spm_adjmean_fmri_ui.m
31022b3a13e6master

spm_adjmean_fmri_ui.m
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			function spm_adjmean_fmri_ui
			% Adjusted means of fMRI via box-car General Linear Model with confounds
			% FORMAT spm_adjmean_fmri_ui
			%_______________________________________________________________________
			%
			% spm_adjmean_fmri_ui uses the General Linear Model to produce adjusted
			% condition images, adjusted for global effects and confounds.
			%
			% This program is designed for collapsing data from a single session
			% fMRI epoch-related design into a set of representative condition
			% images, one for each condition, adjusted for global effects and with
			% low frequency drifts removed via a discrete cosine basis set high
			% pass filter. The resulting data sets are suitable for a (2nd level)
			% random effects analysis of multiple sessions or subjects, or group
			% comparisons.
			%
			% See spm_RandFX.man for further details on implementing random effects
			% analyses in SPM96 using a multi-level approach.
			%
			%
			% Overview
			% ----------------------------------------------------------------------
			% The program works with a single fMRI session, fitting a General
			% Linear Model consisting of simple box-cars (optionally convolved with
			% an estimated haemodynamic response function) and an (optional) high
			% pass filter of discrete cosine basis functions. The effects of
			% interest (the box-cars) are orthogonalised (residualised) with
			% respect to the confounds (the high pass filter) to ensure that all
			% confounds are removed from the data, even if they're correlated with
			% the effects of interest. (For well designed experiments this makes
			% little difference.) Proportional scaling and AnCova global
			% normalisation are supported, the latter including the option to scale
			% all the images such that their the grand mean (GM) is a specified
			% value.
			%
			% The interface is similar to a cut-down SPM-fMRI, and the adjusted
			% means are the parameter estimates from the model. The user is first
			% prompted to select the scans for a single fMRI session. Then the
			% epoch condition order is specified. This should be a r-vector, where
			% r is the number of epochs, of integers 1:n or 0:n-1 where n is the
			% number of conditions (0 can be used to indicate the baseline or
			% "rest" condition. Then the number of scans per epoch is specified:
			% This can be a single integer (all epochs have the same number of
			% scans), or an r-vector of integers specifying number of scans for the
			% corresponding epoch.
			%
			% Once the experimental design has been specified, the user is given
			% various options: The box-cars can be convolved with an approximate
			% haemodynamic reponse function; High-pass filter components can be
			% added to the confounds (at a user-specified cut-off which defaults to
			% twice the maximum period of the experiment); Global normalisation can
			% be omitted, or implemented by proportional scaling or AnCova; and the
			% grand mean scaling options specified.
			%
			% With the design and adjustments specified, the model is constructed,
			% and the user prompted to enter/confirm filenames for the adjusted
			% condition images.
			%
			% The model, filenames, global values and options are saved to a MatLab
			% *.mat file named SPMadj.mat in the current working directory.
			%
			% Implicit masking is carried out: Zero voxels are implicitly assummed
			% to be masked out. Thus, the adjusted mean is calculated at voxels
			% which are non-zero in all the input images pertaining to the
			% adjusted mean (usually those from the appropriate subject). (This is
			% not a softmean.) Data realigned in a single session with SPM'96 (or
			% later) are automatically implicitly zero masked with a consistent
			% mask in this way.
			%
			% GM, the value for grand mean scaling, is user specified.
			% The default value is 100.
			%
			% If computing adjusted means for subsequent (2nd level) modelling, as
			% with a random effects analysis, then it is important to use a
			% seperable model, such that the adjustment for one subject is
			% independent of other subjects entered into the model. Thus,
			% proportional scaling or subject-specific AnCova adjustment must be
			% used. Further, multiple runs must use the same GM value, and should
			% scale Grand mean by subject.
			%
			% ( A separate program (spm_adjmean_ui) is available for computing )
			% ( adjusted condition means of PET data. The functionality is similar )
			% ( to this code, but the two routines have been separated for )
			% ( algorithmic clarity. )
			%
			% Diagnostic output
			% ----------------------------------------------------------------------
			% Diagnostic output consists of two sections:
			%
			% The first page lists the filenames, various parameters (Grand mean
