Complexity Measures in Diverse Dynamical Regimes and Application to States of Consciousness
preprint DOI: 10.20944/preprints202410.0649.v1 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study combining simulated and intracranial depth electrode data |
|---|---|
| Population | Patients undergoing intracranial depth electrode recordings; simulated data from logistic maps and Multivariate Autoregressive models |
| Measures | Statistical Complexity (SC), Lempel-Ziv complexity (LZc), Approximate Entropy (ApEn), Kolmogorov Complexity (KC) |
| Key findings | The authors report that Statistical Complexity captures organized, predictive neural behavior through temporal correlations, whereas Lempel-Ziv complexity is more sensitive to randomness and pattern unpredictability. They state that states of higher consciousness were associated with greater complexity, which they contend supports the entropic brain hypothesis. |
Abstract
The human brain, a highly complex dynamical system, exhibits various states of consciousness—such as wakefulness, sleep, and altered states—each characterized by distinct patterns of neural activity. To capture the dynamical properties of these states, a range of complexity measures is utilized, with a primary focus on Statistical Complexity (SC) and Lempel-Ziv complexity (LZc), and supplemented by Approximate Entropy (ApEn) and Kolmogorov Complexity (KC). These measures are applied to both simulated data, generated through logistic maps and Multivariate Autoregressive (MVAR) models, and intracranial depth electrode recordings from patients. The results demonstrate that these complexity measures effectively capture intricate dynamics of the brain across different states. Specifically, SC captures the structural complexity and information processing within the system, reflecting organized and predictive neural behavior by accounting for temporal correlations in the data. In contrast, LZc is more sensitive to randomness, measuring the diversity and unpredictability of patterns within the data. This distinction allows SC to serve as a more reliable indicator of organized information processing, while LZc highlights the variability in neural signals. Notably, the study reveals that states of higher consciousness are associated with greater complexity, supporting the entropic brain hypothesis. This research contributes to the ongoing efforts to quantify consciousness through mathematical frameworks and offers insights into the neural correlates of different states of awareness.