Difference analysis of multidimensional electroencephalogram characteristics between young and old patients with generalized anxiety disorder

Abstract

Growing evidences indicate that age plays an important role in the development of mental disorders, but few studies focus on the neuro mechanisms of generalized anxiety disorder (GAD) in different age groups. Therefore, this study attempts to reveal the neurodynamics of Young_GAD (patients with GAD under the age of 50) and Old_GAD (patients with GAD over 50 years old) through statistical analysis of multidimensional electroencephalogram (EEG) features and machine learning models. In this study, 10-min resting-state EEG data were collected from 45 Old_GAD and 33 Young_GAD. And multidimensional EEG features were extracted, including absolute power (AP), fuzzy entropy (FE), and phase-lag-index (PLI), on which comparison and analyses were performed later. The results showed that Old_GAD exhibited higher power spectral density (PSD) value and FE value in beta rhythm compared to theta, alpha1, and alpha2 rhythms, and functional connectivity (FC) also demonstrated significant reorganization of brain function in beta rhythm. In addition, the accuracy of machine learning classification between Old_GAD and Young_GAD was 99.67%, further proving the feasibility of classifying GAD patients by age. The above findings provide an objective basis in the field of EEG for the age-specific diagnosis and treatment of GAD.

Enhancing generalized anxiety disorder diagnosis precision: MSTCNN model utilizing high-frequency EEG signals

“…Previous studies have reported a clear correlation between EEG rhythms and alternate EEG features in GAD patients ( 60 ). Additionally, our previous research has pointed to the importance of beta rhythms in GAD ( 20 ).…”

Exploring Abnormal Brain Functional Connectivity in Healthy Adults, Depressive Disorder, and Generalized Anxiety Disorder through EEG Signals: A Machine Learning Approach for Triple Classification

“…The functional organization of the brain determines its connectivity [ 39 ]. Recent research mentions that mental illnesses result from abnormal brain connectivity, where disruptions or even interruptions in the functional structure of the brain lead to disorders such as DD and GAD [ 11 , 17 ]. Brain functional reorganization is closely associated with abnormalities in brain functional connections [ 40 ].…”

“…Additionally, the brain functional reorganization in GAD patients involves alterations in both the number [ 21 ] and the strength of connections [ 13 ]. The abnormal activation in the high-frequency beta band is indicative of anxiety manifestations in both DD and GAD [ 11 , 17 , 21 ].…”

“…Among these, the Phase Lag Index (PLI) represents a prevalent method for analyzing brain functional connectivity, and its strength lies in its minimal sensitivity to volume conduction effects. It has been confirmed as an effective method for distinguishing mental disorders such as DD and GAD through the calculation of EEG features [ 11 , 12 , 17 ]. The research on Qi reveals that PLI, in comparison to linear and nonlinear features, exhibits superior classification performance [ 13 ].…”

Histological and molecular difference in albumen quality between post-adolescent hens and aged hens

“…The histological and molecular differences in generating protein between young and old hens were explored in the present study, which has been demonstrated as a critical way to reveal the targets for regulating albumen quality ( Wang et al, 2022 ). In the current study, albumen quality, magnum tissue and physiological conditions were analyzed and compared between 40- and 100-week-old hens.…”