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course1718:faqs_ws1718 [2018/01/25 06:35] – [Preprocessing] vflanagincourse1718:faqs_ws1718 [2018/01/25 21:20] (current) – [Preprocessing] vflanagin
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       * 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.       * 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.       * 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.
  
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       * 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}}       * 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.        * 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. 
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 +  * **How can I look at the influence of head motion on my data?**
  
  
  
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