Stanford anesthesiologist explores consciousness – and unconsciousness
on November 19th, 2014 No Comments
Stanford Medicine magazine profiled Chander’s work last summer, but I came across it when the title of one of Chander’s recently published papers grabbed my eye: “Electroencephalographic Variation During End Maintenance and Emergence from Surgical Anesthesia.” Okay, that might not pique your curiosity, but when I spotted the words, “for the first time” in the abstract I was hooked. I read on to learn that Chander and her team attach electrodes to the foreheads of patients during surgery, measuring the brain’s electrical signals.
After a bit of scrambling you might expect when trying to get in touch with someone who spends her days in the operating room, I managed to reach Chander on the phone. Our conversation strayed far from the bounds of her paper:
In this work, what did you do for the first time?
It’s not that no one has ever used an EEG during anesthesia. During the middle of the 20th century, several anesthesiologists attempted to record brain activity under increasing levels of anesthesia, just as many neuroscientists were using the EEG to characterize the stages of sleep. The process of recording EEG was really cumbersome back then, unlike today when you can stick a frontal set of leads on a patient’s forehead in the OR in a matter of seconds. Certain general stages of anesthesia were identified, but a formalized staging nomenclature, based on the relative contribution of dominant slow-wave oscillations in the EEG, had never been defined. Non-REM (slow-wave) and REM (rapid eye movement sleep) were staged in this way by sleep neurobiologists, but not anesthesiologists. In our study, we built upon the sleep stage classification system, to define maintenance patterns of general anesthesia. The formalized nomenclature helps us examine the stages of unconsciousness under anesthesia and communicate with other anesthesiologists.What did you find?
We recorded the frontal EEGs (from the forehead) of 100 patients undergoing routine orthopedic surgeries. We discovered four primary electrical patterns that patients exhibit when they’re unconscious, and also as they’re waking up from anesthesia. The unconscious patterns show variety – not all patients’ brains look the same under anesthesia, despite similar drug exposure, meaning there are ‘neural phenotypes,’ or patterns of neuronal activity. The emergence patterns from anesthesia (pathways people’s brains take to reestablish conscious awareness after the anesthetic is turned off) bear some similarity to those pathways traversed when people are awakening from sleep.
When wakening from anesthesia, some people spend a relatively long time in non slow-wave anesthesia, which is similar to REM, the stage of sleep where dreams occur that usually precedes awakening. Others go straight from deep anesthesia, what we call slow-wave anesthesia (because of its dominant EEG patterns) to awakening. Interestingly, these patients were more likely to experience post-surgical pain, a situation akin to awakening from a deep sleep and experiencing confusion or discomfort; some childhood parasomnias like sleep terrors are characterized by moving abruptly from slow wave sleep to waking.
We began to see some tantalizing suggestions certain patterns of wake-ups from anesthesia might be more preferable. Could paying attention to these emergence trajectories prevent some problematic complications, like post-operative cognitive dysfunction? Could we ‘engineer’ or optimize anesthetic delivery to favor certain types of maintenance and emergence patterns? Can we monitor these patterns in a way that makes delivering anesthesia safer? Recognizing the variety of maintenance and emergence patterns under anesthesia also opens an entirely new possibility in the field of personalized medicine – imagine tailoring anesthetics to a person’s genome? I am trying to develop an initiative that addresses this in collaboration with Stanford’s new GenePool Biobank program.
Did the type of drug given to patients affect what you saw?
All of these patients were on the same drug – a commonly used volatile (or gas) anesthetic called sevoflurane. That brings up something very interesting: The anesthetic drugs we use, even when chemically or structurally related (e.g. sevoflurane and isoflurane), may bind to similar receptors but in different neural network distributions. Some of our other anesthetics, like ketamine, work at different receptors entirely. Yet somehow, through some combination of these drugs, we are still able to make people unconscious, we are able to remove their degree of connectedness to the external world to the point they can tolerate really noxious stimuli, like cutting into them with a scalpel, without awareness of what is happening to them.Why doesn’t everyone use EEGs? What do they tell you?
The Holy Grail for me would be to find a way to dynamically characterize EEG states in way that is drug-independent. This would allow anesthesiologists to potentially gauge depth of unconsciousness without worrying about how some drugs differentially affect different EEG frequencies.
All that we’re doing is measuring cortical cells at the very surface of the brain and their electrical activity, which indirectly reflects what happens much, much deeper in the brain. The really cool thing is it’s actually telling us something about the neural networks that serve to maintain or transition between different levels of consciousness.
Unfortunately, interpreting a raw EEG can take a bit of training. Anesthesiologists already have to process so much data to keep their patients alive and safe. Every second, they monitor the patient’s heart rate, blood pressure, oxygen saturation, carbon dioxide, temperature and many other variables.
Some companies have developed frontal (forehead) EEG leads that are analyzed through algorithims, that collapse the complexity of the EEG into a single number. Unfortunately, calculating those numbers has a time delay of up to 1.5 minutes – useless for making clinical decisions, and too slow for preventing things like a patient gaining awareness during an operation or unintentionally waking up. I’m working with other researchers to find algorithms that work more dynamically to improve processing time, down to five seconds – within the reaction time of a clinical decision maker. One-and-a-half minutes is too slow.
Do you consider yourself conscious when you’re dreaming?Yes, I think so.
If I looked at the activity in your brain during REM sleep, it would look very similar to your awake brain. But when you’re dreaming, you’re paralyzed. You can’t tell me what’s going on, and you can’t respond to the external world.You’re also an instructor here. What do you hope to pass on to your students?
So do you have to respond to the external world in a way that I can measure it to be conscious? What if you are in a ‘coma,’ but can respond to requests to visualize things? Are you conscious? What if you can’t move to communicate with me, as in locked-in syndrome? Does that make you unconscious? There are many states we define as lacking conscious awareness in which the brain may still be active in a way that reflects conscious processing. We have to come up with new ways to conceptualize and measure consciousness.
Anesthesia is just so satisfying, so exciting. We’re peering into this privileged, special place to understand what it means to be conscious or unconscious.Previously: Exploring the conscious (and unconscious) brain in everyday life, Advances in anesthesia make it possible for patients to remain awake and watch TV during surgery and Animal study shows sleeping brain behaves as if it’s remembering
Anesthesia is a real art. You want to keep the patient just deep enough, but not so deep that you delay emergence, or they don’t wake up as they were before. The central nervous system is the primary target of our anesthetics; I want the students to understand that monitoring the brain is vitally important to bringing patients safely through anesthetics. Once upon a time we didn’t have pulse oximeters and capnography; now we couldn’t imagine doing anesthesia without these monitors.
Image by DrSJS
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