Roi wts

Return to Source Localization - SAM

Description

The purpose of roi_wts is to leverage the orientation information from the FreeSurfer parcellation of the brain when solving for the beamformer weights. Normally, in a SAM analysis, the orientations calculated by solving the linear eigensystem equations and choosing the vector that gives the largest S/N. In roi_wts, the cortical normal vector is used instead. It is of paramount importance that the registration of the MEG and MRI are accurate. Thus, this program should only be run if you have already run sam_coreg. Obviously, one must have run FreeSurfer before running this program. This program also makes use of whatever parcellation that you have requested FreeSurfer use to annotate your surface.

roi_wts uses the Nolte (realistic) head model, and will ignore other specifications of the Model parameter. By default, a 20th order harmonic expansion of the hull is used. This program represents the signal within the entire ROI using a set of basis functions, and is likely to produce greater signal to noise for the patch than a simple mean.

Usage

 roi_wts -r <dataset_name> -m <parameter_file_name> [options]

The -r flag designates the dataset name (with or without the .ds suffix), and -m designates the parameter file name.

Other options:

 -v  Verbose mode, without this flag sam_wts works silently except for error messages
 -h  Show help

Required Parameters (in the .param file or on the command line):

 CovBand: bandpass for covariance matrix calculation
 Transform: Specifies transform written by sam_coreg
 Atlas: Specifies the file containing the list of cortical normals in each ROI
 ROIList: Specifies the file containing the list of ROIs to be estimated 
 Model: Nolte is required. Other specifications will be ignored.

Optional Parameters:

 MRIDirectory: Specify where to locate the necessary MRI files
 PrefixLength: Specify characters used for identifying MRI and headmodel files.

Output Files

roi_wts creates a log file, containing the rank, number of vertices used, source power, noise power, and s/n. In addition, a .nii file containing the weights is saved, such that there will be a single "voxel" in the X and Y directions, and the Z direction will encode the ROI. The tie dimension encodes the MEG Channels.