Dear Martin
This is just a warning, not an error. Do the maps look correct now- i.e.,
not totally black and the thresholds appear reasonable. If so then it's
fine.
darren
> -----Original Message-----
> From: Martin Kronbichler [mailto:[log in to unmask]]
> Sent: Wednesday, January 31, 2007 4:18 AM
> To: Darren G
> Cc: Martin Kronbichler
> Subject: Re: spm5 bayesian problems
>
> Dear Darren,
>
> thanks for your fast response. However, the fix didn't work for me.
> I tried to reestimate the design with you updated
> spm_spm_Bayes as well as manually replacing "tmp = tmp +
> NaN*(~tmp); " with "tmp(~tmp) = NaN;" in both lines in my
> local spm_spm_Bayes. In both cases i get the same error when
> i test a specific contrast
> (3 conditions -1 0.5 0.5), however when i use a different
> contrast (e.g., condition 1 vs. implicit baseline: 1 0 0) i
> don't get the error mesage and the results look reasonable.
> It seems as if the error occurs whenever i try to contrast
> any of the modelled conditions against one of the other
> modelled conditions, but not when i estimate the design,
> Strangely, the same design gives reasonable results in an
> other patients dataset and normally first-level bayesian
> analysis works extremly well for my data-sets. You can see
> the error returnded when i use dbstop below, to my limited
> understanding the error starts in spm_Ncdf (the cum normal
> distribution..). I might add that i use (still) MATLAB R13).
> Maybe you have any ideas left what's going wrong?
>
>
> Greetings,
>
> Martin
>
>
>
> In /opt/spm5/spm_Ncdf.m at line 80
> In /opt/spm5/spm_contrasts.m at line 214
> In /opt/spm5/spm_getSPM.m at line 474
> In /opt/spm5/spm_results_ui.m at line 264
> In /opt/spm5/spm_input.m at line 1121
> In /opt/spm5/spm_getSPM.m at line 376
> In /opt/spm5/spm_results_ui.m at line 264
>
> Warning in ==> /opt/spm5/spm_Ncdf.m
> On line 80 ==> warning('Returning NaN for out of
> range arguments'), end
>
>
> On Tue, 30 Jan 2007 10:03:24 -0600, d gitelman
> <[log in to unmask]>
> wrote:
>
> > Dear Martin
> >
> >Greetings. The problem is in spm_spm_Bayes.m when one uses
> Matlab R14
> >or greater. If you go to approx lines 294 and 301 this code :
> > tmp = tmp + NaN*(~tmp);
> >
> >produces the NaN's.
> >I have updated this in the FIL version of the file and hopefully it
> >will make its way to wider distribution at a future update
> (or it may
> >have already).
> >
> >In any case replace the above with this line in both places.
> >
> > tmp(~tmp) = NaN;
> >
> >You will have to re-estimate the dataset, but then all
> should be fine.
> >
> >Attached is also an updated version of spm_spm_Bayes.m
> >
> >Regards,
> >Darren
> >
> >> -----Original Message-----
> >> From: SPM (Statistical Parametric Mapping)
> >> [mailto:[log in to unmask]] On Behalf Of Martin Kronbichler
> >> Sent: Tuesday, January 30, 2007 4:12 AM
> >> To: [log in to unmask]
> >> Subject: [SPM] spm5 bayesian problems
> >>
> >> Dear SPM Experts,
> >>
> >> i have recently started to use variational bayesian first level
> >> analyses for analysing presurgical language and motor experiments
> >> because i had the feeling that the posterior probalities
> are easier
> >> to communicate and easier to understand (especially when making
> >> inferences on how likely increased activatity for speech
> or motor is
> >> in regions near to the lesions).
> >> Generally, the bayesian analyses worked extremly well.
