SPM Lab II Tuesday, August 20 2002 1-3PM BME 499.098/Biostat 642 ============================================================================ Name: ___________________________________ Group Number: ___________________________________ Goals of this Lab ============================================================================ After this lab you will... 1. Be able to perform coregistration between the low resolution and high resolution structural images. You will understand the implications for a image to be the "Object" vs the "Target" in terms of the "world space" of each image. 2. Be able to perform spatial normalization, check its success, and apply the transformation to a contrast image. Coregistration ============================================================================ SPM's "Coregister" facility is used for registering different types of images from the same subject (it is a "intermodality, intrasubject" registration). To use it you must specify a "Target" or reference image, which does not move, and an "Object" image, which is moved to match the target. Recall that the rigid body transformation is recorded in the .mat file of the object image. Hence, it is not necessary to write out the transformed image (the Object transformed into the space of the Target), though it is sometimes convenient to do so. Before coregistering the low- and high- resolution structural images we need to (I) make sure the two images are in the same orientation and not too far from one another, and (II) correct for intensity inhomogeneities in the high-resolution SPGR image. 0. First see how close the t1overlay and t1spgr images are to start with. What button do you use to view more than one image? _______________________________________________________________ Multiple images are displayed in the space of the first image. If they are not previously registered this simply means that their origins are lined up. Neither image has had their origin set. What is the default origin? (Not a specific voxel x,y,z, but ...) _______________________________________________________________ 1. If the images were very out of register we could try to get the images closer by manually setting the origin to the Anterior Commissure (AC). A. On the board will be a diagram of how to find the AC on the midsagittal plane. Sketch it here B. Now use the Display button view the t1overlay image and locate the location, as best possible of the AC. You should be able to see it on the axial slice. Hint: Once you are close, it helps to "zoom in". In the display facility, click on the "Full Volume" pop-up menu; select "80x80x80 mm" What is the location of t1overlay's AC in voxels? _________ __________ ___________ Voxels C. Use the Display button view the t1spgr image and locate the location, as best possible of the AC. What is the location of t1spgr's AC in voxels? _________ __________ ___________ Voxels D. At this point we *could* set each image's origin using "HDR edit", but we won't. We don't need to because the images are already quite close, but there's a more important reason. By setting the origin on the Target image (t1overlay), we change its "world space"; specifically, the origin of its world space moves from the center of the volume to the AC. Hence it will no longer have the same world space as the functional data *unless* we identically change the origin on all the functional data and any results (beta*, con*, spm_T* etc) we already created. To summarize, if the images are way off, we can set the origin manually to help the Coregistration. But if we change the origin of the image representing the functional space, we have to similarly change the origin on all functional images. 2. Two processing steps have been performed to the structural images. They have had been scalp-edited and corrected for inhomogenity. The t1 images have signal from the scalp and head. Since this extraneous information can throw off subsequent processing steps the image is scalped edited with FSL's BET software. The resulting images are prefixed with an 'e'. Compare 't1spgr.img' to 'et1spgr.img'. Check the quality of the scalpping. Are there any brain regions accidently 'scooped-out'? Where? ____________________________________________________________ Are there any nonbrain tissues that should have been excluded? Where are they? (Hint: Check the sagittal sinus). ____________________________________________________________ Anatomical images obtained with high-field magnets (>2T), tend to be brighter in the center of the field of view than at the edge. This inhomogenity can throw-off subsequent processing steps, espcially segmentation. The T1 'e'-images have been corrected for inhomogeneity with a script by Gary Glover and Kalina Kristoff; they resulting images are prefixed with an 'h'. Compare 'et1spgr.img' to 'het1spgr.img'. Can you see the greater intensity in the center of the et1spgr image? Does the het1spgr image look better? (Use window controls to make the images look similar over all. 3. Coregister! You will now "Coregister" the high resolution, het1spgr image to the het1overlay image. The het1overlay image has the same space as the functionals, and hence this coregistration will set het1spgr's world space to correspond to the functionals. A. What is your "Target" image? _________________________________________________________ B. What is your "Object" image? _________________________________________________________ (Stop! Ask if this is right before you continue.) C. Click on the Coregister button. You only have one subject and you want to "Coregister & Reslice". You will be asked for the modality of "Target" and "Object" image. What is modality of each? (Circle one) Target Modality: PET T1 MRI T2 MRI PD MRI EPI Transm SPECT Object Modality: PET T1 MRI T2 MRI PD MRI EPI Transm SPECT Select the Target and Object images you identified above. It asks for "Other images". This would be useful if we had other images that were acquired in the same space as the SPGR. We don't, so just click 'Done'. Now it will perform the coregistration. The transformation parameters are