Study finds tracking brain waves could reduce post-op complications

Researchers at MIT have identified brain wave signatures that could help anesthesiologists determine when patients are entering a deeper state of unconsciousness during general anesthesia.

The study analyzed the EEG patterns of patients under anesthesia and discovered distinctive patterns in alpha waves that indicate the transition into a deeper state. These patterns could enable anesthesiologists to intervene and prevent patients from entering a state of burst suppression, which is associated with postoperative cognitive impairments.

The research, published in the Proceedings of the National Academy of Sciences, provides insights into measuring brain activity and may lead to improved monitoring and management of unconsciousness during surgery.

The study involved the analysis of brain waves generated by synchronized neuronal activity, which oscillate at different frequencies depending on the brain’s engagement in different tasks. Anesthesia drugs such as propofol affect these oscillations, inducing a state of unconsciousness characterized by slow-delta-alpha (SDA) oscillations. Higher doses of anesthetic drugs can lead to burst suppression, characterized by periods of inactivity punctuated by low-amplitude oscillations. The study focused on understanding the transition between SDA and burst suppression.

Through EEG analysis of healthy volunteers and surgical patients receiving propofol or sevoflurane, the researchers observed distinct patterns in alpha waves as the dosage of propofol increased. The waxing and waning of alpha waves changed with increasing dosage, eventually leading to burst suppression. The researchers also observed changes in slow and delta waves, reflecting a decrease in brain activity. It is hypothesized that propofol disrupts neuron metabolism, particularly ATP production, resulting in burst suppression.

The findings could provide anesthesiologists with a better understanding of a patient’s level of unconsciousness during surgery. The researchers aim to develop an algorithm that can generate warnings when a patient is approaching burst suppression, offering real-time monitoring in the operating room. Anesthesiologists may be able to identify these patterns in EEG readings and adjust anesthesia dosages accordingly. The team plans to further investigate brain metabolism during the transition to burst suppression using animal models.

The research received funding from the Picower Institute Innovation Fund and the National Institutes of Health.

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