I am trying to evaluate a design with 4 conditions. two of the conditions will occur 32 times and two of the conditions will occur 12 times per run. There will be a total of three runs. I would also like to present the stimuli at sub-TR intervals to increase the sampling of the hemodynamic response. there will be 225 images per run at a TR of 2000 ms. I want to be able to sample at 1 second intervals. I used thefollowing syntax to generate my response functions to test the design. However, I noticed a few things that I did not understand. First, it appears to me that all of the even and odd coefficients are closely correlated to one another. For example, h0 and h2 and h4 all seem to have similar values whereas h1, h3, and h5 all seem to have similar values. In the following examples this difference can be substantial (especially for the 12 trial condition). I reran RSFGEN 3 times with different random seeds. Why are odd coefficients correlated with odds and evens with evens? What am I doing wrong?

FIRST RUN

RSFgen \

-nt 450 -num_stimts 4 -seed 957821451 -one_file -prefix 1RUN2ptr2\

-nreps 1 32 -nreps 2 12 -nreps 3 32 -nreps 4 12

3dDeconvolve \

-nodata \

-num_stimts 4 \

-stim_file 1 "1RUN2ptr2.1D[0]" -stim_maxlag 1 5 -stim_label 1 V1 -stim_nptr 1 2 \

-stim_file 2 "1RUN2ptr2.1D[1]" -stim_maxlag 2 5 -stim_label 2 I1 -stim_nptr 2 2 \

-stim_file 3 "1RUN2ptr2.1D[2]" -stim_maxlag 3 5 -stim_label 3 V8 -stim_nptr 3 2 \

-stim_file 4 "1RUN2ptr2.1D[3]" -stim_maxlag 4 5 -stim_label 4 I8 -stim_nptr 4 2 \

