3dECM¶
Contents
Usage: 3dECM [options] dset
Computes voxelwise local functional connectivity density and
stores the result in a new 3D bucket dataset as floats to
preserve their values. ECM reflects the strength and
extent of a voxel's global connectivity as well as the
importance of the voxels that it is directly connected to.
Conceptually the process involves:
1. Calculating the correlation between voxel time series for
every pair of voxels in the brain (as determined by masking)
2. Calculate the eigenvector corresponding to the largest
eigenvalue of the similarity matrix.
Guaranteeing that this eigenvector is unique and all positive
requires that the similarity matrix is strictly positive. This
is enforced by either adding one to the correlations (Lohmann
et. al. 2010), or by adding one and dividing by two (Wink et al.
2012).
Practically the power iteration algorithm described in Wink et
al. 2012) is used to optimize for computational time and memory
usage.
Lohmann G, Margulies DS, Horstmann A, Pleger B, Lepsien J, et al.
(2010) Eigenvector Centrality Mapping for Analyzing
Connectivity Patterns in fMRI Data of the Human Brain. PLoS
ONE 5(4): e10232. doi: 10.1371/journal.pone.0010232
Wink, A. M., de Munck, J. C., van der Werf, Y. D., van den Heuvel,
O. A., & Barkhof, F. (2012). Fast Eigenvector Centrality
Mapping of Voxel-Wise Connectivity in Functional Magnetic
Resonance Imaging: Implementation, Validation, and
Interpretation. Brain Connectivity, 2(5), 265-274.
doi:10.1089/brain.2012.0087
Options:
-full = uses the full power method (Lohmann et. al. 2010).
Enables the use of thresholding and calculating
thresholded centrality. Uses sparse array to reduce
memory requirement. Automatically selected if
-thresh, or -sparsity are used.
-fecm = uses a shortcut that substantially speeds up
computation, but is less flexibile in what can be
done the similarity matrix. i.e. does not allow
thresholding correlation coefficients. based on
fast eigenvector centrality mapping (Wink et. al
2012). Default when -thresh, or -sparsity
are NOT used.
-thresh r = exclude connections with correlation < r. cannot be
used with FECM
-sparsity p = only include the top p% connectoins in the calculation
cannot be used with FECM method. (default = 100)
-shift s = value that should be added to correlation coeffs to
enforce non-negativity, s >= 0. [default = 0.0, unless
-fecm is specified in which case the default is 1.0
(e.g. Wink et al 2012)].
-scale x = value that correlation coeffs should be multiplied by
after shifting, x >= 0 [default = 1.0, unless -fecm is
specified in which case the default is 0.5 (e.g. Wink et
al 2012)].
-eps p = sets the stopping criterion for the power iteration
l2|v_old - v_new| < eps*|v_old|. default = .001 (0.1%)
-max_iter i = sets the maximum number of iterations to use in
in the power iteration. default = 1000
-polort m = Remove polynomical trend of order 'm', for m=0..3.
[default is m=1; removal is by least squares].
Using m=0 means that just the mean is removed.
-autoclip = Clip off low-intensity regions in the dataset,
-automask = so that the correlation is only computed between
high-intensity (presumably brain) voxels. The
mask is determined the same way that 3dAutomask works.
-mask mmm = Mask to define 'in-brain' voxels. Reducing the number
the number of voxels included in the calculation will
significantly speedup the calculation. Consider using
a mask to constrain the calculations to the grey matter
rather than the whole brain. This is also preferrable
to using -autoclip or -automask.
-prefix p = Save output into dataset with prefix 'p'
[default prefix is 'ecm'].
-memory G = Calculating eignevector centrality can consume alot
of memory. If unchecked this can crash a computer
or cause it to hang. If the memory hits this limit
the tool will error out, rather than affecting the
system [default is 2G].
Notes:
* The output dataset is a bucket type of floats.
* The program prints out an estimate of its memory used
when it ends. It also prints out a progress 'meter'
to keep you pacified.
-- RWCox - 31 Jan 2002 and 16 Jul 2010
-- Cameron Craddock - 13 Nov 2015
-- Daniel Clark - 14 March 2016
=========================================================================
* This binary version of 3dECM is compiled using OpenMP, a semi-
automatic parallelizer software toolkit, which splits the work across
multiple CPUs/cores on the same shared memory computer.
* OpenMP is NOT like MPI -- it does not work with CPUs connected only
by a network (e.g., OpenMP doesn't work with 'cluster' setups).
* For implementation and compilation details, please see
https://afni.nimh.nih.gov/pub/dist/doc/misc/OpenMP.html
* The number of CPU threads used will default to the maximum number on
your system. You can control this value by setting environment variable
OMP_NUM_THREADS to some smaller value (including 1).
* Un-setting OMP_NUM_THREADS resets OpenMP back to its default state of
using all CPUs available.
++ However, on some systems, it seems to be necessary to set variable
OMP_NUM_THREADS explicitly, or you only get one CPU.
++ On other systems with many CPUS, you probably want to limit the CPU
count, since using more than (say) 16 threads is probably useless.
* You must set OMP_NUM_THREADS in the shell BEFORE running the program,
since OpenMP queries this variable BEFORE the program actually starts.
++ You can't usefully set this variable in your ~/.afnirc file or on the
command line with the '-D' option.
* How many threads are useful? That varies with the program, and how well
it was coded. You'll have to experiment on your own systems!
* The number of CPUs on this particular computer system is ...... 2.
* The maximum number of CPUs that will be used is now set to .... 2.
=========================================================================
++ Compile date = Mar 27 2018 {AFNI_18.0.27:linux_ubuntu_16_64}
