ajay:neuroelf_usage_notes
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ajay:neuroelf_usage_notes [2012/11/14 22:34] – [Scripting and Batching: Dangers] ajay | ajay:neuroelf_usage_notes [2013/10/21 12:50] – [Visualization Help] ajay | ||
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- the SPM preprocessing dialog (Run) should also create VTC files at the end (basically the preprocessed image files in another format that allows for rapid processing of data), which you can simply check by using the File -> Open menu option in NeuroElf | - the SPM preprocessing dialog (Run) should also create VTC files at the end (basically the preprocessed image files in another format that allows for rapid processing of data), which you can simply check by using the File -> Open menu option in NeuroElf | ||
+ | |||
+ | - Jochen: The blue bars (boxes, really) represent a spike-detection algorithm that I have implemented together with Katherine Duncan (Daphna' | ||
+ | |||
+ | ==== VTC file formats ==== | ||
+ | - The preprocessed data are transformed from NII to VTC in the final step. | ||
+ | - VTC formats have an advantage in speed of processing for later steps because they put time as the first dimension in the 4D image matrix. | ||
+ | - VTC axes are not in the same exact ordering as NII axes: it goes time, Y, X, Z. | ||
+ | - The defaults set the bounding box of the image to cover the MNI brain. | ||
+ | - The defaults use 3mm voxel sizes, timepointsx66x52x47 size... note that the 66 is in the y direction... | ||
+ | |||
==== Making Design Files (prt files) ==== | ==== Making Design Files (prt files) ==== | ||
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- save the mdm with '' | - save the mdm with '' | ||
- compute the glm (with filter settings): <code matlab ajay_computeglm.m> | - compute the glm (with filter settings): <code matlab ajay_computeglm.m> | ||
+ | - you may prefer to use ' | ||
- to run a FIR model: <code matlab ajay_computefirglm.m> | - to run a FIR model: <code matlab ajay_computefirglm.m> | ||
- the struct allows you to add optional commands and options (such as filtering) | - the struct allows you to add optional commands and options (such as filtering) | ||
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- glm.Study(1).RunTimeVars contains more information, | - glm.Study(1).RunTimeVars contains more information, | ||
- To view the design matrix including filters: plot(ztrans(g1.Study(1).RunTimeVars.SDMMatrix)) | - To view the design matrix including filters: plot(ztrans(g1.Study(1).RunTimeVars.SDMMatrix)) | ||
- | - | + | - To get a sense of the correlations between regressors including the filters |
+ | - mdm.Handles.SDMs{1}.PredictorNames' | ||
+ | - plot(mdm.Handles.SDMs{1}.SDMMatrix) :this plots the matrix. | ||
+ | - corrcoef(mdm.Handles.SDMs{1}.SDMMatrix(:, | ||
+ | ===== Visualization Tools ===== | ||
+ | |||
+ | ==== Scripting Output Displays ==== | ||
+ | For outputting many slices for several maps, this section describes how to script the output visuals at the command line. | ||
+ | |||
===== Using the surface rendering ===== | ===== Using the surface rendering ===== | ||
- load up your VMP and threshold it (height threshold) | - load up your VMP and threshold it (height threshold) | ||
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- then, repeat these steps with the hiresolution ones for figures | - then, repeat these steps with the hiresolution ones for figures | ||
- smoothing values (as a starting suggestion) for hires: 400 iterations, and force between .4 and .6 | - smoothing values (as a starting suggestion) for hires: 400 iterations, and force between .4 and .6 | ||
- | |||
===== Using the voxel rendering ===== | ===== Using the voxel rendering ===== | ||
- load up your VMP and threshold it (height and cluster threshold) | - load up your VMP and threshold it (height and cluster threshold) | ||
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- Starting with your meta-analysis in an xls sheet... | - Starting with your meta-analysis in an xls sheet... | ||
- The header row must have: Study, x, y, z | - The header row must have: Study, x, y, z | ||
+ | - Study must be a string... to ensure this, add a dummy string prior to each study name e.g. ' | ||
- It should also have: CoordSys with " | - It should also have: CoordSys with " | ||
- The header row may optionally and usefully have: N (nr of subjects), FixedRandom (0/1 coding of FFX or RFX) | - The header row may optionally and usefully have: N (nr of subjects), FixedRandom (0/1 coding of FFX or RFX) | ||
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- If a column has ' | - If a column has ' | ||
- Make sure you have a place holder for empty cells or missing values (and not NaNs unless it's a numeric column). E.g. " | - Make sure you have a place holder for empty cells or missing values (and not NaNs unless it's a numeric column). E.g. " | ||
+ | - Do not have any empty columns included. | ||
- Now save your data as a .csv file from Excel. | - Now save your data as a .csv file from Excel. | ||
- Rename as a .txt file. | - Rename as a .txt file. | ||
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- clear the object between analyses. glm.ClearObject; | - clear the object between analyses. glm.ClearObject; | ||
- ' | - ' | ||
+ | |||
+ | A second potential error when making a glm is to not set the options structure correctly. Specifically, | ||
+ | - CORRECT: struct(' | ||
+ | - INCORRECT: struct(' | ||
+ | - where rps is a cell array pointing to files name for realignment parameters | ||
+ | |||
+ | |||
+ | |||
+ | ==== Exporting files to SPM, FSL, etc. ==== | ||
+ | Neuroelf has export functions in the gui menus. | ||
+ | The default options for some of these didn't work well for FSLview. | ||
+ | To deal with this, be sure to use a recent version of NeuroElf (12/12/12 or later). | ||
+ | - vmp.ExportNifti(' | ||
+ | - where the second argument, true, makes the output be written in TAL order, such that it complies with FSL's internal preference. | ||
+ | - also works with vtc, glm, etc. | ||
+ | |||
+ |
ajay/neuroelf_usage_notes.txt · Last modified: 2016/08/03 20:36 by ajay