Hi Patrick,
Yes. Otherwise you can look at the hyperparameters SPM.xVi.h estimated
on a real dataset and use these as weights for a linear combination of
the covariance components SPM.xVi.Vi.
Best regards,
Guillaume.
On 03/02/18 21:23, Patrick Sadil wrote:
> Hello Guillaume,
> Ah, okay. Thanks for the clarifications (and the reference). So, to
> get what they're calling /V, / I would call
>
> V = spm_Q(.3, n_scans);
>
> Best,
> Patrick
>
> ******************************************
> % design matrix convolved with hrf.
> X = SPM.xX.X;
> n_scans = size(X,1);
>
> % assuming AR(1) process with rho = 0.3
> V = spm_Q(.3, n_scans);
>
> % set of contrasts (if want to detect each condition)
> % no derivatives used, so each column is a beta of interest
> % final column is intercept
> n_contrasts = size(X,2)-1;
> C = [eye(n_contrasts), zeros([n_scans,1])];
>
> % frequency confounds (don't want to accidentally have events that will
> ultimately be filtered away)
> S = SPM.xX.K.X0;
>
> IPvs = eye(n_scans) - (V / (V' * V) * V' ) * (S / (S' * S) * S' );
> M = C * ((X' * V' * IPvs * V * X ) \ C' );
>
> a_optimality = n_contrasts / trace(M);
> d_optimality = det(M) ^ (1/n_contrasts);
>
>
>
> On Thu, Feb 1, 2018 at 7:52 AM Guillaume Flandin <[log in to unmask]
> <mailto:[log in to unmask]>> wrote:
>
> Dear Patrick,
>
> I think I would keep the constant term in X. Note that K are the low
> frequency confounds, not the whitening matrix. I don't think you should
> use the covariance components SPM.xVi.Vi <http://SPM.xVi.Vi> but the
> covariance matrix V is
> only available in SPM.xVi.V after seeing data so what you can do is
> impose its shape (eg use spm_Q); I see this is what they do in
> Neurodesign where the assumed autocorrelation coefficient 'rho' is a
> user-specified input:
> https://www.biorxiv.org/content/biorxiv/early/2017/03/23/119594.full.pdf
>
> Best regards,
> Guillaume.
>
>
> On 20/01/18 15:51, Patrick Sadil wrote:
> > I'm looking for some help in calculating the efficiency of a
> design. I'm unsure about some of the components of an SPM.mat file.
> If I generate an SPM structure using:
> >
> > SPM = spm_run_fmri_spec(matlabbatch{1}.spm.stats.fmri_design);
> >
> > where, matlabbatch is, say, a structure generated from entering a
> design into the gui and saving the job script. Is it true that I'll
> still need to whiten and detrend the design given in SPM.xX.X? So,
> If I were calculating a- and d-optimality does the following look
> correct?
> >
> > % design matrix convolved with hrf. exclude the intercept (does
> this need to be done, or can I just have a contrast matrix that
> excludes the final column?)
> > X = SPM.xX.X(:,1:end-1);
> >
> > % autocorrelation to remove
> > V1 = SPM.xVi.Vi <http://SPM.xVi.Vi>{1};
> > V2 = SPM.xVi.Vi <http://SPM.xVi.Vi>{2};
> >
> > % set of contrasts (if want to detect each condition)
> > % no derivatives used, so each column is a beta of interest
> > n_contrasts = size(X,2);
> > C = eye(n_contrasts);
> >
> > % whitening filter
> > K = SPM.xX.K.X0;
> >
> > % detrended and whitened design
> > IPvs = eye(size(K,1)) - ...
> > ((V1 * inv(V1' * V1) * V1') * (V2 * inv(V2' * V2) * V2') * (K
> * inv(K' * K) * K'));
> > M = (X' * V' * IPvs * V * X );
> >
> > a_optimality = n_contrasts / trace(M);
> > d_optimality = det(C * M * C') ^ (1/n_contrasts);
> >
> > Thanks,
> > Patrick
> >
>
> --
> Guillaume Flandin, PhD
> Wellcome Trust Centre for Neuroimaging
> University College London
> 12 Queen Square
> London WC1N 3BG
>
> --
> Patrick Sadil, B.A.
> Graduate Student
> Department of Psychological and Brain Sciences
>
> Lab: http://people.umass.edu/cmap-lab/index.html
> Personal: http://people.umass.edu/psadil/
>
--
Guillaume Flandin, PhD
Wellcome Trust Centre for Neuroimaging
University College London
12 Queen Square
London WC1N 3BG
