BrainVoyager's GLM format requires that, across subjects, the regression outputs of multiple sessions must match in the number of vertices. Given that each subject's brain reconstruction (white/gray matter boundary segmentation result) yields meshes with different numbers of vertices, and that the spatial location requires matching, the most typical approach is spherical inflation followed by a vertex-wise matching of a regular, icosahedron surface.
Each of the regular surface's (target) vertices is then mapped to either a source vertex (SSM) or position within a triangle (TSM). At this point, BrainVoyager QX uses vertex-based matching which has two major advantages: computational speed (no interpolation required) and reduced complexity (without the exact shape of the triangle and surrounding vertices, a high-quality interpolation is impossible). Still, it is a trade-off for whether vertex- or triangle-based matching should be used; potentially to be made based on the application (e.g. derivation of smooth, univariate maps as opposed to multi-variate statistics with data “as raw as possible”).
To use this method a “raw” MTC (single-subject VTC sampled at the coordinates of the smoothed, reconstructed mesh of the respective subject's cortical brain hemisphere) as well as the SSM file created either by BrainVoyager or NeuroElf must be available.
MTC::ApplySSM - resample MTC with SSM mapping FORMAT: newmtc = mtc.ApplySSM(ssm) Input fields: ssm Sphere-to-Sphere-Mapping (SSM) object Output fields: newmtc MTC with resampled timecourses
The usage is straight forward. Create and/or load both objects (which must match in the number of source vertices!) and then apply the method and use (and/or save) the outcome as required.