Hello-

I was hoping that a multiple regression/afni expert could check my logic to see if I am doing what I think that I am doing. I have an experiment where I am presenting auditory and visual stimuli at .5, 1 or 2 Hz and asking participants to tap in time with the stimuli. The stimuli can either occur at different rates in the two modalities or at the same rate. Cues are provided to indicate the modality to attend to - Therefore, I have nine conditions- attend Auditory, attend Visual and attend Both (here the stimuli are presented at the same rate for auditory and visual) at the three rates. The data are presented in blocks of 8 secs with a TR of 2- there is a random rest period between each block (14, 16 or 18 secs). I did your typical pre-processing steps and normalized the data to 100 and concatenated the data across 6 runs- waver was performed with a gamma variate function on individual regressors with a peak of 1-

my regression script is the following

foreach con ( ATT )

3dDeconvolve \
-input ${study}.${subject}.RAT+orig\
-mask DECON.mask25+orig \
-concat ALL_cat.1D \
-polort 1 \
-num_stimts 9 \
-stim_file 1 MR_GAM_CON_{$con}.1D'[0]' \
-stim_label 1 "B_HLF" \
-stim_minlag 1 0 \
-stim_maxlag 1 0 \
-stim_file 2 MR_GAM_CON_{$con}.1D'[1]' \
-stim_label 2 "B_ONE" \
-stim_minlag 2 0 \
-stim_maxlag 2 0 \
-stim_file 3 MR_GAM_CON_{$con}.1D'[2]' \
-stim_label 3 "B_TWO" \
-stim_minlag 3 0 \
-stim_maxlag 3 0 \
-stim_file 4 MR_GAM_CON_{$con}.1D'[3]' \
-stim_label 4 "V_HLF" \
-stim_minlag 4 0 \
-stim_maxlag 4 0 \
-stim_file 5 MR_GAM_CON_{$con}.1D'[4]' \
-stim_label 5 "V_ONE" \
-stim_minlag 5 0 \
-stim_maxlag 5 0 \
-stim_file 6 MR_GAM_CON_{$con}.1D'[5]' \
-stim_label 6 "V_TWO" \
-stim_minlag 6 0 \
-stim_maxlag 6 0 \
-stim_file 7 MR_GAM_CON_{$con}.1D'[6]' \
-stim_label 7 "A_HLF" \
-stim_minlag 7 0 \
-stim_maxlag 7 0 \
-stim_file 8 MR_GAM_CON_{$con}.1D'[7]' \
-stim_label 8 "A_ONE" \
-stim_minlag 8 0 \
-stim_maxlag 8 0 \
-stim_file 9 MR_GAM_CON_{$con}.1D'[8]' \
-stim_label 9 "A_TWO" \
-stim_minlag 9 0 \
-stim_maxlag 9 0 \
-glt 3 ALL_LIN_-101.mat \
-glt_label 3 "ALL_LIN" \
-rout -tout -fout \
-bucket ${study}.${subject}.${con}_MR_9stims_CON_GLTSBASELINEAR.-101_multirowmat


end

where ALL_LIN_-101.mat equals

0 0 0 0 0 0 0 0 0 0 0 0 -1 0 1 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 1 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 0 1

where Both is the first row (.5, 1, 2) Visual is the second and auditory the third.

The primary objective of this study is to measure linear increases in BOLD based on stimulus rate on an individual level (this is a clinical population!)- the resulting beta weights should reflect amplitude of signal change based on stimulus rate (with small errors dependent on voxel baseline)

My question is

1) will the Fstat, corresponding to the above matrix, reflect areas that demonstrate a linear response across all of the conditions? In other words, does coding the matrix this way make it equivalent to a partial F where I am testing the significance of the linear polynomial (across all conditions) versus the reduced model (only a constant)? If not, what would the appropriate matrix look like?
2) Is there any way to directly test a model with a constant + linear + quadratic verses a model with a constant + linear using 3dDeconvolve and the nine regressors? I have played around a bit by collapsing across the nine conditions into a single regressor (constant) and scaling (using waver and different peaks) and collapsing into other regressors (linear or quadratic) and entering these three regressors into 3dDeconvolve and looking at the partial Fs. But I would like to do it all in one fell swoop.

Hopefully all of this information helps! Thanks in advance for any input.