All SAM analyses are directed by a named parameter file. The format of this file is plain text and can be created using any convenient text editor (i.e., vi, gedit, emacs, etc.). The parameter file is normally specified with -m.
Parameters may be abbreviated, and except where noted may also be specified on the command line prefixed with two dashes, e.g., --DataSet. Some also have single dash option flags (-h), and some (e.g., ds) may be specified as exported environment variables.
Parameters specified on the command line or environment override those specified in a parameter file. If command line options are used, the actual parameters that the analysis was run with will be reconstituted as a .param file in the SAM director of the MEG dataset.
Finally, you may also define an optional parameter file called .samrc in the directory from which the program is run, which may contain parameters common to a set of analyses.
Input and Output File Specification
Specifies the MEG dataset to be analyzed. Specified on the command line using -r.
Only used for 4D/BTi datasets, this specifies the additional PDF file. Specified on the command line using -d.
Specifies the MRI directory root. May also be specified on the command line with -i. Several files, derived from a participant’s anatomical MRI, are used in SAM analysis. By default, they are found using this directory and the MRIPattern. The files are the subject’s hull and ortho-space MRI transformed to Talairach space. By default, these are named hull.shape and ortho+tlrc. If MRIDirectory is absent, the SAM programs will look for hull.shape in the dataset directory. There is no default for the ortho MRI. If the Talairach transform is applied to the SAM beamformer coefficients, this is a required parameter. Using MRIDirectory is highly recommended in applications for batch analysis of large numbers of datasets and especially for using AFNI for group statistics.
This parameter controls how the input MRI files are assumed to be named. The default MRIPattern is %M/%P/%s, where %M represents the MRI root directory, %P is the first N characters of the dataset name (the length of the string can be designated with the PrefixLength parameter), and %s is replaced by the actual file name. Thus, under the default behavior, for dataset ABABABAB_rest_01.ds the hull.shape file would be assumed to have the following path:
Other variables which can be used are: %H (hashcode) is the first “_” delimited field of the dataset name, %S is the study type (2nd field of dataset name), %D is the date (3rd field), and %R is the run number (4th field).
This parameter controls the number of characters at the beginning of the MEG dataset that are used to find the required MRI files, and to name the output files. The default is 8, according to the NIH convention for naming MEG datasets with an 8 digit hash code. For sites having a different number of characters for identifying datasets and participants, PrefixLength can be used.
If present, this parameter specifies the directory where the NIFTI image files are written. If absent, the images are written to the SAM subdirectory inside the dataset. This option is highly recommended for batch analysis of large numbers of datasets and for using AFNI for group statistics.
N specifies the number of unique event marker names found in the dataset that are to be used in the analysis. Up to six markers are currently permitted. If no named markers are present, the user must specify zero markers for analyses that are not state dependent. The sam_cov and sam_wts programs, together, read this parameter and parse the dataset for the named markers, generating up to six named SAM beamformer coefficient files plus Global and Sum coefficients.
MarkerN MARKNAME T0 T1 TRUE|FALSE
Specify Marker1 through Marker6. The numbered markers must be sequential and agree with NumMarkers. Each MarkerN parameter requires four arguments: the marker name, the start and end time relative to this marker (in seconds) and a flag (TRUE or FALSE) indicating whether this marker is to be included in the SUM covariance and beamformer coefficients. Markers cannot be specified on the command line. For example:
NumMarkers 3 Marker1 9r0 -0.25 0.25 TRUE Marker2 9r1 -0.25 0.25 TRUE Marker3 9r2 -0.25 0.25 TRUE
Image Coordinate Space Specification
XBounds START END YBounds START END ZBounds START END
Required for sam_wts — except when using an Atlas or a target list (-t option to sam_wts). In the latter case, the image bounds are determined by the atlas ROI limits. XBounds sets the anterior-posterior ROI dimensions in centimeters, where it is positive in the anterior direction. YBounds sets the left-right ROI dimensions, which is positive in the left direction. ZBounds sets the inferior-superior ROI dimensions, which is positive in the superior direction. Units of START and END are in centimeters
Required for all SAM image analyses — except when using an atlas or a target file list. Units are in centimeters
The hull file estimates the boundary of the inner skull surface, which is used for magnetic field calculations when using the Nolte magnetic field model, and also the boundary of the ROI when using MultiSphere. Voxels in the space defined by XBounds, YBounds, and ZBounds that lie outside the hull are not calculated. Hulls are computed using orthohull.
ImageFormat ORIG|TLRC RES
This parameter controls how the SAM beamformer coefficients (weights) are generated. The default is ORIG in which the weights correspond precisely to the ROI and grid spacing specified by XBounds, YBounds, ZBounds and ImageStep. The beamformer weights are written as a NIFTI file. Designating the ImageFormat as TLRC will apply the AFNI program @auto_tlrc to the beamformer weight file, transforming the weights into the common Talairach space according to the Talairach transform included in the ortho+tlrc MRI image. The transform is applied to each SAM coefficient file. The original weight files are retained and the new transformed file names are appended with _at.nii. The resolution of the transform is specified in millimeters. It is recommended that the original ROI grid spacing (specified by ImageStep) be smaller than the Talairach grid spacing because the transform uses nearest neighbor to move the ortho grid to Talairach grid. The Talairach transform can also be applied to weights derived from an atlas. This parameter cannot be specified on the command line.
