Usage: 3dToutcount [options] dataset
Calculates number of 'outliers' a 3D+time dataset, at each
time point, and writes the results to stdout.

 -mask mset = Only count voxels in the mask dataset.
 -qthr q    = Use 'q' instead of 0.001 in the calculation
                of alpha (below): 0 < q < 1.

 -autoclip }= Clip off 'small' voxels (as in 3dClipLevel);
 -automask }=   you can't use this with -mask!

 -fraction  = Output the fraction of (masked) voxels which are
              outliers at each time point, instead of the count.

 -range     = Print out median+3.5*MAD of outlier count with
                each time point; use with 1dplot as in
                3dToutcount -range fred+orig | 1dplot -stdin -one
 -save ppp  = Make a new dataset, and save the outlier Q in each
                voxel, where Q is calculated from voxel value v by
                Q = -log10(qg(abs((v-median)/(sqrt(PI/2)*MAD))))
             or Q = 0 if v is 'close' to the median (not an outlier).
                That is, 10**(-Q) is roughly the p-value of value v
                under the hypothesis that the v's are iid normal.
              The prefix of the new dataset (float format) is 'ppp'.

 -polort nn = Detrend each voxel time series with polynomials of
                order 'nn' prior to outlier estimation.  Default
                value of nn=0, which means just remove the median.
                Detrending is done with L1 regression, not L2.

 -legendre  = Use Legendre polynomials (also allows -polort > 3).

OUTLIERS are defined as follows:
 * The trend and MAD of each time series are calculated.
   - MAD = median absolute deviation
         = median absolute value of time series minus trend.
 * In each time series, points that are 'far away' from the
    trend are called outliers, where 'far' is defined by
      alpha * sqrt(PI/2) * MAD
      alpha = qginv(0.001/N) (inverse of reversed Gaussian CDF)
      N     = length of time series
 * Some outliers are to be expected, but if a large fraction of the
    voxels in a volume are called outliers, you should investigate
    the dataset more fully.

Since the results are written to stdout, you probably want to redirect
them to a file or another program, as in this example:
  3dToutcount -automask v1+orig | 1dplot -stdin

NOTE: also see program 3dTqual for a similar quality check.

This program accepts datasets that are modified on input according to the
following schemes:
  'r1+orig[3..5]'                                    {sub-brick selector}
  'r1+orig<100..200>'                                {sub-range selector}
  'r1+orig[3..5]<100..200>'                          {both selectors}
  '3dcalc( -a r1+orig -b r2+orig -expr 0.5*(a+b) )'  {calculation}
For the gruesome details, see the output of 'afni -help'.

++ Compile date = May  7 2021 {AFNI_21.1.06:linux_ubuntu_16_64}