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.