Usage: 3dToutcount [options] dataset
Calculates number of 'outliers' a 3D+time dataset, at each
time point, and writes the results to stdout.
Options:
-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.
INPUT DATASET NAMES
-------------------
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 = Apr 18 2024 {AFNI_24.1.03:linux_ubuntu_16_64}
