Combining whole-brain models with deep learning, researchers mapped the low-dimensional space of patients with disorders of consciousness and simulated pharmacological interventions by altering neuromodulatory levels. Serotonergic and opioid receptor activation shifted the models toward brain dynamics seen in healthier states, with improvements correlating with the mean density of activated receptors across the brain. This approach provides a way to explore therapeutic potential of psychedelic drugs within ethical and methodological constraints, marking progress toward treatments for disorders of consciousness and other brain diseases.
A method using corticothalamic neural field theory (NFT) fits to EEG spectra can distinguish conscious from unconscious states in healthy and brain-injured subjects. Healthy subjects in wake and REM sleep, those in deep sleep, and brain-injured patients (unresponsive wakefulness syndrome, minimally conscious state, emerged from MCS) cluster into three groups based on two parameters: the difference between corticocortical and corticothalamic feedbacks (X-Y) and mean neural response rates (α and β). X-Y is smaller in conscious states (wake/REM) than in sleep but does not differentiate among brain injuries. The method can be automated on a personal computer, unlike laborious clinical assessments or measures like Φ from integrated information theory.