SPM Lab Friday, August 23 2001 1-3PM BME 499.098/Biostat 642 ============================================================================ Name: ___________________________________ Group Name: ___________________________________ Dataset Used: fmriclass ________ Goals of this Lab ============================================================================ After this lab you will... 1. Be able to examine at data using SPM's single- and multi-volume display facilities. 2. Be able to characterize the susceptibility artifacts and signal voids in functional data, as compared to similar structural data. 3. Be able to model a block-design fMRI dataset. Look at the data! ============================================================================ Look at some functional data -------------------------------------------------------------------------- 0. View a randomly selected image from among your functionals. You should always do this to check orientation (and any possible catastrophic problems.) 1. What is the voxel size? ________ ________ ________ 2. What is the image dimension? ________ ________ ________ 3. What are typical graymatter values? From ________ to _________ 4. Bilinear interpolation is default. Select Nearest Neighbor interpolation. Explore the image Select Sinc interpolation. Explore the image Which one do you prefer? _______________ Why? _________________________________________________________________ Compare the functional data to the anatomical data -------------------------------------------------------------------------- 0. The t1_gre image is in the same space as the functionals (it has the same number of slices with the same spatial location, but it has smaller in plane voxels, i.e. greater in-plane resolution). Use 'Check Reg' to "check" the "registration" between the t1_gre structural and the functionals. Select the t1_gre image for the first image, and select the first functional image as the second image. 1. Look for possible mis-registration (due to subject movement between t1_gre acquisition and first functional scan). Explore the pair of images, making sure to compare the following anatomical regions i. Frontal pole ii. Occipital (posterior) pole iii. Left & Right sides (e.g. superior temporal gyrus) iv. Corpus collosum: (1) Most anterior, (2) most superior and (3) most posterior extent. Extra for experts! You can modify the contrast of the images by playing with the "window", the min-max range of intensity values displayed. To set the window of the first image within 'Check Reg', use the following command spm_orthviews('window',1,[MIN MAX]) where MIN and MAX are the desired minimum and maximum displayed values. This will take some trial and error get good values, but can help a lot with T1 images. To adjust the window on the 2nd image, replace the "1" above with a "2". Do the functional and structural line up well? If not, how so? ______________________________________________________________________ ______________________________________________________________________ 2. Explore the regions of signal loss in the temporal poles and the orbitofrontal cortex. For example, on the T1, go to the medial orbitofrontal cortex and look at the sagittal image; click on the coronal or axial until you have nice view on the sagittal image---that is, not right on the mid-sagittal plane (that usually looks yukky), but just off mid-sag. Now click around on the sagittal view, keeping an eye on the axial view, and carefully compare the T1 and the functional. Similarly, find the signal void in the temporal pole (due to the ear canal). Observe cortex visible in T1 image not visible in T2*. When we get thresholded activation maps, we typically overlay them on the structural images since they have more anatomical detail. But why should we *also* check localization of activation on the functionals? ______________________________________________________________________ ______________________________________________________________________ Preprocessing Steps -------------------------------------------------------------------------- 0. Two key preprocessing steps have already been performed for you, slice time correction and image registration ("coregistriation"). 1. See if there has been appreciable motion. Use 'Check Reg' to compare 3 images (use your block design data, acq 1) aimg/..._0001.img aimg/..._0140.img raimg/.._0140.img If there is any appreciable motion, then the first and last aimg should be out of registration, and the last raimg should be successfully corrected. Can you detect any misregistration between the first and last unregistered image (aimg/..._0001.img & aimg/..._0140.img)? __________________________________________________________________ If so, is it corrected in raimg/..._0140? __________________________________________________________________ 2. The only preprocessing step left for you to do (for an intrasubject analysis) is spatial smoothing. Apply a 8mm FWHM isotropic smoothing to the data. (Isotropic means "the same in all directions"). Extra for experts Specifying a filter size consisting of a single value implies a isotropic smoothing. Define a model for your Block Design Data -------------------------------------------------------------------------- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % Matlab work to define block onsets and durations % TR = 2; Ind = 'FXPXFXPXFPFPFPFPFP'; % % Block durations, in seconds % Sec = [12 8 14 6 16 4 18 2 20 20 20 20 20 20 20 20 20 20]; % % Block durations, in seconds % Dur = Sec/TR; % % Block onsets, in scans % Ons = cumsum([0 Sec(1:end-1)])/TR; % % Faces onsets % Ons(Ind=='F') % 0 20 40 60 80 100 120 % % Faces durations % Dur(Ind=='F') % 6 8 10 10 10 10 10 % % Places onsets % Ons(Ind=='P') % 10 30 50 70 90 110 130 % % Places durations % Dur(Ind=='P') % 7 9 10 10 10 10 10 % Fixation onsets Ons(Ind=='X') % Fixation durations Dur(Ind=='X') %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 1. Specify the model fMRI Models -> Specify Interscan Interval (TR) in sec: 2 scans per session: 140 Number of conditions 2 Enter names; "Faces" for first, "Places" for second Stochastic design: No SOA: Variable Enter vector of onsets (Faces) 0 20 40 60 80 100 120 Variable durations Yes Durations 6 8 10 10 10 10 10 Enter vector of onsets (Places) 10 30 50 70 90 110 130 Variable durations Yes Durations 7 9 10 10 10 10 10 Parametric modulation None Trial type Epochs Type of response fixed response (box-car) Convolve w/ HRF Yes Add temporal derivatives No epoch length (Faces) 1 (If not variable, dur specified here) epoch length (Places) 1 Interactions among trials No User specified regressors 0 2. Estimate the model fMRI Models -> Estimate Select scans remove Global effects Scale High Pass filter Specify session cutoff period (sec) 80 low pass Gaussian Gaussian FWHM 4 Model Intrinsic Autocorr None Set up F-contrasts Yes Estimate Now 3. Look at results Results Specify the 1 -1 "Faces - Places" contrast Specify the -1 1 "Faces - Places" contrast Select the "Faces - Places" contrast 4. Plotting Plot -> Fitted and Adjusted Values Which contrast -> Effects of interest Plot against -> Scan/time ============================================================================ @(#)SPMlab.txt 1.6 01/08/30