Abstract
Statistical parametric mapping (SPM) is an established statistical data analysis framework through which regionally specific effects in structural and functional neuroimaging data can be characterised. SPM is also the name of a free and open source academic software package through which this framework (amongst other things) can be implemented. In summary, SPM analyses contain three key components: data are (a) spatially transformed to bring them into a common space; (b) described in terms of experimental effects, confounding effects and residual variability using a general linear model; and (c) subject to statistical inference using random field theory. In this chapter, we will give an overview of the underlying concepts of the SPM framework and will illustrate these by describing how to analyse a typical block-design functional MRI (fMRI) data set using the SPM software.
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Abbreviations
- DCM:
-
Dynamic causal model
- EPI:
-
Echo planar imaging
- fMRI:
-
Functional magnetic Âresonance imaging
- FFX:
-
Fixed-effects analysis
- FPR:
-
False-positive rate
- FWE:
-
Family-wise error
- FWHM:
-
Full width at half maximum
- GLM:
-
General linear model
- HRF:
-
Haemodynamic response function
- MIP:
-
Maximum intensity projection
- PET:
-
Positron emission tomography
- RFT:
-
Random field theory
- RFX:
-
Random-effects analysis
- SPM:
-
Statistical parametric map(ping)
- SVC:
-
Small volume correction
- TR:
-
Time to repeat
- VBM:
-
Voxel-based morphometry
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Flandin, G., Novak, M.J.U. (2013). fMRI Data Analysis Using SPM. In: Ulmer, S., Jansen, O. (eds) fMRI. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34342-1_6
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