AFNI program: 3dclust
Output of -help
Program: 3dclust
Author: RW Cox et alii
Date: 12 Jul 2017
3dclust - performs simple-minded cluster detection in 3D datasets
*** PLEASE NOTE THAT THE NEWER PROGRAM 3dClusterize ***
*** IS BETTER AND YOU SHOULD USE THAT FROM NOW ON!! ***
This program can be used to find clusters of 'active' voxels and
print out a report about them.
* 'Active' refers to nonzero voxels that survive the threshold
that you (the user) have specified
* Clusters are defined by a connectivity radius parameter 'rmm'
*OR*
Clusters are defined by how close neighboring voxels must
be in the 3D grid:
first nearest neighbors (-NN1)
second nearest neighbors (-NN2)
third nearest neighbors (-NN3)
Note: by default, this program clusters on the absolute values
of the voxels
-----------------------------------------------------------------------
Usage:
3dclust [editing options] [other options] rmm vmul dset ...
*OR*
3dclust [editing options] -NNx dset ...
where '-NNx' is one of '-NN1' or '-NN2' or '-NN3':
-NN1 == 1st nearest-neighbor (faces touching) clustering
-NN2 == 2nd nearest-neighbor (edges touching) clustering
-NN2 == 3rd nearest-neighbor (corners touching) clustering
Optionally, you can put an integer after the '-NNx' option, to
indicate the minimum number of voxels to allow in a cluster;
for example: -NN2 60
-----------------------------------------------------------------------
Examples:
---------
3dclust -1clip 0.3 5 2000 func+orig'[1]'
3dclust -1noneg -1thresh 0.3 5 2000 func+orig'[1]'
3dclust -1noneg -1thresh 0.3 5 2000 func+orig'[1]' func+orig'[3]
3dclust -noabs -1clip 0.5 -dxyz=1 1 10 func+orig'[1]'
3dclust -noabs -1clip 0.5 5 700 func+orig'[1]'
3dclust -noabs -2clip 0 999 -dxyz=1 1 10 func+orig'[1]'
3dclust -1clip 0.3 5 3000 func+orig'[1]'
3dclust -quiet -1clip 0.3 5 3000 func+orig'[1]'
3dclust -summarize -quiet -1clip 0.3 5 3000 func+orig'[1]'
3dclust -1Dformat -1clip 0.3 5 3000 func+orig'[1]' > out.1D
-----------------------------------------------------------------------
Arguments (must be included on command line):
---------
THE OLD WAY TO SPECIFY THE TYPE OF CLUSTERING
rmm : cluster connection radius (in millimeters).
All nonzero voxels closer than rmm millimeters
(center-to-center distance) to the given voxel are
included in the cluster.
* If rmm = 0, then clusters are defined by nearest-
neighbor connectivity
vmul : minimum cluster volume (micro-liters)
i.e., determines the size of the volume cluster.
* If vmul = 0, then all clusters are kept.
* If vmul < 0, then the absolute vmul is the minimum
number of voxels allowed in a cluster.
If you do not use one of the '-NNx' options, you must give the
numbers for rmm and vmul just before the input dataset name(s)
THE NEW WAY TO SPECIFY TYPE OF CLUSTERING [13 Jul 2017]
-NN1 or -NN2 or -NN3
If you use one of these '-NNx' options, you do NOT give the rmm
and vmul values. Instead, after all the options that start with '-',
you just give the input dataset name(s).
If you want to set a minimum cluster size using '-NNx', put the minimum
voxel count immediately after, as in '-NN3 100'.
FOLLOWED BY ONE (or more) DATASETS
dset : input dataset (more than one allowed, but only the
first sub-brick of the dataset)
The results are sent to standard output (i.e., the screen):
if you want to save them in a file, then use redirection, as in
3dclust -1thresh 0.4 -NN2 Elvis.nii'[1]' > Elvis.clust.txt
-----------------------------------------------------------------------
Options:
-------
Editing options are as in 3dmerge (see 3dmerge -help)
(including -1thresh, -1dindex, -1tindex, -dxyz=1 options)
-NN1 => described earlier;
-NN2 => replaces the use of 'rmm' to specify the
-NN3 => clustering method (vmul is set to 2 voxels)
-noabs => Use the signed voxel intensities (not the absolute
value) for calculation of the mean and Standard
Error of the Mean (SEM)
-summarize => Write out only the total nonzero voxel
count and volume for each dataset
-nosum => Suppress printout of the totals
-verb => Print out a progress report (to stderr)
as the computations proceed
-1Dformat => Write output in 1D format (now default). You can
redirect the output to a .1D file and use the file
as input to whereami_afni for obtaining Atlas-based
information on cluster locations.
See whereami_afni -help for more info.
-no_1Dformat => Do not write output in 1D format.
-quiet => Suppress all non-essential output
-mni => If the input dataset has the +tlrc view, this option
will transform the output xyz-coordinates from TLRC to
MNI space.
N.B.0: Only use this option if the dataset is in Talairach
space, NOT when it is already in MNI space.
