A distinctive pattern of brain activity associated with conditions including deep anesthesia, coma and congenital brain disorders appears to represent the brain's shift into a protective, low-activity state in response to reduced metabolic energy. A mathematical model developed by a Massachusetts General Hospital (MGH)-based research team accurately predicts and explains for the first time how the condition called burst suppression is elicited when brain cells' energy supply becomes insufficient. Their report has been released online in PNAS Early Edition.

"The seemingly unrelated brain states that lead to burst suppression -- deep anesthesia, coma, hypothermia and some developmental brain disorders -- all represent a depressed metabolic state," says Emery Brown, MD, PhD, of the MGH Department of Anesthesia, Critical Care and Pain Medicine, senior author of the report. "We believe we have identified something fundamental about brain neurochemistry, neuroanatomy and neurophysiology that may help us plan better therapies for brain protection and design future anesthetics."

Burst suppression is an electroencephalogram (EEG) pattern in which periods of normal, high brain activity -- the bursts -- are interrupted by stretches of greatly reduced activity that can last 10 seconds or longer. Burst suppression has been observed in deep general anesthesia, in induced hypothermia -- used to protect the brain or other structures from damage caused by trauma or reduced blood flow -- in coma, and in infants with serious neurodevelopmental disorders. It also has transiently been observed in some premature infants. Previous investigations of burst suppression focused on characterizing the structure of the EEG patterns and understanding the brain's responsiveness to external stimuli while in this state, not on the underlying mechanism.

Lead author ShiNung Ching, PhD, a postdoctoral fellow in Brown's lab, had been working with Nancy Kopell, PhD, a professor of Mathematics at Boston University and co-author of the PNAS article, to develop mathematical models of different brain states under general anesthesia. In developing a model for burst suppression, they focused on what the associated conditions have in common -- a significant reduction in the brain's metabolic state. In order for a signal to pass from one nerve cell to another, the balance between sodium ions outside the cell and potassium ions within the cell needs to be correct. Maintaining that balance requires that structures called ion pumps, fueled by the cellular energy molecule ATP, function correctly. The model developed by Ching and his colleagues revealed that, when brain energy supplies drop too low and cause a deficiency in ATP, potassium leaks from the nerve cells and signal transmission halts.

"It looks like burst suppression shifts the brain into an altered physiologic state to allow for the regeneration of ATP, which is the essential metabolic substrate," Ching explains. "During suppression, the brain is trying to recover enough ATP to restart. If the substrate doesn't regenerate quickly enough, the system will have these brief bursts of activity, stop and then need to recover again. The length of suppression is governed by how quickly ATP regenerates, which matches the observation that the deeper someone is anesthetized, the longer the periods of suppression."

Brown adds, "When we use general anesthesia to place patients with serious neurologic injuries into induced comas to allow their brains to heal, we take them down to a level of burst suppression. But there are a lot of questions regarding how deeply anesthetized an individual patient should be -- how often the bursts should occur -- and how long we should maintain that state. By elucidating what appears to be a fundamental energy-preserving mechanism within the brain, this model may help us think about using burst suppression to guide induced coma and track recovery from brain injuries. This is also a great example of how studying anesthesia can help us learn something very basic about the brain."

Brown is the Warren Zapol Professor of Anesthesia at Harvard Medical School. He also is a professor of Computational Neuroscience and Health Sciences and Technology at Massachusetts Institute of Technology. Additional co-authors of the PNAS report are Patrick Purdon, PhD, MGH Anesthesia, and Sujith Vijayan, PhD, Boston University Mathematics. The study was supported by grants from the National Institutes of Health and the National Science Foundation.