A set of scripts for visual and quantitative checks of data based on spatio-temporal correlation structure in fMRI data.
Also includes a package to estimate robust covariance matrices.
[score_t score_s score_p h1 h2 h3] = plot_correlations(Xdata(:,new_idx,:));
figure(h1.figure)
export_fig([opts.outputFiles{ii} '_datacheck_subjects.png'])
figure(h2.figure)
export_fig([opts.outputFiles{ii} '_datacheck_regions.png'])
figure(h3.figure)
export_fig([opts.outputFiles{ii} '_datacheck_time.png'])
The influence scores only detect outliers. If the majority of the subjects or regions share the same issues, then the scores do not necessarily give you valuable information. A visual inspection might help flag such cases.
Thus a low influence score (< .5) does not necessarily mean all is okay, but modest scores of (.75-1) are worth inspecting and those (>1) are definitely worth checking for motion, alignment or registration issues. In particular large blocks of high temporal correlation seem to indicate motion. See screenshot for examples.
Needs the ggmClass which has been added as a submodule in ggm-external