> From [log in to unmask] Wed Feb 9 01:51:39 2000
> Date: Tue, 8 Feb 2000 19:48:07 -0600
> Subject: how to get equivalent data to XA.mat in spm99?
Dear Darren,
> I have previously analyzed some data in spm96 but want to move to
> spm99. For this particular analysis (fmri data) I have no covariates of
> interest but several covariates of no interest. I remove the covariates
> of no interest from the raw data and I'm left with data in XA.mat with
> covariates of no interest removed, which I can then use in other
> analyses.
>
> How do I do this in spm99? My guess is this involves taking the data
> saved in Y.mad and removing covariates of no interest but I'm not sure
> of the exact steps. The complicating factor is that I want this
> information basically for all voxels Y.mad at once and it looks like I
> may have to extract the parameter estimates for every voxel. It was
> nice having XA.mat...
The advantage of SPM99 is that you can adjust your data using F
contrasts to specify [different] confounds post hoc. Use V.O.I under
'Data extraction' in 'Results' and a large (e.g. 64mm) VOI radius to
encompass the entire brain. The results are save in a ???.mat file and
include:
Y - first eigenvariate of VOI
xY - structure with:
xY.name - name of VOI
xY.y - voxel-wise data (filtered and adjusted)
xY.u - first eigenvariate
xY.v - first eigenimage
xY.s - eigenimages
xY.XYZmm - Co-ordinates of voxels used within VOI
xY.xyz - centre of VOI (mm)
xY.radius - radius of VOI (mm)
xY.dstr - description of filtering & adjustment applied
(see spm_regions.m). I have included an updated version of
spm_regions.m that handles large numbers of voxels (a similar
functionality is already available in SPM99, as released, but it will be
slower with large VOIs).
> Second question- If I want to effect a low pass filter of a specific Hz
> is there some conversion from the FWHM in seconds which is entered in
> spm99 and the Hz of the filter? Let's say I want .09 Hz?
No. Low-pass filtering uses a convolution scheme (hence the FWHM of
the kernel). The modulation transfer function of this is the Fourier
Transform of the kernel and does not correspond to a Low-pass filter
with a sharp cut-off. This is for computational efficiency - Low pass
filtering uses a sparse convolution matrix and High-pass filtering uses
the residual forming matrix of some low frequency confounds.
I Hope this helps - Karl
___________________________________________________________________________
spm_regions.m
function [Y,xY] = spm_regions(SPM,VOL,xX,xCon,xSDM,hReg)
% VOI time-series extraction of adjusted data (local eigenimage analysis)
% FORMAT [Y xY] = spm_regions(SPM,VOL,xX,xCon,xSDM,hReg);
%
% SPM - structure containing SPM, distribution & filtering detals
% VOL - structure containing details of volume analysed
% xX - Design Matrix structure
% xSDM - structure containing contents of SPM.mat file
% xCon - Contrast definitions structure (see spm_FcUtil.m for details)
% hReg - Handle of results section XYZ registry (see spm_results_ui.m)
%
% Y - first eigenvariate of VOI
% xY - structure with:
% xY.name - name of VOI
% xY.y - voxel-wise data (filtered and adjusted)
% xY.u - first eigenvariate
% xY.v - first eigenimage
% xY.s - eigenimages
% xY.XYZmm - Co-ordinates of voxels used within VOI
% xY.xyz - centre of VOI (mm)
% xY.radius - radius of VOI (mm)
% xY.dstr - description of filtering & adjustment applied
%
% Y and xY are also saved in VOI_*.mat in the SPM working directory
%
% (See spm_getSPM for details on the SPM,VOL, xX & xSDM structures.)
%
%_______________________________________________________________________
%
% spm_regions extracts a representative time course from voxel data in
% Y.mad in terms of the first eigenvariate of the filtered abd adjusted
% data in all suprathreshold voxels saved (in Y.mad) within a spherical VOI
% centered on the nearest Y.mad voxel to the selected location.
%
% If temporal filtering has been specified (fMRI), then the data is
% temporally filtered. Adjustment is with respect to the null space of
% a selected contrast, or can be omitted.
%
% For a VOI of radius 0, the (filtered &) adjusted voxel time-series is
% returned, scaled to have a 2-norm or 1. The actual (filtered &)
% adjusted voxel time series can be extracted from xY.y, and will be
% the same as the (filtered &) adjusted data returned by the plotting
% routine (spm_graph.m) for the same contrast.
%
% See spm_spm.m for further details of how voxels are selected for the
% saving of their raw data in Y.mad.
