TY - GEN
T1 - State dynamics of the epileptic brain
AU - Burns, Samuel P.
AU - Santaniello, Sabato
AU - Anderson, William S.
AU - Sarma, Sridevi V.
PY - 2013
Y1 - 2013
N2 - Communication between specialized regions of the brain is a dynamic process allowing for different connections to accomplish different tasks. While the content of interregional communication is complex, the pattern of connectivity (i.e., which regions communicate) may lie in a lower dimensional state-space. In epilepsy, seizures elicit changes in connectivity, whose patterns shed insight into the nature of seizures and the seizure focus. We investigated connectivity in 3 patients by applying network-based analysis on multi-day subdural electrocorticographic recordings (ECoG). We found that (i) the network connectivity defines a finite set of brain states, (ii) seizures are characterized by a consistent progression of states, and (iii) the focus is isolated from surrounding regions at the seizure onset and becomes most connected in the network towards seizure termination. Our results suggest that a finitedimensional state-space model may characterize the dynamics of the epileptic brain, and may ultimately be used to localize seizure foci.
AB - Communication between specialized regions of the brain is a dynamic process allowing for different connections to accomplish different tasks. While the content of interregional communication is complex, the pattern of connectivity (i.e., which regions communicate) may lie in a lower dimensional state-space. In epilepsy, seizures elicit changes in connectivity, whose patterns shed insight into the nature of seizures and the seizure focus. We investigated connectivity in 3 patients by applying network-based analysis on multi-day subdural electrocorticographic recordings (ECoG). We found that (i) the network connectivity defines a finite set of brain states, (ii) seizures are characterized by a consistent progression of states, and (iii) the focus is isolated from surrounding regions at the seizure onset and becomes most connected in the network towards seizure termination. Our results suggest that a finitedimensional state-space model may characterize the dynamics of the epileptic brain, and may ultimately be used to localize seizure foci.
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U2 - 10.1115/DSCC2013-3708
DO - 10.1115/DSCC2013-3708
M3 - Conference contribution
AN - SCOPUS:84902438587
SN - 9780791856130
T3 - ASME 2013 Dynamic Systems and Control Conference, DSCC 2013
BT - Control, Monitoring, and Energy Harvesting of Vibratory Systems; Cooperative and Networked Control; Delay Systems; Dynamical Modeling and Diagnostics in Biomedical Systems;
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2013 Dynamic Systems and Control Conference, DSCC 2013
Y2 - 21 October 2013 through 23 October 2013
ER -