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Brain network properties in depressed patients receiving seizure therapy: A graph theoretical analysis of peri-treatment resting EEG.

Publication ,  Conference
Deng, Z-D; McClinctock, SM; Lisanby, SH
Published in: Annu Int Conf IEEE Eng Med Biol Soc
August 2015

Electroconvulsive therapy (ECT), the most efficacious antidepressant therapy for treatment-resistant depression, has been reported to alter functional brain network architecture by down-regulating connectivity in frontotemporal circuitry. Magnetic seizure therapy (MST), which induces therapeutic seizures with high dose repetitive transcranial magnetic stimulation, has been introduced to improve the seizure therapy risk/benefit ratio. Unfortunately, there is limited understanding of seizure therapy's underlying mechanisms of action. In this study, we apply graph theory-based connectivity analysis to peri-treatment, resting-state, topographical electroencephalography (EEG) in patients with depression receiving seizure therapy. Functional connectivity was assessed using the de-biased weighted phase lag index, a measure of EEG phase synchronization. Brain network structure was quantified using graph theory metrics, including betweenness centrality, clustering coefficient, network density, and characteristic path length. We found a significant reduction in the phase synchronization and aberration of the small-world architecture in the beta frequency band, which could be related to acute clinical and cognitive effects of seizure therapy.

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Published In

Annu Int Conf IEEE Eng Med Biol Soc

DOI

EISSN

2694-0604

Publication Date

August 2015

Volume

2015

Start / End Page

2203 / 2206

Location

United States

Related Subject Headings

  • Transcranial Magnetic Stimulation
  • Signal Processing, Computer-Assisted
  • Seizures
  • Rest
  • Humans
  • Electroencephalography Phase Synchronization
  • Electroencephalography
  • Electroconvulsive Therapy
  • Cluster Analysis
  • Brain
 

Citation

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Deng, Z.-D., McClinctock, S. M., & Lisanby, S. H. (2015). Brain network properties in depressed patients receiving seizure therapy: A graph theoretical analysis of peri-treatment resting EEG. In Annu Int Conf IEEE Eng Med Biol Soc (Vol. 2015, pp. 2203–2206). United States. https://doi.org/10.1109/EMBC.2015.7318828
Deng, Zhi-De, Shawn M. McClinctock, and Sarah H. Lisanby. “Brain network properties in depressed patients receiving seizure therapy: A graph theoretical analysis of peri-treatment resting EEG.” In Annu Int Conf IEEE Eng Med Biol Soc, 2015:2203–6, 2015. https://doi.org/10.1109/EMBC.2015.7318828.
Deng Z-D, McClinctock SM, Lisanby SH. Brain network properties in depressed patients receiving seizure therapy: A graph theoretical analysis of peri-treatment resting EEG. In: Annu Int Conf IEEE Eng Med Biol Soc. 2015. p. 2203–6.
Deng, Zhi-De, et al. “Brain network properties in depressed patients receiving seizure therapy: A graph theoretical analysis of peri-treatment resting EEG.Annu Int Conf IEEE Eng Med Biol Soc, vol. 2015, 2015, pp. 2203–06. Pubmed, doi:10.1109/EMBC.2015.7318828.
Deng Z-D, McClinctock SM, Lisanby SH. Brain network properties in depressed patients receiving seizure therapy: A graph theoretical analysis of peri-treatment resting EEG. Annu Int Conf IEEE Eng Med Biol Soc. 2015. p. 2203–2206.

Published In

Annu Int Conf IEEE Eng Med Biol Soc

DOI

EISSN

2694-0604

Publication Date

August 2015

Volume

2015

Start / End Page

2203 / 2206

Location

United States

Related Subject Headings

  • Transcranial Magnetic Stimulation
  • Signal Processing, Computer-Assisted
  • Seizures
  • Rest
  • Humans
  • Electroencephalography Phase Synchronization
  • Electroencephalography
  • Electroconvulsive Therapy
  • Cluster Analysis
  • Brain