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1. [attachment:henson-SPM-Grad08-1-preproc.ppt Spatial preprocessing: Realigment, unwarping, normalisation, smoothing, segmentation and Computational Neuroanatomy e.g. voxel-based morphometry (VBM), deformation-based morphometry (DBM) and tensor-based morphometry (TBM)] 1. [attachment:henson-SPM-Grad08-2-glm.ppt The General Linear Model: global effects, correlation/orthogonalisation, time-series convolution models, high-pass filtering, temporal auto-correlation, maximum likelihood (ML) estimation, non-sphericity, Statistical Parametric Maps (SPMs) and Random Field Theory Correction, Random Effects, Parametric Empirical Bayes and PPMs] |
1. [attachment:SPM-Henson-1-preproc.ppt Spatial preprocessing: Realigment, unwarping, normalisation, smoothing, segmentation and Computational Neuroanatomy e.g. voxel-based morphometry (VBM), deformation-based morphometry (DBM) and tensor-based morphometry (TBM)] 1. [attachment:SPM-Henson-2-GLM.ppt The General Linear Model: global effects, correlation/orthogonalisation, time-series convolution models, high-pass filtering, temporal auto-correlation, maximum likelihood (ML) estimation, non-sphericity, Statistical Parametric Maps (SPMs) and Random Field Theory Correction, Random Effects, Parametric Empirical Bayes and PPMs] |
Rik Henson gives a short SPM course for graduates at the CBU and University of Cambridge during February/March every year (though the talks are open to all). Below are copies of the slides:
- [attachment:SPM-Henson-1-preproc.ppt Spatial preprocessing: Realigment, unwarping, normalisation, smoothing, segmentation and Computational Neuroanatomy e.g. voxel-based morphometry (VBM), deformation-based morphometry (DBM) and tensor-based morphometry (TBM)]
- [attachment:SPM-Henson-2-GLM.ppt The General Linear Model: global effects, correlation/orthogonalisation, time-series convolution models, high-pass filtering, temporal auto-correlation, maximum likelihood (ML) estimation, non-sphericity, Statistical Parametric Maps (SPMs) and Random Field Theory Correction, Random Effects, Parametric Empirical Bayes and PPMs]
- [attachment:SPM-Henson-3-design.ppt Experimental design: event-related fMRI, temporal basis functions, design optimisation, nonlinearities, effective connectivity and dynamic causal modelling (DCM)]
- [attachment:SPM-Henson-4-MEEG.ppt SPM for EEG/MEG: space-time maps, inverse-problem, Bayesian formulation, model-evidence, canonical meshes]
You can also watch the [http://www.fil.ion.ucl.ac.uk/spm/course/video/ videos of the SPM Course for fMRI, PET and VBM, at the Wellcome Trust Centre for Neuroimaging] (May 2011), which also contain two recorded practical demonstrations from the 2009 MEG and EEG course on the same page.