SPM Data Structures

SPM has really helpful help files! I suggest reading the below:

• help spm_spm

• help spm_filter

• help spm_vol

• help spm_FcUtil.m

• help spm_fmri_spm_ui.m

• help spm_spm_ui.m

• help spm_fMRI_design.m

• SPM.xY.RT - TR length (RT ="repeat time")

• SPM.xY.P - matrix of file names

• SPM.xY.VY - # of runs x 1 struct array of mapped image volumes (.img file info)

• SPM.modality - the data you're using (PET, FMRI, EEG)

• SPM.stats.[modality].UFp - critical F-threshold for selecting voxels over which the non-sphericity is estimated (if required) [default: 0.001]

• SPM. stats.maxres - maximum number of residual images for smoothness estimation

• SPM. stats.maxmem - maximum amount of data processed at a time (in bytes)

• SPM.SPMid - version of SPM used

• SPM.swd - directory for SPM.mat and img files. default is pwd

• SPM.xBF.name - name of basis function

• SPM.xBF.length - length in seconds of basis

• SPM.xBF.order - order of basis set

• SPM.xBF.T - number of subdivisions of TR

• SPM.xBF.T0 - first time bin (see slice timing)

• SPM.xBF.UNITS - options: 'scans'|'secs' for onsets

• SPM.xBF.Volterra - order of convolution

• SPM.xBF.dt - length of time bin in seconds

• SPM.xBF.bf - basis set matrix

User-specified covariates/regressors (e.g. motion)

• SPM.Sess([sesssion]).C.C - [nxc double] regressor (c#covariates,n#sessions)

• SPM.Sess([sesssion]).C.name - names of covariates

Conditions & modulators specified - i.e. input structure array

• SPM.Sess([sesssion]).U(condition).dt: - time bin length {seconds}

• SPM.Sess([sesssion]).U(condition).name - names of conditions

• SPM.Sess([sesssion]).U(condition).ons - onset for condition's trials

• SPM.Sess([sesssion]).U(condition).dur - duration for condition's trials

• SPM.Sess([sesssion]).U(condition).u - (t x j) inputs or stimulus function matrix

• SPM.Sess([sesssion]).U(condition).pst - (1 x k) peri-stimulus times (seconds)

Parameters/modulators specified

• SPM.Sess([sesssion]).U(condition).P - parameter structure/matrix

• SPM.Sess([sesssion]).U(condition).P.name - names of modulators/parameters

• SPM.Sess([sesssion]).U(condition).P.h - polynomial order of modulating parameter (order of polynomial expansion where 0none)

• SPM.Sess([sesssion]).U(condition).P.P - vector of modulating values

• SPM.Sess([sesssion]).U(condition).P.P.i - sub-indices of U(i).u for plotting

Scan indices for sessions

• SPM.Sess([sesssion]).row

Effect indices for sessions

• SPM.Sess([sesssion]).col

F Contrast information for input-specific effects

• SPM.Sess([sesssion]).Fc

• SPM.Sess([sesssion]).Fc.i - F Contrast columns for input-specific effects

• SPM.Sess([sesssion]).Fc.name - F Contrast names for input-specific effects

• SPM.nscan([session]) - number of scans per session (or if e.g. a t-test, total number of con*.img files)

SPM.xGX.iGXcalc - either "none" or "scaling." for fMRI usually is "none" (no global normalization). if global normalization is "Scaling", see spm_fmri_spm_ui for parameters that will then appear under SPM.xGX.

• SPM.xX.X - Design matrix (raw, not temporally smoothed)

• SPM.xX.name - cellstr of parameter names corresponding to columns of design matrix

• SPM.xX.I - nScan x 4 matrix of factor level indicators. first column is the replication number. other columns are the levels of each experimental factor. SPM.xX.iH - vector of H partition (indicator variables) indices

• SPM.xX.iC - vector of C partition (covariates) indices

• SPM.xX.iB - vector of B partition (block effects) indices

• SPM.xX.iG - vector of G partition (nuisance variables) indices

• SPM.xX.K - cell. low frequency confound: high-pass cutoff (secs)

• SPM.xX.K.HParam - low frequency cutoff value

• SPM.xX.K.X0 - cosines (high-pass filter)

• SPM.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).

• SPM.xX.xKXs - space structure for K*W*X, the 'filtered and whitened' design matrix

• SPM.xX.xKXs.X - Mtx - matrix of trials and betas (columns) in each trial

• SPM.xX.xKXs.tol - tolerance

• SPM.xX.xKXs.ds - vectors of singular values

• SPM.xX.xKXs.u - u as in X u*diag(ds)*v'

• SPM.xX.xKXs.v - v as in X u*diag(ds)*v'

• SPM.xX.xKXs.rk - rank

• SPM.xX.xKXs.oP - orthogonal projector on X

• SPM.xX.xKXs.oPp - orthogonal projector on X'

• SPM.xX.xKXs.ups - space in which this one is embedded

• SPM.xX.xKXs.sus - subspace

• SPM.xX.pKX - pseudoinverse of K*W*X, computed by spm_sp

• SPM.xX.Bcov - xX.pKX*xX.V*xX.pKX - variance-covariance matrix of parameter estimates (when multiplied by the voxel-specific hyperparameter ResMS of the parameter estimates (ResSS/xX.trRV ResMS) )

• SPM.xX.trRV - trace of R*V

• SPM.xX.trRVRV - trace of RVRV

• SPM.xX.erdf - effective residual degrees of freedom (trRV^2/trRVRV)

• SPM.xX.nKX - design matrix (xX.xKXs.X) scaled for display (see spm_DesMtx('sca',... for details) SPM.xX.sF - cellstr of factor names (columns in SPM.xX.I, i think) SPM.xX.D - struct, design definition SPM.xX.xVi - correlation constraints (see non-sphericity below) SPM.xC - struct. array of covariate info

• SPM.P - a matrix of filenames

• SPM.V - a vector of structures containing image volume information.

