What's in an SPM.mat File?

A while back I attempted to write up a document that summarized everything someone would need to know about using SPM. It was called, I believe, the SPM User's Ultimate Guide: For Alpha Males, By Alpha Males. The title was perhaps a little ambitious, since I stopped updating it after only a few dozen pages. In particular I remember a section where I attempted to tease apart everything contained within the SPM.mat files output after first- and second-level analyses. To me this was the most important section, since complex model setups could be executed fairly easily by someone with a good understanding of Matlab code, but I never completed it.

Fortunately, however, there is someone out there who already vivisected the SPM.mat file and publicly displayed its gruesome remains in the online piazza. Researcher Nikki Sullivan has written an excellent short summary of what each field means, broken down into neat, easily digestible categories. You can find it on her website here, and I have also copied and pasted the information below. It makes an excellent shorthand reference, especially if you've forgotten, for example, where contrast weights are stored in the structure, and don't want to go through the tedium of typing SPM, then SPM.xY, then SPM.xY.VY, and so on.

But if you've forgotten how to rock that body? Girl, ain't no remedy for that.


details on experiment:


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

basis function:


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

Session Stucture:


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
effect indices for sessions
F Contrast information for input-specific effects
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)

global variate/normalization details


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.

design matrix information:


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

header 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.
SPM.V.pinfo(1,:) - scale for each plane
SPM.V.pinfo(2,:) - offset for each plane 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).If the size of pinfo is 3x1, then the volume is assumed to be contiguous and each plane has the same scalefactor and offset.

structure describing intrinsic temporal non-sphericity


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 (used by ReML to estimate h)
SPM.xVi.CY - <(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)

filtering information


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

masking information


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

design information (self-explanatory names, for once) 


SPM.xsDes.Basis_functions - type of basis function

details on scannerdata (e.g. smoothness) 


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)

info on beta files:


SPM.Vbeta - struct array of beta image handles
SPM.Vbeta.fname - beta img file names
SPM.Vbeta.descrip - names for each beta file

info on variance of the error


SPM.VResMS - file struct of ResMS image handle
SPM.VResMS.fname - variance of error file name

info on mask


SPM.VM - file struct of Mask image handle
SPM.VM.fname - name of mask img file

contrast details (added after running contrasts)


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

Introduction to Functional Connectivity

It seems as though everyone these days wants to do something more than just the same old standard mass univariate analyses. And why not? Given the embarrassment of riches we have with any FMRI dataset - literally thousands upon thousands of voxels in just a single image, like some gargantuan godsized Rubik's Cube with as many interlocking blocks as there are grains of sand on the beach - it is too tempting not to. In each dataset - hundreds of thousands of voxels. In each experiment - millions. In a lab - tens of millions. In a university - billions. And on and on. As Jeeves would say: The mind boggles, sir.

With so much data at our disposal, one naturally wants to do more sophisticated analyses and test for more interesting types of interactions; and this impulse becomes even stronger among the neurotic academic who has to make things much more complex then they really have to be. To see whether there are any special affinities between different regions of the brain, as it were. Due to the incredibly high number of connections within it, more complex analysis of the interactions between the signals of the voxels themselves becomes compelling - and one of the ways to do this is through a technique called functional connectivity.

All functional connectivity is, in its most basic form, is calculating the correlation between the timeseries of different voxels. The reference timecourse is determined entirely by the investigator; it can be a single peak voxel within a cluster defined by a contrast, it can be an average timecourse across an entire blob of contiguous voxels, or it can be a voxel chosen on the basis of its anatomical location. However it is chosen, this reference timecourse is compared against every other voxel in the brain, and a correlation coefficient computed to measure the similarity between the timecourses. In other words, does this voxel's timeseries, picked from an arbitrary point on the left side of the brain:

Match up well with another voxel picked from the right side of the brain?

Reference timecourse overlaid in red, above the comparison voxel's timecourse in black.

And this same procedure is applied for every other voxel as well.

However, it is worth noting that the name "Functional Connectivity" is misleading at best. Really what it is is a simple correlation analysis, often more appropriately called a bivariate correlation analysis. There is no connectivity to speak of in this kind of analysis; we merely operate under the assumption voxels showing similar timecourses might - might - be connected somehow. But even this is a somewhat laughable assumption to make. No temporal delay is really considered, and no directionality can be inferred.

With this I come to a conclusion and pronounce my judgment. I condemn the name functional connectivity; I raise against this wretched misnomer the most terrible of all the accusations that an accuser ever uttered. It is, to me, the highest of all conceivable corruptions; it seeks to work the ultimate corruption, the worst possible corruption. The name "functional connectivity" has left nothing untouched by its depravity; it has turned every value into worthlessness, and every truth into a lie, and every integrity into vileness of soul. Let any one dare to speak to me of its "scientific" blessings!

Parasitism is the only practice of those who call it so; with its anemic and "connectivity" ideals, sucking all the blood, all the love, all the hope out of life; the will to negate all reality; the word "connectivity" as the mark of recognition for most subterranean conspiracy ever heard of - against health, beauty, well-being, intellect, graciousness of soul - against life itself. This eternal indictment against the name functional connectivity I shall write upon all walls, wherever walls are to be found - I have letters that even the blind will be able to see.

I call that name the one great curse, the one great intrinsic depravity, for which no means are venomous enough, or secret, subterranean and small enough; I call it the one immortal blemish upon the human race.

Of course, if you do want to do a real connectivity analysis, that requires some more steps. We will get to all of that later; but first things first. Here are some of the basics.

What is Percent Signal Change? And Why are People Afraid of It?

A few years ago when I was writing up my first publication, I was gently reprimanded by a postdoc for creating a figure showing my results in percent signal change. After boxing my ears and cuffing me across the pate, he exhorted me to never do that again; his tone was much the same as parents telling their wayward daughters, recently dishonored, to never again darken their door.

Years later, I am beginning to understand the reason for his outburst; much of the time what we speak of as percent signal change, really isn't. All of the major neuroimaging analysis packages scale the data to compare signal across sessions and subjects, but expressing it in terms of percent signal change can be at best misleading, at worst fatal.

Why, then, was I compelled to change my figure to parameter estimates? Because what we usually report are the beta weights themselves, which are not synonymous with percent signal change. When we estimate a beta weight, we are looking at the amount of scaling to best match a canonical BOLD response to the raw data; a better approximation of true percent signal change would be the fitted response, and not the beta weight itself.

Even then, percent signal change is not always appropriate: recall the term "global scaling." This means comparing signal at each voxel against a baseline average of signal taken from the entire brain; this does not take into consideration intrinsic signal differences between, say, white and grey matter, or other tissue classes that one may encounter in the wilderness of those few cubic centimeters within your skull.

You can calculate more accurate percent signal change; see Gläscher (2009), or the MarsBar documentation.

Not everybody should analyze FMRI data; but if they cannot contain, it is better for one to be like me, and report parameter estimates, than to report spurious percent signal change, and burn.