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SnPM - Statistical NonParametric Mapping - A toolbox for SPM

[SnPM]

SnPM

Statistical nonParametric Mapping

A toolbox for SPM

Developed by Tom Nichols and others
URL reference: http://warwick.ac.uk/snpm


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

Introduction

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 http://warwick.ac.uk/snpm; 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.

Documentation

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.

Support

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

References

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

 

Developers

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.

Neuroimaging
Statistics

[brain]


Contact Info

Room D0.03
Deptment of Statistics
University of Warwick
Coventry
CV4 7AL
United Kingdom

Tel: +44(0)24 761 51086
Email: t.e.nichols 'at' warwick.ac.uk
Web: http://warwick.ac.uk/tenichols
Blog: Neuroimaging Tips & Tricks

[Book Cover]


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