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Slepi/Code/Utilities/spm12/toolbox/dcm_fnirs/spm_dcm_fnirs_estimate.m
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spm_dcm_fnirs_estimate.m
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function [DCM] = spm_dcm_fnirs_estimate(P)
% Estimate parameters of a DCM for fNIRS data
% FORMAT [DCM] = spm_dcm_fnirs_estimate(P)
%
% P - Name of DCM file
%
% DCM - DCM structure or its filename
%
% 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
% DCM.Y.X0 % confounds
% DCM.Y.Q % array of precision components
% DCM.n % number of regions
% DCM.v % number of scans
%
% Options
%--------------------------------------------------------------------------
% DCM.options.two_state % two regional populations (E and I)
% DCM.options.stochastic % fluctuations on hidden states
% DCM.options.centre % mean-centre inputs
% DCM.options.nonlinear % interactions among hidden states
% DCM.options.nograph % graphical display
% DCM.options.induced % switch for CSD data features
% DCM.options.P % starting estimates for parameters
% DCM.options.hidden % indices of hidden regions
% DCM.options.nmax % maximum number of (effective) nodes
% DCM.options.nN % maximum number of iterations
%
% Evaluates:
%--------------------------------------------------------------------------
% DCM.M % Model structure
% DCM.Ep % Condition means (parameter structure)
% DCM.Cp % Conditional covariances
% DCM.Vp % Conditional variances
% DCM.Pp % Conditional probabilities
% DCM.H1 % 1st order hemodynamic kernels
% DCM.H2 % 2nd order hemodynamic kernels
% DCM.K1 % 1st order neuronal kernels
% DCM.K2 % 2nd order neuronal kernels
% DCM.R % residuals
% DCM.y % predicted data
% DCM.T % Threshold for Posterior inference
% DCM.Ce % Error variance for each region
% DCM.F % Free-energy bound on log evidence
% DCM.ID % Data ID
% DCM.AIC % Akaike Information criterion
% DCM.BIC % Bayesian Information criterion
%
% This function is based on spm_dcm_estimate.m written by Karl Friston.
%__________________________________________________________________________
% Copyright (C) 2015 Wellcome Trust Centre for Neuroimaging
% Will Penny & Sungho Tak
% $Id: spm_dcm_fnirs_estimate.m 6422 2015-04-23 16:55:52Z spm $
%-Load DCM structure
%--------------------------------------------------------------------------
if ~nargin
%-Display model details
%----------------------------------------------------------------------
Finter = spm_figure('GetWin','Interactive');
set(Finter,'name','Dynamic Causal Modelling')
%-Get DCM
%----------------------------------------------------------------------
[P, sts] = spm_select(1,'^DCM.*\.mat$','select DCM_???.mat');
if ~sts, DCM = []; return; end
spm('Pointer','Watch')
spm('FigName','Estimation in progress');
end
if isstruct(P)
DCM = P;
P = ['DCM-' date '.mat'];
else
load(P)
end
% check options
%--------------------------------------------------------------------------
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.nonlinear; catch, DCM.options.nonlinear = 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.nN; catch, DCM.options.nN = 32; end
try, DCM.options.hidden; catch, DCM.options.hidden = []; end
try, DCM.n; catch, DCM.n = size(DCM.a,1); end
try, DCM.v; catch, DCM.v = size(DCM.Y.y,1); end
try, M.nograph = DCM.options.nograph; catch, M.nograph = spm('CmdLine');end
% Unpack
%--------------------------------------------------------------------------
U = DCM.U; % inputs
Y = DCM.Y; % responses
v = DCM.v; % number of scans
n = DCM.n; % number of sources of interest
m = size(U.u,2); % number of inputs
nwav = DCM.Y.nwav; % number of wavelengths
nch = DCM.Y.nch; % number of channels of interest
nout = nwav * nch; % number of optical measurements
% detrend outputs (and inputs)
%--------------------------------------------------------------------------
% Y.y = spm_detrend(Y.y);
if DCM.options.centre
U.u = spm_detrend(U.u);
end
% generate regressors for confounding effects
%--------------------------------------------------------------------------
X0 = [];
switch DCM.K.type
case 'DCT'
ncf = size(DCM.K.cutoff, 1);
for i = 1:ncf
cutoff = DCM.K.cutoff(i,:);
Lorder = fix(2*(v*DCM.K.RT)/cutoff(1,2) + 1);
Horder = fix(2*(v*DCM.K.RT)/cutoff(1,1) + 1);
X0_t = spm_dctmtx(v, Horder);
X0_t = X0_t(:, Lorder:end);
X0 = [X0 X0_t];
end
case 'Regressors'
X0 = [X0 K.regressor];
end
X0 = [X0 ones(v, 1)];
Y.X0 = X0;
% create priors
%--------------------------------------------------------------------------
% check DCM.d (for nonlinear DCMs)
%--------------------------------------------------------------------------
try
DCM.options.nonlinear = logical(size(DCM.d,3));
catch
DCM.d = zeros(n,n,0);
DCM.options.nonlinear = 0;
end
% specify parameters for spm_int_D (ensuring updates every second or so)
%--------------------------------------------------------------------------
if DCM.options.nonlinear
M.IS = 'spm_int_D';
M.nsteps = round(max(Y.dt,1));
M.states = 1:n;
else
M.IS = 'spm_int';
end
% check for endogenous DCMs, with no exogenous driving effects
%--------------------------------------------------------------------------
if isempty(DCM.c) || isempty(U.u)
DCM.c = zeros(n,1);
DCM.b = zeros(n,n,1);
U.u = zeros(v,1);
U.name = {'null'};
end
if ~any(spm_vec(U.u)) || ~any(spm_vec(DCM.c))
DCM.options.stochastic = 1;
end
% priors (and initial states)
%--------------------------------------------------------------------------
[pE,pC,x] = spm_dcm_fnirs_priors(DCM);
try, M.P = DCM.options.P; end % initial parameters
try, pE = DCM.options.pE; end % prior expectation
try, pC = DCM.options.pC; end % prior covariance
% eigenvector constraints on pC for large models
%--------------------------------------------------------------------------
if n > DCM.options.nmax % This routine will be skipped.
