NeuroImage
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Decision-making in the presence of uncertainty is a complex process that involves both affective and cognitive factors. Both error rate and predictability have been implicated in the process of response selection during decision-making. This study examined the hypothesis that the rate of errors during decision-making differentially affects the activation in prefrontal and cingulate cortex. ⋯ Second, premotor (BA 6) and parahippocampal (BA 36) areas were relatively more active at high error rates, and dorsolateral (BA 9, 46) and inferior prefrontal cortex (BA 44) as well as parietal (BA 40) and cingulate cortex (BA 25, 32) were more active during low error rates. Third, the relationship between the frequency of the dominant strategy underlying decision-making (win-stay/lose-shift) and the activation in the dorsolateral prefrontal cortex and the anterior cingulate was dependent on error rate or outcome predictability. These results support the hypothesis that error rates and predictability affect the activation patterns in the neural systems underlying decision-making because these structures maintain a representation of the reinforcement history for the available response alternatives to select an "optimal strategy."
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Most current analysis methods for fMRI data assume a priori knowledge of the time course of the hemodynamic response (HR) to experimental stimuli or events in brain areas of interest. In addition, they typically assume homogeneity of both the HR and the non-HR "noise" signals, both across brain regions and across similar experimental events. When HRs vary unpredictably, from area to area or from trial to trial, an alternative approach is needed. ⋯ Visualizing sets of BOLD response epochs with novel BOLD-image plots demonstrated that component HRs varied substantially and often systematically across trials as well as across sessions, subjects, and brain areas. Contrary to expectation, in four of the six subjects the V1 component HR contained two positive peaks in response to short-stimulus bursts, while components with nearly identical regions of activity in long-stimulus sessions from the same subjects were associated with single-peaked HRs. Thus, ICA combined with BOLD-image visualization can reveal dramatic and unforeseen HR variations not apparent to researchers analyzing their data with event-related response averaging and fixed HR templates.