One additional comment, you don't have to use 1 and -1. You could, and
I'd actually recommend the following approach:

For each condition, create an onset file with 1s and 0s. Then you will
have N EVs for the psychological variable. For the interaction, in
FSL, you can should multiple each EV by the physiological regressor.
This will create N interaction regressors. Contrasts between them can
be computed with contrasts.

NOTE: In SPM, the physiological regressor is deconvolved prior to
forming the interaction.

In SPM, as I've tested this, splitting the interaction term into N
components for N conditions: (1) improves the model fit, (2) increases
the sensitivity, and (3) decreases false positives. We've submitted a
paper on this to Neuroimage.

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Office: (773) 406-2464
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On Thu, Jul 14, 2011 at 6:27 AM, David Soto <[log in to unmask]> wrote:
> Hello, we have created a step by step manual to perform the PPI in FSL.....we have tried it in one data set and we did not get
> significant results, only uncorrected stuff. Therefore we wonder whether we are doing something wrong at some stage.
> Below I am posting our PPI 'manual', which may be also useful for other people - it this turns out to be fine.
> Would anyone who has performed PPI successfully in FSL comment on the following? I appreciate this is a bit of an onerous task
> but one who may help other people to do PPI in FSL.
> Cheers
>
>
> PPI analysis
>        1.      Set up onset files (PSY regressor).
>               Create a 3 column .txt file containing onsets, durations and weight for each condition of interest. Weight should be 1 or -1, relating to the contrast of interest
>
>        1.      Create file for each subject containing the time series for the seed region (PHYS regressor).
>               Create spherical seed region (3mm radius should be enough) using fslmaths command described above and mm coordinates.
>               Open Misc => featquery.
>        o       Number of feat directories = no. of subjects
>        o       Select feat directories: navigate to EPI.feat directory for each subject
>        o       Stats images of interest: do not select any
>        o       Input ROI selection: Use mask image defined above
>        o       Output options
>               Do not use atlas
>               Select ‘Do not binarise mask’ and ‘Create timeseries plots’
>               Set output directory name to something sensible (otherwise it will automatically create a directory named ‘featquery’; this can get confusing after you’ve run a few analyses).
>               Click Go. Results will pop up in browser.
>               Check the images to ensure that the mask is in the right location in every subject’s space.
>
>
>        1.      First level analysis: click on FEAT fMRI analysis.
> Data
>               Number of inputs: 1
>               Select 4D data: navigate to EPI.feat/filtered_func_data for subject 1
>               Output directory: define PPI directory within subject directory
>               Total volumes: total number – number deleted previously. (e.g. 420-6=414).
>               Number of delete volumes: 0, since the first 6 were deleted during the first FEAT analysis.
>               Set TR = 2.2s and high pass filter cutoff = 100s
>
> Pre-stats
>               Don’t select anything.
>               Set smoothing to ~5mm
>
> Stats
>               Use FILM prewhitening
>               Full model setup
>        o       Number of EVs = 3(PSY, PHYS, PPI) plus any other regressors of no interest (e.g. wm_match, wm_nomatch, dummytask etc.)
>        o       EV1: PSY (onsets of variable of interest; e.g. onsets of priming blocks)
>               Basic shape: Custom (3 column format)
>               Filename: navigate to location of 3 column .txt file containing onset, duration and weight for each condition.
>               Convolution: Double-Gamma HRF
>               Apply temporal derivative and temporal filtering
>        o       EV2: PHYS (timeseries of seed region)
>               Basic shape: Custom (1 entry per volume)
>               Filename: navigate to the directory containing the output of the featquery analysis performed in step 2 (timeseries data). Select the mean_mask_ts.txt file
>               Convolution: none (this is BOLD data and has already been convolved by the brain)
>               Switch off temporal derivative and temporal filtering
>        o       EV3: PPI
>               Basic shape: interaction
>               Between EVs: select EV1 and EV2
>               Make zero: set to Centre for PSY EV (EV1) and Mean for PHYS  EV (EV2).
>               Do not orthogonalise
>               Switch off temporal derivative and temporal filtering
>        o       Remaining EVs
>               Set up as for first level in original FEAT analysis
>               Basic shape = custom (3 column); filename = condition onset text file; convolution = Double-Gamma HRF
>               Do not orthogonalise
>               Apply temporal derivative and temporal filtering
>               Click Contrasts and F-tests
>        o       Set up 3 contrasts, for EV1, EV2 and EV3
>        o       Define the contrasts using the number keys (e.g. [1 0 0 0 0 0 ] for EV1, [0 1 0 0 0 0 0 ] for EV2 etc.)
>
> Post-stats
>               Leave all defaults
>
> Registration
>               Click Main structural image
>        o       Select  T1 from individual subject
>        o       Select Full Search (ensures that images are in same orientation)
>        o       Select 7 DOF (degrees of freedom; 7 is sufficient as registration is within same brain)
>               Standard space: MNI152_T1_2mm
>        o       Select Full Search
>        o       Select 12 DOF (coregistering to different brain)
>
>
> Save first level analysis as, e.g. firstlevel_PPI_precuneus.fsf in first subject’s directory
> Replicate to all other subjects using code listed above.
> Run FEAT from command line.
>
>
>        1.      Higher level analysis
> Click on FEAT fMRI analysis
> Data
>        1.      In top left hand box, select Higher-level analysis; top right-hand box = stats + post-stats
>               Select ‘Inputs are lower level FEAT directories’
>               Selects the directory for each subject containing all copes
>        1.      Number of inputs = number of subjects
>        2.      Select feat directories
>               Navigate to each subject directory and select the .feat directory for the PPi analysis.
>               Select all individually, then click OK.
>        1.      Leave ‘use lower level copes’ boxes selected for all copes.
>        2.      Specify output directory for the 2nd level, otherwise FSL will create one within the directory of the first subject.
>               If the directory name specified was ‘secondlevel’, FSL will save all stats file in another directory named ‘secondlevel.gfeat’.
>
> Stats
>        1.      Select Mixed effects: FLAME 1 if there are more than 10 subjects in the analysis. With fewer than 10 subjects, select Mixed effects: FLAME 1+2, which is considerably slower but more accurate with small samples.
>        2.      Click Full Model Setup, EVs tab
>               Number of main EVs = 1 (EV1 = group)
>               Change weight of each EV (subject) to 1
>        o       Calculates the mean of each contrast across subjects
>               View design: should show a vertical line
>        1.      In Contrasts and F-tests tab, the only contrast to run is C1, group mean (selected by default)
>        2.      Click Done
>
> Post-stats
>        1.      Leave all defaults.
> Press Go to run analysis.
>