Dear Andrew,
I ran into the same problem. I emailed one of the wfu_pickatlas guys about
it but received no answer (I wonder if the contact address at the website
was up to date). Eventually, I copy-pasted the wfu_pickatlas-specific code
bits into spm_getSPM in the spm2_conj directory (and put the spm2_conj
directory above the wfu_pickatlas directory in my matlab path). I attached
the adapated spm_getSPM. For me it seemed to work fine. Use at your own
risk...
Regards
Paul
Andrew Kemp wrote:
> Dear SPMers,
>
>
>
> Has anyone attempted to use WFU Pickatlas as well as Tom Nichol’s new
> conjunction analysis simultaneously? It seems that one cannot do the new
> conjunction analysis as well as an ROI analysis using WFU_Pickatlas in
> the one analysis because both require their parent directory to be
> listed at the top of the matlab search paths.
>
>
>
> Has anyone found a way on how to get around this (for FFX analyses?) bar
> masking swus*.img's?
>
>
>
> Thanks in advance!
>
>
>
> Andrew
>
>
>
--
Paul A.M. Smeets, Phd-fellow
Image Sciences Institute,University Medical Centre Utrecht, Rm Q0S.459
Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
Tel: +31 30 - 250 8197 Fax: +31 30 - 251 3399
URL: http://www.isi.uu.nl/People/?paul
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 statistic images {filtered on u and k}
% .n - conjunction number <= number of contrasts
% .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 fields 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: Usually 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, design 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 multiple effects. The
% conjunction SPM is the minimum of the component SPMs defined by the
% multiple contrasts. Inference on the minimum statistics can be
% performed in different ways. 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; one rejects the conjunction null in
% favor of the alternative that k=nc, that the number of active effects k
% is equal to the number of contrasts nc. No assumptions are needed on
% the dependence between the tests.
%
% Another approach is to make inference on the Global Null (all effects
% null). Rejecting the Global Null of no (u=0) effects real implies an
% alternative that k>0, that one or more effects are real. A third
% Intermediate approach, is to use a null hypothesis of no more than u
% effects are real. Rejecting the intermediate null that k<=u implies an
% alternative that k>u, that more than u of the effects are real.
%
% The Global and Intermediate nulls use results for minimum fields 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 the case of
% the global or intermediate 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 1.14 2004/10/19 15:41:56 nichols Exp $ UM Biostat
SCCSid = '2.51';
%-Adapted version
% Use at your own risk...
%---------------------------------
% Added wfu_pickatlas code-pieces
% Paul Smeets, 26 July 2004
% Updated 22 december 2004 (bug fix of october 2004)
%---------------------------------
%-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);
nc = length(Ic); % Number of contrasts
%-Allow user to extend the null hypothesis for conjunctions
%
% n: conjunction number
% u: Null hyp is k<=u effects real; Alt hyp is k>u effects real
% (NB Here u is from Friston et al 2004 paper, not statistic thresh).
% u n
% Conjunction Null nc-1 1 | u = nc-n
% Intermediate 1..nc-2 nc-u | #effects under null <= u
% Global Null 0 nc | #effects under alt > u, >= u+1
%----------------------------------+-------------------------------------
if (nc > 1)
if nc==2
But='Conjunction|Global'; Val=[1 nc];
else
But='Conj''n|Intermed|Global'; Val=[1 NaN nc];
end
n = spm_input('Null hyp. to assess?','+1','b',But,Val,1);
if isnan(n)
if nc==3,
n = nc-1;
else
n = nc-spm_input('Effects under null ','0','n1','1',nc-1);
end
end
else
n = 1;
end
%-Enforce orthogonality of multiple contrasts for conjunction
% (Orthogonality within subspace spanned by contrasts)
%-----------------------------------------------------------------------
if nc>1 & n>1 & ~spm_FcUtil('|_?',xCon(Ic), xX.xKXs)
OrthWarn = 0;
%-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 < nc), 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
OrthWarn = OrthWarn + 1;
%-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...
if OrthWarn
warning(sprintf('Contrasts changed! %d contrasts orthogonalized ',...
'to allow conjunction inf.', OrthWarn))
end
end % if (orthogonalize)
%-Get contrasts for masking
%-----------------------------------------------------------------------
if spm_input('mask with other contrast(s)','+1','y/n',[1,0],2)
[Im,xCon] = spm_conman(xX,xCon,'T&F',-Inf,...