> >> However i have stumpled across a strange result i do not
> understand
> >> when analysing a block-design language experiment
> >> (2 blockedc onditions: simple tones vs. auditory words
> presented in 2
> >> sessions). Whatever effect size threshold (even completerly
> >> unrealistic values like > 1000) or posterior probality i
> choose the
> >> PPM shows the whole brains as exhibiting posterior prob.
> of 1 (i.e.
> >> the whole brain is "activated"). SPM5 displays the following error
> >> message when is estimate the contrasts:
> >>
> >> spm{P} image 1 : ...computing
> >> Warning: Returning NaN for out of range arguments
> >> > In /opt/spm5/spm_Ncdf.m at line 80
> >> In /opt/spm5/spm_contrasts.m at line 214
> >> In /opt/spm5/spm_getSPM.m at line 474
> >> In /opt/spm5/spm_results_ui.m at line 264
> >> ...written spmP_0001.img
> >> SPM computation :
> >> ...initialising
> >> SPM computation :
> >> ...done
> >> SPM computation :
> >> ...done
> >>
> >> I have tried to remove and add motion parameters, analyse only one
> >> session, use different basis functions etc... but whatever
> i do the
> >> problem persits.
> >> Inspection of the files generated by SPM5 shows that the beta and
> >> SDbeta images look reasonable. The spmP and con_SD images however
> >> contain only NaNs..
> >> I might add that a conventional analysis results in stastically
> >> highly signficant voxels...
> >> Anybody has any idea what is causing this problem?
> >>
> >> Greetings from Salzburg,
> >>
> >> Martin
> >>
> >>
> >> Martin Kronbichler, M.Sc.
> >> ---------------------------------------------
> >> Department of Psychology
> >>
> >>
> >> Center for Neurocognitive Research
> >> University of Salzburg
> >>
> >> Hellbrunnerstr.34, A 5020 Salzburg, Austria
> >> e-mail:[log in to unmask]
> >> Tel.:+43/(0)/662/8044-5162
> >>
> >> Fax:+43/(0)/662/8044-5126
> >>
> >> ---------------------------------------------
> >> Department of Neurology
> >> Center for Neurocognitive Research Christian-Doppler Clinic,
> >> Paracelsus Private Medical University Ignaz Harrerstr.79, A 5020
> >> Salzburg, Austria e-mail:[log in to unmask]
> >> Tel.:+43/(0)/662/4483-3966
> >> ---------------------------------------------
> >>
> >
> >function [SPM] = spm_spm_Bayes(SPM)
> >% Conditional parameter estimation of a General Linear Model
> % FORMAT
> >[SPM] = spm_spm_Bayes(SPM)
> >%____________________________________________________________
> __________
> >_
> >%
> >% For single subject fMRI analysis this function switches to % using
> >voxel-wise GLM-AR models that are spatially regularised %
> using the VB
> >framework. This is implemented using spm_spm_vb.m.
> >% One need not have previously analysed the data using
> classical estimation.
> >% For more on this option please see the help in that file.
> >% Otherwise, read on.
> >%
> >% spm_spm_Bayes returns to voxels identified by spm_spm (ML
> parameter %
> >estimation) to get conditional parameter estimates and ReML hyper- %
> >parameter estimates. These estimates use prior covariances,
> on the %
> >parameters, from emprical Bayes. These PEB prior variances
> come from %
> >the hierarchical model that obtains by considering voxels as
> providing
> >% a second level. Put simply, the variance in parameters,
> over voxels,
> >% is used as a prior variance from the point of view of any
> one voxel.
> >% The error covariance hyperparameters are re-estimated in
> the light of
> >% these priors. The approach adopted is essentially a fully
> Bayesian %
> >analysis at each voxel, using emprical Bayesian prior variance %
> >estimators over voxels.
> >%
> >% Each separable partition (i.e. session) is assigned its own %
> >hyperparameter but within session covariance components are lumped %
> >together, using their relative expectations over voxels.
> This makes %
> >things much more computationally efficient and avoids inefficient %
> >voxel-specific multiple hyperparameter estimates.
