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Analyzing task-dependent brain network changes by whole-brain psychophysiological interactions: a comparison to conventional analysis.

ABSTRACT: While fMRI activation studies contrasting task conditions regularly assess the whole brain, this is usually not true for studies analyzing task-dependent brain connectivity changes by psychophysiological interactions (PPI). Here we combine standard PPI (sPPI) and generalized PPI (gPPI) with a priori brain parcellation by spatially constrained normalized cut spectral clustering (NCUT) to analyze task-dependent connectivity changes in a whole brain manner, and compare the results to multiseed conventional PPI analyses over all activation peaks in an episodic memory recall task. We show that, depending on the chosen parcellation frame, the whole-brain PPI approach is able to detect a large amount of the information that is detected by the conventional approach. Over and above, whole-brain PPI allows identification of several additional task-modulated connections, particularly from seed regions without significant activation differences between conditions.

SUBMITTER: Gerchen MF

PROVIDER: S-EPMC6869077 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Analyzing task-dependent brain network changes by whole-brain psychophysiological interactions: a comparison to conventional analysis.

Gerchen Martin Fungisai MF Bernal-Casas David D Kirsch Peter P

Human brain mapping 20140422 10

While fMRI activation studies contrasting task conditions regularly assess the whole brain, this is usually not true for studies analyzing task-dependent brain connectivity changes by psychophysiological interactions (PPI). Here we combine standard PPI (sPPI) and generalized PPI (gPPI) with a priori brain parcellation by spatially constrained normalized cut spectral clustering (NCUT) to analyze task-dependent connectivity changes in a whole brain manner, and compare the results to multiseed conv ...[more]

PMID: 24753083

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Project description: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.

Dataset Information

Analyzing task-dependent brain network changes by whole-brain psychophysiological interactions: a comparison to conventional analysis.

Publications

Analyzing task-dependent brain network changes by whole-brain psychophysiological interactions: a comparison to conventional analysis.

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