Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework

Abstract

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.

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

“…The increased AP value of beta indicated that the brain activity is in a state of tension and neural nervousness, and the increased FE value indicated increased complexity ( Al-Ezzi et al, 2022 ). Shen et al (2022) has revealed that GAD patients have higher PSD and FE than healthy adults in beta rhythm. In this study, our results further pointed out that Old_GAD has higher anxiety levels and a more complex brain state in the beta rhythm.…”

“…Numerous studies have shown that different symptoms of psychiatric disorders also show diverse abnormal FC patterns ( Meda et al, 2012 ). For example, GAD reduces FC in frontal and other brain regions ( Shen et al, 2022 ). Patients with SAD have a limbic FC deficit, which is manifested by reduced frontal-occipital connectivity in resting-states ( Xing et al, 2017 ).…”

“…Patients with attention deficit show enhanced connectivity in frontal regions ( Choi et al, 2013 ). In this study, PLI was used to estimate brain FC and key FC was further used to investigate mechanistic changes in GAD ( Shen et al, 2022 ). We found that the key FC of Old_GAD patients showed significant reorganization throughout the brain compared to Young_GAD patients.…”

“…The PSD, FE, and PLI have been demonstrated to be effective and feasible for detecting anxiety disorders ( Shen et al, 2022 ). These three methods decode EEG information from three different dimensions, which portrayed the neurophysiological implications of EEG signals from three different perspectives.…”

“…The smaller r value is susceptible to noise, while the larger r value may lead to the loss of useful information. The r is set to k times of the standard deviation of the EEG signal x ( i ), and the common range of k is 0.1 ≤ k ≤ 0.25 ( Shen et al, 2022 ). In this study, the n and k were set to 2 and 0.2, respectively.…”