> >%
> >% spm_spm_Bayes adds the following fields to SPM:
> >%
> >% ----------------
> >%
> >%
> >% SPM.PPM.l = session-specific hyperparameter means
> >% SPM.PPM.Cb = empirical prior parameter covariances
> >% SPM.PPM.C = conditional covariances of parameters
> >% SPM.PPM.dC{i} = dC/dl;
> >% SPM.PPM.ddC{i} = ddC/dldl
> >%
> >% The derivatives are used to compute the conditional variance of
> >various % contrasts in spm_getSPM, using a Taylor expansion
> about the
> >hyperparameter % means.
> >%
> >%
> >% ----------------
> >%
> >% SPM.VCbeta - Handles of conditional parameter estimates
> >% SPM.VHp - Handles of hyperparameter estimates
> >%
> >% ----------------
> >%
> >% Cbeta_????.{img,hdr} - conditional
> parameter images
> >% These are 16-bit (float) images of the conditional estimates. The
> >image % files are numbered according to the corresponding
> column of the
> >% design matrix. Voxels outside the analysis mask (mask.img)
> are given
> >% value NaN.
> >%
> >% ----------------
> >%
> >% CHp_????.{img,hdr} - error covariance
> hyperparamter images
> >% This is a 32-bit (double) image of the ReML error variance
> estimate.
> >% for each separable partition (Session). Voxels outside
> the analysis
> >% mask are given value NaN.
> >%
> >%____________________________________________________________
> __________
> >_ % Copyright (C) 2005 Wellcome Department of Imaging Neuroscience
> >
> >% Karl Friston
> >% $Id: spm_spm_Bayes.m 587 2006-08-07 04:38:22Z Darren $
> >
> >
> >%-Say hello
> >%------------------------------------------------------------
> ----------
> >- Finter = spm('FigName','Stats: Bayesian estimation...');
> >
> >%-Select SPM.mat & change directory
> >%------------------------------------------------------------
> ----------
> >-
> >if ~nargin
> > swd = spm_str_manip(spm_select(1,'^SPM\.mat$','Select
> SPM.mat'),'H');
> > load(fullfile(swd,'SPM.mat'))
> > cd(swd)
> >end
> >
> >try
> > M = SPM.xVol.M;
> > DIM = SPM.xVol.DIM;
> > xdim = DIM(1); ydim = DIM(2); zdim = DIM(3);
> > XYZ = SPM.xVol.XYZ;
> >catch
> > helpdlg({ 'Please do a ML estimation first',...
> > 'This identifies the voxels to analyse'});
> > spm('FigName','Stats: done',Finter); spm('Pointer','Arrow')
> > return
> >end
> >
> >
> >%============================================================
> ===========
> >% - A N A L Y S I S P R E L I M I N A R I E S
> >%============================================================
> ==========
> >=
> >
> >%-Initialise output images
> >%============================================================
> ===========
> >fprintf('%-40s: %30s','Output images','...initialising')
> %-#
> >
> >%-Intialise oonditional estimate image files
> >%------------------------------------------------------------
> -----------
> >xX = SPM.xX;
> >[nScan nBeta] = size(xX.X);
> >Vbeta(1:nBeta) = deal(struct(...
> > 'fname', [],...
> > 'dim', DIM',...
> > 'dt', [spm_type('float32'),
> spm_platform('bigend')],...
> > 'mat', M,...
> > 'pinfo', [1 0 0]',...
> > 'descrip', ''));
> >for i = 1:nBeta
> > Vbeta(i).fname = sprintf('Cbeta_%04d.img',i);
> > Vbeta(i).descrip = sprintf('Cond. beta (%04d) -
> %s',i,xX.name{i});
> > spm_unlink(Vbeta(i).fname)
> >end
> >Vbeta = spm_create_vol(Vbeta);
> >
> >%-Intialise ReML hyperparameter image files
> >%------------------------------------------------------------
> ----------
> >-
> >try
> > nHp = length(SPM.nscan);
> >catch
> > nHp = nScan;
> > SPM.nscan = nScan;
> >end
> >
> >VHp(1:nHp) = deal(struct(...
