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find_variance_lines.tcsh - look for high temporal variance columns
usage : find_variance_lines.tcsh [options] datasets ..."
Look for bars of high variance that might suggest scanner interference.
inputs: multiple runs of EPI datasets
output: a directory containing
- variance maps per run: original and scaled
- cluster reports and x,y coordinates at high averages
- a JPEG image showing locations of high variance
This program takes one or more runs of (presumably) EPI time series data,
and looks for slice locations with consistently high temporal variance across
the (masked) slices.
steps:
- (possibly) automask, erode and require columns of 7 voxels
- (possibly) detrend at regress polort level, default = A
- compute temporal variance volume
- get p90 = 90th %ile in volume mask, default %ile = 90
- scale variance to val/p90, with max of 1
- Localstat -mask mean over columns
- find separate clusters of them,
where a vline is a column with Localstat mean >= 0.90
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Examples:
1. Run using defaults.
find_variance_lines.tcsh epi_r1.nii epi_r2.nii epi_r3.nii
OR
find_variance_lines.tcsh epi_r*.nii
2. What would afni_proc.py do?
find_variance_lines.tcsh -rdir vlines.pb00.tcat -nerode 2 \
pb00*tcat*.HEAD |& tee out.vlines.pb00.tcat.txt
3. Provide a mask (and do not erode). Do not detrend time series.
Use the default output directory, vlines.result.
find_variance_lines.tcsh -mask my_mask.nii.gz -polort -1 \
epi_run*.nii.gz
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Options (terminal):
-help : show this help
-hist : show the version history
-ver : show the current version
Options (processing):
-do_clean VAL : do we clean up a little? (def=1)
VAL in {0,1}
Remove likely unneeded datasets, particular the
large time series datasets.
-do_img VAL : make vline images? (def=1)
VAL in {0,1}
Specify whether to make jpeg images of high
variance locations.
-echo : run script with shell 'echo' set (def=no)
(this is VERY verbose)
With this set, it is as if running the (tcsh) as in:
tcsh -x .../find_variance_lines.tcsh ...
So all shell commands (including setting variables,
"if" evaluations, etc.) are shown. This is useful
for debugging.
-mask VAL : mask for computations (def=AUTO)
VAL in {AUTO, NONE, dataset}
Specify a mask dataset to restrict variance
computations to. VAL should be a dataset, with
exception for special cases:
AUTO : generate automask with 3dAutomask
NONE : do not mask
-min_cvox VAL : min voxels for valid mask column (def=7)
VAL in Z+ (positive integers)
In the input or automask, after any eroding, remove
voxels that do not have at least 'VAL' voxels in the
vertical column. Otherwise, edge voxels might end
up in the result.
-min_nt VAL : minimum number of time points required (def=10)
VAL > 1 (integer)
This is just a minimum limit to be sure the input
time series are long enough to be reasonable.
-nerode VAL : how much to erode input or auto-mask (def=0)
VAL >= 0 (integer)
Specify the number of levels to erode any mask by.
"3dmask_tool -dilate -VAL " is used.
-nfirst VAL : discard the first VAL time points (def=0)
VAL >= 0 (integer)
Specify the number of time points to discard from
the start of each run (pre-steady state, presumably).
-perc VAL : percentile of variance vals to scale to (def=90)
VAL in {0..99}
When looking for high variance, the values are scaled
by this percentile value, with a scaled limit of 1.
So if the 90%-ile of variance values were 876.5, then
variance would be scaled using v_new = v_old/876.5,
with v_new limited to the range [0,1].
This allows evaluation relative to a modestly extreme
value, without worrying about the exact numbers.
-polort VAL : polynomial detrending degree (def=A)
VAL >= -1 (integer), or in {A,AUTO,NONE}
Specify the polynomial degree to use for time series
detrending prior to the variance computation. This
should be an integer >= -1 (or a special case). The
default is the same as that used by afni_proc.py and
3dDeconvolve, which is based on the duration of the
run, in seconds.
Special cases or examples:
A : auto = floor(run_duration/150)+1
AUTO : auto = floor(run_duration/150)+1
NONE : do not detrend (same as -1)
-1 : do not detrend
0 : only remove the mean
3 : remove a cubic polynomial trend
-rdir VAL : name of the output directory (def=vlines.result)
VAL is a new directory name
All output is put into this results directory.
-ignore_edges VAL : ignore vline clusters at edges (def=1)
VAL in {0,1}
Set this option to ignore clusters at the R,L,A,P
edges, so vlines near those edges are not reported.
If a vline cluster traces the outer edge of the brain
(in the j-axis direction), it is probably just due to
motion. Use this option to ignore such clusters, and
therefore not report vlines connected to edges.
Such edges are defined as the outermost edges in the
i and j directions of the 3-D mask. This is because
lines are along the k axis (usually I/S), and the
limits should be perpendicular to the vline axis.
-stdev_power POW : power on stdev to apply before ave/thresh
default : -stdev_power 2
example : -stdev_power 4 -thresh 0.92
The is the power the stdandard deviation is taken to
before any subsequent computations. Higher values
(powers) allow for better contrast when close to 1.0.
Higher values might allow for lower -thresh.
A value of 1 will lead to computations with stdev.
A value of 2 will imply variance.
Higher values continues the pattern.
-thresh THRESH : variance threshold to be considered a variance line
default : -thresh 0.90
This is the minimum 3dLocalstat variance average for
a column to be consider a variance line. A value
just under 1.0 might be reasonable.
-num_pc NUM : number of PCs to calculate per variance line
default : -num_pc 0 (i.e., none estimated)
Preliminary tests with this have found 2 to be a
reasonable value to use, if you want PCs output.
As an example of naming, the info from component #3
in run 2 is named: pc.inner.r02.c03*.
The outputs from the intersection vline dset are
named like: pc.inter.enum.c*.
- R Reynolds, P Taylor, D Glen
Nov, 2022
version 0.6, 8 Jan, 2025
