Brain-machine interface allows anesthesia control

Researchers have developed a brain-machine interface that monitors a patient’s brain activity and adjusts the anesthetic infusion rate to precisely control the level of brain activation in a medically induced coma, according to a study published online Oct. 31 in the journal PLoS Computational Biology.

The team – which includes lead author Maryam Shanechi, a visiting professor and an incoming assistant professor in Cornell’s School of Electrical and Computer Engineering, and Emery Brown and colleagues from Massachusetts General Hospital and Massachusetts Institute of Technology – shows how the brain-machine interface maintains reliable and accurate real-time control of a medically induced coma in rodents. The study suggests that this system could be applied to human patient care.

A brain-machine interface (BMI) connects with the brain to control its state or to allow the brain to control an external device. Examples of BMIs include neural prosthetics for restoring movement in paralyzed patients and cochlear implants to restore hearing. The BMI created by the team is for maintenance of a medically induced coma, which is a drug-induced state of profound brain inactivity and unconsciousness, which is used to treat and manage severe brain conditions.

A medically induced coma can last several days, but in current practice is induced manually by monitoring a patient’s brain activity with an electroencephalogram (EEG) and adjusting the anesthetic infusion rate. The team suggests that this manual maintenance is equivalent to manually flying a plane on a fixed trajectory for several days, rather than using autopilot, and moreover makes accurate control infeasible.

Shanechi’s BMI can automatically and precisely control and change the level of medical coma in real time in individual rodents. The BMI consists of novel estimation and control algorithms that use the EEG to estimate the level of brain activation in coma, then use this estimate as feedback to control the drug infusion rate in real time.

“Our work demonstrates the feasibility of automatic, reliable and accurate control of medical coma that can provide considerable therapeutic benefits,” Shanechi said. Automated control may allow for more efficient use of intensive care unit personnel, as a single nurse per shift would not be solely dedicated to manually managing the anesthetic infusion rate for a single patient for several days.

In addition, reliable and accurate control of medically induced comas could offer the possibility of ensuring adequate brain protection for patients, while using the least amount of anesthetic. It would also make it easier to induce periodic arousals in patients to conduct neurological assessments and prevent anesthetic overdose.

The study further suggests that this system could be adapted to precisely control states of general anesthesia and sedation for patients requiring surgical or nonsurgical procedures. Such an anesthesia delivery system could significantly alter the way anesthesia is administered, particularly during long surgeries.

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