Search

Children and adolescents with Williams-Beuren syndrome (WBS) have been described as having specific memory abilities and emotional reactivity. Although it is well established in the literature that emotion can enhance memory, no such studies have been undertaken in individuals with WBS. In three experiments, the present study tested whether the negative or positive emotional valence of stimuli can influence verbal, visual and visuo-spatial memory by specifically evaluating two memory components: item and associative memory. Sixteen 8- to 18-year-old individuals with WBS performed the first two experiments and, among them, twelve participated in the third. They were compared to equivalent groups of typically developing control children. Participants completed intentional-encoding tasks followed by immediate item recognition, associative recall or item recall tasks. Event-related potential measures during encoding and recognition of pictures were also added in the third experiment. Results demonstrated, for the first time, effects of emotions on visual item memory and visuo-spatial associative memory in individuals with WBS, that were similar to those observed in typically developing children. By combining behavioral and neural measures, our study provides new knowledge of the interaction between emotion and memory in WBS individuals, which seems to be unaffected by their atypical development. [ABSTRACT FROM AUTHOR]

Complex network analysis applied to the resting brain has shown that sets of highly interconnected networks with coherent activity may support a default mode of brain function within a global workspace. Perceptual processing of environmental stimuli induces architectural changes in network topology with higher specialized modules. Evidence shows that, during cognitive tasks, network topology is reconfigured and information is broadcast from modular processors to a connective core, promoting efficient information integration. In this study, we explored how the brain adapts its effective connectivity within the connective core and across behavioral states. We used complex network metrics to identify hubs and proposed a method of classification based on the effective connectivity patterns of information flow. Finally, we interpreted the role of the connective core and each type of hub on the network effectiveness. We also calculated the complexity of electroencephalography microstate sequences across different tasks. We observed that divergent hubs contribute significantly to the network effectiveness and that part of this contribution persists across behavioral states, forming an invariant structure. Moreover, we found that a large quantity of multiple types of hubs may be associated with transitions of functional networks. [ABSTRACT FROM AUTHOR]