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January 1, 2018NeuroImage Clinical105 citationsOpen Access

Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram study

MSMiseon ShimCIChang‐Hwan ImYKYong-Wook Kim

Key Result

Patients with major depressive disorder showed significantly decreased global network strength in the alpha band compared to healthy controls (36.08 vs 39.45, p=0.010).

Study Design

Type

Case-Control (n=145)

Multicenter

No

Structured PICO

Does resting-state EEG-based source-level network analysis identify altered cortical functional networks in patients with major depressive disorder compared to healthy controls?

P
Population
87 patients with major depressive disorder (MDD) and 58 healthy controls
I
Intervention
Resting-state EEG-based source-level network analysis
C
Comparator
Healthy controls
O
Outcome
Network measures including global indices (strength, clustering coefficient, path length, and efficiency) and nodal indices (eigenvector centrality and nodal clustering coefficient) in six frequency bandssurrogate

Source-level EEG network indices, particularly in the alpha band, reveal altered emotional processing networks in MDD and may serve as useful biomarkers for regional brain pathology.

Main Result

Absolute Event Rate: 36.08% vs 39.45%

p-value: p=0.010

Limitations

  • The use of medication was not controlled for in the present study.
  • Individual head models for EEG source imaging were not used as individual MRI data were not available.

Abstract

Background: Electroencephalogram (EEG)-based brain network analysis is a useful biological correlate reflecting brain function. Sensor-level network analysis might be contaminated by volume conduction and does not explain regional brain characteristics. Source-level network analysis could be a useful alternative. We analyzed EEG-based source-level network in major depressive disorder (MDD). Method: Resting-state EEG was recorded in 87 MDD and 58 healthy controls, and cortical source signals were estimated. Network measures were calculated: global indices (strength, clustering coefficient (CC), path length (PL), and efficiency) and nodal indices (eigenvector centrality and nodal CC) in six frequency. Correlation analyses were performed between network indices and symptom scales. Results: At the global level, MDD showed decreased strength, CC in theta and alpha bands, and efficiency in alpha band, while enhanced PL in alpha band. At nodal level, eigenvector centrality of alpha band showed region dependent changes in MDD. Nodal CCs of alpha band were reduced in MDD and were negatively correlated with depression and anxiety scales. Conclusion: Disturbances in EEG-based brain network indices might reflect altered emotional processing in MDD. These source-level network indices might provide useful biomarkers to understand regional brain pathology in MDD.

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Cite This Study

Shim et al. (2018) conducted a case-control in Major depressive disorder (n=145). Resting-state EEG vs. Healthy controls was evaluated on Alpha band global network strength (p=0.010). Patients with major depressive disorder showed significantly decreased global network strength in the alpha band compared to healthy controls (36.08 vs 39.45, p=0.010).

synapsesocial.com/papers/6a128996e407b26696351d94https://doi.org/10.1016/j.nicl.2018.06.012
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