			% scaling etc.), and gives a plot of the image global means against
			% scan number, overlaid on an "image" of the condition effects. Watch
			% out for condition dependent global changes!
			%
			% The second part is a single page depicting the design matrix, effect
			% names, parameter contrasts used, and the corresponding image files
			% written.
			%
			% As always, look at the resulting mean images to make sure they look OK!
			%
			%
			% Algorithm
			% ----------------------------------------------------------------------
			% The model at each voxel is Y = X*B + e, with a set of least squares
			% estimates for the vector of parameters B as b = pinv(X)*Y. For c a
			% vector of contrast weights extracting the appropriate parameter, the
			% contrast of the parameter estimates is c'b = c'pinv(X)*Y, a
			% weighted sum (or weighted mean) of the data at that voxel. These
			% weights are identical for all voxels, so the image of the parameter
			% estimate can be computed as a weighted mean of the images.
			%
			% The design matrix is split into effects of interest [C], a constant
			% term [B], and confounds [G]. The columns of G are centered so that
			% the confound cannot model any of the mean. The effects of interest
			% are orthogonalised wirit. the confounds, using C=C-Gpinv(G)C; This
			% ensures that all confound effects are removed from the data, even
			% if they are correlated with the effects of interest.
			%
			% Once the weights have been worked out for each adjusted mean image,
			% computation proceeds by passing appropriate weights and image
			% filenames to spm_mean, which writes out the appropriate parameter
			% image as an Analyze format image of the same type (see spm_type) as
			% the input images.
			%
			%
			% Variables saved in SPMadj.mat data file
			% ----------------------------------------------------------------------
			% Des Structure containing design parameters & specification
			% .DesName Design name
			% .HForm Form of DesMtx H partition
			% .iSubj Subject indicator vector
			% .iCond Condition indicator vector
			% .iRepl Replication indicator vector
			% .iGloNorm Global normalisation option
			% .sGloNorm Global normalisation description
			% .iGMsca Grand mean scaling option
			% .sGMsca Grand mean scaling description
			% .GM Grand Mean used for scaling
			% .iAdjTo Adjustment (for AnCova) option
			% .sAdjTo Adjustment (for AnCova) description
			% .aGM AnCova adjustment value (subtracted from GX before AnCova)
			% .gSF Image scale factors for global scaling
			% .X Design matrix
			% .nX Normalised (for imaging) design matrix
			% .Xnames Effects corresponding to cols of X (cellstr)
			% .aPMap Additional parameter to effect name mappings (see spm_desMtx)
			% .EXnames English effect names corresponding to TeX parameters of Xnames
			% .iX Structure defining design matrix subpartitions
			% .H Columns of X corresponding to H partition
			% .C Columns of X corresponding to C partition
			% .B Columns of X corresponding to B partition
			% .G Columns of X corresponding to G partition
			% c Matrix of contrasts, contrasts in rows
			% cNames Names associated with contrasts
			% W Weights for images corresponding to contrasts
			% Fnames Filenames of adjusted mean images written (cellstr)
			% rGX raw global means (before any scaling)
			% GX Global means after scaling
			%
			% P String matrix of filenames
			% iCond Condition indicator vector
			% iGloNorm Global normalisation option
			% sGloNorm Global normalisation description
			% iGMsca Grand mean scaling option
			% sGMsca Grand mean scaling description
			% HPFc High pass filter cut-off period (s)
			% HPF High pass filter
			% sHPF Description of high-pass filter
			% rC raw C partition of design matrix, prior to orthogonalisation
			% C C (covariates of interest) partition of design matrix
			% Cnames Names of parameters corresponding to columns of C
			% B B (block) partition of the design matrix
			% Bnames Names of parameters corresponding to columns of B
			% G G (confounding covariates) partition of design matrix
			% Gnames Names of parameters corresponding to columns of G
			% rX raw design matrix, prior to orthogonalisation of C partition
			% X design matrix (=[C,B,G])
			% nrX raw design matrix, normalised for display
			% nX design matrix, normalised for display
			% c Matrix of contrasts, contrasts in rows
			% cNames Names associated with contrasts
			% W Weights for images corresponding to contrasts
			% CWD Current Working Directory (when run)
			% Fnames Filenames of adjusted mean images written
			% rGX raw global means (before any scaling)
			% gSF Image scale factors for global scaling (inc. grand mean scaling)
			% GX Global means after scaling
			% GM Grans Mean used for scaling
			%
			%
			%_______________________________________________________________________
			% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging

