Exploiting Trial-to-Trial Variability in Multimodal Experiments

by PP. Using this method, they demonstrated that the posterior medial frontal cortex (pMFC) increased parametrically with increasing PP. Next, Scheibe et al. (2010) employed an EEG-informed fMRI analysis, which utilizes the variability in EEG data to predict changes in BOLD response (e.g., Goldman et al., 2009). In order to identify voxels whose activity covaried with the CNV, Scheibe et al. (2010) added a regressor using the CNV amplitude for each trial. This identified a network of regions including the middle frontal gyrus (MFG), dorsolateral prefrontal cortex (DLPFC), and putamen (Scheibe et al., 2010; Figure 4A). The authors then regressed out the variance attributable to PP, and used the residuals in a follow up regression model. Finally, by comparing the model derived from the EEG-informed fMRI analysis and the follow up regression model, regions that were only activated in the former and not the latter model were identified as being attributable only to PP processing. Here, the authors showed that the DLPFC, right inferior frontal gyrus (IFG), and right inferior parietal lobule (IPL; Scheibe et al., 2010; Figure 4B) were modulated exclusively by the processing and integration of PP into response preparation. These results provide novel insights into trial-specific information processing of PP during decision making. Scheibe et al. (2010) show that the neural integration of PP has at least three levels. Firstly, increasing PP indexes the need for behavioral adjustment, which is reflected by increases in pMFC and the CNV in order to successfully prepare motor responses and guide future decisions. Previously, the pMFC has been shown to be involved in performance monitoring and cognitive control induced behavioral adjustments (Ridderinkhof et al., 2004). Next, Scheibe et al. (2010) were able to identify regions that covaried with the CNV, which they suggest might index unspecific Exploiting trial-to-trial variability in multimodal experiments