SnPM13 Manual

SnPM: Statistical nonParametric Mapping

The Statistical nonParametric Mapping toolbox 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.

Because the appropriate permutations of the data labellings is highly dependent on the experimental design, different designs must be treated individually. This is handled within SnPM using a PlugIn architecture for "design modules". Currently, PlugIn modules are available for:

  1. SingleSub: Two Sample T test; 2 conditions, replications
  2. SingleSub: Simple Regression (correlation); single covariate of interest
  3. MultiSub: One Sample T test on diffs/contrasts; 1 condition, 1 scan per subject
  4. MultiSub: Simple Regression (correlation); single covariate of interest, 1 scan per subject
  5. MultiSub: Paired T test; 2 conditions, 1 scan per condition
  6. MultiSub: Within Subject ANOVA; multiple scans/subject
  7. 2 Groups: Test diff of response; 2 conditions, 1 scan per condition
  8. 2 Groups: Two Sample T test; 1 scan per subject
  9. >2 Groups: Between Group ANOVA; 1 scan per subject

The approach is described in full in Holmes (1994); Holmes (1996) and Nichols & Holmes (2001). (See references.) These pages give a brief overview of the methodology, and describe the software, it's installation and use.

Statistical nonParametric Mapping refers to the enterprise of making statistical inferences on volumetric statistic images with minimal assumptions using non-parametric statistical techniques. SnPM refers to an implementation of Statistical nonParametric Mapping by Andrew Holmes and Tom Nichols.


Getting started

First download the SnPM13 software alongside an SPM8 or SPM12b installation, ensuring SPM is on the MATLABPATH. Extract the SnPM code from the archive and copy the SnPM13 directory under toolbox in SPM. There is no need to add SnPM to the path as SnPM13 will automatically be loaded when SPM is started.

GUI usage

SnPM runs on top of the SPM Batch environment, and is loaded from within MatLab when starting SPM by typing spm fmri in the command window. This will start the SPM environment if not already done and load SnPM13 toolbox.

Launch SnPM13 from SPM Batch window

Batch usage

SnPM13 can also be used in batch mode using the spm_jobman function as any other SPM batch module. More info on SPM wikibooks.

Output files

Image files generated by SnPM. (SnPM, like SPM, only implements single tailed tests. In the following image files, '+' or '-' correspond to 'positive' or 'negative' effects).

Matlab files generated by SnPM.

(For examples on how to run an analysis please refer to our PET and fMRI examples.)