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iCAPs/packages/spm12/spm_dcm_fmri_mar.m
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spm_dcm_fmri_mar.m
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function DCM = spm_dcm_fmri_mar(DCM)
% Estimates parameters of a DCM using a MAR model of temporal dependencies
% FORMAT DCM = spm_dcm_fmri_mar(DCM)
% DCM - DCM structure
%
% Expects
%--------------------------------------------------------------------------
% DCM.a % switch on endogenous connections
% DCM.b % switch on bilinear modulations
% DCM.c % switch on exogenous connections
% DCM.d % switch on nonlinear modulations
% DCM.U % exogenous inputs
% DCM.Y.y % responses (over time)
% DCM.n % number of regions
% DCM.v % number of scans
%
% This routine estimates the (A and C) parameters of a dynamic causal model
% of fMRI responses, using MAR coefficients under stationarity
% assumptions. The coefficients are estimated from regional timeseries
% (the nodes of the DCM graph) using a Bayesian multivariate autoregressive
% model. The coefficients are then fitted using linear systems
% theory in coefficient space, under the simple assumption that the observed
% spectra are the predicted spectra plus some smooth Gaussian fluctuations
% (noise). The characterisation of the model parameters can then be
% examined in terms of directed transfer functions, spectral density and
% crosscorrelation functions at the neuronal level – having accounted for
% variations in haemodynamics at each node.
%
% Note that neuronal fluctuations are not changes in synaptic activity or
% depolarisation per se but the fluctuations in the power of underlying
% neuronal dynamics. As such, they have much slower time constants than the
% neuronal dynamics.
%
% see also: spm_dcm_estimate
% spm_dcm_fmri_csd
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
% Karl Friston
% $Id: spm_dcm_fmri_mar.m 5633 2013-09-10 13:58:03Z karl $
% get DCM
%--------------------------------------------------------------------------
if ischar(DCM), load(DCM); end
% DCM name
%--------------------------------------------------------------------------
try, DCM.name; catch, DCM.name = sprintf('DCM_%s',date); end
% check options and specification
%==========================================================================
try, DCM.options.two_state; catch, DCM.options.two_state = 0; end
try, DCM.options.stochastic; catch, DCM.options.stochastic = 0; end
try, DCM.options.centre; catch, DCM.options.centre = 0; end
try, DCM.options.nmax; catch, DCM.options.nmax = 8; end
try, DCM.options.nograph; catch, DCM.options.nograph = spm('CmdLine'); end
% parameter initialisation
%--------------------------------------------------------------------------
try, DCM.M.P = DCM.options.P; end
% sizes
%--------------------------------------------------------------------------
try, DCM.U.u; catch, DCM.U.u = []; end
try, DCM.n; catch, DCM.n = size(DCM.a,1); end
try, DCM.v; catch, DCM.v = size(DCM.Y.y,1); end
% analysis and options
%--------------------------------------------------------------------------
DCM.options.induced = 1;
DCM.options.nonlinear = 0;
DCM.options.stochastic = 0;
% organise response variables: detrend outputs (and inputs)
%==========================================================================
DCM.Y.y = spm_detrend(DCM.Y.y);
if DCM.options.centre
DCM.U.u = spm_detrend(DCM.U.u);
end
% check scaling of Y (enforcing a maximum change of 4%
%--------------------------------------------------------------------------
scale = max(max((DCM.Y.y))) - min(min((DCM.Y.y)));
scale = 4/max(scale,4);
DCM.Y.y = DCM.Y.y*scale;
DCM.Y.scale = scale;
% disablehi order parameters and check for models with no inputs
%--------------------------------------------------------------------------
