Diff for "SpmMiniCourse2005" - MRC CBU Imaging Wiki
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 1. [http://imaging.mrc-cbu.cam.ac.uk/ppts/rikSPM-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. [http://imaging.mrc-cbu.cam.ac.uk/ppts/rikSPM-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]
 1. [http://imaging.mrc-cbu.cam.ac.uk/ppts/rikSPM-inf2.ppt 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. [http://imaging.mrc-cbu.cam.ac.uk/ppts/rikSPM-efMRI.ppt Event-related fMRI: Rationale, temporal basis functions, slice-timing correction, latency analysis, efficiency and optimal experimental design, nonlinearities]		  1. [[http://imaging.mrc-cbu.cam.ac.uk/ppts/rikSPM-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. [[http://imaging.mrc-cbu.cam.ac.uk/ppts/rikSPM-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]]
 1. [[http://imaging.mrc-cbu.cam.ac.uk/ppts/rikSPM-inf2.ppt|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. [[http://imaging.mrc-cbu.cam.ac.uk/ppts/rikSPM-efMRI.ppt|Event-related fMRI: Rationale, temporal basis functions, slice-timing correction, latency analysis, efficiency and optimal experimental design, nonlinearities]]

SPM mini course in 2005

Rik Henson gave a short SPM course at the CBU during February 2005. Please see below for a copy of the slides of these talks:

  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

  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: SpmMiniCourse2005 (last edited 2013-03-07 21:24:00 by localhost)