Dear Chiara, Joe, Guillaume, and others,
here is my modification of spm_getSPM for SPM2 (it doesn't have the
optional input for circumventing the GUI input though).
It have tested it previously and it has worked for quite well in the past,
but I would encourage anyone to give it a thorough test.
The way to go about this it to save the T-maps (at a particular threshold)
from both contrast into two images (press 'save') and then make an
(inclusive) conjunction mask of both using ImCalc with the expression:
(i1>0)&(i2>0). This conjunction mask should then correspond to the masked
contrast you get when using my version of spm_getSPM (at the same
thresholds, of course).
If you get any errors, please run spm_getSPM from the Matlab command line
and then save and email me the error messages. I'll then try to fix it.
Best,
Jan
Nosarti, Chiara wrote:
> Dear Jan
> Many thanks I have been struggling with this for ages! I am using SMP2 it would be great if you could send me a compatible version.
> Best wishes
> Chiara
>
> -----Original Message-----
> From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Jan Gläscher
> Sent: 01 March 2007 08:09
> To: [log in to unmask]
> Subject: Re: [SPM] Question rephrased: is it possible to mask from a different statistical model
>
> Dear Chiara,
>
> I ran into a similar problem and I modified the relevant SPM5 function
> (spm_getSPM) to include the feature that you are asking for. If you save the attached function in a directory in the Matlab path (before the SPM distribution), you can go ahead and press 'Results'. When you come to the 'mask with other contrast' section, and you press 'yes', then you can choose between 'current' or 'other' SPM analysis. Then you can select another SPM.mat and a masking contrast.
>
> In addition (and unrelated to your question), my version of spm_getSPM also provides for an struct as an input parameter. In this struct you can specify the information about contrasts, thresholds etc. This way spm_getSPM will run non-interactively (e.g. in a batch mode) and create the usual xSPM struct with the list of voxel exceeding the contrast threshold.
> Please see the comments at the beginning of the function for more on this.
>
> Hope this helps,
> Jan
>
> PS: If you need this for SPM2, let me know and I can email you a version that will run with SPM2.
>
> Guillaume Sescousse wrote:
>> Hi Chiara,
>> I recently run into a similar problem, and the solution I found was to
>> use the PickAtlas toolbox (http://www.fmri.wfubmc.edu/download.htm)
>> which allows you to mask a T-map with any mask of your choice. The
>> only difference with the masking performed by SPM is that you have to
>> create your own mask from the desired contrast beforehand (using the "save"
>> option and then the "ImCalc" function to thresold and binarize your
>> saved image; you will find more helpful details in this post:
>> http://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind04&L=SPM&P=R249758&D=0&I=-3&X=5562383E86C74C74).
>>
>> Hope it helps. All the best
>>
>> Guillaume
>>
>> Nosarti, Chiara a écrit :
>>> Dear list
>>> My question was asked in 1998 and at the time Karl stated that the
>>> contrasts had to come from the same statistical model. Is this still
>>> the case ?
>>>
>>> Best wishes
>>> Chiara Nosarti
>>>
>>>
>>> Date: Tue, 22 Sep 1998 18:03:55 +0100
>>> Reply-To: [log in to unmask] (Karl Friston)
>>> Sender: spm
>>> From: [log in to unmask] (Karl Friston)
>>> Subject: Re: mask from another analysis? (was:Predicted Interval
>>> Analysis
>>>
>>> Dear Ruth,
>>>
>>>
>>>> Dear Karl, May I ask a further question on the subject of
>>>> masks. Does the
>>>>
>>> mask
>>>
>>>> have to come from a contrast created in the very same analysis? Or
>>>> is it possible to "export" a mask from one analysis and use it as a
>>>> mask with another set of scans? Is there a way to save a mask as a
>>>> file
>>>>
>>> for
>>>
>>>> later use?
>>>>
>>> The contrasts have to come from the same statistical model (i.e.
>>> analysis). This is not a mathematical requirement but is required by
>>> the data reduction [to a point list] employed by SPM.
