![]() ![]() For more information, see Atlas Creation and ROI based Analysis. Here, the SPM.mat file of the already estimated statistical design will be used. Optionally estimate results for ROI analysis using Analyze ROIs.Select the results (preferably saved as log-p maps with "Transform SPM-maps" or the TFCE_log maps with the different methods for multiple comparison correction) to display rendering views, slice overlay, and a glassbrain of your results. Optionally use Surface Overlay for visualization of your results.If you are using log-p scaled maps from "Transform SPM-maps" without thresholds or the TFCE_log maps, use the following values as the lower range for the colormap for the thresholding: 1.3 (P<0.05) 2 (P<0.01) 3 (P<0.001). Optionally, you can try Threshold-Free Cluster Enhancement (TFCE) with the SPM.mat file of a previously estimated statistical design.Optionally Transform and Threshold SPM-maps to (log-scaled) p-maps or correlation maps.Estimate the model and finally call Results.For more information, refer to Orthogonality. If you find a considerable correlation between TIV and any other parameter of interest it is advisable to use global scaling with TIV.This threshold can ultimately be increased to 0.2 or even 0.25 if you still notice non-brain areas in your analysis. Select threshold masking with an absolute value of 0.1.Use TIV as covariate (confound) to correct different brain sizes.Use "Flexible factorial" for longitudinal data.Select "Two-sample t-test" or "Multiple regression" or use "Full factorial" for any cross-sectional data.Specify the Basic Models with the smoothed gray or white matter segmentations and check for design orthogonality and sample homogeneity:.Smooth data (recommended start value 6mm 1).Select the gray or white matter segmentations from the first step. Check the data quality with Sample Homogeneity for VBM data (optionally consider TIV and age as nuisance variables).Select the xml-files that are saved in the "report" folder. Get total intracranial volume (TIV) to correct for different brain sizes and volumes.If you have used the longitudinal pipeline, the default segmentations for gray matter are named "mwp1r" or "mwmwp1r" if the longitudinal model for detecting larger changes was selected. The resulting segmentations that can now be used for VBM are saved in the "mri" folder and are named "mwp1" for gray matter and "mwp2" for white matter. Segment data using defaults (use Segment Longitudinal Data for longitudinal data).The guide concludes with additional information about spaces after registration, naming conventions used and other hints.Only the changes are described here - steps such as quality control or smoothing are the same as those described in the basic analysis and are not repeated a second time. Relevant changes to a basic VBM analysis are described here and how these changes can be applied. These cases are longitudinal studies and studies in children or special patient populations. There are some specific cases of VBM analyses, for which the basic analysis workflow has to be adapted.This description should provide all the information necessary to successfully analyze most studies. This is followed by a detailed description of a basic VBM analysis that guides the user step-by-step through the entire process - from preprocessing to contrast selection.Also, a brief overview of the steps of a VBM analysis is given. ![]() This section provides information about downloading and installing the software and starting the Toolbox.
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