-xout > 1RUN2ptr2

RESULTS

Stimulus: V1

h[ 0] norm. std. dev. = 0.2413

h[ 1] norm. std. dev. = 0.3303

h[ 2] norm. std. dev. = 0.2394

h[ 3] norm. std. dev. = 0.3249

h[ 4] norm. std. dev. = 0.2476

h[ 5] norm. std. dev. = 0.3220

Stimulus: I1

h[ 0] norm. std. dev. = 0.5063

h[ 1] norm. std. dev. = 0.4645

h[ 2] norm. std. dev. = 0.4891

h[ 3] norm. std. dev. = 0.4354

h[ 4] norm. std. dev. = 0.4532

h[ 5] norm. std. dev. = 0.4334

Stimulus: V8

h[ 0] norm. std. dev. = 0.3011

h[ 1] norm. std. dev. = 0.2530

h[ 2] norm. std. dev. = 0.3002

h[ 3] norm. std. dev. = 0.2488

h[ 4] norm. std. dev. = 0.2969

h[ 5] norm. std. dev. = 0.2461

Stimulus: I8

h[ 0] norm. std. dev. = 0.4621

h[ 1] norm. std. dev. = 0.4080

h[ 2] norm. std. dev. = 0.4669

h[ 3] norm. std. dev. = 0.4081

h[ 4] norm. std. dev. = 0.4790

h[ 5] norm. std. dev. = 0.4082

SECOND RUN

RSFgen \

-nt 450 -num_stimts 4 -seed 986427531 -one_file -prefix 1RUN2ptr2\

-nreps 1 32 -nreps 2 12 -nreps 3 32 -nreps 4 12

RESULTS

Stimulus: V1

h[ 0] norm. std. dev. = 0.2916

h[ 1] norm. std. dev. = 0.2708

h[ 2] norm. std. dev. = 0.2991

h[ 3] norm. std. dev. = 0.2723

h[ 4] norm. std. dev. = 0.3008

h[ 5] norm. std. dev. = 0.2739

Stimulus: I1

h[ 0] norm. std. dev. = 0.5240

h[ 1] norm. std. dev. = 0.3731

h[ 2] norm. std. dev. = 0.5272

h[ 3] norm. std. dev. = 0.3817

h[ 4] norm. std. dev. = 0.5203

h[ 5] norm. std. dev. = 0.3783

Stimulus: V8

h[ 0] norm. std. dev. = 0.2856

h[ 1] norm. std. dev. = 0.2656

h[ 2] norm. std. dev. = 0.2830

h[ 3] norm. std. dev. = 0.2556

h[ 4] norm. std. dev. = 0.2867

h[ 5] norm. std. dev. = 0.2598

Stimulus: I8

h[ 0] norm. std. dev. = 0.4568

h[ 1] norm. std. dev. = 0.4293

h[ 2] norm. std. dev. = 0.4475

h[ 3] norm. std. dev. = 0.4282

h[ 4] norm. std. dev. = 0.4566

h[ 5] norm. std. dev. = 0.4323

THIRD RUN

RSFgen \

-nt 450 -num_stimts 4 -seed 125347698 -one_file -prefix 1RUN2ptr2\

-nreps 1 32 -nreps 2 12 -nreps 3 32 -nreps 4 12

RESULTS

Stimulus: V1

h[ 0] norm. std. dev. = 0.2621

h[ 1] norm. std. dev. = 0.2941

h[ 2] norm. std. dev. = 0.2576

h[ 3] norm. std. dev. = 0.2864

h[ 4] norm. std. dev. = 0.2682

h[ 5] norm. std. dev. = 0.2896

Stimulus: I1

h[ 0] norm. std. dev. = 0.3993

h[ 1] norm. std. dev. = 0.4892

h[ 2] norm. std. dev. = 0.3981

h[ 3] norm. std. dev. = 0.4759

h[ 4] norm. std. dev. = 0.4058

h[ 5] norm. std. dev. = 0.4931

Stimulus: V8

h[ 0] norm. std. dev. = 0.2631

h[ 1] norm. std. dev. = 0.2760

h[ 2] norm. std. dev. = 0.2655

h[ 3] norm. std. dev. = 0.2765

h[ 4] norm. std. dev. = 0.2642

h[ 5] norm. std. dev. = 0.2789

Stimulus: I8

h[ 0] norm. std. dev. = 0.3809

h[ 1] norm. std. dev. = 0.5644

h[ 2] norm. std. dev. = 0.3835

h[ 3] norm. std. dev. = 0.5606

h[ 4] norm. std. dev. = 0.3863

h[ 5] norm. std. dev. = 0.5352

I also noticed that the estimates seemed better and did not seem to be correlated when I ran RSFGEN without trying to sample at sub-TR resolutions.

FIRST RUN

RSFgen \

-nt 225 -num_stimts 4 -seed 987654321 -one_file -prefix 1RUN1ptr1\

-nreps 1 32 -nreps 2 12 -nreps 3 32 -nreps 4 12

3dDeconvolve \

-nodata \

-num_stimts 4 \

-stim_file 1 "1RUN1ptr1.1D[0]" -stim_maxlag 1 5 -stim_label 1 V1 \

-stim_file 2 "1RUN1ptr1.1D[1]" -stim_maxlag 2 5 -stim_label 2 I1 \

-stim_file 3 "1RUN1ptr1.1D[2]" -stim_maxlag 3 5 -stim_label 3 V8 \

-stim_file 4 "1RUN1ptr1.1D[3]" -stim_maxlag 4 5 -stim_label 4 I8 \

-xout

Stimulus: V1

h[ 0] norm. std. dev. = 0.2071

h[ 1] norm. std. dev. = 0.2102

h[ 2] norm. std. dev. = 0.2120

h[ 3] norm. std. dev. = 0.2119

h[ 4] norm. std. dev. = 0.2141

h[ 5] norm. std. dev. = 0.2146

Stimulus: I1

h[ 0] norm. std. dev. = 0.3178

h[ 1] norm. std. dev. = 0.3138

h[ 2] norm. std. dev. = 0.3160

h[ 3] norm. std. dev. = 0.3180

h[ 4] norm. std. dev. = 0.3147

h[ 5] norm. std. dev. = 0.3144

Stimulus: V8

h[ 0] norm. std. dev. = 0.2136

h[ 1] norm. std. dev. = 0.2117

h[ 2] norm. std. dev. = 0.2098

h[ 3] norm. std. dev. = 0.2087

h[ 4] norm. std. dev. = 0.2100

h[ 5] norm. std. dev. = 0.2112

Stimulus: I8

h[ 0] norm. std. dev. = 0.3192

h[ 1] norm. std. dev. = 0.3190

h[ 2] norm. std. dev. = 0.3193

h[ 3] norm. std. dev. = 0.3260

h[ 4] norm. std. dev. = 0.3282

h[ 5] norm. std. dev. = 0.3257

SECOND RUN

RSFgen -nt 225 -num_stimts 4 -seed 957628431 -one_file -prefix 1RUN1ptr1 -nreps 1 32 \

-nreps 2 12 -nreps 3 32 -nreps 4 12

Stimulus: V1

h[ 0] norm. std. dev. = 0.2088

h[ 1] norm. std. dev. = 0.2099

h[ 2] norm. std. dev. = 0.2127

h[ 3] norm. std. dev. = 0.2134

h[ 4] norm. std. dev. = 0.2146

h[ 5] norm. std. dev. = 0.2122

Stimulus: I1

h[ 0] norm. std. dev. = 0.3200

h[ 1] norm. std. dev. = 0.3197

h[ 2] norm. std. dev. = 0.3180

h[ 3] norm. std. dev. = 0.3132

h[ 4] norm. std. dev. = 0.3125

h[ 5] norm. std. dev. = 0.3119

Stimulus: V8

h[ 0] norm. std. dev. = 0.2056

h[ 1] norm. std. dev. = 0.2060

h[ 2] norm. std. dev. = 0.2032

h[ 3] norm. std. dev. = 0.2061

h[ 4] norm. std. dev. = 0.2108

h[ 5] norm. std. dev. = 0.2101

Stimulus: I8

h[ 0] norm. std. dev. = 0.3116

h[ 1] norm. std. dev. = 0.3116

h[ 2] norm. std. dev. = 0.3100

h[ 3] norm. std. dev. = 0.3114

h[ 4] norm. std. dev. = 0.3112

h[ 5] norm. std. dev. = 0.3142

Based on this analyses it would seem as though it would be better to not sample at sub-TRs. This seems to be a trade-off issue increased temporal resolution but a more "variable" estimate. Is this accurate?

My other questions deal with constraints on the design. If I do sample at sub-TR levels is it better to ensure that half of the trials start with the TR and the other half start exactly one second later to get optimal

sampling? Or should I totally randomize the design? Relatedly, if I want to use a constraint like at least one "blank" trial between every condition is there a way to totally randomize the design? I saw on the website to use something like the following syntax

RSFgen \

-nt 450 -num_stimts 4 -seed 987654321 -one_file -prefix TEST2 \

-nreps 1 32 -nreps 2 10 -nreps 3 32 -nreps 4 1 \

-nblock 1 2 -nblock 2 2 -nblock 3 2 -nblock 4 2

but that places two ones in the column and I would have to go and edit one of them out depending on whether half of the trials should start mid-TR (the first constraint?).

Sorry for the long post and thanks in advance for the help!!!