Hey guys,

To begin with I know what I am trying to do is unconventional, but I still need to accomplish it to test a theory... so foregoing the whys, any help you can provide on the how would be much appreciated.

Basically I am trying to filter out the lower -intrinsic- frequencies from a task based data set, to then run an ROI-to-ROI functional connectivity analysis focused on task effects. From the reading I have been doing there appears to be some potential issues however with bandpassing in relation to censoring, scaling, and regressing out motion, since apparently "3dBandpass wasn't originally intended for use with 3dDeconvolve". Therefore I would like to know if the order processing steps below run me into any of the said conceptual analysis problems. If so advice on how to modify would be most appreciated.

1. basic preprocessing including: Skull strip, autocensoring, 3dvolreg, creating motion regressors, epi + anat alignment, spatial normalization, automask + FWHM spatial smoothing.
2. 3dBandpass this preprocessed data
3. 1dBandpass the motion regressors
4. Concatenate runs of both
5. Apply scaling to func data
6. 3dDeconvolve -errts with the 1dBandpassed motion regressors, censors, and the stim_times of those task conditions of no-interest
7. 3dmaskave the relevant ROIs
8. Move .1D outputs into Matlab to generate necessary correlation coefficents and ttests.

Thats it. Please let me know if I am handling this correctly or missing something. If I am way off-base I figure the alternative would probably be to use something like 3dTproject which encorporates the bandpassing into regression stage. If thats correct let me know how to order steps with program, the correct output format to use from 3dTproject ( ie. similar to -errts) and any other things I may need to consider given its appears used often for RS data.

Thanks all for bearing with me (and my naivety) and the use of your wisdom to correct it,

~`Dane



Edited 2 time(s). Last edit at 06/30/2014 01:07AM by d6anders.