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AFNI class 6//2016:

Quick Note on

You can set put your regression in after building as below, one nice feature is the use of to build censors.

               -regress_censor_motion 0.3                        [.3mm motion max]  

               -regress_censor_outliers 0.1                       [10% volume max]
-regress_censor_first_trs 3                          [remove first 3]
               -regress_censor_extern CENSOR.1D
-regress_opts_3dD OPTS ...   : specify extra options for 3dDeconvolve

3dDeconvolve: time series linear regression

3dNLfim: time series non-linear regression
: removes temporal autocorrelation from time series regression

: useful as a lead into 3dsvm

: AFNI does multivoxel pattern analysis (MVPA)

3dDeconvolve command-line-arguments ... See bottom of page for 3 examples.

-input fname              fname = filename of 3D+time input dataset        

-input1D dname        dname = filename of single (fMRI) .1D time series
-TR_1D tr1d                tr1d = TR for .1D time series [default 1.0 sec].
-mask mname            mname = filename of 3D mask dataset
-automask                  Build a mask automatically from input data;  some masking done as default.     -censor cname           cname = filename of censor .1D time series
-CENSORTR clist        clist = list of strings that specify time indexes
-concat rname           rname = filename for list of concatenated runs  Or '1D: 0 100 200 300'
-nfirst fnum                fnum = trims front; number of first dataset image [default = max maxlag]
-nlast  lnum                lnum = end trim; number of last dataset image [default = last point]
-polort pnum              pnum = degree of polynomial to regress out [A=automatic]
-singvals                      Print out the matrix singular values       
-GOFORIT [g]             Use this to proceed even if the matrix has       
-allzero_OK                Don't error all zero matrix columns
-num_stimts num      num = number of input stimulus time series       

Inputting regressors (k is enumeration of stimli):

Traditional pre wavered:

-stim_file k sname            sname = filename of kth time series input stimulus

-stim_label k slabel          slabel = label for kth input stimulus    
-stim_nptr k p                    p = number of stimulus function points per TR    

Modeled in deconvolution [Preferred]:

-stim_times k tname Rmodel                       is the general format

-stim_times_AM1 k tname Rmodel            provides mean amplitude
-stim_times_AM2 k tname Rmodel            provies mean and difference from mean; preferred model in most instances
-stim_times_FSL k tname Rmodel               3 column [s d p] s=start, d=duration, p=peak [afni dislikes]
-stim_times_IM k tname Rmodel                individually modulated

Picking a Rmodel please note:

ALWAYS convolved:                        BLOCK          dmBLOCK     MION            MIONN

NEVER convolved:                           TENT             CSPLIN          POLY             SIN                EXPR
OPTIONALLY convolved:                GAM              SPMGx          WAV

Rmodel details:

'BLOCK(d,p)'               d=duration p=amplitude (default=1)

'dmUBLOCK(q)'          q = norm at time point
'TENT(b,c,n)'              n = samples in time range, b= start, c=end
'CSPLIN(b,c,n)'           as above different wave form
        'TENTzero' and 'CSPLINzero'         forces HRF to be zero at t=b and t=c
'GAM(p,q,[d])'            p=8.6 q=0.547 if only 'GAM' is used; d = an optional duration of stimulus
'SPMG1([d])'               1 parameter SPM gamma variate basis function; d= optional duration of stimulus
'SPMG2([d])'               2 parameter SPM: gamma variate + d/dt derivative; d= optional duration of stimulus
'SPMG3([d])'               3 parameter SPM basis function set; d= optional duration of stimulus
'POLY(b,c,n)'               n = number of polynomials, b= start, c=end
'SIN(b,c,n)'                  n = number of sine waves, b= start, c=end
'WAV(d)'                      same as waver (cox) d= duration can add;
'WAV(d,2,4,6,0.2,2)' with delay time , rise time , fall time , undershoot fraction, undershoot restore time
'EXPR(b,c) exp1 ... expn'  b= start, c=end
'MION(d)'                    d = duration, a gamma with a long tail
'MIONN(d)'                 d = duration, a gamma with a long tail; flipped to address negative betas

Note on stim files:

Can be a binary stim list by TR, can be a wavered stim list by TR, can be made of the fly :  '1D: 3.2 7.9 | 8.2 16.2 23.7' ['|' = run cat point][doesn’t work with stim_times_*]

-basis_normall a        norm all to following singular peak                                                    

-TR_times dt              change dt for response functions


-num_glt num            num = number of general linear tests (GLTs)      

-glt_label k glabel     glabel = label for kth general linear test   
-gltsym gltname        Read the GLT with symbolic names from the file  or ‘SYM: ….’ 

Output stats:

-fout                            f stat        

-rout                            R^2
-tout                            t stat
-vout                            variance
-bout                            Flag to turn on output of baseline coefs and stats.
-nofull_first                Flag to specify that full model statistics go last 

Output BRIKS:

-bucket bprefix          big output file

-iresp k iprefix            iprefix = output brik of response function for kth labeled stim
-sresp k sprefix          sprefix = output brik of SD function for kth labeled stim
-fitts  fprefix               fprefix = output brik of R (from regression)
-errts  eprefix             eprefix = outpu of error


-xsave                          Flag to save X matrix into file bprefix.xsave      

-xrestore f.xsave       so you can add new glts [other commands ignored
-short                           Write output as scaled shorts [default=float]
-jobs J                          J = number of processors

Testing things out:

-nodata [NT [TR]]   Evaluate experimental design only (no input data) NT=reps, TR=TR sec


      No data: nodata

      Traditional: Traditional

      Using R model: Rmodel