EEG-based Graph Theoretical Measures as Biomarkers of Clinical Outcome in Electroconvulsive and Magnetic Seizure Therapy

Authors

• Z-D Deng
• SM McClintock
• SH Lisanby

Abstract

Electroconvulsive therapy (ECT), the most efficacious treatment of pharmacoresistant depression, has been reported to alter functional brain network architecture by down-regulating connectivity in frontotemporal circuits. 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 two-center, double-masked study, patients were randomized to either ultrabrief pulse right unilateral ECT or circular coil MST. Fifty-nine channel resting EEG was acquired at baseline and after the treatment course in 9 patients (3 responders) for 10 minutes in the eyes opened condition. The data was partitioned into 300 two-second epochs, and spectral analysis was performed using multitaper Fourier transform. Functional connectivity was assessed using the debiased weighted phase lag index (DWPLI), a measure of EEG phase synchronization. Brain network structure was assessed based on graph theory measures: betweenness centrality, clustering coefficient, network density, and characteristic path length. At baseline, responders and nonresponders exhibited similar DWPLI–frequency profile. There was a significant post-treatment increase relative to baseline of the delta band DWPLI in the responders but not in nonresponders (p

Scientific Focus Area: Neuroscience

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