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  • 一般的相关分析代码

    %--brain mask with the brain tissue
    mask_name = 'C:UsersAdministratorDesktopworkspacepreprocessedmaskswithin_brain_mask.nii';
    M   = load_untouch_nii( mask_name ); % load mask NIFTI
    mask     = double(M.img>0);          % get 3d v
    
    %--brain functional 4d data
    data_4d = 'C:UsersAdministratorDesktopworkspacepreprocessed4dfunc_3d.nii';
    % data_4d = 'C:UsersAdministratorDesktopphycaa_workspacephycaa_plus_2104_03_27\_PHYCAA_step1+2.nii';
    
    V   = load_untouch_nii( data_4d ); 
    
    %--transform 4d array to 2d array, using brain_mask
    within_brain_voxels = nifti_to_mat(V,M); 
    
    nt_matrix = within_brain_voxels;
    
    [V_c S_c temp] = svd( nt_matrix' * nt_matrix );
    % PC-space representation
    Q_c = V_c * sqrt( S_c );
    
    offSet=1;
    pcs=4;
    
    % estimate temporal autocorrelation maximized "sources"
    Q1 = Q_c( 1:(end-offSet) , 1:pcs ); % un-offset
    Q2 = Q_c( (offSet+1):end , 1:pcs ); % offsetted timeseries
    
    % canonical correlations on time-lagged data
    [A,B,R,U,V,stats] = canoncorr(Q1,Q2);
    
    % getting stable "average" autocorrelated timeseries
    a=[U(1,:)]; b=[U(2:end,:) + V(1:end-1,:)./2]; c=[V(end,:)];
    
    tset = [a;b;c];
    

      

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  • 原文地址:https://www.cnblogs.com/haore147/p/3797817.html
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