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| Rik Henson gave a short SPM course at the Department of Psychology at the University during February 2008. Please see below for a copy of the slides of these talks: | Rik Henson gives a short SPM course for graduates at the Department of Psychology of the University of Cambridge during February March every year. Below are copies of the slides: |
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| [attachment:C:%5CDocuments%20and%20Settings%5Cuser%5CMy%20Documents%5CTALKS%5Chenson%2DSPM%2DGrad%30%38%2D%31%2Dpreproc.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-Grad08-1-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:henson-SPM-Grad08-3-design.ppt Experimental design: event-related fMRI, temporal basis functions, design optimisation, nonlinearities, effective connectivity and dynamic causal modelling (DCM)] 1. [attachment:henson-SPM-Grad08-4-meeg.ppt SPM for EEG/MEG: space-time maps, inverse-problem, Bayesian formulation, model-evidence, canonical meshes] |
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| The General Linear Model: global effects, correlation/orthogonalisation, time-series convolution models, high-pass filtering, temporal auto-correlation, maximum likelihood (ML) estimation, non-sphericity Inference: Statistical Parametric Maps (SPMs) and Gaussian Field Theory Correction, Random Effects, Parametric Empirical Bayes and PPMs, experimental designs, effective connectivity and dynamic causal modelling (DCM) Event-related fMRI: Rationale, temporal basis functions, slice-timing correction, latency analysis, efficiency and optimal experimental design, nonlinearities |
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. |
Rik Henson gives a short SPM course for graduates at the Department of Psychology of the University of Cambridge during February March every year. Below are copies of the slides:
- [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)]
- [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]
- [attachment:henson-SPM-Grad08-3-design.ppt Experimental design: event-related fMRI, temporal basis functions, design optimisation, nonlinearities, effective connectivity and dynamic causal modelling (DCM)]
- [attachment:henson-SPM-Grad08-4-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.