Hello Afni experts,

I have a question about my design that I ran through 3Ddeconvolve with the no data option.

I want to run a secondary analysis just looking at congruent and incongruent trials for the task. When I run the timing through the no data option it notes very good for each matrix, but when i run 1d_tool.py to look at the correlations it says the correlation between congruent and incongruent regressors is high. I just want to check and see if I will be able to deconvolve it. Here is the output for both below. Thank you!

Tara


++ 3dDeconvolve: AFNI version=AFNI_2011_12_21_1014 (Mar 2 2015) [64-bit]
++ Authored by: B. Douglas Ward, et al.
++ using TR=2 seconds for -stim_times and -nodata
++ using NT=412 time points for -nodata
++ Input polort=3; Longest run=412.0 s; Recommended minimum polort=3 ++ OK ++
++ -stim_times using TR=2 s for stimulus timing conversion
++ -stim_times using TR=2 s for any -iresp output datasets
++ [you can alter the -iresp TR via the -TR_times option]
++ ** -stim_times NOTE ** guessing GLOBAL times if 1 time per line; LOCAL otherwise
++ ** GUESSED ** -stim_times 1 using LOCAL times
++ ** GUESSED ** -stim_times 2 using LOCAL times
------------------------------------------------------------
GLT matrix from 'SYM: incongruent -congruent':
0 0 0 0 0 0 0 0 -1 1

++ Number of time points: 412 (no censoring)
+ Number of parameters: 10 [8 baseline ; 2 signal]
++ Wrote matrix values to file nodata.xmat.1D
++ ----- Signal+Baseline matrix condition [X] (412x10): 2.20943 ++ VERY GOOD ++
++ ----- Signal-only matrix condition [X] (412x2): 1.05394 ++ VERY GOOD ++
++ ----- Baseline-only matrix condition [X] (412x8): 1.01109 ++ VERY GOOD ++
++ ----- polort-only matrix condition [X] (412x8): 1.01109 ++ VERY GOOD ++
++ Wrote matrix values to file nodata_XtXinv.xmat.1D
++ +++++ Matrix inverse average error = 4.35916e-16 ++ VERY GOOD ++
++ Matrix setup time = 0.01 s

Stimulus: congruent
h[ 0] norm. std. dev. = 0.1730

Stimulus: incongruent
h[ 0] norm. std. dev. = 0.1699

General Linear Test: contrast_incon_con
LC[0] norm. std. dev. = 0.1377


[miskovi2@psy-imglab-7 emo1]$ 1d_tool.py -cormat_cutoff 0.1 -show_cormat_warnings -infile nodata.xmat.1D

Warnings regarding Correlation Matrix: nodata.xmat.1D

severity correlation cosine regressor pair
-------- ----------- ------ ----------------------------------------
high: -0.658 0.105 ( 8 vs. 9) congruent#0 vs. incongruent#0