A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

Abstract

A large number of studies demonstrated that major depressive disorder (MDD) is characterized by the alterations in brain functional connections which is also identifiable during the brain's "resting-state." But, in the present study, the approach of constructing functional connectivity is often biased by the choice of the threshold. Besides, more attention was paid to the number and length of links in brain networks, and the clustering partitioning of nodes was unclear. Therefore, minimum spanning tree (MST) analysis and the hierarchical clustering were first used for the depression disease in this study. Resting-state electroencephalogram (EEG) sources were assessed from 15 healthy and 23 major depressive subjects. Then the coherence, MST, and the hierarchical clustering were obtained. In the theta band, coherence analysis showed that the EEG coherence of the MDD patients was significantly higher than that of the healthy controls especially in the left temporal region. The MST results indicated the higher leaf fraction in the depressed group. Compared with the normal group, the major depressive patients lost clustering in frontal regions. Our findings suggested that there was a stronger brain interaction in the MDD group and a left-right functional imbalance in the frontal regions for MDD controls.

A Deep Learning Approach for Mild Depression Recognition Based on Functional Connectivity Using Electroencephalography

“…High coherence between two EEG signals reflects synchronized neuronal oscillations (suggesting functional integration between neural populations), while low coherence indicates independently active populations (suggesting functional segregation) (Murias et al, 2007). Using EEG coherence to study the brain activity of patients with depression (Li et al, 2017), as well as those with Alzheimer's disease (AD) (Wada et al, 1998) and Parkinson's disease (Teramoto et al, 2016), has been quite successful. For example, Murias et al (2007) observed an elevated EEG theta coherence in the frontal and temporal regions of the left hemisphere in individuals with autism spectrum disorder.…”

Random topology organization and decreased visual processing of internet addiction: Evidence from a minimum spanning tree analysis

“…Therefore, the goal of the current study was to examine global functional network connectivity and organization and detect the hubs between IA and controls in resting‐state EEG data. Studying the dynamics of spontaneous (independent‐task) activities in the brain provides us with meaningful information on how the different brain regions communicate and the functional brain network infrastructure (Fraga et al, ; Li et al, gies, such as fMRI (Khanna, Pascual‐Leone, Michel, & Farzan, ). In addition, although traditional graph theory analysis is helpful for understanding brain mechanisms, it still has the limitation of a lack of standard methods (van Diessen et al, ).…”

“…Therefore, the goal of the current study was to examine global functional network connectivity and organization and detect the hubs between IA and controls in resting-state EEG data. Studying the dynamics of spontaneous (independent-task) activities in the brain provides us with meaningful information on how the different brain regions communicate and the functional brain network infrastructure (Fraga et al, 2016;Li et al, 2017). EEG is inexpensive and has a millisecond time resolution, which is finer than the other neuroimaging technologies, such as fMRI (Khanna, Pascual-Leone, Michel, & Farzan, 2015).…”

EEG Feature Extraction Based on a Bilevel Network: Minimum Spanning Tree and Regional Network

“…Fast independent component analysis was used for denoising to obtain more pure EEG signals, ensuring the effectiveness of the extracted features in the subsequent work. The ERD/ERS phenomenon is related to the mu (8-12 Hz) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) rhythms of EEG signals. However, some frequencies in the beta rhythm are the harmonic waves of the mu rhythm.…”

“…MST is the only subnetwork in the weighted network that avoids methodological biases and solves the problem of network connectivity. Moreover, MST can simplify network characteristics and directly compare networks with the same number of nodes [18]. MST is a new and emerging method of human functional connectomics and has been effectively used in the recent research on some neuropsychiatric diseases [19].…”