Diff for "SpmMiniCourse" - MRC CBU Imaging Wiki
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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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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   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 		 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]

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:

  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. [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]
  3. [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)]
  4. [attachment:henson-SPM-Grad08-4-meeg.ppt 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)