wittj
Uploaded by
31 SLIDES
347 VUES
310LIKES

Introduction to General Linear Model in fMRI Data Analysis

DESCRIPTION

Learn the basics of GLM for fMRI analysis and how to improve the model using SPM files. Understand the process of designing a statistical parametric map, from modeling data to making inferences about effects of interest. Discover solutions for model improvement, such as convolution modeling for BOLD responses, high-pass filtering for low-frequency noise, and correcting serial correlations. Enhance your understanding of voxel-wise time series analysis and hypothesis testing in fMRI experiments.

1 / 31

Télécharger la présentation

Introduction to General Linear Model in fMRI Data Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The General Linear Model SPM for fMRI Course Peter Zeidman/Christophe Phillips Methods Group Wellcome Trust Centre for Neuroimaging

  2. Overview • Basics of the GLM • Improving the model • SPM files http://www.fil.ion.ucl.ac.uk/~pzeidman/teaching/GLM.ppt

  3. Basics of the GLM

  4. Image time-series Statistical Parametric Map Design matrix Spatial filter Realignment Smoothing General Linear Model StatisticalInference RFT Normalisation p <0.05 Anatomicalreference Parameter estimates

  5. A very simple fMRI experiment One session Passive word listening versus rest 7 cycles of rest and listening Blocks of 6 scans with 7 sec TR Question: Is there a change in the BOLD response between listening and rest?

  6. Modelling the measured data Make inferences about effects of interest Why? • Decompose data into effects and error • Form statistic using estimates of effects and error How? effects estimate linear model statistic data error estimate

  7. Single voxel regression model error = + + 1 2 Time e x1 x2 BOLD signal

  8. Mass-univariate analysis: voxel-wise GLM X + y = • Model is specified by • Design matrix X • Assumptions about e N: number of scans p: number of regressors The design matrix embodies all available knowledge about experimentally controlled factors and potential confounds.

  9. Model specification Parameter estimation Hypothesis Statistic Voxel-wise time series analysis Time Time BOLD signal single voxel time series SPM

  10. Improving the model

  11. HRF What are the problems of this model? • BOLD responses have a delayed and dispersed form. • The BOLD signal includes substantial amounts of low-frequency noise (eg due to scanner drift). • Due to breathing, heartbeat & unmodeled neuronal activity, the errors are serially correlated. This violates the assumptions of the noise model in the GLM

  12. Problem 1: Shape of BOLD responseSolution: Convolution model Expected BOLD HRF Impulses  = expected BOLD response = input function impulse response function (HRF)

  13. Convolution model of the BOLD response Convolve stimulus function with a canonical hemodynamic response function (HRF):  HRF

  14. blue= data black = mean + low-frequency drift green= predicted response, taking into account low-frequency drift red= predicted response, NOT taking into account low-frequency drift Problem 2: Low-frequency noise Solution: High pass filtering discrete cosine transform (DCT) set

  15. High pass filtering discrete cosine transform (DCT) set

  16. Problem 3: Serial correlations with 1st order autoregressive process: AR(1) autocovariance function

  17. Multiple covariance components enhanced noise model at voxel i error covariance components Q and hyperparameters V Q2 Q1 1 + 2 = Estimation of hyperparameters  with ReML (Restricted Maximum Likelihood).

  18. Spm files

  19. 1.Specify the model

  20. 1.Specify the model

  21. SPM files (after specifying the model)

  22. SPM.mat(after specifying the model) SPM.xY – Filenames of fMRI volumes SPM.Sess – Per-session experiment timing SPM.xX – Design matrix For documentation on these structures, type: help spm_spm

  23. SPM.xX (Design matrix) Design matrix imagesc(SPM.xX.X);

  24. SPM.xX (Design matrix) Confounds (HPF) imagesc(SPM.xX.K.X0);

  25. 2. Estimate the model

  26. SPM files (after estimation)

  27. SPM files (after estimation) beta_0001.nii – beta_0004.nii mask.nii

  28. SPM files (after estimation) RPV.nii ResMS.nii Residual variance estimate Estimated RESELS per voxel

  29. SPM files (after estimation)

  30. SPM files (after contrast estimation)

  31. Summary • We specify a general linear model of the data • The model is combined with the HRF, high-pass filtered and serial correlations corrected • The model is applied to every voxel, producing beta images. • Next we’ll compare betas to make inferences http://www.fil.ion.ucl.ac.uk/~pzeidman/teaching/GLM.ppt

More Related