AFNI program: 3dDeconvolve
Output of -help
Program: 3dDeconvolve
Author: B. Douglas Ward, et al.
Initial Release: 02 September 1998
Latest Revision: 03 August 2004
Program to calculate the deconvolution of a measurement 3d+time dataset
with a specified input stimulus time series. This program will also
perform multiple linear regression using multiple input stimulus time
series. Output consists of an AFNI 'bucket' type dataset containing the
least squares estimates of the linear regression coefficients, t-statistics
for significance of the coefficients, partial F-statistics for significance
of the individual input stimuli, and the F-statistic for significance of
the overall regression. Additional output consists of a 3d+time dataset
containing the estimated system impulse response function.
Usage:
3dDeconvolve
Input data and control options:
-input fname fname = filename of 3d+time input dataset
[-input1D dname] dname = filename of single (fMRI) .1D time series
[-nodata] Evaluate experimental design only (no input data)
[-mask mname] mname = filename of 3d mask dataset
[-censor cname] cname = filename of censor .1D time series
[-concat rname] rname = filename for list of concatenated runs
[-nfirst fnum] fnum = number of first dataset image to use in the
deconvolution procedure. (default = max maxlag)
[-nlast lnum] lnum = number of last dataset image to use in the
deconvolution procedure. (default = last point)
[-polort pnum] pnum = degree of polynomial corresponding to the
null hypothesis (default: pnum = 1)
[-legendre] use Legendre polynomials for null hypothesis
[-nolegendre] use power polynomials for null hypotheses
(default is -legendre)
[-nocond] don't calculate matrix condition number
[-svd] Use SVD instead of Gaussian elimination (default)
[-nosvd] Use Gaussian elimination instead of SVD
[-rmsmin r] r = minimum rms error to reject reduced model
Input stimulus options:
-num_stimts num num = number of input stimulus time series
(0 <= num) (default: num = 0)
-stim_file k sname sname = filename of kth time series input stimulus
[-stim_label k slabel] slabel = label for kth input stimulus
[-stim_base k] kth input stimulus is part of the baseline model
[-stim_minlag k m] m = minimum time lag for kth input stimulus
(default: m = 0)
[-stim_maxlag k n] n = maximum time lag for kth input stimulus
(default: n = 0)
[-stim_nptr k p] p = number of stimulus function points per TR
Note: This option requires 0 slice offset times
(default: p = 1)
General linear test (GLT) options:
-num_glt num num = number of general linear tests (GLTs)
(0 <= num) (default: num = 0)
[-glt s gltname] Perform s simultaneous linear tests, as specified
by the matrix contained in file gltname
[-glt_label k glabel] glabel = label for kth general linear test
[-gltsym gltname] Read the GLT with symbolic names from the file
Options for output 3d+time datasets:
[-iresp k iprefix] iprefix = prefix of 3d+time output dataset which
will contain the kth estimated impulse response
[-tshift] Use cubic spline interpolation to time shift the
estimated impulse response function, in order to
correct for differences in slice acquisition
times. Note that this effects only the 3d+time
output dataset generated by the -iresp option.
[-sresp k sprefix] sprefix = prefix of 3d+time output dataset which
will contain the standard deviations of the
kth impulse response function parameters
[-fitts fprefix] fprefix = prefix of 3d+time output dataset which
will contain the (full model) time series fit
to the input data
[-errts eprefix] eprefix = prefix of 3d+time output dataset which
will contain the residual error time series
from the full model fit to the input data
Options to control the contents of the output bucket dataset:
[-fout] Flag to output the F-statistics
[-rout] Flag to output the R^2 statistics
[-tout] Flag to output the t-statistics
[-vout] Flag to output the sample variance (MSE) map
[-nobout] Flag to suppress output of baseline coefficients
(and associated statistics)
[-nocout] Flag to suppress output of regression coefficients
(and associated statistics)
[-full_first] Flag to specify that the full model statistics will
appear first in the bucket dataset output
[-bucket bprefix] Create one AFNI 'bucket' dataset containing various
parameters of interest, such as the estimated IRF
coefficients, and full model fit statistics.
Output 'bucket' dataset is written to bprefix.
[-xsave] Flag to save X matrix into file bprefix.xsave
(only works if -bucket option is also given)
[-noxsave] Don't save X matrix (this is the default)
[-cbucket cprefix] Save the regression coefficients (no statistics)
into a dataset named 'cprefix'. This dataset
will be used in a -xrestore run instead of the
bucket dataset, if possible.
[-xrestore f.xsave] Restore the X matrix, etc. from a previous run
that was saved into file 'f.xsave'. You can
then carry out new -glt tests. When -xrestore
is used, most other command line options are
ignored.
The following options control the screen output only:
[-quiet] Flag to suppress most screen output
[-xout] Flag to write X and inv(X'X) matrices to screen
[-xjpeg filename] Write a JPEG file graphing the X matrix
[-progress n] Write statistical results for every nth voxel
[-fdisp fval] Write statistical results for those voxels
whose full model F-statistic is > fval
-jobs J Run the program with 'J' jobs (sub-processes).
On a multi-CPU machine, this can speed the
program up considerably. On a single CPU
machine, using this option is silly.
J should be a number from 1 up to the
number of CPU sharing memory on the system.
J=1 is normal (single process) operation.
The maximum allowed value of J is 32.
* For more information on parallelizing, see
http://afni.nimh.nih.gov/afni/doc/misc/parallize.html
* Use -mask to get more speed; cf. 3dAutomask.
** NOTE **
This version of the program has been compiled to use
double precision arithmetic for most internal calculations.
This page generated on
Tue Aug 3 16:42:44 EDT 2004