Predictive computational models in anesthesia and coma

The transition between wakefulness and states of reduced consciousness, whether pharmacologically induced via anesthesia or pathologically necessitated by coma, remains one of the most profound challenges in modern neuroscience. While anesthesia is a controlled, reversible process, and coma is often an unpredictable, fluctuating state, both share underlying neurobiological signatures involving the disruption of global integration and local processing. Traditional clinical monitoring often relies on descriptive scales or simplified indices that may fail to capture the complex, non-linear dynamics of the brain. Computational neuroscience offers a powerful framework to bridge this gap, using mathematical modeling and advanced signal processing to decode the mechanisms of unconsciousness. By simulating neural circuits and analyzing large-scale brain networks, we can better understand the "tipping points" of consciousness, providing a theoretical foundation for more precise clinical interventions.The primary objective of this Research Topic is to address the critical need for predictive tools that can accurately forecast state transitions in patients undergoing anesthesia or recovering from a coma. Currently, there is a lack of robust, individualized models that can predict how a specific brain will respond to anesthetic titration or whether a comatose patient possesses the latent neural capacity for recovery. To achieve this, we aim to bring together multidisciplinary research that utilizes biophysical models, machine learning algorithms, and network science to identify reliable biomarkers of consciousness. We seek to move beyond descriptive analysis toward "predictive neuro-monitoring." This involves developing models that can anticipate emergence from anesthesia, detect the risk of intraoperative awareness, or provide prognostic value in the Intensive Care Unit (ICU). By integrating multi-scale data, from cellular-level effects of GABAergic modulation to macroscopic functional connectivity, we can foster the development of closed-loop systems and personalized prognostic frameworks that improve patient safety and long-term neurological outcomes.We invite contributions that employ computational methods to investigate the dynamics of anesthesia and coma, and welcome all papers that bridge the gap between theoretical modeling and clinical application. We are particularly interested in studies that use diverse data modalities, including EEG, fMRI, ECoG, and pupillometry, to build or validate predictive frameworks.Specific themes of interest include, but are not limited to:- Biophysical and mean-field modeling of anesthetic-induced brain oscillations.- Machine learning and AI architectures for the automated classification and prediction of coma recovery.- Dynamical systems approaches to understanding state transitions and "criticality" in the brain.- Graph-theoretical and network neuroscience models of connectivity loss and restoration.- Development and validation of real-time predictive algorithms for closed-loop anesthetic delivery.- Computational cross-species comparisons (e.g., translating findings from rodent or primate models to human clinical settings).- In-silico testing of therapeutic interventions for "reawakening" the brain from pathological states. Topic editors: Yu-Guo Yu, Mohd Dilshad Ansari, Abdoreza Asadpour Source: https://www.frontiersin.org/research-topics/80579/predictive-computational-models-in-anesthesia-and-coma