> > 'fname', [],...
> > 'dim', DIM',...
> > 'dt', [spm_type('float64'),
> spm_platform('bigend')],...
> > 'mat', M,...
> > 'pinfo', [1 0 0]',...
> > 'descrip', ''));
> >for i = 1:nHp
> > VHp(i).fname = sprintf('Hp_%04d.img',i);
> > VHp(i).descrip = sprintf('Hyperparameter (%04d)',i);
> > spm_unlink(VHp(i).fname)
> >end
> >VHp = spm_create_vol(VHp);
> >
> >fprintf('%s%30s\n',repmat(sprintf('\b'),1,30),'...initialised
') %-#
> >
> >
> >%============================================================
> ==========
> >= % - E M P I R I C A L B A Y E S F O R P R I O R V A R
> I A N C E
> >%============================================================
> ===========
> >fprintf('%s%30s\n',repmat(sprintf('\b'),1,30),'...estimating
> priors') %-#
> >
> >% get row u{i} and column v{i}/v0{i} indices for separable designs
> >%------------------------------------------------------------
> ----------
> >s = nHp;
> >if isfield(SPM,'Sess')
> > for i = 1:s
> > u{i} = SPM.Sess(i).row;
> > v{i} = SPM.Sess(i).col;
> > v0{i} = xX.iB(i);
> > end
> >else
> > u{1} = [1:nScan];
> > v{1} = [xX.iH xX.iC];
> > v0{1} = [xX.iB xX.iG];
> >end
> >
> >% cycle over separarable partitions
> >%------------------------------------------------------------
> ----------
> >-
> >for i = 1:s
> >
> > % Get design X and confounds X0
> > %---------------------------------------------------------------
> > fprintf('%-30s- %i\n',' ReML Session',i);
> > X = xX.X(u{i}, v{i});
> > X0 = xX.X(u{i},v0{i});
> > [m n] = size(X);
> >
> > % add confound in 'filter'
> > %---------------------------------------------------------------
> > if isstruct(xX.K)
> > X0 = full([X0 xX.K(i).X0]);
> > end
> >
> > % orthogonalize X w.r.t. X0
> > %---------------------------------------------------------------
> > X = X - X0*(pinv(X0)*X);
> >
> > % covariance components induced by parameter variations {Q}
> > %---------------------------------------------------------------
> > for j = 1:n
> > Q{j} = X*sparse(j,j,1,n,n)*X';
> > end
> >
> > % covariance components induced by error non-sphericity {V}
> > %---------------------------------------------------------------
> > Q{n + 1} = SPM.xVi.V(u{i},u{i});
> >
> > % ReML covariance component estimation
> > %---------------------------------------------------------------
> > [C h] = spm_reml(SPM.xVi.CY,X0,Q);
> >
> > % check for negative variance components
> > %---------------------------------------------------------------
> > h = abs(h);
> >
> > % 2-level model for this partition using prior variances sP(i)
> > % treat confounds as fixed (i.e. infinite prior variance)
> > %---------------------------------------------------------------
> > n0 = size(X0,2);
> > Cb = blkdiag(diag(h(1:n)),speye(n0,n0)*1e8);
> > P{1}.X = [X X0];
> > P{1}.C = {SPM.xVi.V};
> > P{2}.X = sparse(size(P{1}.X,2),1);
> > P{2}.C = Cb;
> >
> > sP(i).P = P;
> > sP(i).u = u{:};
> > sP(i).v = [v{:} v0{:}];
> >end
> >
> >
> >%============================================================
> ===========
> >% - F I T M O D E L & W R I T E P A R A M E T E R
> I M A G E S
> >%============================================================
> ==========
> >=
> >
> >%-Cycle to avoid memory problems (plane by plane)
