Create Surface-Based Statistical Parametric Maps
Create Surface-Based Statistical Parametric MapsThis process fits a General Linear Model on fMRI data which has previously been converted in a surface-based representation (see Projection of fMRI data onto the cortical surface using convolution kernels). It results in a surface-based SPMt map, according to a given set of regressors and contrast. The process can take deal with several subjects at the same time but it does not compute a group map, instead it computes one individual map per subject.
boldtextures: ListOf ( input )The boldtextures represent the surface-based representation of fMRI BOLD volumes. In other terms, it is a set of textures (one per subject) representing fMRI data projected onto the cortical surface (see Create Surface-Based Functional Data)
protocolfile: Text file ( input )protocol_text is a Python script defining a set of variables.
TR is the repetition time in ms (ex : TR = 2400)
times is a numpy array containing a sequence of timestamps in ms (the starting time of each stimulus)
types is a numpy array defining the type of each stimulus (by an integer index)
names is a list of strings, giving a name for each stimulation type
An example file can be downloaded following this link
contrast: String ( input )a contrast is defined by a vector of integers, according to the protocol described in protocol_text text file.
betamaps: ListOf ( input )betamaps is a set of maps, containing the beta values estimated from the GLM.
spmtmaps: ListOf ( input )spmtmaps is a set of activation (SPMt) maps, assigning each node with a t-test value from the GLM.
Toolbox : Cortical Surface
User level : 0
Identifier :
CreateSurfaceBasedSPMtMapsFile name :
brainvisa/toolboxes/cortical_surface/processes/functional/CreateSurfaceBasedSPMtMaps.pySupported file formats :
protocolfile :Text file