N.B.1: The MNI template brain is about 5 mm higher (in S),
10 mm lower (in I), 5 mm longer (in PA), and tilted
about 3 degrees backwards, relative to the Talairach-
Tournoux Atlas brain. For more details, see, e.g.:
https://imaging.mrc-cbu.cam.ac.uk/imaging/MniTalairach
N.B.2: If the input dataset does not have the +tlrc view,
then the only effect is to flip the output coordinates
to the 'LPI' (neuroscience) orientation, as if you
gave the '-orient LPI' option.)
-isovalue => Clusters will be formed only from contiguous (in the
rmm sense) voxels that also have the same value.
N.B.: The normal method is to cluster all contiguous
nonzero voxels together.
-isomerge => Clusters will be formed from each distinct value
in the dataset; spatial contiguity will not be
used (but you still have to supply rmm and vmul
on the command line).
N.B.: 'Clusters' formed this way may well have components
that are widely separated!
-inmask => If 3dClustSim put an internal attribute into the
input dataset that describes a mask, 3dclust will
use this mask to eliminate voxels before clustering,
if you give this option. '-inmask' is how the AFNI
AFNI Clusterize GUI works by default.
[If there is no internal mask in the dataset]
[header, then '-inmask' doesn't do anything.]
N.B.: The usual way for 3dClustSim to have put this internal
mask into a functional dataset is via afni_proc.py.
-prefix ppp => Write a new dataset that is a copy of the
input, but with all voxels not in a cluster
set to zero; the new dataset's prefix is 'ppp'
N.B.: Use of the -prefix option only affects the
first input dataset.
-savemask q => Write a new dataset that is an ordered mask, such
that the largest cluster is labeled '1', the next
largest '2' and so forth. Should be the same as
'3dmerge -1clust_order' or Clusterize 'SaveMsk'.
-binary => This turns the output of '-savemask' into a binary
(0 or 1) mask, rather than a cluster-index mask.
**-->> If no clusters are found, the mask is not written!
-----------------------------------------------------------------------
N.B.: 'N.B.' is short for 'Nota Bene', Latin for 'Note Well';
also see http://en.wikipedia.org/wiki/Nota_bene
-----------------------------------------------------------------------
E.g., 3dclust -1clip 0.3 5 3000 func+orig'[1]'
The above command tells 3dclust to find potential cluster volumes for
dataset func+orig, sub-brick #1, where the threshold has been set
to 0.3 (i.e., ignore voxels with activation threshold >0.3 or <-0.3).
Voxels must be no more than 5 mm apart, and the cluster volume
must be at least 3000 micro-liters in size.
Explanation of 3dclust Output:
-----------------------------
Volume : Volume that makes up the cluster, in microliters (mm^3)
(or the number of voxels, if -dxyz=1 is given)
CM RL : Center of mass (CM) for the cluster in the Right-Left
direction (i.e., the coordinates for the CM)
CM AP : Center of mass for the cluster in the
Anterior-Posterior direction
CM IS : Center of mass for the cluster in the
Inferior-Superior direction
minRL, maxRL : Bounding box for the cluster, min and max
coordinates in the Right-Left direction
minAP, maxAP : Min and max coordinates in the Anterior-Posterior
direction of the volume cluster
minIS, max IS: Min and max coordinates in the Inferior-Superior
direction of the volume cluster
Mean : Mean value for the volume cluster
SEM : Standard Error of the Mean for the volume cluster
Max Int : Maximum Intensity value for the volume cluster
MI RL : Coordinate of the Maximum Intensity value in the
Right-Left direction of the volume cluster
MI AP : Coordinate of the Maximum Intensity value in the
Anterior-Posterior direction of the volume cluster
MI IS : Coordinate of the Maximum Intensity value in the
Inferior-Superior direction of the volume cluster
-----------------------------------------------------------------------
Nota Bene:
* The program does not work on complex- or rgb-valued datasets!
* Using the -1noneg option is strongly recommended!
* 3D+time datasets are allowed, but only if you use the
-1tindex and -1dindex options.
* Bucket datasets are allowed, but you will almost certainly
want to use the -1tindex and -1dindex options with these.
* SEM values are not realistic for interpolated data sets!
A ROUGH correction is to multiply the SEM of the interpolated
data set by the square root of the number of interpolated
voxels per original voxel.
* If you use -dxyz=1, then rmm should be given in terms of
voxel edges (not mm) and vmul should be given in terms of
voxel counts (not microliters). Thus, to connect to only
3D nearest neighbors and keep clusters of 10 voxels or more,
use something like '3dclust -dxyz=1 1.01 10 dset+orig'.
In the report, 'Volume' will be voxel count, but the rest of
the coordinate dependent information will be in actual xyz
millimeters.
* The default coordinate output order is DICOM. If you prefer
the SPM coordinate order, use the option '-orient LPI' or
set the environment variable AFNI_ORIENT to 'LPI'. For more
information, see file README.environment.
++ Compile date = Dec 17 2024 {AFNI_24.3.10:linux_ubuntu_24_64}
This page auto-generated on
Tue Dec 17 06:52:31 PM EST 2024