Diff for "SpmMiniCourse" - MRC CBU Imaging Wiki
location: Diff for "SpmMiniCourse"
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⇤ ← Revision 1 as of 2008-02-15 16:59:53 →
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Editor: RikHenson
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← Revision 21 as of 2018-01-30 12:05:24 → ⇥
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Editor: JohanCarlin
Comment: Removed mention of Rik's course still being offered
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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: ## page was renamed from SpmMiniCourse2008
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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) Below are copies of Rik Henson's SPM course slides:
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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  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]]
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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.

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Below are copies of Rik Henson's SPM course slides:

  1. 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

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.

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