			% Andrew Holmes
			% $Id: spm_adjmean_fmri_ui.m 4418 2011-08-03 12:00:13Z guillaume $



			%=======================================================================
			% - S E T U P
			%=======================================================================
			SCCSid = '2.9';
			SPMid = spm('FnBanner',mfilename,SCCSid);
			[Finter,Fgraph,CmdLine] = spm('FnUIsetup','AdjMean/fMRI',1);
			spm_help('!ContextHelp',[mfilename,'.m'])


			%=======================================================================
			% - D E S I G N P A R A M E T E R S
			%=======================================================================

			%-Get filenames
			%-----------------------------------------------------------------------
			P = spm_select(Inf,'image','select scans (single session)');
			nScan = size(P,1);
			if nScan==1, error('Only one image - gimme more!'), end

			%-Get condition information & construct vector of conditions indices
			%-----------------------------------------------------------------------
			%-Epoch condition order
			eCond = spm_input('epoch order Eg 121... {0=null}',1,'c')';
			Conds = unique(eCond);

			%-Epoch length(s)
			eLen = [];
			guiPos = spm_input('!NextPos');
			while any(~eLen) \| sum(eLen) ~= nScan
			eLen = spm_input('#scans/epoch Eg 8 or 8 6 8... ',guiPos);
			if length(eLen)==1, eLen=eLen*ones(1,length(eCond)); end
			end
			eLen = eLen(:)';

			%-Epoch onsets, in scans, starting at 1
			eOns = cumsum(eLen) - eLen + 1;

			%-iCond condition indicator vector
			iCond = zeros(nScan,1);
			for i=1:length(eLen), iCond(eOns(i)+[1:eLen(i)]-1)=eCond(i); end


			%-Get Repeat time, construct approximate haemodynamic response function
			%-----------------------------------------------------------------------
			RT = spm_input('interscan interval (in seconds)','+1');
			hrf = spm_hrf(RT);


			%-Construct design matrix C partition
			% (box-cars convolved with approximate HRF if requested)
			% null condition with iCond==0 is ignored by spm_DesMtx with '~' constraint
			%-----------------------------------------------------------------------
			[C,Cnames,Ci] = spm_DesMtx(iCond,'~','\alpha');
			% C = spm_detrend(C);

			%-Convolve with hemodynamic response function, if requested
			bHRFconv = spm_input('Conv. box-cars w/ approx HRF?','+1','y/n',[1,0],1);
			if bHRFconv
			d = length(hrf);
			C = [ones(d,1)*C(1,:); C];
			C = spm_sptop(hrf,nScan + d,1)*C;
			C = C([1:nScan]+d,:);
			end


			%-Construct design matrix block (B) partition
			%-----------------------------------------------------------------------
			B = ones(nScan,1); Bnames = '\mu';


			%-Construct design matrix confound (G) partition?
			%-----------------------------------------------------------------------
			%*** Allow user specified confounds?
			%*** ...and user specified convolution of confounds with approximate HRF?
			G = []; Gnames = '';


			%-Add high-pass filter using discrete cosine set
			%-----------------------------------------------------------------------
			bHPF = spm_input('Use high pass filter?','+1','y/n',[1 0],1);
			if bHPF
			HPFc=nScan;
			for i=Conds', HPFc = min([HPFc,max(diff(eOns(eCond==i)))]); end
			HPFc=spm_input('HPF cut-off period {in seconds}','0','e',2HPFcRT);
			%-Find max order for discrete cosine set, HPFk
			% (from period>HPFc, period=nScan*RT/(k/2) for order k; k<nScan/2)
			HPFk = fix(min(2(nScanRT)/HPFc,nScan/2));
			HPF = []; HPFnames = '';
			for k = 1:HPFk
			HPF = [HPF, cos(kpi([1:nScan]-1)'/(nScan-1))];
			HPFnames = ...
			strvcat(HPFnames,sprintf('LowHz (%4.0fs)',2RTnScan/k));
			end
			HPF = HPF - ones(nScan,1)*mean(HPF); %-Mean correct (by column)
			sHPF = sprintf('High-pass filter of %d components, cut off = %ds',...
			HPFk,HPFc);

			G = [G, HPF]; Gnames = strvcat(Gnames,HPFnames);
			else
			HPFc=0; HPF=[]; sHPF='No high-pass filter';
			end

			%-Global normalization options
			%-----------------------------------------------------------------------
			sGloNorm = strvcat('None','Proportional scaling','AnCova');
			iGloNorm = spm_input('Select global normalisation',...
			'+1','m',sGloNorm,[],2);
			sGloNorm = deblank(sGloNorm(iGloNorm,:));


			%-Grand mean scaling
			%-----------------------------------------------------------------------
			sGMsca = strvcat('None','Scaling of overall Grand Mean',...
			'(Implicitly via PropSca global normalisation)');
			if iGloNorm==2, iGMsca=3; else, iGMsca=2; end
			sGMsca = deblank(sGMsca(iGMsca,:));
			GM = 100;


			%-Temporal smoothing
			%-----------------------------------------------------------------------
			%**** What to do with temporal smoothing?
			%SIGMA = spm_input('Temporal smoothing FWHM {secs}','+1','e',6);
			%SIGMA = SIGMA/sqrt(8*log(2))/RT;



			%=======================================================================
			% - C O M P U T A T I O N
			%=======================================================================
			spm('FigName','AdjMean/fMRI: configuring',Finter,CmdLine);
			fprintf('\tconfiguring: ')
			spm('Pointer','Watch');

			%-Memory map files
			%-----------------------------------------------------------------------
			V = spm_vol(char(P));
			spm_check_orientations(V);

			%-Work out required Analyze header info from handles
			%-----------------------------------------------------------------------
			DIM = V(1).dim(1:3);
			VOX = sqrt(sum(V(1).mat(1:3,1:3).^2));
			ORIGIN = (V(1).mat\[0 0 0 1]')';
			ORIGIN = round(ORIGIN(1:3));

			%-Compute global values
			%-----------------------------------------------------------------------
			fprintf('(globals)')
			GX = zeros(nScan,1);
			for i = 1:nScan, GX(i) = spm_global(V(i)); end
			fprintf('\b - done)\n')

			%-Scaling: compute global scaling factors required to implement proportional
			% scaling global normalisation or Grand mean scaling, as requested
			%-----------------------------------------------------------------------
			rGX = GX;
			if iGloNorm==2
			%-Proportional scaling global normalisation
			gSF = GM./GX;
			GX = GM*ones(nScan,1);
			sGloNorm = sprintf('%s, to %g',sGloNorm,GM);
			elseif iGMsca==2
			%-Grand mean scaling (overall)
			gSF = GM/mean(GX);
			GX = GX*gSF;
			sGMsca = sprintf('%s, to %g',sGMsca,GM);
			else %-No scaling
			gSF = ones(nScan,1);
			end


			%-AnCova options: Construct Global covariates of no interest partition
			%-----------------------------------------------------------------------
			if iGloNorm==3
			G = [G, GX-mean(GX)];
			Gnames = strvcat(Gnames,'Global');
			sGloNorm = [sGloNorm,', to Grand Mean'];
			end

			%-Design matrix - raw & with effects of interest orthogonalised wirit G
			%-----------------------------------------------------------------------
			rC = C;
			rX = [rC B G];
			[nrX,Xnames] = spm_DesMtx('sca',C,Cnames,B,Bnames,G,Gnames);
			if size(G,2)
			C = C - Gpinv(G)C;
			end
			X = [C B G];
			nX = spm_DesMtx('sca',X,Xnames);
			EXnames = spm_DesMtx('ETeXNames',Xnames);
			iX = struct( 'H', [], 'C', [1:size(C,2)],...
			'B', size(C,2)+1, 'G', size(C,2) +1 +[1:size(G,2)]);

			%-Temporal smoothing?
			%-----------------------------------------------------------------------
			%**** Include temporal smoothing? Theoretically, shouldn't make any
			% difference to parameter estimates, but would improve efficiency and
			% match SPM-fMRI.
			%**** Q:Orthogonalisation of C wirit G pre or post temporal smoothing?

			%-Parameter estimation matrix & data weights matrix for contrasts
			%-----------------------------------------------------------------------
			pX = pinv(X);

			%-Contrasts (c) & associated weight matrix (W)
			%-----------------------------------------------------------------------
			nc = size(C,2);
			c = [eye(nc), ones(nc,size(B,2)), zeros(nc,size(G,2))];
			cNames = spm_DesMtx('Fnames',EXnames(iX.C));
			%-if we have a null condition, then constant is null condition effect
			if any(Conds==0)
			c = [zeros(1,nc), 1, zeros(1,size(G,2)); c];
			cNames = [{'baseline'};cNames];
			nc = nc+1;
			end

			W = c * pX;
			Fnames = cNames;


			%=======================================================================
			% - D I A G N O S T I C O U T P U T
			%=======================================================================
			fprintf('\tdisplaying diagnostic output...\n')
			figure(Fgraph);


			%-Display files and variables
			%=======================================================================

			%-Display parameters
			%-----------------------------------------------------------------------
			figure(Fgraph); spm_clf; axis off
			text(0.30,1.00,'Adjusted means (fMRI)','Fontsize',16,'Fontweight','Bold')
			text(-.10,0.96,'Files:','Fontweight','Bold')
			text(-.10,0.94,sprintf('1 to %4d: %s...',nScan,P{1}),'FontSize',10)
			text(-.10,0.92,sprintf('Repeat time = %.4f seconds',RT),'FontSize',10)
			text(-.10,0.88,'Image Global means (& conditions):','Fontweight','Bold')

			text(-.1,0.20,['Grand mean scaling: ',sGMsca])
			text(-.1,0.17,['Global normalisation: ',sGloNorm])
			text(-.1,0.14,sHPF)
			text(-.1,0.11,'Effects of interest orthogonalised (residualised) to confounds')
			text(-.1,0.08,'No temporal smoothing (only required for inference)')
			text(-.1,0.05,sprintf('Parameters saved to: %s/SPMadj.mat',pwd),'FontSize',10)

			%-Global mean values plot, with underlayed conditions
			%-----------------------------------------------------------------------
			%-Conditions plot (for comparison with global means plot)
			axes('Position',[0.1,0.5,0.8,0.3])
			image(32+(rC'-min(rC(:)))32(max(rC(:))-min(rC(:))))
			set(gca,'Visible','off')

			axes('Position',[0.1,0.5,0.8,0.3])
			if iGloNorm==2
			plot(rGX)
			ylabel('global mean (pre-scaling)')
			else
			plot(GX)
			ylabel('global mean (post-scaling)')
			end
			set(gca,'XLim',[0.5,nScan+0.5],'Color','none')
			xlabel('scan index')

			spm_print


			%-Depict and label design matrix, depict & label contrasts
			%=======================================================================
			spm_clf(Fgraph); axes('Position',[0 0 1 .95],'Visible','off')
			text(.2,1,'Design Matrix, contrasts & contrast files',...
			'Fontsize',16,'Fontweight','Bold');

			%-Image scaled design matrix, label the effects, add filenames (if <=32)
			%-----------------------------------------------------------------------
			hDesMtx = axes('Position',[.07 .5 .6 .3]);
			image((nX + 1)*32);
			set(hDesMtx,'TickDir','out')
			ylabel('scans')
			xlabel('parameters')
			axes('Position',[.07 .8 .6 .1],'Visible','off')
			dx = 1/size(nX,2);
			tXnames = spm_DesMtx('TeXnames',Xnames);
			for i = 1:size(nX,2)
			text((i - .5)*dx+.01,.3,EXnames{i},'Fontsize',9,'Rotation',90)
			if ~strcmp(Xnames{i},EXnames{i})
			text((i - .5)*dx,.1,tXnames{i},'Fontsize',10,'Interpreter','TeX')
			end
			end
			if nScan<=32
			set(hDesMtx,'YTick',1:nScan)
			axes('Position',[.68 .5 .3 .3],'Visible','off')
			dy = 1/nScan;
			tP = spm_str_manip(P,'k40');
			for i=1:nScan
			text(0,(nScan-i+0.5)*dy,tP{i},'FontSize',8)
			end
			end

			%-Depict contrasts and associated (preliminary) filenames
			%-----------------------------------------------------------------------
			dy = .4/nc;
			axes('Position',[.025 .05 .05 .4],'Visible','off')
			text(0,.5,'contrasts','HorizontalAlignment','Center','Rotation',90,...
			'FontSize',14,'FontWeight','Bold')
			axes('Position',[.6 .44 .40 .02],'Visible','off')
			text(0,1,'Contrast files...','FontSize',10,'FontWeight','Bold')
			text(0,0,sprintf('...in %s',pwd),'FontSize',8)
			hFnames = zeros(1,nc);
			for i = 1:nc
			axes('Position',[.1 (.45 -dyi) .6 .9dy])
			h = bar(c(i,:),1);
			set(h,'FaceColor',[1 1 1]*.8)
			set(gca,'XLim',[.5,size(c,2)+.5],'Visible','off')
			text(0,0,num2str(i),'HorizontalAlignment','Right','FontSize',10)
			hFnames(i) = text(size(c,2)+.55,.1,Fnames{i},'FontSize',10,...
			'Color',[1 1 1]*.5,'FontAngle','Italic');
			end



