Mariela,
I've pooled a list (although it might not be exhaustive and
repetitious in place). Its compiled from the help sections of
spm_spm.m, spm_fmri_spm_ui.m, and spm_spm_ui.m.
% Required fields of SPM:
%
% xY.VY - nScan x 1 struct array of image handles (see spm_vol)
% Images must have the same orientation, voxel size and data type
% - Any scaling should have already been applied via the image handle
% scalefactors.
%
% xX - Structure containing design matrix information
% - Required fields are:
% xX.X - Design matrix (raw, not temporally smoothed)
% xX.name - cellstr of parameter names corresponding to columns
% of design matrix
% - Optional fields are:
% xX.K - cell of session-specific structures (see spm_filter)
% - Design & data are pre-multiplied by K
% (K*Y = K*X*beta + K*e)
% - Note that K should not smooth across block boundaries
% - defaults to speye(size(xX.X,1))
% xX.W - Optional whitening/weighting matrix used to give
% weighted least squares estimates (WLS). If not specified
% spm_spm will set this to whiten the data and render
% the OLS estimates maximum likelihood
% i.e. W*W' = inv(xVi.V).
%
% xVi - Structure describing intrinsic temporal non-sphericity
% - Required fields are:
% xVi.Vi - array of non-sphericity components
% - defaults to {speye(size(xX.X,1))} - i.i.d.
% - specifying a cell array of constraints (Qi)
% These constraints invoke spm_reml to estimate
% hyperparameters assuming V is constant over voxels.
% that provide a high precise estimate of xX.V
% - Optional fields are:
% xX.V - Optional non-sphericity matrix. Cov(e) = sigma^2*V
% If not specified spm_spm will compute this using
% a 1st pass to identify significant voxels over which
% to estimate V. A 2nd pass is then used to re-estimate
% the parameters with WLS and save the ML estimates
% (unless xX.W is already specified).
%
% xM - Structure containing masking information, or a simple column vector
% of thresholds corresponding to the images in VY [default: -Inf]
% - If a structure, the required fields are:
% xM.TH - nVar x nScan matrix of analysis thresholds, one per image
% xM.I - Implicit masking (0=>none, 1 => implicit zero/NaN mask)
% xM.VM - struct array of explicit mask image handles
% - (empty if no explicit masks)
% - Explicit mask images are >0 for valid voxels to assess.
% - Mask images can have any orientation, voxel size or data
% type. They are interpolated using nearest neighbour
% interpolation to the voxel locations of the data Y.
% - Note that voxels with constant data (i.e. the same value across
% scans) are also automatically masked out.
%
% swd - Directory where the output files will be saved [default: pwd]
% If exists, it becomes the current working directory.
% creates SPM with the following fields
%
% xY: [1x1 struct] - data structure
% nscan: [double] - vector of scans per session
% xBF: [1x1 struct] - Basis function structure (see spm_fMRI_design)
% Sess: [1x1 struct] - Session structure (see spm_fMRI_design)
% xX: [1x1 struct] - Design matrix structure (see spm_fMRI_design)
% xGX: [1x1 struct] - Global variate structure
% xVi: [1x1 struct] - Non-sphericity structure
% xM: [1x1 struct] - Masking structure
% xsDes: [1x1 struct] - Design description structure
%
%
% SPM.xY
% P: [n x ? char] - filenames
% VY: [n x 1 struct] - filehandles
% RT: Repeat time
%
% SPM.xGX
%
% iGXcalc: {'none'|'Scaling'} - Global normalization option
% sGXcalc: 'mean voxel value' - Calculation method
% sGMsca: 'session specific' - Grand mean scaling
% rg: [n x 1 double] - Global estimate
% GM: 100 - Grand mean
% gSF: [n x 1 double] - Global scaling factor
%
% SPM.xVi
% Vi: {[n x n sparse]..} - covariance components
% form: {'none'|'AR(1)'} - form of non-sphericity
%
% SPM.xM
% T: [n x 1 double] - Masking index
% TH: [n x 1 double] - Threshold
% I: 0
% VM: - Mask filehandles
% xs: [1x1 struct] - cellstr description
