPM software are best described in the Nichols & Holmes (2001) paper; see references below.


In addition to the main SnPM documentation, you are encouraged to read:
  • The appropriate peer reviewed articles.
  • The PET and fMRI example pages.
  • The main SPM documentation.
  • Basic non-parametric statistical texts, such as Good (1994) & Edgington (1980) will help clarify the underlying concepts of permutation/randomisation testing.


SnPM is an academic package that is supported by its developers and users. If you have a question, try these steps:


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 [Pubmed]
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 [Preprint|Pubmed]
Nichols TE, Holmes AP (2001)
Human Brain Mapping, 15:1-25.
Holmes & Watson, on ``Sherlock'' [Preprint|Pubmed]
Holmes & Nichols (and John Watson) 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.
Permutation inference for the general linear model
Winkler, Ridgway, Webster, Smith & Nichols (2014). [Pubmed]
NeuroImage, 92, 381–97.
  • 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



SnPM was originally developed by Andrew Holmes and Tom Nichols between 1995 and 1996, and Tom Nichols has led the development since 2001, with the valuable help of a number of people which we acknowledge here.

  • Camille Maumet, a Post Doctoral Research Fellow at WMG, University of Warwick, completed the Matlab Batch system porting as well as various bug fixes and improvements, 2013-.
  • Emma Thomas, an undergraduate student at the Department of Engineering, University of Warwick, began porting the sequential Q & A interface to the current (SPM) Matlab Batch system, 2010-2011.
  • Jun Ding, University of Michigan Biostatistics, worked on the SnPM3 version, 2005-2006.
  • Yanjun Xu, of the Mental Health Research Institute, University of Michigan, did important work on porting SnPM96 to Matlab 5 in 2001.
  • You! Please join the SnPM development efforts on GitHub.



Contact Info

Room D0.03
Deptment of Statistics
University of Warwick
United Kingdom

Tel: +44(0)24 761 51086
Email: t.e.nichols 'at'
Blog: Neuroimaging Tips & Tricks

[Book Cover]

Handbook of fMRI Data Analysis by Russ Poldrack, Thomas Nichols and Jeanette Mumford