'Select contrasts for masking...',' for masking',1);
%-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
%-Get ATLAS ROIS for masking
%-----------------------------------------------------------------------
wfu_atlas_filename = [];
if spm_input('ROI Analysis','+1','y/n',[1,0],2)
wfu_mask_type = spm_input('ROI analysis from','+1','b','Saved File|Pickatlas GUI',[0,1]);
if wfu_mask_type == 0
wfu_atlas_filename = spm_get(1,'*.img','Select ROI mask')
else
atlas_mask_filename=[swd '/atlas_mask_file'];
[wfu_atlas_region,wfu_atlas_mask,wfu_atlas_filename] = wfu_pickatlas(atlas_mask_filename);
end
if ~isempty(wfu_atlas_filename)
P = spm_get('Files',swd,'beta*.img');
P = {char(P(1,:)),wfu_atlas_filename};
flags.mean=0;
flags.hold = 0;
flags.which=1;
flags.mask=0;
spm_reslice(P,flags);
wfu_atlas_filename=prepend(wfu_atlas_filename,'r');
end
%To get mat use spm_get_space(filename)
end
%-Create/Get title string for comparison
%-----------------------------------------------------------------------
if nc == 1
str = xCon(Ic).name;
else
str = [sprintf('contrasts {%d',Ic(1)),sprintf(',%d',Ic(2:end)),'}'];
if n==nc
str = [str ' (global null)'];
elseif n==1
str = [str ' (conj. null)'];
else
str = [str sprintf(' (Ha: k>=%d)',(nc-n)+1)];
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);
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 nc == 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;
SPM = spm_contrasts(SPM,unique([Ic,Im]));
xCon = SPM.xCon;
VspmSv = cat(1,xCon(Ic).Vspm);
STAT = xCon(Ic(1)).STAT;
%-Check conjunctions - Must be same STAT w/ same df
%-----------------------------------------------------------------------
if (nc > 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 nc>1
if n>1
str = sprintf('^{%d \\{Ha:k\\geq%d\\}}',nc,(nc-n)+1);
else
str = sprintf('^{%d \\{Ha:k=%d\\}}',nc,(nc-n)+1);
end
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 atlas mask first
%J Maldjian 8-6-02
%-----------------------------------------------------------------------
if ~isempty(wfu_atlas_filename)
fprintf('%s%30s',sprintf('\b')*ones(1,30),'...Atlas ROI analysis')
SPACE='I';
D = spm_vol(wfu_atlas_filename);
tM = D.mat \ SPM.xVol.M;
if any(tM([2:5,7:10,12]))
spm('alert!',{ 'Mask image rotated/sheared!',...
'(relative to SPM image)',...
'Can''t use for SVC.'},mfilename,0);
spm('FigName',['SPM{',SPM.STAT,'}: Results']);
return
end
XYZmm = SPM.xVol.M(1:3,:)*[XYZ; ones(1, SPM.xVol.S)];
FWHM = SPM.xVol.FWHM;
% FWHM = FWHM.*(VOX./VOX);
% XYZ = D.mat \ [XYZmm; ones(1, size(XYZmm, 2))];
j = find(spm_sample_vol(D, XYZ(1,:), XYZ(2,:), XYZ(3,:),0) > 0);
k = find(spm_sample_vol(D, XYZ(1,:), XYZ(2,:), XYZ(3,:),0) > 0);
S = length(k);
R = spm_resels(FWHM,D,SPACE);
Z = Z(j);
XYZ = XYZ(:,j);
XYZmm = XYZmm(:,j);
Ps = Ps(k);
SPM.xVol.S = S;
SPM.xVol.R = R;
% SPM.xVol.FWHM = FWHM % shouldn't change?
end
%-Compute mask and eliminate masked voxels
%-----------------------------------------------------------------------
for i = Im
fprintf('%s%30s',sprintf('\b')*ones(1,30),'...masking')
Mask = spm_get_data(xCon(i).Vspm,XYZ);
um = spm_u(pm,[xCon(i).eidf,xX.erdf],xCon(i).STAT);
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; u = spm_u(u^(1/n),df,STAT); 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) & nc == 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
%_______________________________________________________________________
function PO = prepend(PI,pre)
[pth,nm,xt,vr] = fileparts(deblank(PI));
PO = fullfile(pth,[pre nm xt vr]);
return;
%_______________________________________________________________________
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