create_latency_corrected_amplitude_maps.m~

function create_latency_corrected_amplitude_maps(Canonical, Derivative, Conditions)
% Creates latency corrected amplitude maps according to Calhoun et al.,
% 2004. 
% Requires Matlab 2017b or later for niftiread function. Otherwise
% change function for reading and writing in volumes.
%
% Input: 
%       Canonical   -- file name of beta_????.img of canonical term
%       Derivative  -- file name of beta_????.img of derivative term
%       Conditions  -- cell with condition/ session name as needed for output
%                name,  e.g.: Conditions  = {'PreStress', 'Stress', 'PostStress'}
%       
% ----------------------------------------------------------------------
%
% Authors: Immanuel Elbau and Philipp Sämann, MPI of Psychiatry- 2017
% % Email: immanuel_elbau@psych.mpg.de or saemann@psych.mpg.de

% -------------------------------------------------------------------

% Define Output name

Output = ['LatencyCorrectedAmplitudeMap_',Conditions,'.nii'];

% Load in Beta of canonical term 

Info = niftiinfo(Canonical);

V1   = niftiread(Info);

V1 = reshape(V1, [1 size(V1,1).*size(V1,2).*size(V1,3)]);

% Load in Beta of derivative term

Info = niftiinfo(Derivative);

V2   = niftiread(Info);

V2 = reshape(V2, [1 size(V2,1).*size(V2,2).*size(V2,3)]);

% Index all voxel with beta1 > beta2 and leave the other ones out of
% amplitude corrected amplitude computation. The beta1 < beta2 voxels get
% simply beta1 value asssigned

Indi = abs(V1) > abs(V2);

% Perform claculation according to Formula by Calhoun et al. 2004

LatencyCorrectedAmplitudes(Indi)  = sign(V1(Indi)) .* sqrt(V1(Indi).^2 + V2(Indi).^2);

LatencyCorrectedAmplitudes(~Indi) = V1(~Indi);

% Save output image 

LatencyCorrectedAmplitudes = reshape(LatencyCorrectedAmplitudes, [Info.ImageSize]);

niftiwrite(LatencyCorrectedAmplitudes, Output, Info);

% The resulting images were smoothed as described in the SI Appendix (p.7) 
% with a 6 mm (FWHM) kernel
end