Tools of the trade: psychophysiological interactions and functional connectivity

Abstract

Psychophysiological interactions (PPIs) analysis is a method for investigating task-specific changes in the relationship between activity in different brain areas, using functional magnetic resonance imaging (fMRI) data. Specifically, PPI analyses identify voxels in which activity is more related to activity in a seed region of interest (seed ROI) in a given psychological context, such as during attention or in the presence of emotive stimuli. In this tutorial, we aim to give a simple conceptual explanation of how PPI analysis works, in order to assist readers in planning and interpreting their own PPI experiments.

Figures

Fig. 1

Defining the seed region of interest. PPI analysis investigates task-dependent relationships with activity in a seed mask. This seed mask may be defined in several ways. (a) A common approach is to select the voxels with the strongest task effect in a group analysis (e.g. the voxels most active during navigation). (b) Alternatively the mask may be defined anatomically, if there is a strong hypothesis about a particular anatomical region, and that region can be easily delineated on an anatomical scan (here we have selected the entire putamen). (c) We may define the region of interest individually for each participant. First we constrain our search to a volume of interest (here we use our anatomical putamen mask, but we could equally use a mask based on a group fMRI analysis). Then we select the voxels in each participant with the strongest task effect. This allows for inter-individual differences in functional anatomy and is probably the most sensitive approach. Note that in cases ‘a’ and ‘c’, we are selecting an ROI based on the results of our analysis. However, we need not be concerned about circularity in this case, because as long as we model the main effect of task when we run the PPI analysis, the PPI will only detect functional connectivity effects over and above (orthogonal to) the main effect of task.

Fig. 2

Generating a PPI regressor. (a) We start with a regressor representing the main effect of task (in this case, a block design) (dashed line), and convolve it with the HRF to get an HRF convolved task regressor (black line). The horizontal grey line is zero. (b) We extract a time course from our seed region of interest (blue line). If this region of interest was active during the task, the time course of activity from the seed region will be correlated with the HRF convolved task regressor. (c) We generate a PPI regressor (red line) as an element-by-element product of the HRF convolved task (black line) and seed ROI (blue line) regressors. Note that the PPI regressor is correlated with the seed region time course during task blocks, but anti-correlated with it during rest blocks. Consequently, voxels that are always correlated with the seed ROI (e.g. due to anatomical connections that are not task-relevant) will have an overall regression co-efficient of zero for the PPI regressor, but voxels which are more correlated with the seed ROI during task blocks than during rest will show a positive correlation with the PPI regressor.