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.

Recent studies have suggested a relationship between abnormal neurophysiological functions and generalized anxiety disorder (GAD). However, studies on its electrophysiological characteristics, such as its power spectrum and functional connectivity are relatively few and scattered than those on other mental disorders (e.g., depression, ADHD, etc.). The present study aims to reveal the multidimensional electrophysiological characteristics of GAD via comparative analysis of electroencephalogram (EEG) data between GAD patients and healthy controls. Specifically, resting-state EEG, with a duration of 10 min, was recorded from 98 GAD patients and 92 healthy control participants. The electrophysiological characteristics, including the power spectrum, alpha asymmetry, and functional connectivity, were extracted and compared between the two groups. The results revealed significantly increased beta-band activity; decreased ipsilateral fronto-temporal and parieto-temporal functional connectivities in the lower frequency bands (theta–beta band); as well as decreased frontal‒parietal and frontal‒occipital connectivities in the higher frequency bands (beta‒gamma band) in GAD patients. Additionally, alpha asymmetry analysis revealed a significantly greater rightward temporal alpha asymmetry in GAD patients. These findings suggest the existence of significant EEG characteristics in patients with GAD, supporting previous conclusions regarding abnormal neurophysiological functions in psychiatric disorders and potentially leading to the identification of biomarkers for clinical diagnosis.

The gut-brain axis is a bidirectional pathway linking the gastrointestinal microbiota to neurological functions. While its significance in the pathogenesis of gastrointestinal conditions is well-documented, emerging evidence indicates that dysbiosis of the gut microbiota could also be implicated in various neuropsychiatric disorders, specifically major depressive disorder (MDD). MDD represents a debilitating illness that accounts for a significant portion of global disability. Although numerous medications have been developed to manage depression, they are frequently plagued by variable efficacy and unpleasant adverse effects. The inconsistency of antidepressant effects highlights the complexity and poorly understood pathophysiology underlying this condition. Recent studies suggest that MDD may involve disruptions in the gut-brain axis via gut dysbiosis, induction of inflammation, metabolic disturbances of neuroactive substances, and dysregulation of the hypothalamic-pituitary-adrenal axis, along with the autonomic and enteric nervous systems. Given the direct and indirect connections between the microbiota and these physiological processes, probiotics are increasingly being explored as a prospective therapeutic option for MDD. Multiple probiotic formulations have shown promise in both preclinical and clinical settings, demonstrating effectiveness in attenuating symptoms associated with MDD. This review provides an overview of the pathophysiologic attributes of MDD, with particular focus on disturbances along the gut-brain axis, and investigates current findings regarding the role of probiotics in addressing these challenges. We conclude by identifying persistent gaps in the literature and proposing directions for future studies.

Objective: Anxiety disorder (AD) is a common disabling disease. The prolonged disease course may lead to impaired cognitive performance, brain function, and a bad prognosis. Few studies have examined the effect of disease course on brain function by electroencephalogram (EEG). Methods: Resting-state EEG analysis was performed in 34 AD patients. The 34 patients with AD were divided into two groups according to the duration of their illness: anxious state (AS) and generalized anxiety disorder (GAD). Then, EEG features, including univariate power spectral density (PSD), fuzzy entropy (FE), and multivariable functional connectivity (FC), were extracted and compared between AS and GAD. These features were evaluated by three previously validated machine learning methods to test the accuracy of classification in AS and GAD. Results: Significant decreased PSD and FE in GAD were detected compared with AS, especially in the Alpha 2 band. In addition, FC analysis indicated that GAD patients’ connection between the left and right hemispheres decreased. Based on machine learning, AS and GAD are classified on a six-month criterion with the highest classification accuracy of up to 0.99 ± 0.0015. Conclusion: The brain function of patients is more severely impaired in AD patients with longer illness duration. Resting-state EEG demonstrated to be a promising examination in the classification in GAD and AS using machine learning methods with better classification accuracy.