written in the .mat file of the object image het1spgr.img; this sets the world space of het1spgr to match that of het1overlay. Since we asked it to "Reslice", it will also create a rhet1spgr.img, an image with the same dimensions and world space as het1overlay. D. Note changes in world spaces. "Display" het1overlay.img and use the "World Space"/"Voxel Space" pop-up button to change between the two spaces. Does the image move as you flip between the two spaces? _____________________________________________________________ "Display" het1spgr.img and do the same again. Does the image move as you flip between the two spaces? _____________________________________________________________ Why does one move and the other doesn't? _____________________________________________________________ _____________________________________________________________ D. Check the success of the Coregistration. Use check reg to check the registration. Check the points mentioned in the last lab: 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. Also try tracing sulci. Has the coregistration succeeded? _____________________________________________________________ _____________________________________________________________ Spatial Normalization ============================================================================ Spatial Normalization is SPMese for intersubject registration. It is essential for performing intersubject analyses or, for intrasubject analyses, determining Talairach/MNI coordinates of activation foci. While the "Normalize" button can accept any kind of image (T1, T2, etc), to register your subject into the standard atlas space, it is important to use the highest resolution structural data. 1. For us, that is which image: _________________________________________________ (Check your answer before continuing on!) Spatial normalization (or, with a British accent, normalisation) takes three types of images (i) "Images to determine parameters from" These are the high resolution anatomical images from which the spatial transformation is determined. Typically you only specify *one* such image per subject. (ii) "Images to write normalised" These are other images *with* *the* *same* *world* *space* as image (i) above. Typical examples would be statistic or contrast images, or whole set of ra* functional images. (iii) "Template image(s)" Images that define the standard atlas space. You get to choose from images that match the type (or "modality") of the image in (i). It produces a "*_sn3d.mat" file, which records the nonlinear transformation *from* the *world* *space* of image (i) *to* the atlas space. 2. Now that we have coregistered, what does the world space of het1spgr correspond to? __________________________________________________________ 3. Thus if we spatially normalize het1spgr the resulting _sn3d.mat file will not just be good for het1spgr, but for... __________________________________________________________ Click 'Normalize' to start the spatial normalization process. Select 'Determine Parameters Only'. For "Images to determine parameters from", select het1spgr.img For "Template images", select T1.img. This image is an average of 152 subject's T1 images, from the MNI/ICBM, smoothed with an 8mm filter. (ICBM=International Consortium for Brain Mapping). After a short while it will finish. This will create a _sn3d.mat and reslice the "image to determine parameter from", creating a nhet1spgr.img. Check the success of the registration, comparing the normalized image (nhet1spgr.img) to the unsmoothed version of the template image, that is avg152T1.img in the spm99/canonical directory. 4. Using the landmarks suggested above, has the spatial normalization succeeded? _______________________________________________________________ _______________________________________________________________ Analyzing Event-Related Data ========================================================================= First! Create a new directory, like g?ER (except replace ? with your group number). Copy the onset data to those directories. Load these event timing data: FacOns = load('g2_onsets_faces.dat'); ObjOns = load('g2_onsets_objects.dat'); Look at them with this matlab command (type carefully!) figure plot(FacOns,.9*ones(size(FacOns)),'v',... ObjOns,-.9*ones(size(ObjOns)),'^') Do they look random? Note how you never have two events at the same time. First, smooth the data. Click the 'Smooth' button and select all of the ra*vol.img you will analyze. As yesterday, use a 8mm isotropic smoothing. This creates sra*vol.img's. Now specify a model. Breifly... "Interscal interval {secs}" 1.5 "Scans per session" 213 "number of conditions or trials" 2 "name for condition/trial 1" Faces "name for condition/trial 2" Objects "stocastic design" No "SOA" Variable Because the events are randomly spaced, the SOAs vary from trial to trial. "vector of onsets (scans) - Faces" FacOns "variable durations" No "vector of onsets (scans) - Objects" ObjOns "variable durations" No This assumes you've previously loaded the onsets into Matlab's memory. If you forgot, you can enter spm_load(spm_get) instead and you'll be prompted for the file. "parametric modulation" None "Are these trials" Events "Select basis set..." hrf alone "interactions among trials" No "Users specified regressors" 0 OK. Now explore the design. Be sure to look at both Faces and Objects. How is the energy plot different from the block design? ______________________________________________________________________ ______________________________________________________________________ OK. Now estimate by clicking 'fMRI Models' "Estimate a specified model" Select this option "Select scans for session 1" Select your sra vols "remove global effects" Scale "High-pass filter?" Specify "session cutoff period (secs)" "Low-pass filter?" Gaussian "Gaussian FWHM (secs)" 4 "Model intrinsic autocorrelation" None "Setup trial-specific F-contrasts?" Yes Create contrasts and examine the results. Compare to the block results. Does it seem like the event-related design more or less sensitive? @(#)SPMlab2.txt 1.5 01/08/30