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+ | ====== GLM::SingleStudy_tMap ====== | ||
+ | ===== Motivation ===== | ||
+ | After running the regression for a single study (run, i.e. one FMR, VTC, or MTC object), creating t-contrast maps for just this study can be beneficial in determining whether or not a certain run contains too much noise (or specific artefacts) for it to be included in second-level statistics. Also, it is sometimes desired to use the actually standard-error-normalized effect-statistic (t instead of regression beta) for further computations (e.g. to incorporate a measure of within-subject-data noise on the second level). | ||
+ | |||
+ | ===== Requirements ===== | ||
+ | You need to have a GLM file/object available that represents the regression outcome of a single run. **Please note that, at this time, the support for FMR (MAP creation) has not been implemented; but as the MAP format lacks a lot of the properties (and thus flexibility) of the VMP format, it is also not suggested other than for very specific application, such as MVPA.** | ||
+ | |||
+ | ===== Method reference ('glm.Help('SingleStudy_tMap')') ===== | ||
+ | <file> GLM::SingleStudy_tMap - calculate a t contrast map | ||
+ | |||
+ | FORMAT: map = glm.SingleStudy_tMap([c, mapopts]) | ||
+ | |||
+ | Input fields: | ||
+ | |||
+ | c NxC contrast vector (default: full model and main eff) | ||
+ | mapopts structure with optional fields | ||
+ | .interp mesh-based interpolation (default: true) | ||
+ | .srf surface file, required for interpolation | ||
+ | |||
+ | Output fields: | ||
+ | |||
+ | map MAP/VMP/SMP object with C maps</file> | ||
+ | |||
+ | ===== Reference notes ===== | ||
+ | **The ''.interp'' option** was intended to cover those cases where in (fairly "old" versions of BrainVoyager QX, 1.7.x), vertex nodes would sometimes lack an appropriate target when a Sphere-to-Sphere-Mapping (SSM) object had been specified. As this bug has been fixed (and a workaround is still available on the SMP side), this option **will be removed in future versions**. | ||
+ | |||
+ | ===== Usage example ===== | ||
+ | Say you have a study with 5 regressors of interest and 1 confound (mean study level, automatically added by BrainVoyager QX/NeuroElf), whereas the conditions of interest are: | ||
+ | |||
+ | * instruction | ||
+ | * motion in left visual field | ||
+ | * motion in right visual field | ||
+ | * motion in both visual fields (at the same time) | ||
+ | * static (trials without any motion) | ||
+ | |||
+ | then the syntax to create a contrast over all conditions sharing motion in any visual field would be coded as: | ||
+ | |||
+ | <code matlab glm_singlestudy_tmap_ex1.m>% load a GLM (only needed if not yet loaded!) | ||
+ | glm = xff('*.glm', 'Please select the single-study GLM...'); | ||
+ | |||
+ | % create the contrast | ||
+ | contrast = glm.SingleStudy_tMap([0;1;1;1;0]); | ||
+ | |||
+ | % name the contrast | ||
+ | contrast.Map.Name = 'Motion in any visual field'; | ||
+ | |||
+ | % save the contrast | ||
+ | contrast.SaveAs; | ||
+ | |||
+ | % clean up | ||
+ | contrast.ClearObject; | ||
+ | glm.ClearObject;</code> | ||
+ | |||
+ | ===== Usage notes ===== | ||
+ | Please note the following details about this method and the example: | ||
+ | |||
+ | * at this time, the name of the contrast(s) cannot be specified but has to be set after the call | ||
+ | * multiple contrasts can be given whereas the number of contrasts is the number of columns in the weights argument | ||
+ | * each contrast should either have its weights be all positive (or all negative, weighted contrast over baseline, or reversed) or sum up to 0 (weighted differential contrast between conditions); in other words, contrasts with weights of different sign where the weights do not sum up to 0 are invalid for direct hypothesis testing! |