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Seunghwan Kim

2 papers in the library · 169 citations · publishing 2013

Papers

Reconfiguration of Network Hub Structure after Propofol-induced Unconsciousness

Anesthesiology September 7, 2013 Heonsoo Lee, George A. Mashour, Gyu‐jeong Noh et al. 153 citations

General anesthesia with propofol reconfigures the brain's functional network hub structure and reverses the phase relationship between frontal and parietal regions. Using graph theoretical analysis of 21-channel electroencephalogram data from 10 volunteers, the study found that network topology—not connection strength—correlates with states of consciousness. After propofol administration, average path length, clustering coefficient, and modularity increased, long-range connections were disrupted, and hub node strength decreased. The primary hub shifted from parietal to frontal regions. The phase lead of frontal to parietal areas in the alpha frequency band (8-13 Hz) during wakefulness reversed direction after propofol and returned during recovery. Changes in network topology may be the primary mechanism for loss of frontal to parietal feedback during 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.