>>>
>>> All the best - Karl
>>>
>>>
>
--
Jan Gläscher, Ph.D. Div. Humanities & Social Sciences
+1 (626) 395-4976 (office) Caltech, Broad Center, M/C 114-96
+1 (626) 395-2000 (fax) 1200 California Blvd
[log in to unmask] Pasadena, CA 91125
function [SPM,xSPM] = spm_getSPM
% computes a specified and thresholded SPM/PPM following parameter estimation
% FORMAT [SPM,xSPM] = spm_getSPM;
%
% xSPM - structure containing SPM, distribution & filtering details
% .swd - SPM working directory - directory containing current SPM.mat
% .title - title for comparison (string)
% .Z - minimum of n Statistics {filtered on u and k}
% .n - abs(n) is the number of conjoint tests; if n<0, then global
% null, instead of conjunction null, is used.
% .STAT - distribution {Z, T, X, F or P}
% .df - degrees of freedom [df{interest}, df{residual}]
% .STATstr - description string
% .Ic - indices of contrasts (in SPM.xCon)
% .Im - indices of masking contrasts (in xCon)
% .pm - p-value for masking (uncorrected)
% .Ex - flag for exclusive or inclusive masking
% .u - height threshold
% .k - extent threshold {voxels}
% .XYZ - location of voxels {voxel coords}
% .XYZmm - location of voxels {mm}
% .S - search Volume {voxels}
% .R - search Volume {resels}
% .FWHM - smoothness {voxels}
% .M - voxels -> mm matrix
% .iM - mm -> voxels matrix
% .VOX - voxel dimensions {mm} - column vector
% .DIM - image dimensions {voxels} - column vector
% .Vspm - Mapped statistic image(s)
% .Ps - list of P values for voxels at SPM.xVol.XYZ (used by FDR)
%
% Required feilds of SPM
%
% xVol - structure containing details of volume analysed
%
% xX - Design Matrix structure
% - (see spm_spm.m for structure)
%
% xCon - Contrast definitions structure array
% - (see also spm_FcUtil.m for structure, rules & handling)
% .name - Contrast name
% .STAT - Statistic indicator character ('T', 'F' or 'P')
% .c - Contrast weights (column vector contrasts)
% .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',...
% .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: Usuall one of {'c','c+','X0'}
% .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',...
% .eidf - Effective interest degrees of freedom (numerator df)
% - Or effect-size threshold for Posterior probability
% .Vcon - Name of contrast (for 'T's) or ESS (for 'F's) image
% .Vspm - Name of SPM image
%
% In addition, the xCon.mat file is updated. For newly evaluated
% contrasts, SPM images (spmT_????.{img,hdr}) are written, along with
% contrast (con_????.{img,hdr}) images for SPM{T}'s, or Extra
% Sum-of-Squares images (ess_????.{img,hdr}) for SPM{F}'s.
%
% The contrast images are the weighted sum of the parameter images,
% where the weights are the contrast weights, and are uniquely
% estimable since contrasts are checked for estimability by the
% contrast manager. These contrast images (for appropriate contrasts)
% are suitable summary images of an effect at this level, and can be
% used as input at a higher level when effecting a random effects
% analysis. (Note that the ess_????.{img,hdr} and
% SPM{T,F}_????.{img,hdr} images are not suitable input for a higher
% level analysis.) See spm_RandFX.man for further details.
%
%_______________________________________________________________________
%
% spm_getSPM prompts for an SPM and applies thresholds {u & k}
% to a point list of voxel values (specified with their locations {XYZ})
% This allows the SPM be displayed and characterized in terms of regionally
% significant effects by subsequent routines.
%
% For general linear model Y = XB + E with data Y, desgin matrix X,
% parameter vector B, and (independent) errors E, a contrast c'B of the
% parameters (with contrast weights c) is estimated by c'b, where b are
% the parameter estimates given by b=pinv(X)*Y.
%
% Either single contrasts can be examined or conjunctions of different
% contrasts. Contrasts are estimable linear combinations of the
% parameters, and are specified using the SPM contrast manager
% interface [spm_conman.m]. SPMs are generated for the null hypotheses
% that the contrast is zero (or zero vector in the case of
% F-contrasts). See the help for the contrast manager [spm_conman.m]
% for a further details on contrasts and contrast specification.
%
% A conjunction assesses the conjoint expression of two or more
% effects. The conjunction SPM is the minimum of the component SPMs
% defined by the multiple contrasts. Inference on the conjunction null
% (one or more of the effects null) is accomplished by assessing the
% minimum as if it were a single statistic. No assumptions are needed on
% the dependence between the tests.
