Hi Sarah,

One simple general way I could recommend to obtain seed-to-voxel first-level connectivity maps corrected for multiple comparisons would be to use Fisher approximation to the standard error of correlation coefficient estimates to obtain first uncorrected p-values for each voxel, and then apply an FDR-correction to obtain FDR-corrected p-values. The simplest way to approximate the standard error of the seed-to-voxel correlations would be assuming non-colored noise over the (typically small) frequency band defined during the preprocessing step (this is the same approach used by the previous version of the toolbox when using fixed-effect analyses; alternatively you could also use Welch-Satterthwaite approximation for a more precise result but that would require estimating the spectra of the residuals first).

In any way, if you have already run the setup/preprocessing/first-level analyses (and obtained in this way fisher-transformed BETA_Subject*_Condition*_Source*.nii files; one map for each source/condition/subject), you could easily transform these files to the associated uncorrected and FDR-corrected p-values (following the approach described above) using the following script:


nsub=1;       % subject number
ncondition=1; % condition number
nroi=1;       % source/seed number
nanalysis=1;  % analysis number

global CONN_x;
filepath=CONN_x.folders.preprocessing;
filepathresults=fullfile(CONN_x.folders.firstlevel,CONN_x.Analyses(nanalysis).name);
filename=fullfile(filepath,['DATA_Subject',num2str(nsub,'%03d'),'_Condition',num2str(ncondition,'%03d'),'.mat']);
Y=conn_vol(filename);
DOF=max(0,Y.size.Nt*(min(1/(2*CONN_x.Setup.RT),CONN_x.Preproc.filter(2))-max(0,CONN_x.Preproc.filter(1)))*(2*CONN_x.Setup.RT)+1);
filename=fullfile(filepathresults,['BETA_Subject',num2str(nsub),'_Condition',num2str(ncondition,'%03d'),'_Source',num2str(nroi,'%03d'),'.nii']);
a=spm_vol(filename);
Z=spm_read_vols(a);          % fisher-transformed correlation values
z=Z*max(0,sqrt(DOF-3));    % z-scores
p=spm_Ncdf(z);                  % p-values from normal distribution
p=2*min(p,1-p);                 % note: two-sided tests (remove this line for one-sided tests; i.e. only positive correlations)
P=p;
P(:)=conn_fdr(p(:));            % FDR-corrected p-values
a.fname=fullfile(filepathresults,['p_Subject',num2str(nsub),'_Condition',num2str(ncondition,'%03d'),'_Source',num2str(nroi,'%03d'),'.nii']);
spm_write_vol(a,p);
a.fname=fullfile(filepathresults,['pFDR_Subject',num2str(nsub),'_Condition',num2str(ncondition,'%03d'),'_Source',num2str(nroi,'%03d'),'.nii']);
spm_write_vol(a,P);


This will create two files:

   p_Subject1_Condition1_Source1.nii 
   pFDR_Subject1_Condition1_Source1.nii

with the uncorrected and FDR-corrected p-values, respectively, of the seed-to-voxel maps for the first subject/condition/source (edit the first four lines to change the subject/condition/source numbers of interest)

Hope this helps, and let me know if this addresses your question. You might need to do something more complex if you want to look instead at more complex contrasts across conditions or sources, let me know.

Best
Alfonso

Originally posted by Sarah Buetof: