SPM12

Introduction

SPM12, first released 1st October 2014 and last updated 13th January 2020, is a major update to the SPM software, containing substantial theoretical, algorithmic, structural and interface enhancements over previous versions.

A description of the new features is available in the Release Notes.

The software is available after completing a brief Download Form.

A PDF Manual is also available and some extra information can be obtained on the SPM online documentation (such as installation and getting started).

Requirements

You need the following to run SPM12:

MATLAB: MATLAB (MathWorks) is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numeric computation.

SPM12 is designed to work with MATLAB versions R2007a (7.4) to R2023b (9.15), and will not work with earlier versions. It only requires core MATLAB to run (i.e. no toolboxes).
See the System Requirements page for a list of suitable platforms to run MATLAB and the Platform Roadmap for the correspondance between MATLAB versions and supported platforms.

Alternatively, a Standalone SPM (built using the MATLAB Compiler) is also available and does not require the availability of a MATLAB licence (see limitations).

MEX files: Whilst the majority of the code is implemented as standard MATLAB M-files, SPM also uses external MEX files, written in C, to perform some of the more computationally intensive operations. Pre-compiled binaries of these external C-MEX routines are provided for:

Statistical Parametric Mapping

Statistical Parametric Mapping refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional imaging data. These ideas have been instantiated in software that is called SPM.

The SPM software package has been designed for the analysis of brain imaging data sequences. The sequences can be a series of images from different cohorts, or time-series from the same subject. The current release is designed for the analysis of fMRI, PET, SPECT, EEG and MEG.

Getting Started

The best starting point is to read the introductory article on SPM available here. You could then download the latest version of the software and a data set to analyse. Step-by-step instructions for this analysis are available in the SPM manual.

If you’re new to imaging, perhaps an epoch fMRI data set would be appropriate. The data sets are provided with instructions on how to use SPM to analyse them. These tutorials therefore give practical instructions on how to implement the various methodologies. Our methods have been written up in books, technical reports and journal papers which are available from our Online Bibliography. This groups documentation according to year, category, author and keyword.

If you’re looking for help on a particular topic you can find the relevant papers from the Online Bibliography. Alternatively, you can search the SPM pages using the search facility that appears at the bottom of every page. Also browse and search the SPM WikiBook and please feel free to edit it if you can. If you still can’t find what you need, you could send an email to the SPM Email list, which gives you access to our community of experts.
You should also be aware of the many courses on SPM. If there isn’t one in your country this year then there’s always the annual short course in London. Finally, once you’ve mastered SPM you can learn about the various extensions provided by experts in the wider community.

The SPM approach in brief

The Statistical Parametric Mapping approach is voxel based:

Images are realigned, spatially normalised into a standard space, and smoothed.
Parametric statistical models are assumed at each voxel, using the General Linear Model GLM to describe the data in terms of experimental and confounding effects, and residual variability.
For fMRI the GLM is used in combination with a temporal convolution model.
Classical statistical inference is used to test hypotheses that are expressed in terms of GLM parameters. This uses an image whose voxel values are statistics, a Statistic Image, or Statistical Parametric Map (SPM{t}, SPM{Z}, SPM{F})
For such classical inferences, the multiple comparisons problem is addressed using continuous random field theory RFT, assuming the statistic image to be a good lattice representation of an underlying continuous stationary random field. This results in inference based on corrected p-values.
Bayesian inference can be used in place of classical inference resulting in Posterior Probability Maps PPMs.
For fMRI, analyses of effective connectivity can be implemented using Dynamic Causal Modelling DCM

官方网站：http://www.fil.ion.ucl.ac.uk/spm/