A complex system is often associated with emergence of new phenomena from the interactions between the system’s components. General anesthesia reduces brain complexity and so inhibits the emergence of consciousness. An understanding of complexity is necessary for the interpretation of brain monitoring algorithms. Complexity indices capture the “difficulty” of understanding brain activity over time and/or space. Complexity–entropy plots reveal the types of complexity indices and their balance of randomness and structure. Lempel–Ziv complexity is a common index of temporal complexity for single-channel electroencephalogram containing both power spectral and nonlinear effects, revealed by phase-randomized surrogate data. Computing spatial complexities involves forming a connectivity matrix and calculating the complexity of connectivity patterns. Spatiotemporal complexity can be estimated in multiple ways including temporal or spatial concatenation, estimation of state switching, or integrated information. This article illustrates the concept and application of various complexities by providing working examples; a website with interactive demonstrations has also been created.
Brain Complexities and Anesthesia: Their Meaning and Measurement
This article is featured in “This Month in Anesthesiology,” page A1.
Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are available in both the HTML and PDF versions of this article. Links to the digital files are provided in the HTML text of this article on the Journal’s Web site (www.anesthesiology.org).
Submitted for publication January 3, 2022. Accepted for publication June 2, 2022. Published online first on August 4, 2022.
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Duan Li, Marco S. Fabus, Jamie W. Sleigh; Brain Complexities and Anesthesia: Their Meaning and Measurement. Anesthesiology 2022; 137:290–302 doi: https://doi.org/10.1097/ALN.0000000000004293
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