Digital twin brain simulator for real-time consciousness monitoring and virtual intervention using primate electrocorticogram data.
NPJ digital medicine – February 10, 2025
Source: PubMed
Summary
A groundbreaking electrocorticogram (ECoG) simulator accurately predicts brain signals in real-time, enhancing personalized medicine. By analyzing ECoG data from 12 macaque monkeys in both awake and anesthetized states, the model achieved high precision in simulating brain activity. It effectively updated its latent states dynamically, showcasing a self-organization process that mirrors individual brain functions. This innovative approach not only simulates virtual drug effects but also reveals crucial functional networks, offering insights into brain dynamics during anesthesia and beyond.
Abstract
At the forefront of bridging computational brain modeling with personalized medicine, this study introduces a novel, real-time, electrocorticogram (ECoG) simulator, based on the digital twin brain concept. Utilizing advanced data assimilation techniques, specifically a Variational Bayesian Recurrent Neural Network model with hierarchical latent units, the simulator dynamically predicts ECoG signals reflecting real-time brain latent states. By assimilating broad ECoG signals from macaque monkeys across awake and anesthetized conditions, the model successfully updated its latent states in real-time, enhancing precision of ECoG signal simulations. Behind successful data assimilation, self-organization of latent states in the model was observed, reflecting brain states and individuality. This self-organization facilitated simulation of virtual drug administration and uncovered functional networks underlying changes in brain function during anesthesia. These results show that the proposed model can simulate brain signals in real-time with high accuracy and is also useful for revealing underlying information processing dynamics.