%
% Inference on the global null (all of the effects null) uses results
% for minimum fileds, which require the SPMs to be identically
% distributed and independent. Thus, all component SPMs must be either
% SPM{t}'s, or SPM{F}'s with the same degrees of freedom. Independence is
% roughly guaranteed for large degrees of freedom (and independent data) by
% ensuring that the contrasts are "orthogonal". Note that it is *not*
% the contrast weight vectors per se that are required to be
% orthogonal, but the subspaces of the data space implied by the null
% hypotheses defined by the contrasts (c'pinv(X)). Furthermore,
% this assumes that the errors are i.i.d. (i.e. the estimates are
% maximum likelihood or Gauss-Markov. This is the default in spm_spm).
%
% To ensure approximate independence of the component SPMs in inference
% on the global null, non-orthogonal contrasts are serially
% orthogonalised in the order specified, possibly generating new
% contrasts, such that the second is orthogonal to the first, the third
% to the first two, and so on.
%
% Note that significant inference on the global null only allows one to
% conclude that one or more of the effects are real. Significant
% inference on the conjunction null allows one to conclude that all of
% the effects are real.
%
% Masking simply eliminates voxels from the current contrast if they
% do not survive an uncorrected p value (based on height) in one or
% more further contrasts. No account is taken of this masking in the
% statistical inference pertaining to the masked contrast.
%
% The SPM is subject to thresholding on the basis of height (u) and the
% number of voxels comprising its clusters {k}. The height threshold is
% specified as above in terms of an [un]corrected p value or
% statistic. Clusters can also be thresholded on the basis of their
% spatial extent. If you want to see all voxels simply enter 0. In this
% instance the 'set-level' inference can be considered an 'omnibus test'
% based on the number of clusters that obtain.
%
% BAYESIAN INFERENCE AND PPMS - POSTERIOR PROBABILITY MAPS
%
% If conditional estimates are available (and your contrast is a T
% contrast) then you are asked whether the inference should be 'Bayesian'
% or 'classical' (using GRF). If you choose Bayesian the contrasts are of
% conditional (i.e. MAP) estimators and the inference image is a
% posterior probability map (PPM). PPMs encode the probability that the
% contrast exceeds a specified threshold. This threshold is stored in
% the xCon.eidf. Subsequent plotting and tables will use the conditional
% estimates and associated posterior or conditional probabilities.
%
% see spm_results_ui.m for further details of the SPM results section.
% see also spm_contrasts.m
%_______________________________________________________________________
% @(#)spm_getSPM.m 2.51 Andrew Holmes, Karl Friston & Jean-Baptiste Poline 03/05/22
% $Id: spm_getSPM.m,v 1.2 2004/06/30 14:29:34 nichols Exp $ UM Biostat
SCCSid = '2.51';
%-GUI setup
%-----------------------------------------------------------------------
SPMid = spm('SFnBanner',mfilename,SCCSid);
spm_help('!ContextHelp',mfilename)
%-Select SPM.mat & note SPM results directory
%-----------------------------------------------------------------------
swd = spm_str_manip(spm_get(1,'SPM.mat','Select SPM.mat'),'H');
%-Preliminaries...
%=======================================================================
%-Load SPM.mat
%-----------------------------------------------------------------------
load(fullfile(swd,'SPM.mat'));
SPM.swd = swd;
%-Get volumetric data from SPM.mat
%-----------------------------------------------------------------------
try
xX = SPM.xX; %-Design definition structure
XYZ = SPM.xVol.XYZ; %-XYZ coordinates
S = SPM.xVol.S; %-search Volume {voxels}
R = SPM.xVol.R; %-search Volume {resels}
M = SPM.xVol.M(1:3,1:3); %-voxels to mm matrix
VOX = sqrt(diag(M'*M))'; %-voxel dimensions
catch
% check the model has been estimated
%---------------------------------------------------------------
str = { 'This model has not been estimated.';...
'Would you like to estimate it now?'};
if spm_input(str,1,'bd','yes|no',[1,0],1)
[SPM] = spm_spm(SPM);
else
return
end
end
%-Contrast definitions
%=======================================================================
%-Load contrast definitions (if available)
%-----------------------------------------------------------------------
try
xCon = SPM.xCon;
catch
xCon = {};
end
%=======================================================================
% - C O N T R A S T S , S P M C O M P U T A T I O N , M A S K I N G
%=======================================================================
%-Get contrasts
%-----------------------------------------------------------------------
[Ic,xCon] = spm_conman(xX,xCon,'T&F',Inf,...
