Skip to content

Seungwoo Ku

2 papers in the library · 452 citations · publishing 2013

Papers

Disruption of Frontal–Parietal Communication by Ketamine, Propofol, and Sevoflurane

Anesthesiology May 22, 2013 UnCheol Lee, Seungwoo Ku, Gyu‐jeong Noh et al. 436 citations

Ketamine, like propofol and sevoflurane, inhibits feedback (anterior-to-posterior) connectivity between frontal and parietal brain regions after loss of consciousness, while preserving feedforward (posterior-to-anterior) connectivity. In 30 surgical patients given intravenous ketamine (2 mg/kg), electroencephalography showed that feedback connectivity gradually diminished and was significantly reduced after loss of consciousness (mean baseline 0.0074 vs. anesthesia 0.0055). Feedforward connectivity remained unchanged. Ketamine reduced alpha power and increased gamma power, unlike propofol and sevoflurane. Despite molecular and neurophysiologic differences, diverse anesthetics disrupt frontal-parietal communication, suggesting that directional connectivity analysis could provide a common metric for general anesthesia.

Subgraph “Backbone” Analysis of Dynamic Brain Networks during Consciousness and Anesthesia

PLoS ONE August 15, 2013 Jeongkyu Shin, George A. Mashour, Seungwoo Ku et al. 16 citations

General anesthesia rapidly alters brain network connectivity, and standard graph-theoretical methods that assume static networks are poorly suited to capture these changes. A new technique was introduced to track the temporal evolution of network modules. Multichannel EEG was recorded from 18 surgical patients anesthetized with propofol or sevoflurane. Networks were reconstructed by treating each EEG channel as a node and correlated activity between channels as a link. The frequency of subgraphs with a defined number of links was analyzed; subgraphs with high occurrence probability were called network "backbones." Constitutive, variable, and state-specific backbones were identified across consciousness, induction, maintenance, and recovery. This approach enables a granular, dynamic description of network evolution.