Instead of using a rectilinear space of voxels, beam former weights can be calculated for each "voxel" or greyordinate on a cortical surface derived from the MRI using FreeSurfer. The atlas file contains the coordinates and orientation vector for each neocortical location. An atlas may be generated by applying FSnormals.py and reduce_surface.py to the subject’s surface data obtained from FreeSurfer. If present, it overrides the XBounds, YBounds, ZBounds, and ImageStep parameters. The default location of the atlas is set using the MRIDirectory and MRIPattern parameters.
CovBand LO HI
Passband frequencies (in Hz) for filtering the data prior to computing the covariance matrices. A wide bandwidth covariance (much wider than ImageBand) is useful when analyzing MEG data that has very large artifacts, due to tDCS, tACS, or VNS. In such cases, a wide bandwidth will improve rejection of interference by including interference in the beamformer coefficient computations.
ImageBand LO HI
Passband frequencies (in Hz) for SAM imaging. In instances where you may want to use a wider bandwidth for calculation of the covariance matrix to improve rejection of interference, you can still calculate your desired image metric with a more narrow bandwidth.
OrientBand LO HI
A separate covariance matrix, using all data samples is used for estimating the dipole orientation in the two-step scalar SAM beamformer. If OrientBand is present, the data are filtered to this passband prior to computing the Orient.cov file. Otherwise, the Orient.cov file will be identical to Global.cov. This option allows the user to optimize the source orientation by specifying a passband where signal-to-noise is high (such as beta). As a first principle, the source orientation is assumed to be stationary, which is why all data samples are used. For example:
NoiseBand LO HI
The default method of determining mean sensor noise is finding the rank where the first derivative is at a minimum in the covariance eigenvalue spectrum; this yields only a mean noise value for all sensors. In practice, however, there may be differences in individual SQUID sensor noise. This factor becomes important when imaging high frequency power (fast ripples, etc.) where signal-to-noise is marginal. The mean sensor noise will result in a non-uniform SAM image magnitude, which may be misleading. To overcome this limitation, the user can specify a NoiseBand passband that extends well above the frequencies of interest where the MEG signal is (presumably) vanishingly small. The product of the noise covariance with the SAM beamformer coefficients will give the best estimate of the actual projected noise for each voxel.
SmoothBand LO HI
Required for sam_4d, sam_4dc, sam_ersc, and sam_hfo, The lowpass filter (the HI value) is used to smooth the moment, Hilbert envelope of power, excess kurtosis or RVE time-series prior to saving multiple images for each latency. If LO is non-zero, then the metric is bandpass filtered (a highpass other than zero is not recommended).
Enables/disables notch filtering for power mains frequency and its harmonics. The default notch frequencies are based on 60 Hz power mains. This can be switched to 50 Hz using the Hz parameter. The default notch width is 4 Hz (+/- 2 Hz), using a 20th-order FFT filter. Up to 6 notch frequencies are used. Usually this is done by filtering the raw dataset using newDs. Notch filtering is recommended when operating on raw datasets.
CovType determines which covariance file(s) and SAM beamformer coefficients will be used in an analysis. GLOBAL selects a covariance matrix based on all dataset samples, without regard to markers or data segments. If NumMarkers is greater than zero, the SAM beamformer coefficients are computed for each marker using the GLOBAL covariance. SUM selects a covariance matrix based on the data corresponding to the designated marker segments. It can only be computed if NumMarkers is greater than zero. The beamformer coefficients are computed from the covariance of the sum over the designated markers. Note that there is a flag, TRUE or FALSE, on the line giving the numbered markers. If TRUE, then that marker’s data segments are included in the computation of the sum covariance. ALL selects independent covariance matrices for each marker. The beamformer coefficients for each marker are computed from the corresponding covariance matrices. Although contrast may be improved by selecting ALL, this may also introduce a bias in the results due to the differences in beamformer coefficients for each marker condition. Note, the CovType parameter does not affect the computation of the covariance matrices or the beamformer coefficients — only which of them is used for the analyses. All are computed by sam_cov.
Model SingleSphere x y z | MultiSphere | Nolte
This parameter is used to select the conductive model needed for computation of the forward solution for sam_wts and sam_Nwts There are three available. SingleSphere uses a single homogeneously conducting sphere to approximate the brain (using the Sarvas equation) with the origin specified by the x, y, z coordinates, in centimeters. The MultiSphere model creates a best-fit sphere for each primary sensor. If this model is specified, SAM will look for a compatible default.hdm (or hs_file for 4D/BTi MEG) in the MEG data directory. The Nolte model is a realistic head model. Both MultiSphere and Nolte require the hull.shape file located in the participant’s MRI directory or in the MEG dataset.
This parameter is used to specify the regularization applied, in . Prior to the computation of the SAM weights, the diagonal of the covariance matrices may be adjusted. This is a form of “whitening”, since increasing the magnitude of the diagonal of the covariance has the same effect as adding white (uncorrelated) noise to the data. Regularization is used when the MEG data are noisy, or contain linear dependencies that may be introduced by (for example) ICA pre-processing. Regularization is frequently used in connectivity analyses. Regularization will decrease the resolution of the beamformed images. See also regularization
In detail, for additive MU, the specified value is squared, and multiplied by the scaled (to Tesla) bandwidth:
while for multiplicative MU, the specified value multiplies the covariance diagonal directly.
This is required only for ste_ers - calculation of transfer entropy. The marker name designates the epochs or blocks to be processed for computation of the covariance matrix and probability distribution functions (PDFs).