SPM Lab III Wednesday, August 21 2001 1-3PM BME 499.098/Biostat 642 ============================================================================ Name: ___________________________________ Group Number: ___________________________________ Goals of this Lab ============================================================================ After this lab you will... 1. Be able to apply previously determined spatial normalization parameters to newly created results, making way for a random effects analysis. 2. Be able to explore the results, understanding whether an observed result is due to a large signal as small variance, or both. The rest of the lab time (and Wednesday's and Thursday's lab) are dedicated to your project. Spatially Normalizing "Other" Images ============================================================================ Yesterday you should have normalized your het1spgr image into the standard MNI space. This created a het1spgr_sn3d.mat file, which contains the nonlinear transformations required to move het1spgr's world space to the MNI space. You now will apply those transformations to other files. You will transform two contrast images and a mean functional image into the MNI space. The contrast image will be used for random effects inference, the mean functional image will be used to precisely characterize each subject's signal voids. First, create a mean functional. Press 'Means... -> Mean'. Select all of your ravol* images. This will create a "mean.img" image in your *current* directory. Take a moment and change the name of this file to a more meaningful name (for example run_01_ramean). Important! Manipulating an image file name means you have to do the same thing to all three components! This means you have to change the names of three files: mean.img mean.hdr mean.mat Now, collect all files to be spatially normalized 1. What is the filename of your Faces-Places contrast for your block design data with temporal derivative? (It should be con_XXXX.img, where XXXX is the contrast number) __________________________________________________________________ 2. What is the filename of your ra-functional mean image __________________________________________________________________ Press 'Normalize'. Select "Write Normalized Only..." Select the _sn3d.mat file, and then select the two above images to "write normalized". After this is done, examine this images in Check Reg. In particular, pay attention to how the signal voids have mapped onto the MNI space. 3. Are there regions of functional data outside the MNI brain? If so, where? __________________________________________________________________ __________________________________________________________________ __________________________________________________________________ 4. Are there regions of the MNI brain where there is no functional data? If so, where? __________________________________________________________________ __________________________________________________________________ __________________________________________________________________ As a final step, ftp your results to stanley and put them into the ~/Groups/ directory. Ftp the following files ncon_XXXX.{hdr,img,mat} - Normalized Faces-Places contrast nhet1spgr - Normalized structural image run_01_ramean - Normalized functional image Visualization of results ============================================================================ The "glass brain" MIP viewer is a very crude way to visualize your results. While conveniently summarizing your 3D results in a fixed 2D picture, there is so much more to your data. In this section you will learn a much better way to view your results. In addition to using the interactive 3D viewer in check reg, you will view both the "signal" and the "noise", instead of just the "signal-to-noise ratio" of the t statistic. First, we need a "noise" image. SPM creates a "ResMS" image, which is the sigma-squared-hat of the GLM. However, this image has the usual problem of variance, in that it is in squared units. Make a standard deviation image with ImCalc... Click 'ImCalc'. For "Images to work on" select the ResMS image. For output image enter "Stdev". For the equation enter sqrt(i1) Now, use Check Reg to view four images simultaneously I. run_01_mean ... For anatomical reference II. spmT_XXXX ... The statistic image of your Faces-Places contrast III. con_XXXX ... The image filename of your F-P contrast IV. Stdev ... The standard deviation image Now explore the statistic image. If necessary, adjust the contrast of each image with the command spm_orthviews('window'...) command. For example, to set the max and min values of the t statistic image to be -6 and 6, do spm_orthviews('window',2,[-6 6]) 0. Is there alot of structure in the standard deviation image? Can you see anatomical features? _______________________________________________________________ 1. Can you see any correspondences between regions of significance in the t image and structure in the standard deviation image? _______________________________________________________________ 2. Go to a region of large positive change. Is this due to a large effect (in the contrast) or a relative dip in the standard deviation _______________________________________________________________ Use an approach like this to really understand what is going on in your experimental effects. Of course, when you have to make a decision, or "statistical inference", you have to return to the "Results" part of SPM. This concludes the guided part of the lab. You can now start on your project. @(#)Lb_SPM3.txt 1.3 02/08/21