Description: SAMNwts is a simultaneous solution for a sparse number of linearly constrained minimum variance (LCMV) beamformer coefficients (SAM weights). Unlike SAMwts SAMNwts can correctly compute coefficients for both correlated and uncorrelated sources. It cannot be applied to a large ROI because each target coordinate costs one degree of freedom from the covariance matrix. For example, a 275-channel MEG has only 275 degrees of freedom with which to attenuate unwanted signal such as "brain noise" and environmental noise.
SAMNwts reads the covariance files within the named dataset for the covariance bandpass (CovBand) and a list of target voxels (specified by the parameter keyword "TargetName". Like SAMwts, SAMNwts is a scalar beamformer in which the optimal source dipole orientation is determined first - followed by [simultaneous] computation of the scalar beamformer coefficients. Source orientation is estimated from a separate orientation covariance matrix (Orient.cov) that uses all data samples and a passband in which signal-to-noise is optimal (see SAMcov). It is assumed that source orientation is stationary, whereas source amplitude may be non-stationary. An "atlas" file containing the position and orientation vectors of [neocortical] sources can be designated in the parameter file. If the atlas file is present, SAMNwts searches for the nearest voxel to each target and bypasses the orientation step by using the predetermined source orientation. In addition, the parameter file can designate a transform used to refine the translation and rotation of the atlas file. Generation of an atlas file requires FreeSurfer, FSnormals.py, and reduce_surface.py. At present, methods for computing the transform file are still under test. Three forward models are available for computing weights that are selectable using the "Model" keyword in that parameter file: 1) a single homogeneously conducting sphere (Sarvas), 2) multiple local spheres, and 3) realistic head model (Nolte).
If desired, the covariance matrix can be regularized by using the "Mu" keyword. If the Mu value is preceded by a "+" then a fixed noise value (units of fT/√Hz) is added to the covariance diagonal. If the Mu value is preceded by a "*" then the covariance diagonal is multiplied by that value. Regularization of the covariance matrix degrades the spatial selectivity of the beamformer coefficients. Use this optional parameter with caution!
Usage:
SAMNwts -r <dataset_name> -m < parameter_file_name> (optional flags) -v
where -r designates the dataset name (with or without the .ds suffix), -m designates the parameter file name without the .param suffix, and -v is "verbose" mode. Without -v, SAMNwts works silently, except for error messages.
Optional flags:
-i <mri_directory_path>: designates an optional pathname for the MRI directory,
taking precedence over the path designated in the parameter file
-Z: normalize SAM weights by projected noise
Parameters & Keywords: The parameters specific to SAMNwts include:
NumMarkers
Marker1
Marker2
...
Marker6
XBounds
YBounds
ZBounds
ImageStep
ImageFormat
TargetName
MRIDirectory
PrefixLength
Atlas
Transform
Mu
Model
Example Parameter File:
NumMarkers | 3 | Marker1 | 9r0 | -0.25 | 0.25 | TRUE | Marker2 | 9r1 | -0.25 | 0.25 | TRUE | Marker3 | 9r2 | -0.25 | 0.25 | TRUE |
CovBand | 4.0 | 100.0 |
ImageBand | 35.0 | 45.0 |
OrientBand | 14.0 | 30.0 |
TargetName | example_target | |
MRIDirectory | WXYZABCD | |
PrefixLength | 8 | |
Model | Nolte | |
CovType | SUM |
Input File Formats:
Notes: