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spm_VOI.m
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function TabDat = spm_VOI(SPM,xSPM,hReg)
% List of local maxima and adjusted p-values for a small Volume of Interest
% FORMAT TabDat = spm_VOI(SPM,xSPM,hReg)
%
% SPM - structure containing analysis details (see spm_spm)
%
% xSPM - structure containing SPM, distribution & filtering details
% - required fields are:
% .swd - SPM working directory - directory containing current SPM.mat
% .Z - minimum of n Statistics {filtered on u and k}
% .n - number of conjoint tests
% .STAT - distribution {Z, T, X or F}
% .df - degrees of freedom [df{interest}, df{residual}]
% .u - height threshold
% .k - extent threshold {resels}
% .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
% .VOX - voxel dimensions {mm}
% .DIM - image dimensions {voxels} - column vector
% .Vspm - Mapped statistic image(s)
% .Ps - P vlues in searched voxels (for FDR)
%
% hReg - Handle of results section XYZ registry (see spm_results_ui.m)
%
% TabDat - Structure containing table data
% - see spm_list for definition
%
%_______________________________________________________________________
%
% spm_VOI is called by the SPM results section and takes variables in
% SPM to compute p-values corrected for a specified volume of interest.
%
% The volume of interest may be defined as a box or sphere centred on
% the current voxel or by a mask image.
%
% If the VOI is defined by a mask this mask must have been defined
% independently of the SPM (e.g.using a mask based on an orthogonal
% contrast)
%
% External mask images should be in the same orientation as the SPM
% (i.e. as the input used in stats estimation). The VOI is defined by
% voxels with values greater than 0.
%
% FDR computations are similarly resticted by the small search volume
%
% See also: spm_list
%_______________________________________________________________________
% @(#)spm_VOI.m 2.18 Karl Friston 02/10/30
%-Parse arguments
%-----------------------------------------------------------------------
if nargin < 2, error('insufficient arguments'), end
if nargin < 3, hReg = []; end
Num = 16; % maxima per cluster
Dis = 04; % distance among maxima (mm)
%-Title
%-----------------------------------------------------------------------
spm('FigName',['SPM{',xSPM.STAT,'}: Small Volume Correction']);
%-Get current location {mm}
%-----------------------------------------------------------------------
xyzmm = spm_results_ui('GetCoords');
%-Specify search volume
%-----------------------------------------------------------------------
str = sprintf(' at [%.0f,%.0f,%.0f]',xyzmm(1),xyzmm(2),xyzmm(3));
SPACE = spm_input('Search volume...',-1,'m',...
{['Sphere',str],['Box',str],'Image'},['S','B','I']);
% voxels in entire search volume {mm}
%-----------------------------------------------------------------------
XYZmm = SPM.xVol.M(1:3,:)*[SPM.xVol.XYZ; ones(1, SPM.xVol.S)];
Q = ones(1,size(xSPM.XYZmm,2));
O = ones(1,size( XYZmm,2));
FWHM = xSPM.FWHM;
switch SPACE
case 'S' %-Sphere
%---------------------------------------------------------------
D = spm_input('radius of VOI {mm}',-2);
str = sprintf('%0.1fmm sphere',D);
j = find(sum((xSPM.XYZmm - xyzmm*Q).^2) <= D^2);
k = find(sum(( XYZmm - xyzmm*O).^2) <= D^2);
D = D./xSPM.VOX;
case 'B' %-Box
%---------------------------------------------------------------
D = spm_input('box dimensions [k l m] {mm}',-2);
str = sprintf('%0.1f x %0.1f x %0.1f mm box',D(1),D(2),D(3));
j = find(all(abs(xSPM.XYZmm - xyzmm*Q) <= D(:)*Q/2));
k = find(all(abs( XYZmm - xyzmm*O) <= D(:)*O/2));
D = D./xSPM.VOX;
case 'I' %-Mask Image
%---------------------------------------------------------------
Msk = spm_get(1,'.img','Image defining search volume');
D = spm_vol(Msk);
str = sprintf('image mask: %s',spm_str_manip(Msk,'a30'));
VOX = sqrt(sum(D.mat(1:3,1:3).^2));
FWHM = FWHM.*(xSPM.VOX./VOX);
XYZ = D.mat \ [xSPM.XYZmm; ones(1, size(xSPM.XYZmm, 2))];
j = find(spm_sample_vol(D, XYZ(1,:), XYZ(2,:), XYZ(3,:),0) > 0);
XYZ = D.mat \ [ XYZmm; ones(1, size( XYZmm, 2))];
k = find(spm_sample_vol(D, XYZ(1,:), XYZ(2,:), XYZ(3,:),0) > 0);
end
xSPM.S = length(k);
xSPM.R = spm_resels(FWHM,D,SPACE);
xSPM.Z = xSPM.Z(j);
xSPM.XYZ = xSPM.XYZ(:,j);
xSPM.XYZmm = xSPM.XYZmm(:,j);
xSPM.Ps = xSPM.Ps(k);
%-Tabulate p values
%-----------------------------------------------------------------------
str = sprintf('search volume: %s',str);
if any(strcmp(SPACE,{'S','B'}))
str = sprintf('%s at [%.0f,%.0f,%.0f]',str,xyzmm(1),xyzmm(2),xyzmm(3));
end
TabDat = spm_list('List',xSPM,hReg,Num,Dis,str);
%-Reset title
%-----------------------------------------------------------------------
spm('FigName',['SPM{',xSPM.STAT,'}: Results']);