====== 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!