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RunTCM_Script_transfun.m
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RunTCM_Script_transfun.m
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function RunTCM_Script_transfun(i)
% Top level script showing how to apply the thalamo-cortical neural mass
% model decribed in Shaw et al 2020 NeuroImage, to M/EEG data.
%
% This version using a linearisation and transfer function (numerical
% Laplace) rather than brute numerical integration.
%
% Requires atcm (thalamo cortical modelling package) and aoptim
% (optimisation package)
%
% atcm: https://github.com/alexandershaw4/atcm
%
% Overview of contents
%--------------------------------------------------
% - atcm. contains:
% - the equations of motion for an 8 pop thalamo-cortical
% model described by parameterised Morris-Lecar/Hodgkin-Hux
% conductance ODEs
% - numerical integration (Euler, RK, Newton-Cotes +) and spectral
% response functions
% - lots of helper functions (integration, differentiation,
% continuation, decomposition methods etc%
%
% AS2020/21/22 {alexandershaw4[@]gmail.com}
% EXAMPLE ONE NODE SETUP:
%==========================================================================
% Data & Design
%--------------------------------------------------------------------------
Data.Datasets = 'NewSZ.txt';%'MeanSZDatasets.txt';%'AllSZNoMerge.txt'; % textfile list of LFP SPM datasets (.txt)
Data.Design.X = []; % design matrix
Data.Design.name = {'undefined'}; % condition names
Data.Design.tCode = [1]; % condition codes in SPM
Data.Design.Ic = [1]; % channel indices
Data.Design.Sname = {'V1'}; % channel (node) names
Data.Prefix = 'LM_Laplace_TCM_'; % outputted DCM prefix
Data.Datasets = atcm.fun.ReadDatasets(Data.Datasets);
% Model space - T = ns x ns, where 1 = Fwd, 2 = Bkw
%--------------------------------------------------------------------------
T = [... % this is a 1-node model; nothing to put here...
0];
F = (T==1);
B = (T==2);
C = [1]'; % input(s)
L = sparse(1,1);
[p]=fileparts(which('atcm.integrate_1'));p=strrep(p,'+atcm','');addpath(p);
% Set up, over subjects
%--------------------------------------------------------------------------
for i = i;%1:length(Data.Datasets)
% Data Naming & Design Matrix
%----------------------------------------------------------------------
DCM = [];
[fp fn fe] = fileparts(Data.Datasets{i});
DCM.name = [Data.Prefix fn fe];
DCM.xY.Dfile = Data.Datasets{i}; % original spm datafile
Ns = length(F); % number of regions / modes
DCM.xU.X = Data.Design.X; % design matrix
DCM.xU.name = Data.Design.name; % condition names
tCode = Data.Design.tCode; % condition index (in SPM)
DCM.xY.Ic = Data.Design.Ic; % channel indices
DCM.Sname = Data.Design.Sname; % channel names
if exist(DCM.name);
fprintf('Skipping model %d/%d - already exists!\n( %s )\n',i,length(Data.Datasets),DCM.name);
continue;
end
% Extrinsic Connectivity - Model Space
%----------------------------------------------------------------------
DCM.A{1} = F;
DCM.A{2} = B;
DCM.A{3} = L;
DCM.B{1} = DCM.A{1} | DCM.A{2};
DCM.B(2:length(DCM.xU.X)) = DCM.B;
DCM.C = C;
% Function Handles
%----------------------------------------------------------------------
DCM.M.f = @atcm.tc_hilge2; % model function handle
DCM.M.IS = @atcm.fun.alex_tf; % Alex integrator/transfer function
DCM.options.SpecFun = @atcm.fun.Afft; % fft function for IS
% Print Progress
%----------------------------------------------------------------------
fprintf('Running Dataset %d / %d\n',i,length(Data.Datasets));
% Frequency range of interest
fq = [1 90];
% Prepare Data
%----------------------------------------------------------------------
DCM.M.U = sparse(diag(ones(Ns,1))); %... ignore [modes]
DCM.options.trials = tCode; %... trial code [GroupDataLocs]
DCM.options.Tdcm = [300 1300]; %... peristimulus time
DCM.options.Fdcm = fq; %... frequency window
DCM.options.D = 1; %... downsample
DCM.options.han = 1; %... apply hanning window
DCM.options.h = 4; %... number of confounds (DCT)
DCM.options.DoData = 1; %... leave on [custom]
%DCM.options.baseTdcm = [-200 0]; %... baseline times [new!]
DCM.options.Fltdcm = fq; %... bp filter [new!]
