The processing of emotional nonlinguistic information in speech is defined as emotional prosody. This auditory nonlinguistic information is essential in the decoding of social interactions and in our capacity to adapt and react adequately by taking into account contextual information. An integrated model is proposed at the functional and brain levels, encompassing 5 main systems that involve cortical and subcortical neural networks relevant for the processing of emotional prosody in its major dimensions, including perception and sound organization; related action tendencies; (...) and associated values that integrate complex social contexts and ambiguous situations. (shrink)

Recent fMRI studies have shown that functional networks can be extracted even from resting state data, the so called “resting state networks” (RSNs) by applying independent component analysis (ICA). However, compared to fMRI, EEG and MEG have much higher temporal resolution and provide a direct estimation of cortical activity. To date, MEG studies have applied ICA for separate frequency bands only, disregarding cross-frequency couplings. In this study, we aimed to detect EEG-RSNs and their interactions in all frequency bands. We applied (...) low resolution brain electromagnetic tomography-ICA (LORETA-ICA) to resting-state EEG data in 80 healthy subjects using five frequency bands (delta, theta, alpha, beta and gamma band) and found five RSNs in alpha, beta and gamma frequency bands. Next, taking into account these frequency properties, five RSNs were identified; 1) the visual network, 2) dual-process of visual perception network, characterized by a negative correlation between the right ventral visual pathway (VVP) and left posterior dorsal visual pathway (DVP), 3) self-referential processing network, characterized by a positive correlation between the medial PFC and right VVP, 4) dual-process of memory perception network, functionally related to a negative correlation between the left VVP and the precuneus and 5) sensorimotor network. To detect aging-related changes of these five RSNs, the subjects were divided into three age groups: younger, middle aged, and elderly group, and Student's t test with Bonferroni correction on each coefficient of five independent components were performed. We found a significant attenuation in dual-process of visual perception network in elderly relative to middle aged subjects. Overall findings indicate that LORETA-ICA with EEG data can precisely identify five RSNs in their intrinsic frequency bands, and correct correlations and aging-related changes between and within RSNs. (shrink)