Washington, D.C. (December 14, 2010) — While the causes of epileptic seizures continue to confound brain researchers, scientists have been exploring how changes in the coordinated activity of brain networks, as monitored through electrodes, might help predict impending seizures. A report in the American Institute of Physics’ journal CHAOS offers new insight into this possibility.
Two properties are commonly used to measure fluctuations in the activity of a brain network; one, known as L, relates to the overall connectedness between the activities of brain regions (or nodes), and the other, C, represents the probability that any two nodes are both interacting with a third node. Tracking changes in these variables, neuroscientists suspect, might offer a way to spot seizures in advance.
Most studies of complex brain networks have used only short-duration recordings of brain function, no more than a few minutes long. And, says physicist Marie-Therese Kuhnert — a graduate student at the University of Bonn and first author of the CHAOS paper — to really find seizure-predicting patterns, you need longer-term data.
Kuhnert and her colleagues, professors Christian Elger and Klaus Lehnertz, studied the brain recordings of 13 epilepsy patients undergoing pre-surgical evaluations. The data — representing, in all cases, days of continuous recordings and seizure activity — did indeed show fluctuations in L and C, but the two measures were “strongly influenced by the daily rhythms of the patient, sleep — wake cycles, and alterations of anticonvulsive medication,” Kuhnert says. Upcoming seizures and even seizures themselves had little effect.
Surprisingly, Kuhnert and her colleagues found much more regularization of brain network activity at night. Previously, such regularization has been seen in healthy individuals, but never in epilepsy patients. “It remains to be investigated whether the increased regularization at night is causally related to epilepsy, whether it requires some treatment, or whether it can be regarded as a seizure-preventing mechanism,” she says.
The article “Long-term variability of global statistical properties of epileptic brain networks” by Marie-Therese Kuhnert, Christian E. Elger, and Klaus Lehnertz appears in the journal Chaos. See: http://link.aip.org/link/chaoeh/v20/i4/p043126/s1
Journalists may request a free PDF of this article by contacting [email protected]
ABOUT CHAOS
Chaos is an interdisciplinary journal of non-linear science. The journal is published quarterly by the American Institute of Physics and is devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines. Special focus issues are published periodically each year and cover topics as diverse as the complex behavior of the human heart to chaotic fluid flow problems. See: http://chaos.aip.org/
ABOUT AIP
The American Institute of Physics is a federation of 10 physical science societies representing more than 135,000 scientists, engineers, and educators and is one of the world’s largest publishers of scientific information in the physical sciences. Offering partnership solutions for scientific societies and for similar organizations in science and engineering, AIP is a leader in the field of electronic publishing of scholarly journals. AIP publishes 12 journals (some of which are the most highly cited in their respective fields), two magazines, including its flagship publication Physics Today; and the AIP Conference Proceedings series. Its online publishing platform Scitation hosts nearly two million articles from more than 185 scholarly journals and other publications of 28 learned society publishers.
