Run PPI in FSL: Difference between revisions

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=Functional Connectivity & PPI in FSL=
PLEASE NOTE: This work is in progress. Follow these steps at your own risk.


Psychophysiological Interaction (PPI) is a type of fMRI functional connectivity analysis that is specifically useful for looking at the interaction of two regions during a block of time (i.e. a task). This following describes a process for executing a PPI with FSL, based on a block designed experiment.
==Essential web references for running a PPI analysis with FSL==
http://www.fmrib.ox.ac.uk/Members/joreilly/what-is-ppi/
http://www.fmrib.ox.ac.uk/Members/joreilly/how-to-run-a-ppi-analysis-in-feat
==References of interest==
Friston, KJ, Buechel, C, et al. (1997).
Psychophysiological and Modulatory Interactions in Neuroimaging. Neuroimage, 6, 218-229.
http://dx.doi.org/10.1006/nimg.1997.0291
Gitelman DR, Penny WD, Ashburner J, et al. (2003).
Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution. NeuroImage, 19(1), 200-7.
http://dx.doi.org/10.1016/S1053-8119(03)00058-2
Fair DA, Schlaggar BL, Cohen AL, et al. (2007).
A method for using blocked and event-related fMRI data to study 'resting state' functional connectivity. NeuroImage, 35(1), 396-405.
http://dx.doi.org/10.1016/j.neuroimage.2006.11.051
Murphy K, Birn RM, Handwerker DA, et al. (2009).
The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?. NeuroImage, 44(3), 893-905.
http://dx.doi.org/10.1016/j.neuroimage.2008.09.036
==Overview==
The basic PPI model is: H(s)=H(t)+H(s*t)
where s is the seed timeseries, t is the task, s*t is the interaction between seed region and task, and H is HRF convolution. Our sequence of an analysis is:
# fMRI pre-processing of raw data (skull stripping, motion correction, high-pass filtering)  and (optionally) removal of nuisance variables (motion, CSF & white matter timeseries)
# Determining a seed region of interest, based on a functional activation, an anatomical region, or a meta-analytic ROI.
# Obtaining an ROI mask in each subject’s native functional space and extracting the mean time series for the region.
# Running the PPI analysis using the FSL General Linear Model with regressors for the task, the seed region timeseries, and the interaction of these two terms.
==Steps==
===Preprocessing Model===
Goal: obtain a cleaned up BOLD functional file after brain extraction, filtering, and motion correction.
*Enter raw 4D data as input
*BET (skull stripping)
*MCFlirt (motion correction)
*Highpass filter
===ROI selection===
Goal: Obtain a mask file for your Region Of Interest. Your ROI might be a voxel (based on peak activation from your fMRI analysis or meta-analysis), a sphere around a voxel, or an anatomical region (from a cortical/subcortical parcellation or an atlas).
====Voxel processing====
Work in progress. Will take voxel from standard space, get a sphere around voxel, register to functional space for each subject, and get mean timeseries.
===MPRAGE processing (optional)===
Goal: obtain an anatomical region of interest mask, along with white matter and CSF masks.
*BET MPRAGE
*Run FAST tissue-type segmentation on MPRAGE
*Register MPRAGE to functional space
*Transform CSF, white matter, gray matter masks to functional space
*Extract mean timeseries for CSF, WM voxels in filtered_func_data file output by preprocessing model; combine into one file
===Nuisance Model (optional)===
Goal: Obtain the residuals file that accounts for nuisance variables such as CSF and white matter signal.
*Enter WM and CSF mean timeseries
*Add motion parameters to model
*Set contrasts to 1; these are not important as we're only interested in the res4d file, not the COPE images
===Get seed timeseries===
The mean timeseries for your seed ROI will always come from the same functional file you use to do the PPI analysis step.
If you model out nuisance parameters:
*nuisance.feat/stats/res4D.nii.gz is the residual functional data that you will use in the actual analysis.
:Important: Res4D has a mean ~0, so you must add (10000 * mask) to create input data. You also must put '–odt float' at the end of the fslmaths command to get proper values.
:>> fslmaths mask –mul 10000 –add Res4D Res4D_input –odt float
*Extract seed timeseries from res4d_10000.nii.gz file
If not:
*ppi_preprocessing.feat/filtered_func_data.nii.gz
===PPI Model===
*Highpass filter at 10000
*Prewhiten
*Set up EVs for:
*#Psychological Regressor: your task, custom entry with 1 column, convolution Double Gamma (or whatever model you prefer)
*#Physiological Regressor: your seed timeseries, custom entry with 1 column
*#Interaction: the intereaction between Psych and Phys, basic shape is interaction, make zero Center for Psych and Mean for Phys.
*Set up contrasts as:
:1 0 0
:0 1 0
:0 0 1
:0 0 -1
Which will give copes for psych mean, phys mean, interaction positive mean, and interaciton negative mean.
===Group Analysis===
*Enter single-subject COPEs into higher level mixed-effect simple OLS or FLAME model.
*Notes:
**Contrast masking can be used to separate the effects of positive correlations and negative correlations in group differences.
**A gray matter mask in MNI152 2mm standard space can be used as a pre-threshold mask in order to limit analysis to gray matter voxels.
==Useful scripts==
[http://www.ccn.ucla.edu/media/fslgroup/PPI.tar.gz PPI scripts] contains:
*run_preprocessing_model.sh
*mprage_processing.sh
*run_nuisance_model.sh
*get_seed_ts.sh
*run_ppi_model.sh
*design_preprocessing_template.fsf
*design_nuisance_template.fsf
*design_ppi_template.fsf

Latest revision as of 03:42, 16 January 2014

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