From Single Voxels to Statistical Maps
The statistical analysis steps were described in the previous sections for a single voxel's time course. The described GLM analysis is performed independently for the time course of each voxel. Since a typical fMRI data set contains about 100,000 voxels, the statistical analysis is performed 100,000 times using the same design matrix X for each voxel's data. Running a GLM results in a set of estimated beta values attached to each voxel. Likewise, a specified contrast c'bv will be performed using the same contrast vector c for each voxel v, but it will use a voxel's "own" vector of beta values bv to obtain a voxel-specific t and p value. Statistical test results for individual voxels are accumulated in a 3D data set called a statistical parametric map. Here we often use simply the term "map" since the data stored can be produced from more general processes than a statistical test. To visualize a calculate map, the obtained values, e.g. t values, can be shown at the position of each voxel in the original data, which will replace the original intensity values at each voxel (showing anatomical information). A more useful approach shows the statistical values only for those voxels exceeding a specified statistical threshold. This allows visualizing "background" (anatomical) information in large parts of the brain while statistical information is shown only in those regions exhibiting significant signal modulations. While anatomical information is normally visualized using a range of grey values, supra-threshold statistical test values are typically visualized using multiple colours, for example, a red-to-yellow color range for positive values and a green-to-blue color range for negative values. With these colours, a positive (negative) t value just passing a specified threshold would be colored in red (green), while a very high positive (negative) t value would be colored in yellow (blue).
The topics Overlaying Volume Maps and Overlaying Surface Maps show examples of thresholded statistical maps overlaid on normalized (MNI) space anatomical data (VMR-VTC data) and on cortex meshes (SRF-MTC data), respectively. Maps can also be overlaid directly on slice-based (FMR-STC) data. Maps calculated in FMR space, usually from slice time course (STC) data, are called MAPs, maps in VMR space, usually calculated from volume time course (VTC) data, are called VMPs, and maps in cortex SRF space, usually calculated from MTC data, are called SMPs. These map formats can be loaded and saved with corresponding file extensions from respective dialogs.
Calculating and Overlaying Volume Maps
Calculating and Overlaying Surface Maps
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