n = DCM.n;
DCM.b = zeros(n,n,0);
DCM.d = zeros(n,n,0);
if isempty(DCM.c) || isempty(DCM.U.u)
DCM.c = zeros(DCM.n,1);
DCM.U.u = zeros(DCM.v,1);
DCM.U.name = {'null'};
end
% priors (and initial states)
%==========================================================================
[pE,pC,x] = spm_dcm_fmri_priors(DCM.a,DCM.b,DCM.c,DCM.d,DCM.options);
% eigenvector constraints on pC for large models
%--------------------------------------------------------------------------
if n > DCM.options.nmax
% remove confounds and find principal (nmax) modes
%----------------------------------------------------------------------
y = DCM.Y.y - DCM.Y.X0*(pinv(DCM.Y.X0)*DCM.Y.y);
V = spm_svd(y');
V = V(:,1:DCM.options.nmax);
% remove minor modes from priors on A
%----------------------------------------------------------------------
j = 1:(n*n);
V = kron(V*V',V*V');
pC(j,j) = V*pC(j,j)*V';
end
% create DCM
%--------------------------------------------------------------------------
DCM.M.IS = 'spm_csd_fmri_mar';
DCM.M.g = 'spm_gx_fmri';
DCM.M.f = 'spm_fx_fmri';
DCM.M.x = x;
DCM.M.pE = pE;
DCM.M.pC = pC;
DCM.M.hE = 8;
DCM.M.hC = 1/128;
DCM.M.n = length(spm_vec(x));
DCM.M.m = size(DCM.U.u,2);
DCM.M.l = n;
% specify M.u - endogenous input (fluctuations) and intial states
%--------------------------------------------------------------------------
DCM.M.u = sparse(n,1);
% get data-features
%==========================================================================
DCM = spm_dcm_fmri_csd_data(DCM);
DCM.M.Hz = DCM.Y.Hz;
DCM.M.p = DCM.Y.p;
DCM.M.dt = DCM.Y.dt;
DCM.M.N = 32;
% Variational Laplace: model inversion (using spectral responses)
%==========================================================================
Y = DCM.Y;
Y.y = Y.mar;
Y.Q = speye(length(Y.y(:)));
[Ep,Cp,Eh,F] = spm_nlsi_GN(DCM.M,DCM.U,Y);
% Bayesian inference {threshold = prior}
%--------------------------------------------------------------------------
warning('off','SPM:negativeVariance');
dp = spm_vec(Ep) - spm_vec(pE);
Pp = spm_unvec(1 - spm_Ncdf(0,abs(dp),diag(Cp)),Ep);
warning('on', 'SPM:negativeVariance');
% predictions (csd) and error (haemodynamics)
%==========================================================================
Hc = spm_csd_fmri_mtf(Ep,DCM.M,DCM.U); % prediction
Ec = spm_unvec(spm_vec(Y.csd) - spm_vec(Hc),Hc); % prediction error
% Bilinear representation and first-order hemodynamic kernel
%--------------------------------------------------------------------------
M = DCM.M; % model
Qp = Ep; % posterior parameters
Qp.C = 1; % Switch to endogenous
[S,H1] = spm_dcm_mtf(Qp,M); % haemodynamic kernel
% and neuronal kernels
%==========================================================================
M.g = @(x,u,P,M) x(:,1); % neuronal observer
[S,K1] = spm_dcm_mtf(Qp,M); % neuronal kernel
% predictions (at the level of neuronal interactions)
%--------------------------------------------------------------------------
[Hs Hz dtf] = spm_csd_fmri_mtf(Ep,M,DCM.U);
[ccf pst] = spm_csd2ccf(Hs,Hz);
[coh fsd] = spm_csd2coh(Hs,Hz);
DCM.dtf = dtf;
DCM.ccf = ccf;
DCM.coh = coh;
DCM.fsd = fsd;
DCM.pst = pst;
DCM.Hz = Hz;
DCM.H1 = H1;
DCM.K1 = K1;
% store estimates in DCM
%--------------------------------------------------------------------------
DCM.Ep = Ep; % conditional expectation
DCM.Cp = Cp; % conditional covariance
DCM.Pp = Pp; % conditional probability
DCM.Hc = Hc; % conditional responses (y), BOLD space
DCM.Rc = Ec; % conditional residuals (y), BOLD space
DCM.Hs = Hs; % conditional responses (y), neural space
DCM.Ce = exp(-Eh); % ReML error covariance
DCM.F = F; % Laplace log evidence
% and save
%--------------------------------------------------------------------------
save(DCM.name,'DCM','F','Ep','Cp', spm_get_defaults('mat.format'));
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