> >%============================================================
> ==========
> >= spm_progress_bar('Init',100,'Bayesian estimation','');
> helpdlg('This
> >could take some time');
> >spm('Pointer','Watch')
> >
> >%-maxMem is the maximum amount of data processed at a time (bytes)
> >%------------------------------------------------------------
> ----------
> >-
> >global defaults
> >MAXMEM = defaults.stats.maxmem;
> >blksz = ceil(MAXMEM/8/nScan);
> >SHp = 0; % sum of hyperparameters
> >for z = 1:zdim
> >
> > % current plane-specific parameters
> >
> %-------------------------------------------------------------------
> > U = find(XYZ(3,:) == z);
> > nbch = ceil(length(U)/blksz);
> > CrBl = zeros(nBeta,length(U)); %-conditional
> parameter estimates
> > CrHp = zeros(nHp, length(U)); %-ReML
> hyperparameter estimates
> > for bch = 1:nbch %-loop over bunches of
> lines (planks)
> >
> > %-construct list of voxels in this block
> > %---------------------------------------------------------------
> > I = [1:blksz] + (bch - 1)*blksz;
> > I = I(I <= length(U));
> > xyz = XYZ(:,U(I));
> > nVox = size(xyz,2);
> >
> > %-Get response variable
> > %---------------------------------------------------------------
> > Y = spm_get_data(SPM.xY.VY,xyz);
> >
> > %-Conditional estimates (per partition, per voxel)
> > %---------------------------------------------------------------
> > beta = zeros(nBeta,nVox);
> > Hp = zeros(nHp, nVox);
> > for j = 1:s
> > P = sP(j).P;
> > u = sP(j).u;
> > v = sP(j).v;
> > for i = 1:nVox
> > [C P] = spm_PEB(Y(u,i),P);
> > beta(v,i) = C{2}.E(1:length(v));
> > Hp(j,i) = C{1}.h;
> > end
> > end
> >
> > %-Save for current plane in memory as we go along
> > %---------------------------------------------------------------
> > CrBl(:,I) = beta;
> > CrHp(:,I) = Hp;
> > SHp = SHp + sum(Hp,2);
> >
> > end % (bch)
> >
> >
> > %-write out plane data to image files
> >
> > %===================================================================
> >
> > %-Write conditional beta images
> >
> %-------------------------------------------------------------------
> > for i = 1:nBeta
> > tmp = sparse(XYZ(1,U),XYZ(2,U),CrBl(i,:),xdim,ydim);
> >% The following line (and the same code on line 306
> below) results in
> >% images with all NaN's (in Matlab R14 or greater). It has been
> >% commented out by DRG, and replaced by the line:
> tmp(~tmp) = NaN;
> >% tmp = tmp + NaN*(~tmp);
> > tmp(~tmp) = NaN;
> > Vbeta(i) = spm_write_plane(Vbeta(i),tmp,z);
> > end
> >
> > %-Write hyperparameter images
> >
> %-------------------------------------------------------------------
> > for i = 1:nHp
> > tmp = sparse(XYZ(1,U),XYZ(2,U),CrHp(i,:),xdim,ydim);
> >% tmp = tmp + NaN*(~tmp);
> > tmp(~tmp) = NaN;
> > VHp(i) = spm_write_plane(VHp(i),tmp,z);
> > end
> >
> >
> > %-Report progress
> >
> %-------------------------------------------------------------------
> > spm_progress_bar('Set',100*(z - 1)/zdim);
> >
> >
> >end % (for z = 1:zdim)
> >fprintf('\n')
> %-#
> >spm_progress_bar('Clear')
> >
> >%============================================================
> ===========
> >% - P O S T E S T I M A T I O N
> >%============================================================
