J Korean Neurol Assoc.  2002 Mar;20(2):147-152.

Spatio-Temporal Distribution and Propagation of Temporal Lobe Seizures: Application of Nonlinear Mutual Cross Prediction

Affiliations
  • 1Department of Neurology, Chungnam National University, College of Medicine, Korea. kyjung@cnuh.co.kr
  • 2Institude for Brain Research, Chungnam National University, Korea.
  • 3Department of Neurology, Sung Kyun Kwan University, Samsung Medical Center, Korea.
  • 4Department of Neurology, Inje University, Chaos and Nonlinear Biological Laboratory, Korea.

Abstract

BACKGROUND: Nonlinear mutual cross prediction (MCP) characterizes dynamic interdependence among nonlinear systems. MCP also reveal relative strength of the coupling between systems, thus provides information about the direc-tion of interdependence. The aim of this study is to apply MCP algorithm to multi-channel EEG and to characterize spatio- temporal pattern of seizure.
METHODS
We analyzed MCP of EEG of three medically intractable temporal lobe epilepsy patients, who underwent temporal lobectomy (left 2, right 1). Asymmetry of nonlinear cross predictability between channels was investigated. Five epochs of interictal EEG free from epileptiform discharge(s) and of ictal EEG were analyzed.
RESULTS
In interictal period, both frontal and occipital region appeared a weak driving force while awake and this driving force was further weakened during sleep. Before the onset of the seizure (preictal phase), the intensity of driving system became slightly stronger around seizure foci in 3 out of 8 seizures while no significant change was seen on the naked eyes. However this change was dim and not continuous. At the onset of seizure (ictal phase), 5 out of 8 seizures showed strong driving force around seizure foci. Three seizures without significant change initially had strong driving force as synchronous seizure discharges became built-up and spreading to surrounding areas in the middle of seizure. All seizures showed ipsilateral frontotemporal strong driving force and centroparietal response system, which was typical spatio-temporal distribution of MCP.
CONCLUSION
MCP analysis may be a useful method for detecting spatio-temporal distribution and propagation pattern in temporal lobe epilepsy.

Keyword

Temporal lobe epilepsy; EEG; Nonlinear interdependece; Mutual cross prediction (MCP); Spatio-tempo-ral distribution

MeSH Terms

Electroencephalography
Epilepsy, Temporal Lobe
Humans
Seizures*
Temporal Lobe*
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