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Thursday, February 26, 2009

SPM version 8b statistical parametric mapping, SPM approach

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.

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