Graph Theory Analysis of Functional Connectivity in Major Depression Disorder With High-Density Resting State EEG Data

Sun, Shuting; Li, Xiaowei; Zhu, Jing; Wang, Ying; La, Rong; Zhang, Xuemin; Wei, Liuqing; Hu, Bin

doi:10.1109/tnsre.2019.2894423

Cited by 117 publications

(85 citation statements)

References 64 publications

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“…15 These results provide specific evidence of disruption in the alpha band connectivity that directly correlates with the ineffective activity of the brain network and the random network structure of depressed people. 60 Also, altered brain networks in the alpha rhythm may be due to disrupted emotional processing of MDD. 15 It should be noted that other comparative EEG analysis on depression obtained increased functional connectivity in MDD patients compared to controls.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal

Mohammadi

Moradi

2020

Clin EEG Neurosci

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Background Depression is one of the most common mental disorders and the leading cause of functional disabilities. This study aims to specify whether functional connectivity and complexity of brain activity can predict the severity of depression (Beck Depression Inventory–II scores). Methods Resting-state, eyes-closed EEG data were recorded from 60 depressed patients. A phase synchronization measure was used to estimate functional connectivity between all pairs of the EEG channels in the delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequency bands. To quantify the local value of functional connectivity, 2 graph theory metrics, degree, and clustering coefficient (CC), were measured. Moreover, Lempel-Ziv complexity (LZC) and fuzzy entropy (FuzzyEn) were used to measure the complexity of the EEG signal. Results Through correlation analysis, a significant negative relationship was found between graph metrics and depression severity in the alpha band. This association was strongly positive for the complexity measures in alpha and delta bands. Also, the linear regression model represented a substantial performance of depression severity prediction based on EEG features of the alpha band ( r = 0.839; P < .0001, root mean square error score of 7.69). Conclusion We found that the brain activity of patients with depression was related to depression severity. Abnormal brain activity reflects an increase in the severity of depression. The presented regression model provides a quantitative depression severity prediction, which can inform the development of EEG state and exhibit potential desirable application for the medical treatment of the depressive disorder.

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Section: Discussionmentioning

confidence: 99%

Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal

Mohammadi

Moradi

2020

Clin EEG Neurosci

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“…Previous studies have mostly found that alpha FC is increased in MDD ( Fingelkurts et al, 2007 ; Leuchter et al, 2012 ; Olbrich et al, 2014 ). Although changes in alpha SW in MDD are unclear, some studies have found that small-world measures of alpha EEG were decreased for subjects with MDD ( Zhang et al, 2018 ; Sun et al, 2019 ). Therefore, in MDD, the relationship between FC and SW found in the current study is probably not disrupted.…”

Section: Discussionmentioning

confidence: 99%

“…Most studies that compare two groups of subjects, obtain values above 1 for SW. Since those metrics are obtained by comparing original networks to random networks, decrease in those values is generally interpreted as a more random network structure ( Rubinov et al, 2009 ; Zhang et al, 2018 ; Sun et al, 2019 ). In the current study we showed that although decrease in SW MSC and SW SL can be interpreted as a more random network structure ( Figures 3A,B ), decrease in SW ICOH resulted in a less random network structure ( Figure 3C ).…”

Section: Discussionmentioning

confidence: 99%

“…In the current study, the network densities from 10 to 50% with a step of 5% were used. These are one of the commonly analyzed densities, ensuring that the network is sparse enough to show small-world properties and at the same time is still fully connected ( Bullmore and Bassett, 2011 ; Sun et al, 2019 ). Obtained graphs were binarized: edge values were 0 or 1, depending on whether there was a connection between two nodes or not.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, we will focus on frequency bands, where the most frequent and consistent results were reported. Major depressive disorder (MDD) is mostly characterized by increased FC ( Fingelkurts et al, 2007 ; Leuchter et al, 2012 ; Olbrich et al, 2014 ; Li et al, 2017 ) and more random network structure ( Li et al, 2017 ; Zhang et al, 2018 ; Sun et al, 2019 ) in theta and alpha frequency bands. However, few studies have also found a decrease in alpha FC ( Shim et al, 2018 ; Zhang et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Negative Correlation Between Functional Connectivity and Small-Worldness in the Alpha Frequency Band of a Healthy Brain

et al. 2020

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The aim of the study was to analyze the relationship between resting state electroencephalographic (EEG) alpha functional connectivity (FC) and small-world organization. For that purpose, Pearson correlation was calculated between FC and small-worldness (SW). Three undirected FC measures were used: magnitude-squared coherence (MSC), imaginary part of coherency (ICOH), and synchronization likelihood (SL). As a result, statistically significant negative correlation occurred between FC and SW for all three FC measures. Small-worldness of MSC and SL were mostly above 1, but lower than 1 for ICOH, suggesting that functional EEG networks did not have small-world properties. Based on the results of the current study, we suggest that decreased alpha small-world organization is compensated with increased connectivity of alpha oscillations in a healthy brain.

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Graph Strength for Identification of Pre-training Desynchronization

Castano

Morales²,

Álvarez-Meza

et al. 2023

Lecture Notes in Networks and Systems

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Graph Theory Analysis of Functional Connectivity in Major Depression Disorder With High-Density Resting State EEG Data

Cited by 117 publications

References 64 publications

Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal

Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal

Negative Correlation Between Functional Connectivity and Small-Worldness in the Alpha Frequency Band of a Healthy Brain

Graph Strength for Identification of Pre-training Desynchronization

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