:orphan: .. _ahelp_3dNLfim: ******* 3dNLfim ******* .. contents:: :local: | .. code-block:: none This program calculates a nonlinear regression for each voxel of the input AFNI 3d+time data set. The nonlinear regression is calculated by means of a least squares fit to the signal plus noise models which are specified by the user. Usage with terminal options: 3dNLfim -help show this help -help_models show model help from any that have it (can come via AFNI_MODEL_HELP_ALL) One can get help for an individual model, *if* it exists, by setting a similar environment variable, and providing some non-trivial function option (like -load_models), e.g., 3dNLfim -DAFNI_MODEL_HELP_CONV_PRF_6=Y -load_models Indifidual help should be available for any model with help via -help_models. -load_models simply load all models and exit (this is for testing or getting model help) General usage: 3dNLfim -input fname fname = filename of 3d + time data file for input [-mask mset] Use the 0 sub-brick of dataset 'mset' as a mask to indicate which voxels to analyze (a sub-brick selector is allowed) [default = use all voxels] [-ignore num] num = skip this number of initial images in the time series for regression analysis; default = 0 ****N.B.: default ignore value changed from 3 to 0, on 04 Nov 2008 (BHO day). [-inTR] set delt = TR of the input 3d+time dataset [The default is to compute with delt = 1.0 ] [The model functions are calculated using a time grid of: 0, delt, 2*delt, 3*delt, ... ] [-TR delt] directly set the TR of the time series model; can be useful if the input file is a .1D file (transposed with the \' operator) [-time fname] fname = ASCII file containing each time point in the time series. Defaults to even spacing given by TR (this option overrides -inTR). -signal slabel slabel = name of (non-linear) signal model -noise nlabel nlabel = name of (linear) noise model -sconstr k c d constraints for kth signal parameter: c <= gs[k] <= d **N.B.: It is important to set the parameter constraints with care! **N.B.: -sconstr and -nconstr options must appear AFTER -signal and -noise on the command line -nconstr k c d constraints for kth noise parameter: c+b[k] <= gn[k] <= d+b[k] [-nabs] use absolute constraints for noise parameters: c <= gn[k] <= d [default=relative, as above] [-nrand n] n = number of random test points [default=19999] [-nbest b] b = use b best test points to start [default=9] [-rmsmin r] r = minimum rms error to reject reduced model [-fdisp fval] display (to screen) results for those voxels whose f-statistic is > fval [default=999.0] [-progress ival] display (to screen) results for those voxels every ival number of voxels [-voxel_count] display (to screen) the current voxel index --- These options choose the least-square minimization algorithm --- [-SIMPLEX] use Nelder-Mead simplex method [default] [-POWELL] use Powell's NEWUOA method instead of the Nelder-Mead simplex method to find the nonlinear least-squares solution [slower; usually more accurate, but not always!] [-BOTH] use both Powell's and Nelder-Mead method [slowest, but should be most accurate] --- These options generate individual AFNI 2 sub-brick datasets --- --- [All these options must be AFTER options -signal and -noise]--- [-freg fname] perform f-test for significance of the regression; output 'fift' is written to prefix filename fname [-frsqr fname] calculate R^2 (coef. of multiple determination); store along with f-test for regression; output 'fift' is written to prefix filename fname [-fsmax fname] estimate signed maximum of signal; store along with f-test for regression; output 'fift' is written to prefix filename fname [-ftmax fname] estimate time of signed maximum; store along with f-test for regression; output 'fift' is written to prefix filename fname [-fpsmax fname] calculate (signed) maximum percentage change of signal from baseline; output 'fift' is written to prefix filename fname [-farea fname] calculate area between signal and baseline; store with f-test for regression; output 'fift' is written to prefix filename fname [-fparea fname] percentage area of signal relative to baseline; store with f-test for regression; output 'fift' is written to prefix filename fname [-fscoef k fname] estimate kth signal parameter gs[k]; store along with f-test for regression; output 'fift' is written to prefix filename fname [-fncoef k fname] estimate kth noise parameter gn[k]; store along with f-test for regression; output 'fift' is written to prefix filename fname [-tscoef k fname] perform t-test for significance of the kth signal parameter gs[k]; output 'fitt' is written to prefix filename fname [-tncoef k fname] perform t-test for significance of the kth noise parameter gn[k]; output 'fitt' is written to prefix filename fname --- These options generate one AFNI 'bucket' type dataset --- [-bucket