A generalized form of context-dependent psychophysiological interactions (gPPI): a comparison to standard approaches

Abstract

Functional MRI (fMRI) allows one to study task-related regional responses and task-dependent connectivity analysis using psychophysiological interaction (PPI) methods. The latter affords the additional opportunity to understand how brain regions interact in a task-dependent manner. The current implementation of PPI in Statistical Parametric Mapping (SPM8) is configured primarily to assess connectivity differences between two task conditions, when in practice fMRI tasks frequently employ more than two conditions. Here we evaluate how a generalized form of context-dependent PPI (gPPI; http://www.nitrc.org/projects/gppi), which is configured to automatically accommodate more than two task conditions in the same PPI model by spanning the entire experimental space, compares to the standard implementation in SPM8. These comparisons are made using both simulations and an empirical dataset. In the simulated dataset, we compare the interaction beta estimates to their expected values and model fit using the Akaike information criterion (AIC). We found that interaction beta estimates in gPPI were robust to different simulated data models, were not different from the expected beta value, and had better model fits than when using standard PPI (sPPI) methods. In the empirical dataset, we compare the model fit of the gPPI approach to sPPI. We found that the gPPI approach improved model fit compared to sPPI. There were several regions that became non-significant with gPPI. These regions all showed significantly better model fits with gPPI. Also, there were several regions where task-dependent connectivity was only detected using gPPI methods, also with improved model fit. Regions that were detected with all methods had more similar model fits. These results suggest that gPPI may have greater sensitivity and specificity than standard implementation in SPM. This notion is tempered slightly as there is no gold standard; however, data simulations with a known outcome support our conclusions about gPPI. In sum, the generalized form of context-dependent PPI approach has increased flexibility of statistical modeling, and potentially improves model fit, specificity to true negative findings, and sensitivity to true positive findings.

Published by Elsevier Inc.

Figures

Figure 1. Exemplars of inputs and outputs of the sPPI and the gPPI approaches

A: A vector of condition on times (A, B, C). B: SPM8 canonical hemodynamic response function (HRF). C: Psychological vector for GLM for the sPPI approach using [1 −1 −1] for A, B, and C, respectively, formed by multiplying the vector of condition ON times (A) by the weights and convolving the result with the canonical HRF. D: Psychological vectors for the GLM for the gPPI approach formed by separately convolving a vector of each conditions’ on times with the canonical HRF. E: Extracted BOLD signal from a region of interest for use in the GLM for both models as well as in the deconvolution process to estimate the neural response. F: Psychophysiological interaction vector for GLM for the sPPI approach using [1 −1 −1] for A, B, and C, respectively, formed by multiplying the condition on times (A) by the weights, then multiplying by the neural signal and convolving the with the canonical HRF. G: Psychophysiological interaction vectors for the GLM for the gPPI approach formed by separately multiplying a vector of each condition’s ON times with the neural signal and then convolving the canonical HRF.

Figure 2. Comparison on sPPI and gPPI approaches using the Akaike information criterion (AIC) on the PALS cortical surface (Van Essen, 2005)

Top row: mask of regions where either sPPI or gPPI revealed significant interaction effect for PV>N at p<.01 in at least contiguous voxels. second row: the mean aic change from sppi to gppi. third row:: significant clusters of decreased values within mask threshold a family-wise error corrected p-value voxels.. bottom number subjects with decrease gppi2.>

Figure 3. Conjunction maps for the PV conditions and for all conditions projected onto the PALS cortical surface (Van Essen, 2005)

First row: logical OR between PVself and PVsem each thresholded at FWE-corrected p