This preview shows pages 1–7. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: This Time Experimental Design Initial GLM Intro GLM General Linear Model Single subject fMRI modeling Single Subject fMRI Data Data at one voxel Rest vs. passive word listening Is there an effect? A Linear Model Intensity Time = 1 2 + + error x 1 x 2 Linear in parameters 1 & 2 Linear model, in image form = + + 1 2 Y 1 1 x 2 2 x in image matrix form = + 2 1 Y X in matrix form. X Y = + Y Y X N 1 N N 1 1 p p N: Number of scans, p: Number of regressors Linear Model Predictors Signal Predictors Block designs Eventrelated responses Nuisance Predictors Drift Regression parameters X Y Signal Predictors Linear TimeInvariant system LTI specified solely by Stimulus function of experiment Hemodynamic Response Function (HRF) Response to instantaneous impulse Blocks Events X Y Convolution Examples Hemodynamic Response Function Predicted Response Block Design Experimental Stimulus Function X Y EventRelated LTI Pet Peeve LTI/convolution approach implies antisymmetry Shape of rise must match inverted shape of fall Bump here must match... ...bump here X Y HRF Models Canonical HRF Most sensitive if it is correct If wrong, leads to bias and/or poor fit E.g. True response may be faster/slower E.g. True response may have smaller/ bigger undershoot SPMs HRF X Y HRF Models Smooth Basis HRFs More flexible Less interpretable No one parameter explains the response Less sensitive relative to canonical (only if canonical is correct) Gamma Basis Fourier Basis X Y HRF Models Deconvolution Most flexible Allows any shape Even bizarre, nonsensical ones Least sensitive relative to canonical (again, if canonical is correct) Deconvolution Basis X Y Drift Models Drift Slowly varying Nuisance variability Even seen in cadavers! A. Smith et al, NI, 1999, 9:526533 Models Linear, quadratic Discrete Cosine Transform Discrete Cosine Transform Basis X Y Some Terminology Some Terminology Some Terminology SPM (Statistical Parametric Mapping) is a massively univariate approach  meaning that a statistic (e.g., Tvalue) is calculated for every voxel  using the General Linear Model Experimental manipulations are specified in a model (design matrix) which is fit to each voxel to estimate the size of the experimental effects (parameter estimates) in that voxel on which one or more hypotheses (contrasts) are tested to make statistical inferences (pvalues), correcting for multiple comparisons across voxels (using Random Field Theory) The parametric statistics assume continuousvalued data and...
View
Full
Document
This note was uploaded on 02/10/2010 for the course TBE 2300 taught by Professor Cudeback during the Spring '10 term at Webber.
 Spring '10
 Cudeback

Click to edit the document details