A Virtual Clinical Trial of Psychedelics to Treat Patients With Disorders of Consciousness

Disorders of consciousness after severe brain injury are marked by reduced complexity of brain activity and limited treatment options. Using personalized whole‐brain models, this study shows that simulated lysergic acid diethylamide (LSD) and psilocybin shift patient brain dynamics closer to criticality. This proof‐of‐principle study investigates this novel therapeutic avenue and demonstrates the potential of virtual clinical trials in precision medicine. Abstract Disorders of consciousness (DoC), including unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS), have limited treatment options and are characterized by low complexity of brain activity. Recent research suggests that psychedelic drugs, which enhance the complexity of brain activity, could offer promising therapies. Here, individualized whole‐brain computational models are developed for patients with DoC, optimized with empirical functional magnetic resonance imaging data and diffusion‐weighted imaging data, upon which the administration of lysergic acid diethylamide (LSD) and psilocybin is simulated. An in silico perturbation protocol is applied to assess brain dynamics, first distinguishing between different states of consciousness, including DoC, anesthesia, and the psychedelic state. Then, brain dynamics are assessed before and after a simulation of psychedelic drugs on patients with DoC. Findings indicated that the simulation of LSD and psilocybin shifted the brain activity of patients with DoC closer to criticality (the point at a phase transition between order and chaos), with a greater effect in patients in the MCS. In patients with UWS, the treatment response correlated with structural connectivity, while in patients in the MCS, it aligned with baseline functional connectivity. These results offer a computational foundation for using psychedelics in DoC treatment and highlight the potential future role of computational modeling in drug discovery and personalized medicine. Disorders of consciousness after severe brain injury are marked by reduced complexity of brain activity and limited treatment options. Using personalized whole-brain models, this study shows that simulated lysergic acid diethylamide (LSD) and psilocybin shift patient brain dynamics closer to criticality. This proof-of-principle study investigates this novel therapeutic avenue and demonstrates the potential of virtual clinical trials in precision medicine. Abstract Disorders of consciousness (DoC), including unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS), have limited treatment options and are characterized by low complexity of brain activity. Recent research suggests that psychedelic drugs, which enhance the complexity of brain activity, could offer promising therapies. Here, individualized whole-brain computational models are developed for patients with DoC, optimized with empirical functional magnetic resonance imaging data and diffusion-weighted imaging data, upon which the administration of lysergic acid diethylamide (LSD) and psilocybin is simulated. An in silico perturbation protocol is applied to assess brain dynamics, first distinguishing between different states of consciousness, including DoC, anesthesia, and the psychedelic state. Then, brain dynamics are assessed before and after a simulation of psychedelic drugs on patients with DoC. Findings indicated that the simulation of LSD and psilocybin shifted the brain activity of patients with DoC closer to criticality (the point at a phase transition between order and chaos), with a greater effect in patients in the MCS. In patients with UWS, the treatment response correlated with structural connectivity, while in patients in the MCS, it aligned with baseline functional connectivity. These results offer a computational foundation for using psychedelics in DoC treatment and highlight the potential future role of computational modeling in drug discovery and personalized medicine. Advanced Science, EarlyView.