Upload files to 'temporal_denoise/QC'

temporal_denoise/QC/calculate_dvars.m

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+function [dvars_per_vol, mean_dvars]=calculate_dvars(data_matrix,censored_vols)
+% routine for calculation of dvars for QC'ing dHCP denoised fMRI data 
+% 
+% data_matrix - Ntimepoints by Nvoxels
+% censored_vols  - as per file: sub-{sub}_ses-{ses}_task-rest_desc-censored_vols.txt
+
+% dvars_per_vol -  Ntimepoints by 1; will be zero-valued if differential
+% involves a censored volume; the first element is always zero-valued
+% mean_dvars - mean over non-zeroed dvars_per_vol
+
+
+censored_vols=censored_vols+1; % as they are counted from 0
+
+ind_not_to_include=unique([1 censored_vols censored_vols+1]); 
+ind_not_to_include(ind_not_to_include>size(data_matrix,1))=[];
+dY=diff(data_matrix).^2;
+dvars_per_vol=[0;mean(dY,2)];
+dvars_per_vol(ind_not_to_include)=0;
+mean_dvars=mean(dvars_per_vol(dvars_per_vol>0));

temporal_denoise/QC/calculate_tsnr.m

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+function [tsnr, mean_tsnr]=calculate_tsnr(data_matrix,censored_vols)
+% routine for calculation of tsnr for QC'ing dHCP denoised fMRI data 
+% 
+% data_matrix - Ntimepoints by Nvoxels
+% censored_vols  - as per file: sub-{sub}_ses-{ses}_task-rest_desc-censored_vols.txt
+
+censored_vols=censored_vols+1; % as they are counted from 0 
+data_matrix(censored_vols,:)=[]; 
+mean_signal=mean(data_matrix);
+std_signal=std(data_matrix);
+tsnr=mean_signal./std_signal;
+mean_tsnr=median(tsnr);