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We recently demonstrated that the feedback negativity may be better understood as a reward-related positivity that is absent on nonreward trials, and source localization revealed that this reward response may reflect activity in the striatum. In a commentary on our report, Cohen et al. argue against this latter finding, claiming it is unlikely that the striatum contributes to the scalp-recorded event-related potential. We disagree with the line of reasoning presented by Cohen et al., and we respond here to each of their points. Based on all the available evidence, we argue that the striatum is a plausible generator of a reward-related response observed at the scalp, and this possibility warrants further investigation., (Copyright © 2011 Wiley Periodicals, Inc.)

Event-related potential studies of reward processing have consistently identified the feedback negativity (FN), an early neural response that differentiates feedback indicating unfavorable versus favorable outcomes. Several important questions remain, however, about the nature of this response. In this study, the FN was recorded in response to monetary gains and losses during a laboratory gambling task, and temporospatial principal components analysis was used to separate the FN from overlapping responses. The FN was identified as a positive deflection at frontocentral recording sites that was enhanced for rewards compared with nonrewards. Furthermore, source localization techniques identified the striatum as a likely neural generator. These data indicate that this apparent FN reflects increased striatal activation in response to favorable outcomes that is reduced or absent for unfavorable outcomes, thereby providing unique information about the timing and nature of basal ganglia activity related to reward processing., (Copyright © 2011 Wiley Periodicals, Inc.)

Independent components analysis (ICA) and principal components analysis (PCA) are methods used to analyze event-related potential (ERP) and functional imaging (fMRI) data. In the present study, ICA and PCA were directly compared by applying them to simulated ERP datasets. Specifically, PCA was used to generate a subspace of the dataset followed by the application of PCA Promax or ICA Infomax rotations. The simulated datasets were composed of real background EEG activity plus two ERP simulated components. The results suggest that Promax is most effective for temporal analysis, whereas Infomax is most effective for spatial analysis. Failed analyses were examined and used to devise potential diagnostic strategies for both rotations. Finally, the results also showed that decomposition of subject averages yield better results than of grand averages across subjects., (2006 Wiley-Liss, Inc.)

The P300 and Novelty P3 are positive components of the event related brain potential (ERP) with a latency of at least 300 ms, which are manifestations of brain activity evoked by deviant events. Spencer et al. [1999, 2001] demonstrated that these are two distinct components, both of which may be elicited, with different amplitudes, by both rare and novel events. However, the locations of the intracranial sources of the components remain unknown. We describe the application of cortical potential imaging (CPI) analysis to the data described by Spencer et al. [1999]. The ERPs were recorded from 15 healthy subjects presented with auditory oddball sequences. Cortical potential maps (CPMs) were reconstructed from the scalp potential maps (SPMs) corresponding to the P300 and Novelty P3 components by deblurring the smoothing effect of the head volume conductor. The reconstructed CPMs, derived from the SPMs by means of the CPI, showed localized areas of activity distributed in both the frontal and parietal lobes; the parietal region was active throughout the period of the late positivities. The reconstructed CPMs associated with novel events showed prominent activity at the frontal lobe (Novelty P3) followed by progressively pronounced parietal lobe activity (P300), and these two components can be well separated by the CPMs. These analyses show how the CPI can be used to relate the scalp electrical recordings to the underlying brain activity.