A photo of a person playing tennis
Mohamed Nuzrath from Pixabay

Cyborgs

Brain signals can reveal how “awake” a fly’s brain is

A new test for measuring awareness in fruit flies could change the way neuroscientists think about and measure consciousness.

A photo of a person playing tennis

Levels of consciousness are notoriously difficult to measure. The gold standard is to study functional magnetic resonance images of the brain as an individual receives various stimuli, such as instructions to think about playing tennis. This changes the patterns of activity in the brain in a measurable way.

But fMRI machines are bulky and expensive, and the tests are difficult to perform, particularly for patients in a coma or state of minimal awareness. Another option is to measure the electrical activity of the brain using EEG scans. This is easier, but neuroscientists have to agree on a clear marker of awareness in these signals.

So a better way to measure what is known as "conscious arousal" is desperately needed.

Enter Roberto Muñoz at Monash University in Australia and a number of colleagues. These folks have found a way to measure the level of conscious arousal in fruit flies using the complexity of the signals produced by the brain. Their technique allows them to distinguish between flies that have been anesthetized and those that have not, simply by looking at the signals.

The new work offers an objective way to measure conscious arousal, based on well-established ideas from complexity theory. It is potentially applicable to humans. And it reflects a growing interest in new theories of consciousness that are experimentally testable.

First, some background. One of the most important breakthroughs in the study of consciousness in recent years is an idea known as integrated information theory. Developed by the neuroscientist Giulio Tonini, the idea is that a conscious system must have two specific traits.

The first is that it must process large amounts of information. The second is that this information must be integrated into a whole that cannot be decomposed into independent parts. This reflects the experience that each moment of consciousness is a unified whole. So consciousness is a phenomenon of information with specific properties.

One of the big advantages of this theory is that it lends itself to mathematical analysis. Indeed, physicists like Max Tegmark at MIT have developed mathematical models of integrated information theory that make testable predictions and can be modified to account for observational data.

For example, the theory predicts that the information associated with conscious arousal must have a certain level of complexity. And therefore the complexity of the information it produces is a measure of conscious arousal.

However, there is problem. The information associated with conscious arousal is clearly linked to many different parts of the brain. Measuring this “integrated information” is a difficult task.

But there is a simpler approach. This is to look at the stream of information from specific locations in the brain and measure the complexity of the time series it produces. Since this time series is correlated with the mechanisms that integrate information in the brain, it should offer some insight into the level of consciousness behind it.

At least, that’s the theory. To find out its practical value, Muñoz and co study the brain signals produced by 13 fruit flies both when they are awake and when they are anesthetized. They then study the signals to see how complex they are.

The results make for interesting reading. “We found the statistical complexity to be larger on average when a fly is awake than when the same fly is anaesthetised,” they say.

That’s important because it suggests a reliable way to determine the level of conscious arousal using data from a single channel, rather than from lots of different data sources. It also suggests that there is a clear marker of conscious arousal that does not depend on specific external stimuli.

That’s interesting work that raises the possibility of more detailed studies. For example, the data from single channels could offer more insights into the nature of consciousness. “It is likely that applying a similar analysis to other datasets, in particular, human EEG data will lead to new discoveries regarding the relationship between consciousness and complexity,” say Muñoz and co.

It wasn’t so long ago that consciousness was a taboo subject for researchers because of the perceived inability to tackle it scientifically. But the new work reflects a newfound interest and enthusiasm in exploring consciousness with testable hypotheses and reproducible observations. Clearly there are exciting times ahead.

Ref: arxiv.org/abs/1905.13173 : Distinguishing States Of Conscious Arousal Using Statistical Complexity