n prefixname] create one AFNI 'bucket' dataset containing n sub-bricks; n=0 creates default output; output 'bucket' is written to prefixname The mth sub-brick will contain: [-brick m scoef k label] kth signal parameter regression coefficient [-brick m ncoef k label] kth noise parameter regression coefficient [-brick m tmax label] time at max. abs. value of signal [-brick m smax label] signed max. value of signal [-brick m psmax label] signed max. value of signal as percent above baseline level [-brick m area label] area between signal and baseline [-brick m parea label] signed area between signal and baseline as percent of baseline area [-brick m tscoef k label] t-stat for kth signal parameter coefficient [-brick m tncoef k label] t-stat for kth noise parameter coefficient [-brick m resid label] std. dev. of the full model fit residuals [-brick m rsqr label] R^2 (coefficient of multiple determination) [-brick m fstat label] F-stat for significance of the regression [-noFDR] Don't write the FDR (q vs. threshold) curves into the output dataset. (Same as 'setenv AFNI_AUTOMATIC_FDR NO') --- These options write time series fit for --- --- each voxel to an AFNI 3d+time dataset --- [-sfit fname] fname = prefix for output 3d+time signal model fit [-snfit fname] fname = prefix for output 3d+time signal+noise fit -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 https://sscc.nimh.nih.gov/afni/doc/misc/afni_parallelize/index_html/view * Use -mask to get more speed; cf. 3dAutomask. ---------------------------------------------------------------------- Signal Models (see the appropriate model_*.c file for exact details) : Null : No Signal (no parameters) see model_null.c SineWave_AP : Sinusoidal Response (amplitude, phase) see model_sinewave_ap.c SquareWave_AP : Square Wave Response (amplitude, phase) see model_squarewave_ap.c TrnglWave_AP : Triangular Wave Response (amplitude, phase) see model_trnglwave_ap.c SineWave_APF : Sinusoidal Wave Response (amplitude, phase, frequency) see model_sinewave_apf.c SquareWave_APF : Sinusoidal Wave Response (amplitude, phase, frequency) see model_squarewave_apf.c TrnglWave_APF : Sinusoidal Wave Response (amplitude, phase, frequency) see model_trnglwave_apf.c Exp : Exponential Function (a,b): a * exp(b * t) see model_exp.c DiffExp : Differential-Exponential Drug Response (t0, k, alpha1, alpha2) see model_diffexp.c GammaVar : Gamma-Variate Function Drug Response (t0, k, r, b) see model_gammavar.c Beta : Beta Distribution Model (t0, tf, k, alpha, beta) see model_beta.c * The following convolved functions are generally convolved with the time series in AFNI_CONVMODEL_REF, allowing one to specify multiple event onsets, varying durations and varying response magnitudes. ConvGamma : Gamma Vairate Response Model (t0, amp, r, b) see model_convgamma.c ConvGamma2a : Gamma Convolution with 2 Input Time Series (t0, r, b) see model_convgamma2a.c ConvDiffGam : Difference of 2 Gamma Variates (A0, T0, E0, D0, A1, T1, E1, D1) see model_conv_diffgamma.c for help : setenv AFNI_MODEL_HELP_CONVDIFFGAM YES 3dNLfim -signal ConvDiffGam demri_3 : Dynamic (contrast) Enhanced MRI (K_trans, Ve, k_ep) see model_demri_3.c for help : setenv AFNI_MODEL_HELP_DEMRI_3 YES 3dNLfim -signal demri_3 ADC : Diffusion Signal Model (So, D) see model_diffusion.c michaelis_menton : Michaelis/Menten Concentration Model (v, vmax, k12, k21, mag) see model_michaelis_menton.c Expr2 : generic (3dcalc-like) expression with exactly 2 'free' parameters and using symbol 't' as the time variable; see model_expr2.c for details. ConvCosine4 : 4-piece Cosine Convolution Model (A, C1, C2, M1, M2, M3, M4) see model_conv_cosine4.c for help : setenv AFNI_MODEL_HELP_CONV_COSINE4 YES 3dNLfim -signal ConvCosine4 Conv_PRF : 4-param Population Receptive Field Model (A, X, Y, sigma) see model_conv_PRF.c for help : setenv AFNI_MODEL_HELP_CONV_PRF YES 3dNLfim -signal bunnies Conv_PRF_6 : 6-param Population Receptive Field Model (A, X, Y, sigma, sigrat, theta) see model_conv_PRF_6.c for help : setenv AFNI_MODEL_HELP_CONV_PRF_6 YES 3dNLfim -signal bunnies Conv_PRF_DOG : 6-param 'Difference of Gaussians' PRF Model (as Conv_PRF, but with second A and sigma) (A, X, Y, sig, A2, sig2) see model_conv_PRF_DOG.c for help : setenv AFNI_MODEL_HELP_CONV_PRF_DOG YES 3dNLfim -signal bunnies ---------------------------------------- Noise Models (see the appropriate model_*.c file for exact details) : Zero : Zero Noise Model (no parameters) see model_zero.c Constant : Constant Noise Model (constant) see model_constant.c Linear : Linear Noise Model (constant, linear) see model_linear.c Linear+Ort : Linear+Ort Noise Model (constant, linear, Ort) see model_linplusort.c Quadratic : Quadratic Noise Model (constant, linear, quadratic) see model_quadratic.c ++ Compile date = Apr 24 2024 {AFNI_24.1.05:linux_ubuntu_16_64}