% remove confounds and find principal (nmax) modes
%----------------------------------------------------------------------
y = Y.y - Y.X0*(pinv(Y.X0)*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
% calculate sensitivity matrix using precalculated Green's functions
%--------------------------------------------------------------------------
xyz_h = reshape(extractfield(DCM.xY, 'xyz'), [3 n]);
load(Y.fgreen);
A = spm_fnirs_sensitivity(G, Y.pos, xyz_h, DCM.options.rs.* 3, Y.ch);
clear G;
% hyperpriors over precision - expectation and covariance
%--------------------------------------------------------------------------
hE = sparse(nout,1) + 6;
hC = speye(nout,nout)/128;
i = DCM.options.hidden;
hE(i) = -4;
hC(i,i) = exp(-16);
Y.Q = spm_Ce(ones(1,nout)*v); % error precision components
% complete model specification
%--------------------------------------------------------------------------
M.rs = DCM.options.rs; % radius of source
M.eps= Y.eps; % extinction coefficients [hbo, hbr (wave1); hbo, hbr (wave2)]
M.f = 'spm_fx_fnirs'; % hemodynamic state equation
M.g = 'spm_gx_state_fnirs'; % optics equation
M.x = x;
M.A = A; % sensitivity matrix
M.pE = pE; % prior expectation (parameters)
M.pC = pC; % prior covariance (parameters)
M.m = size(U.u,2);
M.n = size(M.x(:),1);
M.nwav = nwav; % number of wavelengths
M.nch = nch; % number of channels
M.l = nout;
M.hE = hE; % prior expectation (precisions)
M.hC = hC; % prior covariance (precisions)
M.N = 32;
M.dt = 16/M.N;
M.ns = v;
% nonlinear system identification (nlsi)
%--------------------------------------------------------------------------
if ~DCM.options.stochastic
% nonlinear system identification (Variational EM) - deterministic DCM
%----------------------------------------------------------------------
[Ep,Cp,Eh,F] = spm_nlsi_GN(M,U,Y);
% predicted responses (y) and residuals (R)
%----------------------------------------------------------------------
y = feval(M.IS,Ep,M,U);
R = Y.y - y;
R = R - Y.X0*spm_inv(Y.X0'*Y.X0)*(Y.X0'*R);
Ce = exp(-Eh);
end
% Bilinear representation and first-order hemodynamic kernel
%--------------------------------------------------------------------------
[M0,M1,L1,L2] = spm_bireduce(M,Ep);
[H0,H1] = spm_kernels(M0,M1,L1,L2,M.N,M.dt);
% and neuronal kernels
%--------------------------------------------------------------------------
L = sparse(1:n,(1:n) + 1,1,n,length(M0));
[K0,K1] = spm_kernels(M0,M1,L,M.N,M.dt);
% Bayesian inference and variance {threshold: prior mean plus T = 0}
%--------------------------------------------------------------------------
T = full(spm_vec(pE));
sw = warning('off','SPM:negativeVariance');
Pp = spm_unvec(1 - spm_Ncdf(T,abs(spm_vec(Ep)),diag(Cp)),Ep);
Vp = spm_unvec(full(diag(Cp)),Ep);
warning(sw);
try, M = rmfield(M,'nograph'); end
% Store parameter estimates
%--------------------------------------------------------------------------
DCM.M = M;
DCM.Y = Y;
DCM.U = U;
DCM.Ce = Ce;
DCM.Ep = Ep;
DCM.Cp = Cp;
DCM.Pp = Pp;
DCM.Vp = Vp;
DCM.H1 = H1;
DCM.K1 = K1;
DCM.R = R;
DCM.y = y;
DCM.T = 0;
% Data ID and log-evidence
%--------------------------------------------------------------------------
if isfield(M,'FS')
try
ID = spm_data_id(feval(M.FS,Y.y,M));
catch
ID = spm_data_id(feval(M.FS,Y.y));
end
else
ID = spm_data_id(Y.y);
end
% Save approximations to model evidence: negative free energy, AIC, BIC
%--------------------------------------------------------------------------
evidence = spm_dcm_evidence(DCM);
DCM.F = F;
DCM.ID = ID;
DCM.AIC = evidence.aic_overall;
DCM.BIC = evidence.bic_overall;
%-Save DCM
%--------------------------------------------------------------------------
if ~isstruct(P)
save(P, 'DCM', 'F', 'Ep', 'Cp', spm_get_defaults('mat.format'));
end
if ~nargin
spm('Pointer','Arrow');
spm('FigName','Done');
end
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