% Variables saved in the SPM stucture
%
% xY.VY - nScan x 1 struct array of memory mapped images
% (see spm_vol for definition of the map structure)
% xX - structure describing design matrix
% xX.D - design definition structure
% (See definition in main body of spm_spm_ui.m)
% xX.I - nScan x 4 matrix of factor level indicators
% I(n,i) is the level of factor i corresponding to image n
% xX.sF - 1x4 cellstr containing the names of the four factors
% xX.sF{i} is the name of factor i
% xX.X - design matrix
% xX.xVi - correlation constraints for non-spericity correction
% xX.iH - vector of H partition (condition effects) indices,
% identifying columns of X correspoding to H
% xX.iC - vector of C partition (covariates of interest) indices
% xX.iB - vector of B partition (block effects) indices
% xX.iG - vector of G partition (nuisance variables) indices
% xX.name - p x 1 cellstr of effect names corresponding to columns
% of the design matrix
%
% xC - structure array of covariate details
% xC(i).rc - raw (as entered) i-th covariate
% xC(i).rcname - name of this covariate (string)
% xC(i).c - covariate as appears in design matrix (after any scaling,
% centering of interactions)
% xC(i).cname - cellstr containing names for effects corresponding to
% columns of xC(i).c
% xC(i).iCC - covariate centering option
% xC(i).iCFI - covariate by factor interaction option
% xC(i).type - covariate type: 1=interest, 2=nuisance, 3=global
% xC(i).cols - columns of design matrix corresponding to xC(i).c
% xC(i).descrip - cellstr containing a description of the covariate
%
% xGX - structure describing global options and values
% xGX.iGXcalc - global calculation option used
% xGX.sGXcalc - string describing global calculation used
% xGX.rg - raw globals (before scaling and such like)
% xGX.iGMsca - grand mean scaling option
% xGX.sGMsca - string describing grand mean scaling
% xGX.GM - value for grand mean (/proportional) scaling
% xGX.gSF - global scaling factor (applied to xGX.rg)
% xGX.iGC - global covariate centering option
% xGX.sGC - string describing global covariate centering option
% xGX.gc - center for global covariate
% xGX.iGloNorm - Global normalisation option
% xGX.sGloNorm - string describing global normalisation option
%
% xM - structure describing masking options
% xM.T - Threshold masking value (-Inf=>None,
% real=>absolute, complex=>proportional (i.e. times global) )
% xM.TH - nScan x 1 vector of analysis thresholds, one per image
% xM.I - Implicit masking (0=>none, 1=>implicit zero/NaN mask)
% xM.VM - struct array of explicit mask images
% (empty if no explicit masks)
% xM.xs - structure describing masking options
% (format is same as for xsDes described below)
%
% xsDes - structure of strings describing the design:
% Fieldnames are essentially topic strings (use "_"'s for
% spaces), and the field values should be strings or cellstr's
% of information regarding that topic. spm_DesRep.m
% uses this structure to produce a printed description
% of the design, displaying the fieldnames (with "_"'s
% converted to spaces) in bold as topics, with
% the corresponding text to the right
%
% SPMid - String identifying SPM and program versions
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Postdoctoral Research Fellow, GRECC, Bedford VA
Website: http://www.martinos.org/~mclaren
Office: (773) 406-2464
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On Thu, Sep 13, 2012 at 10:57 AM, Mariela Hidalgo
<[log in to unmask]> wrote:
> Dear SPMers:
>
> I have a question about the variables that the SPM. mat file has integrate
> (e.g. xY, xGx, SPMid, VBeta, etc.).
> Do you have any information about each variable and what mean each one of
> them?
>
> I hope any answer from you.
> Regards,
>
> --
> Mariela Hidalgo
> Biomedical Engineering student
> Universidad de Valparaíso,
> Chile
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