DCM.options.UseButterband = fq;
DCM.options.analysis = 'CSD'; %... analyse type
DCM.xY.modality = 'LFP'; %... ECD or LFP data? [LFP]
DCM.options.spatial = 'LFP'; %... spatial model [LFP]
DCM.options.model = 'tc6'; %... neural model
DCM.options.Nmodes = length(DCM.M.U); %... number of modes
DCM.options.UseWelch = 1010;
DCM.options.FFTSmooth = 0;
DCM.options.BeRobust = 0;
DCM.options.FrequencyStep = 1;
DCM.xY.name = DCM.Sname;
DCM = atcm.fun.prepcsd(DCM);
DCM.options.DATA = 1 ;
DCM.xY.y{:} = agauss_smooth(abs(DCM.xY.y{:}),1)';
% Subfunctions and default priors
%----------------------------------------------------------------------
DCM = atcm.parameters(DCM,Ns);
% other model options
%----------------------------------------------------------------------
DCM.M.solvefixed=0; %
DCM.M.x = zeros(1,8,7); % init state space: ns x np x nstates
DCM.M.x(:,:,1)=-70; % init pop membrane pot [mV]
load([p '/newpoints3.mat'],'pE','pC')
pE = spm_unvec(spm_vec(pE)*0,pE);
pC.ID = pC.ID * 0;
pC.T = pC.T *0;
pE.J = pE.J-1000;
pE.J(1:8) = log([.6 .8 .4 .6 .4 .6 .4 .4]);
%pC.ID = pC.ID + 1/8;
pE.L = 0;
pC.a = pC.a*0;
pE.Gb = pE.H;
pC.Gb = [1 0 0 0 0 0 0 0;
0 1 1 0 0 0 0 0;
0 0 1 0 0 0 0 0;
0 0 0 1 1 0 0 0;
0 0 0 0 1 0 0 0;
0 0 0 0 1 1 0 0;
0 0 0 0 0 0 0 0;
0 0 0 0 0 0 1 0]/64;
pC.J(1:8)=1/8;
pC.d(1) = 1/8;
pC.d(3) = 1/8;
% Make changes here;
%-----------------------------------------------------------
DCM.M.pE = pE;
DCM.M.pC = pC;
% Optimise using AO.m -- a Newton scheme with add-ons and multiple
% objective functions built in, including free energy
%----------------------------------------------------------------------
w = DCM.xY.Hz;
Y = DCM.xY.y{:};
DCM.M.y = DCM.xY.y;
DCM.M.Hz = DCM.xY.Hz;
ppE = DCM.M.pE;
ppC = DCM.M.pC;
fprintf('--------------- STATE ESTIMATION ---------------\n');
fprintf('Search for a stable fixed point\n');
xx = load([p '/newx.mat']); DCM.M.x = spm_unvec(xx.x,DCM.M.x);
load('init_14dec','x');
DCM.M.x = spm_unvec(x,DCM.M.x);
x = atcm.fun.alexfixed(DCM.M.pE,DCM.M,1e-10);
DCM.M.x = spm_unvec(x,DCM.M.x);
norm(DCM.M.f(DCM.M.x,0,DCM.M.pE,DCM.M))
fprintf('Finished...\n');
fprintf('--------------- PARAM ESTIMATION ---------------\n');
%fprintf('iteration %d\n',j);
% Fit with DCM VB routine:
%----------------------------------------------------------------------
[Qp,Cp,Eh,F] = spm_nlsi_GN(DCM.M,DCM.xU,DCM.xY);
% Fit with LM (Log Likelihood estimation):
%----------------------------------------------------------------------
% M = aFitDCM(DCM)
%
% M.aloglik
% M.update_parameters(M.Ep)
% M.aloglik
% M.update_parameters(M.Ep)
% M.aloglik
%Qp = spm_unvec(M.Ep,DCM.M.pE);
%Cp = M.CP;
% Fit with LM (Free energy estimation):
%----------------------------------------------------------------------
% M = aFitDCM(DCM)
%
% M.aloglikFE
% M.update_parameters(M.Ep)
% M.aloglikFE
% M.update_parameters(M.Ep)
% M.aloglikFE
%Qp = spm_unvec(M.Ep,DCM.M.pE);
%Cp = M.CP;
% save in DCM structures after optim
%----------------------------------------------------------------------
DCM.M.pE = ppE;
DCM.Ep = Qp;%spm_unvec(M.Ep,DCM.M.pE);
DCM.Cp = Cp;
DCM.M.sim.dt = 1./600;
DCM.M.sim.pst = 1000*((0:DCM.M.sim.dt:(2)-DCM.M.sim.dt)');
[y,w,G,s] = feval(DCM.M.IS,DCM.Ep,DCM.M,DCM.xU);
DCM.pred = y;
DCM.w = w;
DCM.G = G;
DCM.series = s;
%DCM.Cp = atcm.fun.reembedreducedcovariancematrix(DCM,M.CP);
%DCM.Cp = makeposdef(DCM.Cp);
DCM.F = F;%M.FreeEnergyF;
%DCM.Cp = M.CP;
save(DCM.name); close all; clear global;
end
end