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--- course1718:faqs_ws1718 [2018/01/24 12:53] – [Preprocessing] vflanagin
+++ course1718:faqs_ws1718 [2018/01/25 21:20] (current) – [Preprocessing] vflanagin
@@ Line 19: / Line 19: @@
   * **What went wrong if my preprocessed functional images do not match the template image?**
       * Unfortunately, there are a lot of different reasons why the preprocessing could go wrong. It is extremely important that you are systematic and careful about setting up your preprocessing so that you are not the cause of the error. You could have accidentally mixed up two subjects so that you are normalizing the functional data with the deformation field of a different subject. Or you could have neglected to select ALL of the functional image frames/volumes when setting the origin. However, assuming you did everything correctly, the most error-prone step of the preprocessing is the coregistration step, which is highly dependent on how you set your origin. Therefore I can recommend that you make sure that the structural image from each subject fits well to the functional images after setting the origin. What do I mean by that? You should make sure that the origin looks like it is at the same location in the image, and that large anatomical landmarks (e.g. ventricles, or the cerebellum) are approximately in the same place. This should speed up preprocessing and eliminate the necessity to repeat steps because the normalization failed.
   * **What do I do if the preprocessed functional images do not match the template?**
       * You will need to first figure out what step did not work (typically coregistration) and then, understanding what is done in the different steps decide on what steps you will need to repeat. I typically work backwards. If the segmentation step did not work, then the anatomical image should not fit well to the template image. So I open both the template and the normalized anatomical image with Check Reg to see if the segmentation worked. If it did and if the anatomical image was the reference image then the anatomical image can be left alone. Then I would check the output images from the realignment, or the input images to the coregistration step. Check whether the functional images that were used as input to the coregistration step look like they fit well to the unchanged anatomical image. If they do not then the problem was the coregistration step and you will need to reset the origin of the functional images to match the anatomical image. You can do this with the output of the realignment step, and then you do not need to redo the realignment.
   * **Why does the normalization step only need one functional NIFTI-file and not multiple frames from the NIFTI-file? **
       * The normalization step is part of the DARTEL toolbox which is a separate toolbox that works within SPM. This toolbox was designed to use 4D NIFTI inputs and will automatically read all frames, as opposed to SPM, which still needs you to specify each individual frame.
   * **Is it possible to load more than one image to be normalized?**
       * Yes, you may load multiple 4D NIFTI files (for instance from two sessions of the same subject) to the images that you wish to be normalized as long as you can use the SAME deformation field for all of the images you select.
-  * **When do I select "Many subjects" as opposed to "few subjects" for the normalization?**
+  * **When do I select "many subjects" as opposed to "few subjects" for the normalization?**
       * The "many subjects" option in the normalization step is for voxel-based morphometry, or a structural analysis method. It is use when the number of images, or frames for one subject is small compared to the number of subjects (e.g. 1 image volume per subject). In fMRI we will use the option "few subject, although we may now have more than one.
   * **Is it possible to add additional subjects to one normalization batch step?**
       * Yes, you can normalize all of the subjects (if you want the same base parameters like voxel resolution) in one batch, by adding new subjects. It is important that you add as many subjects as you have deformation fields.
+==== Single-subject analysis ====
+  * **Because we want to compare the stimuli with rest, shouldn't we also explicitly model the rest periods as columns of the design matrix?**
+      * It is possible but not recommended to model everything that happened in the experiment. This is because, in order to get the best estimate for the betas, or parameter estimates, the design matrix must be invertible. For a matrix to be invertible it must be full rank, full rank meaning that the number of dimensions that the matrix spans is equal to the number of columns in the matrix. If we consider the situation where we model both stimuli and the rest, (the mean is always modeled as well, see figure below) then if Stim1 + Stim2 + Rest = Mean. This means that the matrix is not full rank, and the Beta-values or parameter estimates for the individual columns may not be well estimated. It is possible that they are well estimated, but it could be that it works for 1 subject but for the rest not. {{ :course1718:designmatrix.png?600 |DesMat}}
+      * However, if you use the positive contrasts (e.g. 1 0 0) for stimulus 1, you are in essence comparing them to the rest periods.
+  * **How can I look at the influence of head motion on my data?**