TY - CHAP
T1 - Brain dynamics and modeling in epilepsy
T2 - Prediction and control studies
AU - Iasemidis, Leonidas
AU - Sabesan, Shivkumar
AU - Chakravarthy, Niranjan
AU - Prasad, Awadhesh
AU - Tsakalis, Konstantinos
PY - 2009
Y1 - 2009
N2 - Epilepsy is a major neurological disorder characterized by intermittent paroxysmal neuronal electrical activity, that may remain localized or spread, and severely disrupt the brain's normal operation. Epileptic seizures are typical manifestations of such pathology. It is in the last 20 years that prediction and control of epileptic seizures has been the subject of intensive interdisciplinary research. In this communication, we investigate epilepsy from the point of view of pathology of the dynamics of the electrical activity of the brain. In this framework, we revisit two critical aspects of the dynamics of epileptic seizures - the seizure predictability and seizure resetting - that may prove to be the keys for improved seizure prediction and seizure control schemes. We use human EEG data and the concepts of spatial synchronization of chaos, phase and energy to first show that seizures could be predictable in the order of tens of minutes prior to their onset. We then present additional statistical evidence that the pathology of the brain dynamics prior to seizures is reset mostly upon seizures' occurrence, a phenomenon we have called seizure resetting. Finally, using a biologically-plausible neural population mathematical model that can exhibit seizure-like behavior, we provide evidence for the effectiveness of a recently devised seizure control scheme we have called 'feedback decoupling'. This scheme also provides an interesting dynamical model for ictogenesis (generation of seizures).
AB - Epilepsy is a major neurological disorder characterized by intermittent paroxysmal neuronal electrical activity, that may remain localized or spread, and severely disrupt the brain's normal operation. Epileptic seizures are typical manifestations of such pathology. It is in the last 20 years that prediction and control of epileptic seizures has been the subject of intensive interdisciplinary research. In this communication, we investigate epilepsy from the point of view of pathology of the dynamics of the electrical activity of the brain. In this framework, we revisit two critical aspects of the dynamics of epileptic seizures - the seizure predictability and seizure resetting - that may prove to be the keys for improved seizure prediction and seizure control schemes. We use human EEG data and the concepts of spatial synchronization of chaos, phase and energy to first show that seizures could be predictable in the order of tens of minutes prior to their onset. We then present additional statistical evidence that the pathology of the brain dynamics prior to seizures is reset mostly upon seizures' occurrence, a phenomenon we have called seizure resetting. Finally, using a biologically-plausible neural population mathematical model that can exhibit seizure-like behavior, we provide evidence for the effectiveness of a recently devised seizure control scheme we have called 'feedback decoupling'. This scheme also provides an interesting dynamical model for ictogenesis (generation of seizures).
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U2 - 10.1007/978-1-4020-9143-8_12
DO - 10.1007/978-1-4020-9143-8_12
M3 - Chapter
AN - SCOPUS:56349137823
SN - 9781402091421
T3 - Understanding Complex Systems
SP - 185
EP - 214
BT - Complex Dynamics in Physiological Systems
A2 - Dana, Syamal
A2 - Roy, Prodyot
A2 - Kurths, Jurgen
ER -