' Select contrasts...',' for conjunction',1);
% Number of tests to conjoin
n = length(Ic);
%-Transistional conjunction question; eliminate in next release?
if (n>1)
n = spm_input('Null hyp. to assess?','+1','b',...
'Conjunction|Global',[n -n],1);
% Conjunction Null -> positive n
% Global Null -> negative n
end
%-Enforce orthogonality of multiple contrasts for conjunction.
% Not needed when asssessing conjunction null.
% (Orthogonality within subspace spanned by contrasts)
%-----------------------------------------------------------------------
if n < -1 & ~spm_FcUtil('|_?',xCon(Ic), xX.xKXs)
%-Successively orthogonalise
%-NB: This loop is peculiarly controlled to account for the
% possibility that Ic may shrink if some contrasts diasppear
% on orthogonalisation (i.e. if there are colinearities)
%-------------------------------------------------------------------
i = 1; while(i < abs(n)), i = i + 1;
%-Orthogonalise (subspace spanned by) contrast i wirit previous
%---------------------------------------------------------------
oxCon = spm_FcUtil('|_',xCon(Ic(i)), xX.xKXs, xCon(Ic(1:i-1)));
%-See if this orthogonalised contrast has already been entered
% or is colinear with a previous one. Define a new contrast if
% neither is the case.
%---------------------------------------------------------------
d = spm_FcUtil('In',oxCon,xX.xKXs,xCon);
if spm_FcUtil('0|[]',oxCon,xX.xKXs)
%-Contrast was colinear with a previous one - drop it
%-----------------------------------------------------------
Ic(i) = [];
i = i - 1;
elseif any(d)
%-Contrast unchanged or already defined - note index
%-----------------------------------------------------------
Ic(i) = min(d);
else
%-Define orthogonalised contrast as new contrast
%-----------------------------------------------------------
oxCon.name = [xCon(Ic(i)).name,' (orth. w.r.t {',...
sprintf('%d,',Ic(1:i-2)), sprintf('%d})',Ic(i-1))];
xCon = [xCon, oxCon];
Ic(i) = length(xCon);
end
end % while...
end % if n < -1 ...
%-Get contrasts for masking
%-----------------------------------------------------------------------
if spm_input('mask with other contrast(s)','+1','y/n',[1,0],2)
%- select analysis for masking contrasts
%---------------------------------------------------------------
omask = spm_input('which analysis?','!+1','b','current|other',[0,1],1);
if omask == 1
otherSPM = load(spm_get(1,'SPM.mat','Select SPM.mat from other analysis'));
otherxCon = otherSPM.SPM.xCon;
otherxX = otherSPM.SPM.xX;
[Im,otherxCon] = spm_conman(otherxX,otherxCon,'T&F',-Inf,...
'Select contrasts for masking...',' for masking',1);
else
[Im,xCon] = spm_conman(xX,xCon,'T&F',-Inf,...
'Select contrasts for masking...',' for masking',1);
end
%-Threshold for mask (uncorrected p-value)
%---------------------------------------------------------------
pm = spm_input('uncorrected mask p-value','+1','r',0.05,1,[0,1]);
%-Inclusive or exclusive masking
%---------------------------------------------------------------
Ex = spm_input('nature of mask','+1','b','inclusive|exclusive',[0,1]);
else
Im = [];
pm = [];
Ex = [];
end
%-Create/Get title string for comparison
%-----------------------------------------------------------------------
if abs(n) == 1
str = xCon(Ic).name;
else
str = [sprintf('contrasts {%d',Ic(1)),sprintf(',%d',Ic(2:end)),'}'];
if n<0
str = [ str ' (global null)'];
end
end
if Ex
mstr = 'masked [excl.] by';
else
mstr = 'masked [incl.] by';
end
if length(Im) == 1
str = sprintf('%s (%s %s at p=%g)',str,mstr,xCon(Im).name,pm);
if exist('omask','var') & omask == 1
str = sprintf('%s (%s %s at p=%g)',str,mstr,otherxCon(Im).name,pm);
else
str = sprintf('%s (%s %s at p=%g)',str,mstr,xCon(Im).name,pm);
end
elseif ~isempty(Im)
str = [sprintf('%s (%s {%d',str,mstr,Im(1)),...
sprintf(',%d',Im(2:end)),...
sprintf('} at p=%g)',pm)];
end
titlestr = spm_input('title for comparison','+1','s',str);
%-Bayesian or classical Inference?
