Measures of Consciousness

Can EEG be used to tell whether someone is conscious, and to what degree?

The remarkable discovery that some people with no outward sign of consciousness are still, within, quite conscious indeed, has been both sobering and a spur to better ways to detect consciousness directly. The orginal studies used fMRI, which is extremely laborious and expensive. Far cheaper would be to be able to tell a patient's state from an EEG, i.e. reading their brain waves from electrodes on the scalp. A recent study attempted to do that, using a high density array of 96 electrodes.

Patients with disorders of consciousness come in many kinds. From the EEG perspective, one is tempted to say that all happy brains are alike; every unhappy brain is unhappy in its own way. But the very unhappy ones are put in two classes these days, those in a vegetative state, and those in a minimally conscious state, which is significantly better, particularly with the ability to follow action with the eyes, or some similar ability to react to the surroundings, at least sporadically.

The researchers used high density EEG recordings, ten minutes long, from 26 normal people, 13 in a vegetative state, and 19 in a minimally conscious state. This status was evaluated according to a standard checklist of tests, called CRS-R. The hunt was to develop some kind of algorithm out of the large amount of EEG data that would reliably classify their patients, using the CRS-R tool as a standard of comparison. One would assume that if these researchers are successful, they could go on to run blinded trials to validate their  EEG analysis for clinical use, but it didn't sound like they got that far.

Brain wave power vs frequency, for human controls, those with minimal consciousness (MCS), and those in a vegetative state (VS).

There are dramatic differences, though, between the subjects. A summary of their brain wave power vs frequency (above) shows that the alpha band has a strong peak in normal people, which is utterly missing in those who are impaired. Conversely, patients with disorders of consciousness (DoC) had much higher delta band power. As discussed in a recent post, alpha waves are a sign of consciousness, but not really of active thought, rather they characterize an alert, relaxed state, especially in the visual cortex, ready to process data, but not actively processing. Theta and delta bands are more common in sleep and coma. One might surmise that they have something to do with healing and regeneration, which fits with this current data. The graphs also show raised gamma wave power in those with impaired consciousness, which the authors interpret as an incidental phenomenon due to involuntary muscle movement that causes electromyographic noise, i.e. electrical activity from muscles, not from brain waves.

But is there a quantifiable test to be made out of this? While there are significant differences in the wave pattern, it is merely statistically detectable, not an absolute, every-time kind of distinction.

Connectivity metrics among EEG electrodes, clustered into color-sets. Note long-range and strong connectivity in delta band of VS patients, theta band of MCS patients, and alpha band of controls. 

The researchers also provide more detailed maps of connecting / correlating activity among their electrodes, which makes interesting viewing. Note here again that the most active and longest-range connections are in the alpha waves in the control group, the theta waves in the MCS group, and the delta waves in the VS group. They point out that not only strength, but also the distance of interaction is a significant metric. The VS group shows mostly localized connections in the alpha waves, for instance.

Comparison of EEG node connectivity computed for three patients in a vegetative state,  after being asked to imagine themselves playing tennis. Patient P3 is clearly following along. Colors come from an arbitrary clustering algorithm that groups more-connected nodes.

They even go on to reproduce the "locked-in" patient test, asking patients to imagine playing tennis. As shown, two of the three patients show quite disorganized patterns in the alpha waves, while the third had dramatically long-distance and strong wave action. The third patient was classed as being in a vegetative state, but scored highly on all the EEG metrics that this group used, and thus appears to be a case that is more accurately diagnosed by newer methods of EEG or fMRI than by the standard CRS-R diagnostic checklist. Indeed, this patient appears to be a locked-in candidate, but the researchers note that in other brain wave frequencies, this patient looked more impaired, and say little more about her or him.

"In particular, though these prominent differences between the brain networks of P1 and P3 could perhaps be attributed to aetiology, it could not explain away the differences between P2 and P3, as both had suffered traumatic brain injury. It is also interesting to note that though P3's alpha network properties were clearly very prominent outliers as compared to P1 and P2, delta and alpha power in P3 were much less exceptional. Hence characterising network signatures of spectral connectivity could considerably improve our understanding of residual brain function in behaviourally uncommunicative patients who nevertheless demonstrate covert awareness."

The researchers go on to analyze their data in various ways, but while very interesting, none looks like a slam-dunk for telling the classes of patients apart in definitive fashion, quite yet. Such methods are sure to arrive at some point, however, as better tools are developed, and now that we know how much can be hidden behind the facade of immobility after traumatic brain injury.

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