A computational method uses a simplified brain model fitted to a patient's EEG power spectrum to design personalized electrical stimulation signals. In computer simulations, these signals induce healthy-like brain activity patterns in models of people with disorders of consciousness. When the model's parameters were near a stability boundary, stimulation caused a lasting change in activity beyond the stimulation period. The approach may activate plasticity mechanisms during long-term treatment, potentially leading to sustained improvements. Further clinical adjustments and validation are needed, but the method holds promise for improving therapeutic outcomes in disorders of consciousness and may extend to other neurological conditions.
Neural field theory (NFT) can model brain activity across different states of consciousness. By fitting a corticothalamic NFT model to EEG data from healthy individuals and patients with disorders of consciousness, researchers identified correlations between NFT parameters and features of both experimental and simulated EEG. These correlations distinguish healthy from impaired consciousness and point to potential physiological biomarkers. The findings clarify how consciousness levels are represented in the NFT framework and highlight its value for in-silico experimentation in consciousness research.