Neural field modeling and analysis of consciousness states in the brain.

Neural field theory (NFT) can model brain activity across different states of consciousness. By fitting a corticothalamic NFT model to EEG data from healthy individuals and patients with disorders of consciousness, researchers identified correlations between NFT parameters and features of both experimental and simulated EEG. These correlations distinguish healthy from impaired consciousness and point to potential physiological biomarkers. The findings clarify how consciousness levels are represented in the NFT framework and highlight its value for in-silico experimentation in consciousness research.

Study at a glance

Abstract

Understanding the neural correlates of consciousness remains a central challenge in neuroscience. In this study, we explore the potential of neural field theory (NFT) as a computational framework for representing consciousness states. While prior research has validated NFT's capacity to differentiate between normal and pathological states of consciousness, the relationship of its parameters to the representation of consciousness states remains unclear. Here, we fitted a corticothalamic NFT model to the electroencephalography (EEG) data collected from healthy individuals and patients with disorders of consciousness. We then comprehensively explored the correlations between the fitted NFT parameters and features extracted from both experimental and simulated EEG data across various states of consciousness. The identified correlations not only highlight the model's ability to differentiate between healthy and impaired states of consciousness, but also shed light on the physiological bases of these states, pinpointing potential biomarkers. Our results provide valuable insights into how consciousness levels are represented within the NFT framework and into the dynamics of brain activity across normal and pathological states of consciousness. This underscores the potential of NFT as a useful tool for consciousness research, facilitating in-silico experimentation.