An ROI-analysis of Activation in FG2, Amygdala lb and dlPFC : How are they Functionally Organized in a Face Working Memory task

Abstract: Working memory (WM) for facial identity and WM for facial expressions of emotions is important in everyday functioning and seems to have different neurobiological correlates. We investigated the level of neural activation in three regions of interest (ROI): the fusiform face area (FFA), dorsolateral prefrontal cortex (dlPFC), and amygdala; and how they are related to behavioral performance during an n-back task involving face stimuli with a complex background figure within an fMRI-paradigm. Participants performed three different 2-back tasks, one for facial expressions of emotions (EMO), one for the facial identity (ID), and one for a background figure presented behind the face (FIG). We hypothesized that the FFA would activate more in ID, the amygdala would activate more during EMO, and that the dlPFC would activate in all n-back tasks. An ROI analysis was done to extract mean activation values from the participants (N = 32) in the fusiform gyrus area 2 (FG2), the laterobasal amygdala (amygdala lb), and dlPFC in the different tasks. A one way repeated measures ANOVA revealed a similar activation in FG2 and amygdala lb in both ID and EMO. During the FIG task higher activation in FG2 was shown in comparison with ID and EMO, and lower activation in amygdala lb was shown in comparison to ID. dlPFC was activated in all tasks. Furthermore, there was a negative correlation between amygdala lb activation and reaction time in the FIG task, where an abstract figure was kept in WM and facial information was to be ignored. These results indicate that the activation in FG2 and amygdala lb might not differ between WM for facial identity and WM for facial expressions of emotions, which is unexpected in comparison to perception studies where a difference in these nodes has been reported for processing these two different types of information. This might suggest that the role of these neural nodes differ depending on WM load and task irrelevant features.