%-----------------------------------------------------------------------
if isfield(SPM,'PPM') & xCon(Ic(1)).STAT == 'T'
if abs(n) == 1 & isempty(xCon(Ic).Vcon)
if spm_input('Inference',1,'b',{'Bayesian','classical'},[1 0]);
% set STAT to 'P'
%---------------------------------------------------------------
xCon(Ic).STAT = 'P';
%-Get Bayesian threshold (Gamma) stored in xCon(Ic).eidf
% The default is one conditional s.d. of the contrast
%---------------------------------------------------------------
str = 'threshold {default: prior s.d.}';
Gamma = sqrt(xCon(Ic).c'*SPM.PPM.Cb*xCon(Ic).c);
xCon(Ic).eidf = spm_input(str,'+1','e',sprintf('%0.2f',Gamma));
end
end
end
%-Compute & store contrast parameters, contrast/ESS images, & SPM images
%=======================================================================
SPM.xCon = xCon;
if exist('omask','var') & omask == 1
SPM = spm_contrasts(SPM,unique([Ic,Im]));
else
SPM = spm_contrasts(SPM,unique([Ic,Im]));
end
xCon = SPM.xCon;
VspmSv = cat(1,xCon(Ic).Vspm);
STAT = xCon(Ic(1)).STAT;
%-Check conjunctions - Must be same STAT w/ same df
%-----------------------------------------------------------------------
if (abs(n) > 1) & (any(diff(double(cat(1,xCon(Ic).STAT)))) | ...
any(abs(diff(cat(1,xCon(Ic).eidf))) > 1))
error('illegal conjunction: can only conjoin SPMs of same STAT & df')
end
%-Degrees of Freedom and STAT string describing marginal distribution
%-----------------------------------------------------------------------
df = [xCon(Ic(1)).eidf xX.erdf];
if n > 1
str = sprintf('^{%dC}',n); % Conjunction Null
elseif n < -1
str = sprintf('^{%dG}',-n); % Global Null
else
str = '';
end
switch STAT
case 'T'
STATstr = sprintf('%c%s_{%.0f}','T',str,df(2));
case 'F'
STATstr = sprintf('%c%s_{%.0f,%.0f}','F',str,df(1),df(2));
case 'P'
STATstr = sprintf('%s^{%0.2f}','PPM',df(1));
end
%-Compute (unfiltered) SPM pointlist for masked conjunction requested
%=======================================================================
fprintf('\t%-32s: %30s\n','SPM computation','...initialising') %-#
%-Compute conjunction as minimum of SPMs
%-----------------------------------------------------------------------
Z = Inf;
for i = Ic
Z = min(Z,spm_get_data(xCon(i).Vspm,XYZ));
end
% P values for False Discovery FDR rate computation (all search voxels)
%=======================================================================
switch STAT
case 'T'
Ps = (1 - spm_Tcdf(Z,df(2))).^n;
case 'P'
Ps = (1 - Z).^n;
case 'F'
Ps = (1 - spm_Fcdf(Z,df)).^n;
end
%-Compute mask and eliminate masked voxels
%-----------------------------------------------------------------------
for i = Im
fprintf('%s%30s',sprintf('\b')*ones(1,30),'...masking')
if exist('omask','var') & omask == 1
otherVspm = spm_vol(fullfile(otherSPM.SPM.swd,otherxCon(i).Vspm.fname));
Mask = spm_get_data(otherVspm,XYZ);
um = spm_u(pm,[otherxCon(i).eidf,otherxX.erdf],otherxCon(i).STAT);
else
Mask = spm_get_data(xCon(i).Vspm,XYZ);
um = spm_u(pm,[xCon(i).eidf,xX.erdf],xCon(i).STAT);
end
if Ex
Q = Mask <= um;
else
Q = Mask > um;
end
XYZ = XYZ(:,Q);
Z = Z(Q);
if isempty(Q)
fprintf('\n') %-#
warning(sprintf('No voxels survive masking at p=%4.2f',pm))
break
end
end
%-clean up interface
%-----------------------------------------------------------------------
fprintf('\t%-32s: %30s\n','SPM computation','...done') %-#
spm('Pointer','Arrow')
%=======================================================================
% - H E I G H T & E X T E N T T H R E S H O L D S
%=======================================================================