> ==========
> >=
> >
> >% Taylor expansion for conditional covariance
> >%------------------------------------------------------------
> ----------
> >-
> >fprintf('%-40s: %30s\n','Non-sphericity','...REML estimation') %-#
> >
> >% expansion point (mean hyperparameters)
> >%------------------------------------------------------------
> -----------
> >l = SHp/SPM.xVol.S;
> >
> >% change in conditional coavriance w.r.t. hyperparameters
> >%------------------------------------------------------------
> -----------
> >n = size(xX.X,2);
> >PPM.l = l;
> >for i = 1:s
> > PPM.dC{i} = sparse(n,n);
> > PPM.ddC{i} = sparse(n,n);
> >end
> >for i = 1:s
> >
> > P = sP(i).P;
> > u = sP(i).u;
> > v = sP(i).v;
> >
> > % derivatives of conditional covariance w.r.t. hyperparameters
> > %---------------------------------------------------------------
> > if spm_matlab_version_chk('6.5.1') < 0
> > % MATLAB 6.5.0 R13 has a bug in dealing with sparse
> matrices so
> revert to full matrices
> > fprintf('%-40s','MATLAB 6.5.0 R13: must use full matrices -
> > this
> could cause memory problems...');
> > d = full(P{1}.X'*inv(P{1}.C{1})*P{1}.X);
> > Cby = full(inv(d/l(i) + inv(P{2}.C)));
> > d = full(d*Cby);
> > dC = full(Cby*d/(l(i)^2));
> > ddC = full(2*(dC/(l(i)^2) - Cby/(l(i)^3))*d);
> > else
> > % all other MATLAB versions: use sparse matrices
> > d = P{1}.X'*inv(P{1}.C{1})*P{1}.X;
> > Cby = inv(d/l(i) + inv(P{2}.C));
> > d = d*Cby;
> > dC = Cby*d/(l(i)^2);
> > ddC = 2*(dC/(l(i)^2) - Cby/(l(i)^3))*d;
> > end
> >
> > % place in output structure
> > %---------------------------------------------------------------
> > if spm_matlab_version_chk('6.5.1') < 0,
> > % MATLAB 6.5.0 R13: revert to full matrices
> > j = 1:length(v);
> > PPM.Cb(v,v) = full(P{2}.C(j,j));
> > PPM.Cby(v,v) = full(Cby(j,j));
> > PPM.dC{i}(v,v) = full(dC(j,j));
> > PPM.ddC{i}(v,v) = full(ddC(j,j));
> > else
> > % all other MATLAB versions: use sparse matrices
> > j = 1:length(v);
> > PPM.Cb(v,v) = P{2}.C(j,j);
> > PPM.Cby(v,v) = Cby(j,j);
> > PPM.dC{i}(v,v) = dC(j,j);
> > PPM.ddC{i}(v,v) = ddC(j,j);
> > end
> >
> >end
> >
> >%-Save remaining results files and analysis parameters
> >%============================================================
> ===========
> >fprintf('%-40s: %30s','Saving results','...writing')
> %-#
> >
> >%-Save analysis parameters in SPM.mat file
> >%------------------------------------------------------------
> -----------
> >SPM.VCbeta = Vbeta; % Filenames - parameters
> >SPM.VHp = VHp; % Filenames - hyperparameters
> >SPM.PPM = PPM; % PPM structure
> >
> >if spm_matlab_version_chk('7') >=0
> > save('SPM', 'SPM', '-V6');
> >else
> > save('SPM', 'SPM');
> >end;
> >
> >fprintf('%s%30s\n',repmat(sprintf('\b'),1,30),'...done')
> %-#
> >
> >
> >%============================================================
> ==========
> >=
> >%- E N D: Cleanup GUI
> >%============================================================
> ==========
> >=
> >spm('FigName','Stats: done',Finter); spm('Pointer','Arrow')
> >fprintf('%-40s: %30s\n','Completed',spm('time'))
> %-#
> >fprintf('...use the results section for assessment\n\n')
> %-#
> >
>
>
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