Adversarial AI framework reveals mechanisms behind impaired consciousness and a potential therapy

Consciousness, and the ways in which it can become impaired after certain brain injuries, are not well understood, making disorders of consciousness (DOC), like coma, vegetative states and minimally conscious states difficult to treat. But a new study, published in Nature Neuroscience, indicates that AI might be able to help researchers gain some traction with this problem. The research team involved in the new study has developed an adversarial AI framework to help them determine what exactly is going on in states of reduced consciousness and how to approach a solution.

To better understand the mechanisms behind impaired consciousness, the researchers developed two types of AI models and had them play a kind of game where one model determined different levels of consciousness based on EEGs simulated to look like those of real unconscious and conscious brains. The AI agents guessing consciousness levels, called deep convolutional neural networks (DCNNs), were first trained on 680,000 ten-second recordings of brain activity from conscious and unconscious humans, monkeys, bats and rats to detect which neural signals related to differing levels of consciousness. The AI showing EEG data was a biologically plausible simulation of the human brain.

"To decode consciousness from these signals, we trained three separate DCNNs, each specialized for a different brain region, to output a continuous score from 0 (unconscious) to 1 (fully conscious): a cortical consciousness detector (ctx-DCNN), a thalamic consciousness detector (th-DCNN) and a pallidal consciousness detector (pal-DCNN). The ctx-DCNN was trained on continuous consciousness levels derived from clinical scales (GCS and CRS-R), enabling it to recognize graded states of consciousness," the study authors explain.

Without explicit programming, the AI model was able to deduce known responses to brain stimulation that occur in DOC. The team then analyzed the parameters that the simulation model tweaked in order to find testable predictions about the underlying mechanisms of unconsciousness.

The researchers say that the model predicted two previously unknown mechanisms for unconsciousness that they were able to validate. The first is an increased inhibitory-to-inhibitory neuron coupling in the cortex, in which more neurons are restraining the firing of other neurons. This results in reduced overall activity. The researchers were able to validate this prediction from RNA sequencing data of brain tissue from comatose patients and in data from rats with brain damage from strokes. The team found that those with impaired consciousness showed an upregulation of genes that drive cortical inhibitory synapse formation.

The AI model also predicted that those with impaired consciousness have a selective disruption of the basal ganglia indirect pathway—a neural circuit that increases inhibition of the thalamus, thereby suppressing unwanted movements and motor actions. To validate the prediction, the researchers analyzed diffusion tensor imaging (DTI) scans from 51 patients with different DOC disorders. They say their analysis provided supporting evidence for the plausibility of selective basal ganglia pathway disruption in pathological unconsciousness, although some limitations, like a lack of cell-type specificity in DTI, of the study warrant further validation studies.