🤖 AI Summary
This study addresses the limited model interpretability and lack of standardization in classifying states of consciousness and modeling anesthetized brain dynamics by proposing a hybrid, generalizable architecture that integrates physiological priors. By combining deep neural networks, multiscale computational modeling, and clustering algorithms, this work jointly analyzes EEG, fMRI, and LFP data to achieve automated brain state classification and structure–function dynamical modeling, while supporting real-time EEG/LFP-based monitoring. The proposed framework is validated for its efficacy in predicting brain states and elucidating connectivity patterns, thereby overcoming conventional black-box limitations. It significantly enhances the capacity to resolve the specificity of consciousness markers and establishes its core potential for translational applications in clinical neuroscience.
📝 Abstract
A critical analysis of contemporary approaches to the study of conscious states. The review focuses on methods of classification, clustering, modeling of brain states under anesthesia and identification of measurable neurobiological characteristics of brain function. A comparative analysis was conducted in the following three major areas: automatic detection of states of consciousness using neural networks based on EEG and fMRI data; modeling of the structural-functional dynamics of the brain under the effects of anesthetics; and detection of neurophysiological indicators which correlate with the level of consciousness. The obtained conclusions demonstrate the growing effectiveness of deep neural models in the classification and prediction of brain states and the analysis of dynamic structural-functional connectivity. Nonetheless, significant limitations were also identified, including the limited interpretability of the models, the lack of standardized metrics, and the problem of the specificity of consciousness markers. Our findings support the need for developing hybrid, generalizible, physiologically grounded architectures. Furthermore, such approaches may improve the translational potential of computational models in clinical neuroscience. Diverse methods of machine and computational modeling have demonstrated their effectiveness in tasks of automatic clustering and classification of brain states, the development of multilevel models and the identification of connectivity patterns correlated with levels of consciousness. A larger-scale analysis and a larger dataset, as well as the implementation of model interpretability approaches are required for the practical application of the analyzed models. The models based on EEG and LFP are the most promising for clinical application due to their availability and the possibility of real-time monitoring.