Abstracts

Rationale:
Impaired awareness is one of the most disabling aspects of epilepsy. However, mechanisms underlying consciousness awareness remain poorly understood. Many theories of consciousness are based on changes in functional connectivity between different brain regions. These changes may fall into two major categories: 1) alterations in the overall dynamic state of brain regions. An alteration in the dynamic state of brain regions may be reflected by a “global ignition” leading to a conscious state, such as that suggested in the Global Neuronal Workspace model.  2) changes in directional flows of information across regions. The purpose of this study was to determine the role of local and global network characteristics in different natural states of consciousness (awake and asleep) as well as seizures.
Method:
Scalp EEG was recorded from patients undergoing video EEG monitoring as part of their standard clinical care. EEG was acquired with electrodes conforming to the international 10-20 system for electrode placement, using a Nihon Kohden EEG system (Nihon Kohden America, Irvine CA), sampling at 200 Hz. Graph theory metrics such global and local efficiency, participation coefficient, and clustering coefficient were computed over sequential 2s non-overlapping sliding windows to capture network characteristics. Artifacts such as eyeblinks and muscle were excluded. Calculations were performed using the Brain Connectivity Toolbox in Matlab (Mathworks, Natick, MA), and visualized using Microsoft Excel (Redmond, WA).
Results:
Network metrics based on graph theory were dynamic (i.e., fluctuated over time). However, on average, the asleep state was associated with increased local efficiency and clustering coefficient in comparison to the awake state. This relation was less robust with participation coefficient. These patterns were not consistent in the hemisphere that was affected by seizures. However, similar to asleep, local efficiency, clustering coefficient and participation coefficient were increased during the seizure state.
Conclusion:
Brain network characteristics are not static and fluctuate over time, even during a specific state of consciousness. The asleep state is generally associated more robust “local” connectivity. Similar characteristics can occur during seizures, another form of altered consciousness. Baseline network characteristics can also be affected by seizures during the interictal state and may depend on the location of seizures. Graph theory metrics capturing network level information processing as a biomarker for states of consciousness may have limitations in the setting of neurological disorders such as epilepsy. 
Funding:
:None.