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EEG scan detects hidden consciousness in unresponsive brain injury patients

EEG scan detects hidden consciousness in unresponsive brain injury patients
Using machine learning, scientists could identify patterns of hidden consciousness in EEG data from unresponsive patients, and predict their likelihood of recovery
Using machine learning, scientists could identify patterns of hidden consciousness in EEG data from unresponsive patients, and predict their likelihood of recovery
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Using machine learning, scientists could identify patterns of hidden consciousness in EEG data from unresponsive patients, and predict their likelihood of recovery
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Using machine learning, scientists could identify patterns of hidden consciousness in EEG data from unresponsive patients, and predict their likelihood of recovery

A landmark study from neurologists at Columbia University has described a new method to detect signs of consciousness in unresponsive brain-injured patients using a simple EEG scan. The research also suggests the EEG data can predict which patients have a high chance of regaining consciousness and recovering.

About a decade ago advances in medical imaging allowed scientists to better track deep signals of consciousness in brain-injured comatose patients. MRI data revealed what was dubbed "hidden consciousness", sometimes also referred to as covert consciousness.

This undeniable medical breakthrough broadened the definition of consciousness for academic purposes, but several challenges remained before this knowledge could be effectively translated into something clinically functional. For one, MRI tests are not easy to perform in intensive care situations, and even then, it was unclear exactly what these signs of hidden consciousness actually meant in terms of patient recovery.

"One of the most challenging problems in the ICU involves predicting recovery, and not just survival, for patients who are unconscious after a brain injury," says Jan Claassen, lead author on the new research. "Since the first studies describing hidden consciousness, we've been looking for a practical way to do this in the early days after brain injury, when treatment decisions that affect outcomes are often made."

The new study followed 104 patients in intensive care after suffering major brain injury. All the subjects were completely unresponsive, but not paralyzed. While EEG scans were conducted, the subjects were asked to open and close their hands. A machine learning algorithm was then tasked with analyzing the EEG scans to ascertain whether the physical commands triggered any tiny patterns in the neurological data.

The results revealed unique EEG patterns in response to the commands in 15 percent of the unresponsive subjects within the first four days of the brain injury. Around half of patients identified with these signs of hidden consciousness improved to the point they could follow verbal commands by the time they were discharged from hospital. Only 26 percent of subjects without these initial signs improved to the same point.

A year later 44 percent of the group displaying the initial EEG activity were independently functional for up to eight hours everyday. This compares to only 14 percent reaching the same point a year later without initially displaying these EEG activity signs.

Although these results are incredibly promising, much more work is still needed before they are translated into clinical applications. The study was not large enough to separate out different kinds of brain injuries. Early indications suggest the EEG test is most effective in cases of brain haemorrhage and trauma, as opposed to injury suffered from oxygen deprivation, but this will need to be clarified through larger trials.

Claassen also says there are some technical hurdles that need to be overcome before this test is broadly implemented. Computers are needed to process the EEG data, and because patients tend to drift in and out of consciousness after a brain injury, the tests need to be conducted several times a day in the first few days following an injury for the most accurate results.

"It's important to begin monitoring with EEG as early as possible and to assess at several time points as recovery after a severe brain injury is a complex process," says Claassen.

Still, this new discovery points to potentially groundbreaking changes in how clinicians approach unresponsive patients in the first few days following brain injury. If the technique can be effectively fine-tuned, it could be easily rolled out into intensive care units around the world and assist doctors to better target those patients most likely to recover consciousness following a brain injury.

"Though our study was small, it suggests that EEG – a tool that's readily available at the patient's bedside in the ICU in almost any hospital across the globe – has the potential to completely change how we manage patients with acute brain injury," says Claassen.

The research was published in The New England Journal of Medicine.

Source: Columbia University

2 comments
2 comments
Edward Vix
More evidence against the concept of "brain death" which has been used for several decades now by transplant surgeons to subjectively select young healthy organ donors, especially of heart, liver and lung, and then remove these organs while the victims are un-anaensthetized, because that works best for the organs. Common sense says that if the heart is beating on its own and begins to race when the first incision is made, the victim is responding in pain and the action of removing the heart, for example, is what really causes death. This is kind of like what the Aztecs used to do to make sure the sun came up the next day. Just saying.
Joneseyboy
Autonomic response explains racing of heart. Your use of the word 'Victim" shows where you're coming from.