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Slepi/Code/Utilities/spm12/spm_est_non_sphericity.m
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spm_est_non_sphericity.m
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function [xVi, mask] = spm_est_non_sphericity(SPM)
% Non-sphericity estimation using ReML
% FORMAT [xVi, mask] = spm_est_non_sphericity(SPM)
%
% Required fields of SPM structure (see spm_spm):
% SPM.xY.VY - nScan x 1 struct array of file handles
% SPM.xX - structure containing design matrix information
% SPM.xX.W - optional whitening/weighting matrix
% SPM.xVi - structure describing intrinsic non-sphericity
% SPM.xM - structure containing masking information
%
% Return xVi from SPM.xVi with extra fields:
% xVi.V - estimated non-sphericity, trace(V) = rank(V)
% xVi.h - hyperparameters xVi.V = xVi.h(1)*xVi.Vi{1} + ...
% xVi.Cy - spatially whitened <Y*Y'> (used by ReML to estimate h)
%
% mask - logical array of voxels within analysis mask
%__________________________________________________________________________
%
% In a first pass, voxels over which non-sphericity will be estimated are
% selected using an 'effects of interest' F-contrast (can be specified in
% SPM.xVi.Fcontrast) and critical threshold taken from SPM defaults
% stats.<modality>.UFp.
% The sample covariance matrix (xVi.Cy) is then estimated by pooling over
% these voxels, assuming V is constant over them.
% Finally, SPM will invoke ReML to estimate hyperparameters (xVi.h) of an
% array of non-sphericity components (xVi.Vi), providing a high precise
% estimate of the non-sphericity matrix (xVi.V).
%__________________________________________________________________________
% Copyright (C) 1994-2012 Wellcome Trust Centre for Neuroimaging
% Karl Friston & Guillaume Flandin
% $Id: spm_est_non_sphericity.m 6015 2014-05-23 15:46:19Z guillaume $
SVNid = '$Rev: 6015 $';
%-Say hello
%--------------------------------------------------------------------------
spm('FnBanner',mfilename,SVNid);
%-Get data, design, mask and variance components details
%--------------------------------------------------------------------------
VY = SPM.xY.VY;
DIM = VY(1).dim;
YNaNrep = spm_type(VY(1).dt(1),'nanrep');
xX = SPM.xX;
nScan = size(xX.X,1);
if ~isfield(xX,'W')
xX.W = speye(nScan,nScan);
end
xM = SPM.xM;
xVi = SPM.xVi;
%-Compute Hsqr and F-threshold under i.i.d.
%--------------------------------------------------------------------------
xX.xKXs = spm_sp('Set',spm_filter(xX.K,xX.W*xX.X));
xX.xKXs.X = full(xX.xKXs.X);
xX.pKX = spm_sp('x-',xX.xKXs);
if isfield(xVi,'Fcontrast')
Fcname = 'User-specified contrast';
xCon = spm_FcUtil('Set',Fcname,'F','c',xVi.Fcontrast,xX.xKXs);
else
Fcname = 'effects of interest';
iX0 = [xX.iB xX.iG];
xCon = spm_FcUtil('Set',Fcname,'F','iX0',iX0,xX.xKXs);
end
if ~isempty(xCon(1).c)
X1o = spm_FcUtil('X1o', xCon(1),xX.xKXs);
Hsqr = spm_FcUtil('Hsqr',xCon(1),xX.xKXs);
trMV = spm_SpUtil('trMV',X1o);
else
% Force all voxels to enter non-sphericity
trMV = 1;
Hsqr = Inf;
end
trRV = spm_SpUtil('trRV',xX.xKXs);
%-Threshold for voxels entering non-sphericity estimates
%--------------------------------------------------------------------------
try
modality = lower(spm_get_defaults('modality'));
UFp = spm_get_defaults(['stats.' modality '.ufp']);
catch
UFp = 0.001;
end
xVi.UFp = UFp;
UF = spm_invFcdf(1 - UFp,[trMV,trRV]);
%==========================================================================
%- P O O L E D V A R I A N C E E S T I M A T I O N
%==========================================================================
%-Get explicit mask(s)
%==========================================================================
mask = true(DIM);
for i = 1:numel(xM.VM)
C = spm_bsplinc(xM.VM(i), [0 0 0 0 0 0]');
v = true(DIM);
[x1,x2] = ndgrid(1:DIM(1),1:DIM(2));
for x3 = 1:DIM(3)
M2 = inv(VY(1).mat\xM.VM(i).mat);
y1 = M2(1,1)*x1+M2(1,2)*x2+(M2(1,3)*x3+M2(1,4));
y2 = M2(2,1)*x1+M2(2,2)*x2+(M2(2,3)*x3+M2(2,4));
y3 = M2(3,1)*x1+M2(3,2)*x2+(M2(3,3)*x3+M2(3,4));
v(:,:,x3) = spm_bsplins(C, y1,y2,y3, [0 0 0 0 0 0]') > 0;
end
mask = mask & v;
clear C v x1 x2 x3 M2 y1 y2 y3
end
Cy = 0; %-<Y*Y'> spatially whitened
Cm = mask; %-mask of selected voxels
%-Split data into chunks
%==========================================================================
chunksize = floor(spm_get_defaults('stats.maxmem') / 8 / nScan);
nbchunks = ceil(prod(DIM) / chunksize);
chunks = min(cumsum([1 repmat(chunksize,1,nbchunks)]),prod(DIM)+1);
spm_progress_bar('Init',nbchunks,'Hyperparameter estimation','Chunks');
for i=1:nbchunks
chunk = chunks(i):chunks(i+1)-1;
%-Report progress
%======================================================================
if i > 1, fprintf(repmat(sprintf('\b'),1,72)); end %-#
fprintf('%-40s: %30s', sprintf('Chunk %3d/%-3d',i,nbchunks),...
