Abstract
SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools.
Highlights
Statistical parametric mapping (SPM) is a free and open source academic software distributed under GNU General Public License
The idea here is to use standard SPM to find significant regions in time × frequency or time × channels search spaces, whose P-values are properly adjusted for multiple comparisons and profound correlations over the implicit search spaces
The ensuing SPM may be the final analysis or used to specify the number and location of sources that form the basis of dynamic causal modelling (DCM) analysis
Summary
Statistical parametric mapping (SPM) is a free and open source academic software distributed under GNU General Public License. The origins of SPM software go back to 1990, when SPM was first formulated for the statistical analysis of positron emission tomography (PET) data [1, 2]. As functional magnetic resonance imaging (fMRI) gained popularity later in the decade, SPM was further developed to support this new imaging modality, introducing the notion of a hemodynamic response function and associated convolution models for serially correlated time series. This formulation became an established standard in the field and most other free and commercial packages for fMRI analysis implement variants of it. In addition to finessing fMRI and PET analyses, these methods made it possible to apply SPM to structural MRIs [6], which became the field of voxel-based morphometry (VBM)
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