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PPI – what and why? (and how)

Learn about the functional connectivity measure, PPI, and how it can be used to identify voxels linked to a region of interest. Explore the interaction between psychological context and physiological input in the prefrontal cortex and hippocampus for tasks like maze navigation. Discover how to set up PPI in FEAT for hypothesis-driven analyses and considerations for using MELODIC data.

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PPI – what and why? (and how)

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  1. PPI – what and why? (and how) • Functional connectivity measure • A way of looking for voxels which may be functionally linked to a region of interest • Interaction between psychological context and physiological input

  2. dl prefrontal cortex  planning Hippocampus  spatial memory A hypothetical example: Maze navigation task Condition of interest: Navigating around a virtual reality maze Control condition: Travelling passively through the maze INTERACTION ?

  3. Key concept of PPI: • If two areas are interacting, their activity will go up and down in synch • This effect may be task dependent • It should be more than can be explained by the shared main effect of task

  4. PPI strategy “Look for all the voxels in which the level of activity is well explained by the level of activity in the hippocampus ROI” >> fslmeants –i filtered_func.data –o hippocampus.txt –m hpc_mask.nii.gz

  5. Problem: • Some brain areas will have a similar time-course to the seed area regardless of what task participants are doing • e.g. •  Shared sub-cortical or neuro-modulatory input • Shared sensory input • Anatomical connections

  6. PSY main effect (task variable) .* Overlay: PHYS main effect (time-course from seed region) PPI = PSY.*PHYS Solution: use a ‘psychophysiological interaction’ regressor

  7. Caveat: covariates of no interest  Must include main effects (PSY and PHYS) in model Design matrix

  8. Considerations for running PPI • Hypothesis driven (must choose seed region and task EV a-priori) • If you don’t have a hypothesis (!), or even if you do, - It can be helpful to run MELODIC on group data first - MELODIC gives you an idea of the different functional NETWORKS in your data - ROIs from MELODIC blobs might work better as PPI seed regions • Should only use block designs (deconvolution issue)

  9. How to do it • Choose your seed ROI and make masks • either anatomical or based on foci of activity from GLM or MELODIC analysis • Extract time-course with fslmeants • Go into FEAT…

  10. Setting up your PPI in Feat In FEAT stats tab… EV1 = your task regressor EV2 = your ROI timecourse EV3 = PPI EV4 all your other EV5 task regressors EV6 • Click on “basic shape” dropdown • Interaction • Between EVs 1 and 2 Make zero • centre for task regressor • mean for ROI timecourse Orthogonalise, temporal derivative, temporal filtering --- OFF

  11. The end.

  12. What do all those “mean” options mean? zero min zero mean zero centre

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