'...processing'); %-#
%-Get data & construct analysis mask
%----------------------------------------------------------------------
Y = zeros(nScan,numel(chunk));
cmask = mask(chunk);
for j=1:nScan
if ~any(cmask), break, end %-Break if empty mask
Y(j,cmask) = spm_data_read(VY(j),chunk(cmask));%-Read chunk of data
cmask(cmask) = Y(j,cmask) > xM.TH(j); %-Threshold (& NaN) mask
if xM.I && ~YNaNrep && xM.TH(j) < 0 %-Use implicit mask
cmask(cmask) = abs(Y(j,cmask)) > eps;
end
end
cmask(cmask) = any(diff(Y(:,cmask),1)); %-Mask constant data
mask(chunk) = cmask;
Cm(chunk) = cmask;
if ~any(cmask), continue, end
Y = Y(:,cmask); %-Data within mask
%-Remove filter confounds
%----------------------------------------------------------------------
KWY = spm_filter(xX.K,xX.W*Y);
%-Ordinary Least Squares estimation
%----------------------------------------------------------------------
beta = xX.pKX*KWY; %-Parameter estimates
if any(cmask)
res = spm_sp('r',xX.xKXs,KWY); %-Residuals
else
res = zeros(nScan,0);
end
ResSS = sum(res.^2); %-Residual SSQ
clear res
%-F-threshold & accumulate spatially whitened Y*Y'
%----------------------------------------------------------------------
j = sum((Hsqr*beta).^2,1)/trMV > UF*ResSS/trRV;
Cm(chunk(cmask)) = j;
q = nnz(j);
if q
q = spdiags(sqrt(trRV./ResSS(j)'),0,q,q);
Y = Y(:,j)*q;
Cy = Cy + Y*Y';
end
%-Report progress
%======================================================================
fprintf('%s%30s',repmat(sprintf('\b'),1,30),'...done'); %-#
spm_progress_bar('Set',i);
end
fprintf('\n'); %-#
spm_progress_bar('Clear');
s = nnz(Cm); %-Number of selected voxels
if ~s
error('Please check your data: There are no significant voxels.');
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%-Estimate sample covariance matrix Y*Y' from voxels in Cm
% Cy = 0;
% nbchunks = ceil(s / chunksize);
% chunks = min(cumsum([1 repmat(chunksize,1,nbchunks)]),s+1);
% vx = find(Cm);
% for i=1:nbchunks
% chunk = chunks(i):chunks(i+1)-1;
% n = numel(chunk);
% Y = zeros(nScan,n);
% for j=1:nScan
% Y(j,:) = spm_data_read(VY(j),vx(chunk));
% end
% % store ResSS above to prevent recomputing it here:
% %--------------------------------------------------
% KY = spm_filter(xX.K,Y);
% ResSS = spm_sp('r',xX.xKXs,KY);
% ResSS = sum(ResSS.^2);
% %--------------------------------------------------
% q = spdiags(sqrt(trRV./ResSS'),0,n,n);
% Y = Y*q;
% Cy = Cy + Y*Y';
% end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Cy = Cy / s; %-Sample covariance matrix
%==========================================================================
%- R e M L E S T I M A T I O N
%==========================================================================
%-ReML estimate of residual correlations through hyperparameters (h)
%--------------------------------------------------------------------------
str = sprintf('Temporal non-sphericity (%d voxels)',s);
fprintf('%-40s: %30s\n',str,'...ReML estimation'); %-#
%-ReML for separable designs and covariance components
%--------------------------------------------------------------------------
if isstruct(xX.K)
m = length(xVi.Vi);
h = zeros(m,1);
V = sparse(nScan,nScan);
for i = 1:length(xX.K)
% extract blocks from bases
%------------------------------------------------------------------
q = xX.K(i).row;
p = [];
Qp = {};
for j = 1:m
if nnz(xVi.Vi{j}(q,q))
Qp{end + 1} = xVi.Vi{j}(q,q);
p = [p j];
end
end
% design space for ReML (with confounds in filter)
%------------------------------------------------------------------
Xp = xX.X(q,:);
try
Xp = [Xp xX.K(i).X0];
end
% ReML
%------------------------------------------------------------------
fprintf('%-30s\n',sprintf(' ReML Block %i',i));
[Vp,hp] = spm_reml(Cy(q,q),Xp,Qp);
V(q,q) = V(q,q) + Vp;
h(p) = hp;
end
else
[V,h] = spm_reml(Cy,xX.X,xVi.Vi);
end
%-Normalize non-sphericity and save hyperparameters
%--------------------------------------------------------------------------
V = V*nScan/trace(V);
xVi.h = h;
xVi.V = V; % Save non-sphericity xVi.V
xVi.Cy = Cy; % spatially whitened <Y*Y'>
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