212018-01-30 12:05:24JohanCarlinRemoved mention of Rik's course still being offered202013-03-07 21:23:04localhostconverted to 1.6 markup192013-02-28 12:58:37OlafHauk182012-03-05 15:07:26RikHenson172012-03-05 15:07:03RikHenson162012-03-05 15:03:06RikHensonNot specific to 2008 (or any specific version of SPM)152012-02-29 14:54:12RikHenson142012-02-24 11:31:36RikHenson132012-02-24 11:29:53RikHenson122011-11-21 12:34:12YaaraErez112009-01-21 15:43:59RikHenson102008-03-07 20:11:27RikHenson92008-02-29 16:25:23RikHenson82008-02-25 09:36:34RikHenson72008-02-15 17:12:26RikHenson62008-02-15 17:12:03RikHenson52008-02-15 17:06:47RikHenson42008-02-15 17:06:21RikHenson32008-02-15 17:03:21RikHenson22008-02-15 17:01:37RikHenson12008-02-15 16:59:53RikHensonmrclogo.gif Below are copies of Rik Henson's SPM course slides: 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) 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 Experimental design: event-related fMRI, temporal basis functions, design optimisation, nonlinearities, effective connectivity and dynamic causal modelling (DCM) SPM for EEG/MEG: space-time maps, inverse-problem, Bayesian formulation, model-evidence, canonical meshes 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.