			%=======================================================================
			% - W R I T E C O N T R A S T I M A G E S
			%=======================================================================
			fprintf('\tspecify filenames for contrast images...\n')
			spm('Pointer');
			guiPos = spm_input('!NextPos');
			for i = 1:nc
			Fnames{i} = spm_input(sprintf('Contrast %d: filename?',i),...
			guiPos,'s',Fnames{i});
			set(hFnames(i),'string',Fnames{i},'Color','k','FontAngle','Normal')
			end
			spm_print

			%-Parameter images (of interest) - Adjusted mean images
			%-----------------------------------------------------------------------
			spm('FigName','AdjMean/fMRI - writing',Finter,CmdLine);
			spm('Pointer','Watch');

			%-Computation - calculations handled by spm_add.c
			%-Using implicit zero masking feature of spm_add: The resultant image
			% will be zero at voxels where any of the input images are zero.

			%-Create handle template for output images as 16bit Int16's
			Vo = struct( 'fname', '',...
			'dim', V(1).dim(1:3),...
			'dt', [4 spm_platform('bigend')],...
			'mat', V(1).mat,...
			'pinfo' , [1,0,0]',...
			'descrip', '');

			%-Loop over contrasts
			for i = 1:nc
			fprintf('\t...writing image %d/%d: %-20s',i,nc,Fnames{i})
			%-Implement weighted sum by weighting scalefactors in image handles
			w = W(i,:).*gSF';
			Q = find(abs(w)>0);
			w = w(Q); wV = V(Q);
			for j=1:length(Q), wV(j).pinfo(1:2,:)=wV(j).pinfo(1:2,:)*w(j); end
			%-Write header
			Vo.fname = [Fnames{i},'.img'];
			Vo.descrip = sprintf('Adjusted mean (spm_adjmean_fmri) - %s',Fnames{i});
			Vo.pinfo = [1,0,0]';
			Vo = spm_create_vol(Vo);
			%-Compute & rewrite header scalefactor
			Vo.pinfo(1) = spm_add(wV,Vo,'m');
			Vo = spm_create_vol(Vo);

			fprintf(' (done)\n')
			end

			%-Prepend PWD to Fnames
			Fnames = cellstr([repmat([pwd,filesep],nc,1),char(Fnames)]);


			%-Save parameters to SPMadj.mat in current directory
			%-----------------------------------------------------------------------
			%-Pack design parameters up in a structure
			Des = struct(...
			'DesName', 'Single sess. fMRI box-cars',...
			'HForm', [],...
			'iSubj', [],...
			'iCond', iCond,...
			'iGloNorm', iGloNorm,...
			'sGloNorm', sGloNorm,...
			'iGMsca', iGMsca,...
			'sGMsca', sGMsca,...
			'GM', GM,...
			'gSF', gSF,...
			'iAdjTo', [],...
			'sAdjTo', [],...
			'aGM', [],...
			'X', X,...
			'nX', nX,...
			'Xnames', Xnames,...
			'aPMap', [],...
			'EXnames', EXnames,...
			'iX', iX );
			if spm_check_version('matlab','7') >= 0
			save('SPMadj.mat','-V6',...
			'SPMid','Des','V','c','cNames','W','Fnames','HPFc','HPF','sHPF','rX','nrX','rGX','GX');
			else
			save('SPMadj.mat',...
			'SPMid','Des','V','c','cNames','W','Fnames','HPFc','HPF','sHPF','rX','nrX','rGX','GX');
			end;


			%=======================================================================
			% - E N D
			%=======================================================================
			spm('FigName','AdjMean/fMRI - done',Finter,CmdLine);
			spm('Pointer','Arrow')
			fprintf('\n\n')

iCAPs/packages/spm12/compat/spm_adjmean_fmri_ui.m31022b3a13e6master

spm_adjmean_fmri_ui.mView Options

iCAPs/packages/spm12/compat/spm_adjmean_fmri_ui.m
31022b3a13e6master

spm_adjmean_fmri_ui.m
View Options