Diff for "SpmMiniCourse" - MRC CBU Imaging Wiki
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Editor: RikHenson
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 1. The General Linear Model: global effects, correlation/orthogonalisation, time-series convolution models, high-pass filtering, temporal auto-correlation, maximum likelihood (ML) estimation, non-sphericity
 1. 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)
 1. Event-related fMRI: Rationale, temporal basis functions, slice-timing correction, latency analysis, efficiency and optimal experimental design, nonlinearities		  1. 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. Experimental design: event-related fMRI, temporal basis functions, design optimisation, nonlinearities, effective connectivity and dynamic causal modelling (DCM)  
 1. SPM for EEG/MEG: space-time maps, inverse-problem, Bayesian formulation, model-evidence, canonical meshes

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:

  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)]
  2. 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
  3. Experimental design: event-related fMRI, temporal basis functions, design optimisation, nonlinearities, effective connectivity and dynamic causal modelling (DCM)
  4. SPM for EEG/MEG: space-time maps, inverse-problem, Bayesian formulation, model-evidence, canonical meshes

CbuImaging: SpmMiniCourse (last edited 2018-01-30 12:05:24 by JohanCarlin)