There are several things that can go wrong during the acquisition phase (scanning) of a subject, some of which severely impact the usability of a subject's dataset. While any given project (study) is still in the stage of data collection (subjects are still being scanned), there is always the chance to decide that a particular subject might introduce too much noise into the eventually performed group statistic and should be discarded (and in this case replaced by another subject).

Relevant for that decision could be one of the following issues:

• the subject had to exit the scanner before the experiment was completed → usually such a dataset needs to be discarded
• the subject couldn't restrain from moving their head during the experiment → depending on how difficult it is to find a replacement subject, it is advised to discard such a dataset
• the scanner produced disproportionally strong noise in the data → if possible, such a dataset should also be discarded

Of course there are still many other possible reasons to discard any given subject (e.g. a score on a questionnaire/behavioral measure indicates that the subject does not fall into the distribution of the examined population of subjects), but especially the second and third issue mentioned above can be detected even before entering a subject's dataset into any given group analysis.

This function, fmriquality allows to assess some of the more general quality measures in a unified way.

To run the fMRI quality checking function, the images need to be in one of the functional imaging data formats currently supported by the xff class (Analyze/NIftI, FMR/STC, VTC).

  fmriquality  - get some quality assurance for fMRI data
 
  FORMAT:       [fq, fmridata = ] fmriquality(images [, opts])
 
  Input fields:
 
        images      list of images (or fMRI datatype, like FMR/VTC)
        opts        optional settings
         .motcor    perform motion-correction (and re-do stats, def: false)
         .nuisreg   nuisance regressors/confounds for temp. filtering
         .pbar      1x1 xfigure::ProgressBar or xprogress object
         .prange    progress bar range (default: [0 .. 1])
         .qasheet   flag, display quality assessment sheet (default: false)
         .res       resolution (for motion detection, default: from file)
         .robfilt   flag, perform filtering robustly (default: false)
         .robmotcor flag, do motion detection robustly (default: false)
         .tempffrq  temp filter frequency cut-off (as TRs, default: 80)
         .tempfset  temp filter set, either of 'DC', {'Fourrier'}
 
  Output fields:
 
        fq          complex struct, containing masks, time courses, etc.
        fmridata    4-D data slab (motion corrected, if selected)
 
  Note: the data of one run must at least fit into memory (in single
        precision), plus some temporary arrays and, if motion correction
        is selected, with further memory allocation required!

The most basic (and pre-configured) way of running fmriquality is by simply passing in the filename(s) or object of the run to check:

• using Analyze files:

  fmriquality_example1.m

  qas = fmriquality(findfiles(sessionfolder, '*.img'));

• using a BrainVoyager QX FMR file:

  fmriquality_example2.m

  fmr = xff('*.fmr', 'Select FMR for which you want to check the data quality...');
  qas = fmriquality(fmr);

• also checking motion parameters with 64 TRs as the filtering cutoff, showing the result immediately:

  fmriquality_example3.m

  % selecting files with findfiles
  qafiles = fmriquality(findfiles(pwd, '*.img'));
   
  % options
  qaopts = struct( ...
      'motcor',   true, ...
      'qasheet',  true, ...
      'robfilt',  true, ...
      'tempffrq', 64);
   
  % running fmriquality
  fmriquality(qafiles, qaopts);

If the .qasheet option is not set to true, the returned variable can later be passed to fmriqasheet manually (for scripted QA-ing). This variable is of type struct and contains (at least) the following fields:

   .Dims         1x4 array, size
   .Filename     the first filename given
   .Masks        automatically detected masks (foreground, background, etc)
   .Raw          mean, stdev, and null-voxel image
   .TempFiltered re-created summary values/maps after applying temporal filtering
   .Quality      summary images trying to capture overall quality measures (SNR, CNR, etc.)
   .TC           diverse time courses

When passed to fmriqasheet, this function then creates a new figure and displays part of the information contained in the structure, which can be used to decide on whether or not a subject would likely introduce too much noise/bias at the group level.