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| Rik Henson gave a short SPM course at the Department of Psychology at the University during four lectures from February-March 2008. Below are copies of the slides: | ## page was renamed from SpmMiniCourse2008 {{attachment:mrclogo.gif}} |
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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: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] |
Below are copies of Rik Henson's SPM course slides: 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]] 1. [[attachment:SPM-Henson-3-design.ppt|Experimental design: event-related fMRI, temporal basis functions, design optimisation, nonlinearities, effective connectivity and dynamic causal modelling (DCM)]] 1. [[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. |
Below are copies of Rik Henson's SPM course slides:
You can also watch the 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.