BrainVoyager Support

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Please keep in mind the toolbox is still under (ongoing) development, and the author has (unfortunately...) put more emphasis on the implementation of features than writing a truly comprehensive BVQXtools Documentation.

However, for objects of a given type, help is available via a method list. This can be produced (displayed) with a call to .Help on any valid object:

glm = BVQXfile('new:glm');

ans =

GLM::FillMissingVertices - fill missing vertices in beta maps / Syntax: [glm] = glm.FillMissingVertices(srf [, method])
GLM::JoinFFX - join two fixed effects GLMs / Syntax: combined = glm1.JoinFFX(glm2);
GLM::JoinRFX - join two random effects GLMs / Syntax: combined = glm1.JoinRFX(glm2);
GLM::MapNames - return a cell array (Nx1) of map names / Syntax: mnames = glm.MapNames;
GLM::PlotBetas - plot betas / Syntax: [ax] = glm.PlotBetas(c [, plotopts])
GLM::PSCMaps - calculate PSC maps for single study GLM / Syntax: pscmap = glm.PSCMaps;
GLM::RemoveSubject - remove subject(s) from GLM / Syntax: [glm = ] glm.RemoveSubject(sid);
GLM::RFX_RemovePredictors - removes predictors for each subject / Syntax: [glm] = glm.RFX_RemovePredictors(removep)
GLM::RFX_rMap - calculate a second-level r contrast map / Syntax: map = glm.RFX_rMap(c, r [, mapopts])
GLM::RFX_tMap - calculate a t contrast map / Syntax: map = glm.RFX_tMap([c, mapopts])
GLM::SingleStudy_tMap - calculate a t contrast map / Syntax: map = glm.SingleStudy_tMap([c, mapopts])
GLM::SubjectPredictors - return list of subject predictor names / Syntax: spreds = glm.SubjectPredictors;
GLM::Subjects - return list of subjects of multi-subject GLM / Syntax: subjects = glm.Subjects;
GLM::VOIBetas - returns a table of VOI betas (per subjects) / Syntax: vb = glm.VOIBetas(voi [, opts]);
GLM::WriteAnalyzeBetas - write beta images as Analyze / Syntax: glm.WriteAnalyzeBetas(tfolder [, cv [, aformat]])
GLM::WriteAnalyzeVols - write Analyze images from a GLM file / Syntax: [success = ] glm.WriteAnalyzeVols(cons)

And, naturally, the details are still accessible with specifying the method in question:


ans =

GLM::VOIBetas - returns a table of VOI betas (per subjects)

FORMAT: vb = glm.VOIBetas(voi [, opts]);

Input fields:

voi VOI object
opts optional settings
.c contrasts (defaults: each beta map on its own)
.interp either of {'nearest'}, 'linear', 'cubic'
.rmean remove mean (of map) first
.vl indices of VOIs in object to sample (default: all)

Output fields:

vb SxCxV double table with data

A complete list of all available methods (on all object types) can be produced by a call to Help using the root object:

root = BVQXfile;