Alterations in Patients With First-Episode Depression in the Eyes-Open and Eyes-Closed Conditions: A Resting-State EEG Study

Abstract

Altered resting-state EEG activity has been repeatedly reported in major depressive disorder (MDD), but no robust biomarkers have been identified until now. The poor consistency of EEG alterations may be due to inconsistent resting conditions; that is, the eyes-open (EO) and eyes-closed (EC) conditions. Here, we explored the effect of the EO and EC conditions on EEG biomarkers for discriminating MDD subjects and healthy control (HC) subjects. EEG data were recorded from 30 first-episode MDD and 26 HC subjects during an 8-min resting-state session. The features were extracted using spectral power, Lempel-Ziv complexity, and detrended fluctuation analysis. Significant features were further selected via the sequential backward feature selection algorithm. Support vector machine (SVM), logistic regression, and linear discriminate analysis were used to determine a better resting condition to provide more reliable estimates for identifying MDD. Compared with the HC group, we found that the MDD group exhibited widespread increased β and γ powers (p < 0.01) in both conditions. In the EO condition, the MDD group showed increased complexity and scaling exponents in the α band relative to HC subjects (p < 0.05). The best classification performance of the combined feature sets was found in the EO condition, with the leave-one-out classification accuracy of 89.29%, sensitivity of 90.00%, and specificity of 88.46% using SVM with the linear kernel classifier when the threshold was set to 0.7, followed by the β and γ spectral features with an average accuracy of 83.93%. Overall, EO and EC conditions indeed affected the between-group variance, and the EO condition is suggested as the more separable resting condition to identify depression. Specially, the β and γ powers are suggested as potential biomarkers for first-episode MDD.

Aperiodic and Hurst EEG exponents across early human brain development: A systematic review

“…In YA, Smit et al (2013) identify EOR alpha band HE maxima in the central midline consistent with other ECR and EOR studies ( Linkenkaer-Hansen, 2001 ). Moreover, other EOR studies illustrate increasing HE with age; both Nakao et al (2019) and Liu et al (2022) reported consistent ECR alpha band HE across the scalp. However, Natarajan et al (2004) report considerably lower global HE (0.29 vs ≥0.70–0.80 in other studies), and Sleimen-Malkoun et al (2015) identify a broadband global HE of 1.69, suggesting non-stationary (variation unrestricted to a singular mean/setpoint) with occipital maxima and frontal minima.…”

“…5, Section 3.3, Auth Corr. 38 Natarajan et al, (2004) Young Adult (20.00±3.00) HE Global (1–50 Hz) 0.29±0.06 -0.42 ECR 30(15,15) Table 1 39 Liu et al, (2022) Young Adult (20−30) HE Channelwise est (Broadband)[0.5–120 Hz] EOR∼0.80–0.82 EOR0.60–0.64 ECR, EOR 26(?,?) Fig.…”

Aperiodic and Hurst EEG exponents across early human brain development: a systematic review

“…In YA, Smit, Linkenkaer-Hansen and de Geus (2013) identify EOR alpha band HE maxima in the central midline consistent with other ECR and EOR studies (Linkenkaer-Hansen et al ., 2001). Moreover, other EOR studies illustrate increasing HE with age; both Nakao et al ., (2019) and Liu et al ., (2022) reported consistent ECR alpha band HE across the scalp. However, Natarajan et al ., (2004) report considerably lower global HE (0.29 vs ≥0.70-0.80 in other studies), and Sleimen-Malkoun et al ., (2015) identify a broadband global HE of 1.69, suggesting non-stationary (variation unrestricted to a singular mean/setpoint) with occipital maxima and frontal minima.…”

Multi-Granularity Graph Convolution Network for Major Depressive Disorder Recognition

“…Early researchers try to utilize traditional machine learning methods, i.e. support vector machine (SVM) [11]- [14], naive bayesian (NB) [15] [16], random forest (RF) [17], k-nearest neighbor (KNN) [18], logistic regression (LR) [19] [20] and decision tree (DT) [21] for MDD recognition. Among these methods, feature extraction is crucial for the accuracy improvement of MDD recognition.…”