%-Height threshold - classical inference
%-----------------------------------------------------------------------
u = -Inf;
k = 0;
if STAT ~= 'P'
%-Get height threshold
%-------------------------------------------------------------------
str = 'FWE|FDR|none';
% str = 'FWE|none'; % Use this line to disable FDR threshold
switch spm_input('p value adjustment to control','+1','b',str,[],1)
case 'FWE' % family-wise false positive rate
%---------------------------------------------------------------
u = spm_input('p value (family-wise error)','+0','r',0.05,1,[0,1]);
u = spm_uc(u,df,STAT,R,n,S);
case 'FDR' % False discovery rate
%---------------------------------------------------------------
u = spm_input('p value (false discovery rate)','+0','r',0.05,1,[0,1]);
u = spm_uc_FDR(u,df,STAT,n,VspmSv,0);
otherwise %-NB: no adjustment
% p for conjunctions is p of the conjunction SPM
%---------------------------------------------------------------
u = spm_input(['threshold {',STAT,' or p value}'],'+0','r',0.001,1);
if u <= 1
if n > 0
u = spm_u(u ,df,STAT); % Univar or Conjunction null
else
u = spm_u(u^(1/abs(n)),df,STAT); % Global conjunction null
end
end
end
%-Height threshold - Bayesian inference
%-----------------------------------------------------------------------
elseif STAT == 'P'
u = spm_input(['p value threshold for PPM'],'+0','r',.95,1);
end % (if STAT)
%-Calculate height threshold filtering
%-------------------------------------------------------------------
Q = find(Z > u);
%-Apply height threshold
%-------------------------------------------------------------------
Z = Z(:,Q);
XYZ = XYZ(:,Q);
if isempty(Q)
warning(sprintf('No voxels survive height threshold u=%0.2g',u))
end
%-Extent threshold (disallowed for conjunctions)
%-----------------------------------------------------------------------
if ~isempty(XYZ) & abs(n) == 1
%-Get extent threshold [default = 0]
%-------------------------------------------------------------------
k = spm_input('& extent threshold {voxels}','+1','r',0,1,[0,Inf]);
%-Calculate extent threshold filtering
%-------------------------------------------------------------------
A = spm_clusters(XYZ);
Q = [];
for i = 1:max(A)
j = find(A == i);
if length(j) >= k; Q = [Q j]; end
end
% ...eliminate voxels
%-------------------------------------------------------------------
Z = Z(:,Q);
XYZ = XYZ(:,Q);
if isempty(Q)
warning(sprintf('No voxels survive extent threshold k=%0.2g',k))
end
else
k = 0;
end % (if ~isempty(XYZ))
%=======================================================================
% - E N D
%=======================================================================
fprintf('\t%-32s: %30s\n','SPM computation','...done') %-#
%-Assemble output structures of unfiltered data
%=======================================================================
xSPM = struct('swd', swd,...
'title', titlestr,...
'Z', Z,...
'n', n,...
'STAT', STAT,...
'df', df,...
'STATstr', STATstr,...
'Ic', Ic,...
'Im', Im,...
'pm', pm,...
'Ex', Ex,...
'u', u,...
'k', k,...
'XYZ', XYZ,...
'XYZmm', SPM.xVol.M(1:3,:)*[XYZ; ones(1,size(XYZ,2))],...
'S', SPM.xVol.S,...
'R', SPM.xVol.R,...
'FWHM', SPM.xVol.FWHM,...
'M', SPM.xVol.M,...
'iM', SPM.xVol.iM,...
'DIM', SPM.xVol.DIM,...
'VOX', VOX,...
'Vspm', VspmSv,...
'Ps', Ps);
% RESELS per voxel (density) if it exists
%-----------------------------------------------------------------------
if isfield(SPM,'VRpv'), xSPM.VRpv = SPM.VRpv; end
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