A real-time electrocorticogram (ECoG) simulator based on a digital twin brain concept was developed. Using a Variational Bayesian Recurrent Neural Network with hierarchical latent units, the model dynamically predicted ECoG signals by assimilating data from macaque monkeys in awake and anesthetized conditions. The model updated its latent states in real-time, improving simulation precision. Self-organization of latent states reflected brain states and individuality, enabling simulation of virtual drug administration and revealing functional networks underlying anesthesia-induced changes. The simulator achieves high-accuracy real-time brain signal simulation and helps uncover underlying information processing dynamics.
Diphenidine (DPD) and its analogues 4-methoxydiphenidine (4MeO-DPD) and 4-hydroxydiphenidine (4OH-DPD) all penetrate the blood-brain barrier and trigger dopamine release in rats. 4OH-DPD produced the highest brain concentrations and dopamine release. Pretreatment with verapamil, a P-glycoprotein inhibitor, increased brain levels and prolonged elimination of all compounds, especially 4MeO-DPD, indicating that P-glycoprotein normally restricts their brain entry. Diphenhydramine, an organic cation transporter inhibitor, had no effect. The findings suggest that P-glycoprotein activity is a key factor in the toxicological risk of these emerging novel psychoactive substances.