Although increasing evidences support the notion that psychiatric disorders are associated with abnormal communication between brain regions, scattered studies have investigated brain electrophysiological disconnectivity of patients with generalized anxiety disorder (GAD). To this end, this study intends to develop an analysis framework for automatic GAD detection through incorporating multidimensional EEG feature extraction and machine learning techniques. Specifically, resting-state EEG signals with a duration of 10 min were obtained from 45 patients with GAD and 36 healthy controls (HC). Then, an analysis framework of multidimensional EEG characteristics (including univariate power spectral density (PSD) and fuzzy entropy (FE), and multivariate functional connectivity (FC), which can decode the EEG information from three different dimensions) were introduced for extracting aberrated multidimensional EEG features via statistical inter-group comparisons. These aberrated features were subsequently fused and fed into three previously validated machine learning methods to evaluate classification performance for automatic patient detection. We showed that patients exhibited a significant increase in beta rhythm and decrease in alpha1 rhythm of PSD, together with the reduced long-range FC between frontal and other brain areas in all frequency bands. Moreover, these aberrated features contributed to a very good classification performance with 97.83 ± 0.40% of accuracy, 97.55 ± 0.31% of sensitivity, 97.78 ± 0.36% of specificity, and 97.95 ± 0.17% of F1. These findings corroborate previous hypothesis of disconnectivity in psychiatric disorders and further shed light on distribution patterns of aberrant spatio-spectral EEG characteristics, which may lead to potential application of automatic diagnosis of GAD.

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Antigen-specific immune diseases such as rheumatoid arthritis are often accompanied by pain and hyperalgesia. Our previous studies have demonstrated that Fc-gamma-receptor type I (FcγRI) is expressed in a subpopulation of rat dorsal root ganglion (DRG) neurons and can be directly activated by IgG immune complex (IgG-IC). In this study we investigated whether neuronal FcγRI contributes to antigen-specific pain in the naïve and rheumatoid arthritis model rats. In vitro calcium imaging and whole-cell patch clamp recordings in dissociated DRG neurons revealed that only the small-, but not medium- or large-sized DRG neurons responded to IgG-IC. Accordingly, in vivo electrophysiological recordings showed that intradermal injection of IgG-IC into the peripheral receptive field could sensitize only the C- (but not A-) type sensory neurons and evoke action potential discharges. Pain-related behavioral tests showed that intradermal injection of IgG-IC dose-dependently produced mechanical and thermal hyperalgesia in the hindpaw of rats. These behavioral effects could be alleviated by localized administration of non-specific IgG or an FcγRI antibody, but not by mast cell stabilizer or histamine antagonist. In a rat model of antigen-induced arthritis (AIA) produced by methylated bovine serum albumin, FcγRI were found upregulated exclusively in the small-sized DRG neurons. In vitro calcium imaging revealed that significantly more small-sized DRG neurons responded to IgG-IC in the AIA rats, although there was no significant difference between the AIA and control rats in the magnitude of calcium changes in the DRG neurons. Moreover, in vivo electrophysiological recordings showed that C-nociceptive neurons in the AIA rats exhibited a greater incidence of action potential discharges and stronger responses to mechanical stimuli after IgG-IC was injected to the receptive fields. These results suggest that FcγRI expressed in the peripheral nociceptors might be directly activated by IgG-IC and contribute to antigen-specific pain in pathological conditions.