• SPM.V.fname - the filename of the image.

• SPM.V.dim - the x, y and z dimensions of the volume.

• SPM.V.dt - a 1x2 array. First element is datatype (see spm_type). The second is 1 or 0 depending on the endian-ness.

• SPM.V.mat - a 4x4 affine transformation matrix mapping from voxel coordinates to "real world" coordinates.

• SPM.V.pinfo - plane info for each plane of the volume, as follows:

• SPM.V.pinfo(1,:) - scale for each plane.

• SPM.V.pinfo(2,:) - offset for each plane. note: the true voxel intensities of the jth image are given by: val*V.pinfo(1,j) + V.pinfo(2,j)

• SPM.V.pinfo(3,:) - offset into image (in bytes).

NOTE: If the size of pinfo is 3x1, then the volume is assumed to be contiguous and each plane has the same scalefactor and offset.

• SPM.xVi.I - typically the same as SPM.xX.I SPM.xVi.h - hyperparameters

• SPM.xVi.V xVi.h(1)*xVi.Vi{1} + ...

• SPM.xVi.Cy - spatially whitened <Y*Y'> (used by ReML to estimate h)

• SPM.xVi.CY - <(Y - <Y>)*(Y - <Y>)'>(used by spm_spm_Bayes)

• SPM.xVi.Vi - array of non-sphericity components

  • defaults to {speye(size(xX.X,1))} - i.ii.d.

  • specifying a cell array of contraints ((Qi)

  • These contraints invoke spm_reml to estimate hyperparameters assuming V is constant over voxels that provide a high precise estimate of xX.V

• SPM.xVi.form - form of non-sphericity (either 'none' or 'AR(1)')

• SPM.xX.V - Optional non-sphericity matrix. CCov(e)sigma^2*V.

  • If not specified spm_spm will compute this using a 1st pass to identify signifcant 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)

SPM.K - filter matrix or filtered structure:

• SPM.K(s) - struct array containing partition-specific specifications

• SPM.K(s).RT - observation interval in seconds

• SPM.K(s).row - row of Y constituting block/partitions

• SPM.K(s).HParam - cut-off period in seconds

• SPM.K(s).X0 - low frequencies to be removed (DCT)

• SPM.Y - filtered data matrix

• SPM.xM - Structure containing masking information, or a simple column vector of thresholds corresponding to the images in VY.

• SPM.xM.T - [n x 1 double] - Masking index

• SPM.xM.TH - nVar x nScan matrix of analysis thresholds, one per image

• SPM.xM.I - Implicit masking (0 --> none; 1 --> implicit zero/NaN mask)

• SPM.xM.VM - struct array of mapped explicit mask image volumes

• SPM.xM.xs - [1x1 struct] cellstr description

• SPM.xsDes.Basis_functions - type of basis function

• SPM.xsDes.Number_of_sessions

• SPM.xsDes.Trials_per_session

• SPM.xsDes.Interscan_interval

• SPM.xsDes.High_pass_Filter

• SPM.xsDes.Global_calculation

• SPM.xsDes.Grand_mean_scaling

• SPM.xsDes.Global_normalisation

• SPM.xVol - structure containing details of volume analyzed

• SPM.xVol.M- 4x4 voxel --> mm transformation matrix

• SPM.xVol.iM - 4x4 mm --> voxel transformation matrix

• SPM.xVol.DIM - image dimensions - column vector (in voxels)

• SPM.xVol.XYZ - 3 x S vector of in-mask voxel coordinates

• SPM.xVol.S- Lebesgue measure or volume (in voxels)

• SPM.xVol.R- vector of resel counts (in resels)

• SPM.xVol.FWHM - Smoothness of components - FWHM, (in voxels)

• SPM.Vbeta - struct array of beta image handles

• SPM.Vbeta.fname - beta img file names

• SPM.Vbeta.descrip - names for each beta file

• SPM.VResMS - file struct of ResMS image handle

• SPM.VResMS.fname - variance of error file name

• SPM.VM - file struct of Mask image handle

• SPM.VM.fname - name of mask img file

• SPM.xCon - Contrast definitions structure array

  • (see also spm_FcUtil.m for structure, rules &handling)

• SPM.xCon.name - Contrast name

• SPM.xCon.STAT - Statistic indicator character ('T', 'F' or 'P')

• SPM.xCon.c - Contrast weights (column vector contrasts)

• SPM.xCon.X0 - Reduced design matrix data (spans design space under Ho)

  • Stored as coordinates in the orthogonal basis of xX.X from spm_sp

  • (Matrix in SPM99b)

  • Extract using X0 spm_FcUtil('X0',...

• SPM.xCon.iX0 - Indicates how contrast was specified:

  • If by columns for reduced design matrix then iX0 contains the column indices.

  • Otherwise, it's a string containing the spm_FcUtil 'Set' action: Usually one of {'c','c+','X0'} defines the indices of the columns that will not be tested. Can be empty.

• SPM.xCon.X1o - Remaining design space data (X1o is orthogonal to X0)

  • Stored as coordinates in the orthogonal basis of xX.X from spm_sp (Matrix in SPM99b) Extract using X1o spm_FcUtil('X1o',...

• SPM.xCon.eidf - Effective interest degrees of freedom (numerator df)

  • Or effect-size threshold for Posterior probability

• SPM.xCon.Vcon - Name of contrast (for 'T's) or ESS (for 'F's) image

• SPM.xCon.Vspm - Name of SPM image