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Professor hunts epileptic seizure clues


April 11, 2007

Research promising to break new ground in the understanding and treatment of epilepsy and similar maladies is detailed in a paper by Ira A. Fulton School of Engineering faculty member Ying-Cheng Lai published in March by a leading physics journal.

“Characterization of synchrony with applications to epileptic brain signals” in Physical Review Letters reveals recent findings by Lai, a professor in the Department of Electrical Engineering. Lai also is a professor in the Department of Physics in the College of Liberal Arts and Sciences.

The paper delves into one of the most important and challenging problems in biomedical sciences: understanding the dynamics of seizures.

Epileptic seizures affect about one percent of the population in industrialized countries – about 3 million people in the United States alone.

It's thought that a necessary condition leading to seizures is neuronal hypersyncrony, a state where many neurons in certain regions of the brain become synchronized in their activities. But Lai says recent experimental study reveals that seizure-like events are associated with desynchronization, a state in which the activities of neurons involved in seizures become less synchronized.

Resolving the question about which of these conditions is the best predictor of a seizure is critical to a basic understanding of epilepsy, Lai says.

Lai and Liang Huang, a doctoral candidate in electrical engineering at ASU, together with collaborators at the University of Kansas Medical Center, are developing a method to analyze synchrony using recordings from multichannel electroencephalograms (EEGs) or electrocorticogroams (ECoG) from laboratory and clinical studies of epilepsy.

The electrical signals from the channels look quite random in general, but there could be correlations between these signals, Lai says, such as any tendency for them to follow each other.

By detecting and characterizing the degree of possible synchronous activities among the channels, it may be possible to answer the question of whether seizures are associated with enhanced or reduced synchrony.

Lai's analyses are based on a matrix whose elements are various times for pairs of channels to maintain temporal synchronization in their phases. To focus on such times is important, as the brain signals are typically noisy, and synchrony, if it exists, can last for only a finite amount of time, he says.

The advantage with this matrix-based approach is that it can effectively use all information provided by signals from all available channels.

To make the matrix robust against noise but at the same time sensitive to changes in synchrony, Lai and his collaborators used the mathematical theory of random matrices to guide the construction of the matrix.

Monitoring of the properties of the matrix provides an effective way to assess changes in synchrony. The method is validated by a control model of coupled nonlinear oscillators, and tested using clinical EEG and ECoG data. One finding is that, at a systems level, whether epileptic seizures are accompanied by enhanced or reduced synchrony is highly case-dependent.

Since multichannel data are also common in many other disciplines of science and engineering, Lai says he believes the new method can be used in other areas in addition to epilepsy studies, such as seismology and large-scale data analysis from sensor networks that are increasingly employed in civil and defense applications.

The American Physical Society's Physical Review Letters is the foremost journal for publishing short reports on significant fundamental research in all areas of physics.

Lai is a fellow of the American Physical Society, an honor given to those who have made significant contributions to the physical sciences.