Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder

Abstract

To investigate the differences in functional brain network structures between patients with a high level of generalized anxiety disorder (HGAD) and those with a low level of generalized anxiety disorder (LGAD), a resting-state electroencephalogram (EEG) was recorded in 30 LGAD patients and 21 HGAD patients. Functional connectivity between all pairs of brain regions was determined by the Phase Lag Index (PLI) to construct a functional brain network. Then, the characteristic path length, clustering coefficient, and small world were calculated to estimate functional brain network structures. The results showed that the PLI values of HGAD were significantly increased in alpha2, and significantly decreased in the theta and alpha1 rhythms, and the small-world attributes for both HGAD patients and LGAD patients were less than one for all the rhythms. Moreover, the small-world values of HGAD were significantly lower than those of LGAD in the theta and alpha2 rhythms, which indicated that the brain functional network structure would deteriorate with the increase in generalized anxiety disorder (GAD) severity. Our findings may play a role in the development and understanding of LGAD and HGAD to determine whether interventions that target these brain changes may be effective in treating GAD.

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

“…It has been suggested that individuals with DD sacrifice connections between the frontal and parietal regions to achieve long-distance connections [ 45 ]. 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 ].…”

“…In EEG signals, there are often numerous artifacts. In previous studies [ 11 , 12 , 13 ], we utilized an independent component analysis (ICA) combined with manual artifact removal to achieve satisfactory results. Additionally, traditional methods of EEG analysis involve linear analysis [ 14 ], nonlinear analysis [ 15 ], and brain functional connectivity analysis [ 16 ].…”

“…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 ]. Moreover, the frequency band activities in EEG serve as reflective indicators of the subject’s mental state [ 18 ].…”

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

“…In recent years, the application of EEG to GAD has been continuously explored to help uncover the complex neuro-electrophysiological mechanism and provide more effective detection methods. Previous studies have utilized EEG to observe changes in the brain of GAD patients, such as increased brain activity ( 18 ) and alterations in brain network structure ( 19 ). Furthermore, by extracting various types of EEG features, such as functional connectivity ( 19 ), power spectral density ( 20 ), and correlation dimension ( 21 ), researchers found significant differences in features between GAD patients and healthy controls.…”

“…Previous studies have utilized EEG to observe changes in the brain of GAD patients, such as increased brain activity ( 18 ) and alterations in brain network structure ( 19 ). Furthermore, by extracting various types of EEG features, such as functional connectivity ( 19 ), power spectral density ( 20 ), and correlation dimension ( 21 ), researchers found significant differences in features between GAD patients and healthy controls. Until now, EEG has been widely used to assist in the diagnosis of various psychiatric disorders, such as anxiety ( 22 , 23 ), depression ( 24 , 25 ), obsessive-compulsive disorder ( 26 , 27 ), Alzheimer’s ( 28 , 29 ), schizophrenia ( 30 , 31 ).…”