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 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:
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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. 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

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
  3. 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)
  4. Event-related fMRI: Rationale, temporal basis functions, slice-timing correction, latency analysis, efficiency and optimal experimental design, nonlinearities

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