%_______________________________________________________________________
% @(#)spm_regions.m 2.4 Karl Friston 00/02/09
% get figure handles
%-----------------------------------------------------------------------
Finter = spm_figure('GetWin','Interactive');
Fgraph = spm_figure('GetWin','Graphics');
set(Finter,'Name','VOI time-series extraction')
%-Find nearest voxel [Euclidean distance] in point list with data saved
% in Y.mad, and update GUI location
%-----------------------------------------------------------------------
if ~length(SPM.XYZmm)
spm('alert!','No suprathreshold voxels!',mfilename,0);
Y = []; xY = [];
return
elseif exist(fullfile(SPM.swd,'Y.mad')) ~= 2
spm('alert!','No raw data saved with this analysis!',mfilename,0);
Y = []; xY = [];
return
elseif ~any(SPM.QQ)
spm('alert!','No raw data saved for any suprathreshold location!',...
mfilename,0);
Y = []; xY = [];
return
end
[xyz,i] = spm_XYZreg('NearestXYZ',...
spm_XYZreg('GetCoords',hReg),SPM.XYZmm(:,find(SPM.QQ)));
spm_XYZreg('SetCoords',xyz,hReg);
%-Get adjustment options, VOI name and radius
%-----------------------------------------------------------------------
spm_input(sprintf('at [%3.0f %3.0f %3.0f]',xyz),1,'d',...
'VOI time-series extraction')
Ic = spm_input('Adjust data for (select contrast)',2,'m',...
{'<don''t adjust>',xCon.name})-1;
Rname = spm_input('name of region',3,'s');
R = spm_input('VOI radius (mm)',4,'r',0,1,[0,Inf]);
%-Find suprathreshold voxels within radius, & note those also in Y.mad
%-----------------------------------------------------------------------
d = [SPM.XYZmm(1,:)-xyz(1); SPM.XYZmm(2,:)-xyz(2); SPM.XYZmm(3,:)-xyz(3)];
Q = find(sum(d.^2) <= R^2);
q = find(SPM.QQ(Q));
if any(SPM.QQ(Q)==0)
spm('alert"',{...
sprintf('Don''t have raw data for all %d suprathreshold',length(Q)),...
sprintf('voxels within %.1gmm of [%3.0f %3.0f %3.0f]',R,xyz),' ',...
sprintf('Proceeding using the %d voxels that are.',length(q)),...
},mfilename,sqrt(-1));
end
%-Extract required data from results files
%=======================================================================
%-Get (approximate) raw data y from Y.mad file
%-NB: Data in Y.mad file is compressed, and therefore not fully accurate
%-----------------------------------------------------------------------
y = spm_extract(fullfile(SPM.swd,'Y.mad'),SPM.QQ(Q(q)));
rcp = VOL.iM(1:3,:)*[SPM.XYZmm(:,Q(q));ones(size(q))];
%-Parameter estimates: beta = xX.pKX*xX.K*y; (load from file for accuracy)
%-----------------------------------------------------------------------
nBeta = length(xSDM.Vbeta);
beta = ones(nBeta,size(q,2));
for i = 1:nBeta
beta(i,:) = spm_sample_vol(xSDM.Vbeta(i),rcp(1,:),rcp(2,:),rcp(3,:),0);
end
%-Computation
%=======================================================================
% filter and remove confounds
%-----------------------------------------------------------------------
y = spm_filter('apply',xX.K, y);
if Ic
y = y - spm_FcUtil('Y0',xCon(Ic),xX.xKXs,beta);
dstr = ['adjusted for ',xCon(Ic).name];
else
dstr = 'not adjusted';
end
% compute regional response in terms of first eigenvariate
%-----------------------------------------------------------------------
[m n] = size(y);
if m > n
[v s v] = svd(spm_atranspa(y));
s = diag(s);
v = v(:,1);
u = y'*v/sqrt(s(1));
else
[u s u] = svd(spm_atranspa(y'));
s = diag(s);
u = u(:,1);
v = y'*u/sqrt(s(1));
end
d = sign(sum(v));
u = u*d;
v = v*d;
Y = u;
%-Display MIP of VOI weighting and timecourse
% NB: timecourse axes overlap MIP: This obscures the coronal MIP (for MIP96)
% in 3D mode, but for 2D data half the plane image will be obscured.
%-----------------------------------------------------------------------
spm_results_ui('Clear',Fgraph);
figure(Fgraph);
axes('Position',[0.0500,0.1100,0.5500,0.3388])
spm_mip(v(:,1),SPM.XYZmm(:,Q(q)),VOL.M,VOL.DIM)
title('VOI weighting ')
axes('Position',[0.376,0.130,0.520,0.294])
plot(Y)
set(gca,'YAxisLocation','Right')
title(['1st eigenvariate: ' Rname],'FontSize',16)
str = { 'scan number';' ';sprintf(...
'%d voxels in sphere of radius %.1gmm at [%3.0f %3.0f %3.0f]',...
length(q),R,xyz);dstr;sprintf('Variance: %0.2f%%',s(1)*100/sum(s))};
xlabel(str)
% create structure
%-----------------------------------------------------------------------
xY = struct('name', Rname,...
'y', y,...
'u', u,...
'v', v,...
's', s,...
'XYZmm', SPM.XYZmm(:,Q(q)),...
'xyz', xyz,...
'radius', R,...
'dstr', dstr);
% save
%-----------------------------------------------------------------------
save(fullfile(SPM.swd,['VOI_',Rname]),'Y','xY')
%-Reset title
%-----------------------------------------------------------------------
spm('FigName',['SPM{',SPM.STAT,'}: Results']);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
