Cortical complexity and connectivity during isoflurane-induced general anesthesia: a rat study.
basic_science · Level V
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- Record sourced from PubMed, PMID 35472693.
- Also identified by DOI 10.1088/1741-2552/ac6a7b.
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Abstract
<i>Objective.</i>The investigation of neurophysiologic mechanisms of anesthetic drug-induced loss of consciousness (LOC) by using the entropy, complexity, and information integration theories at the mesoscopic level has been a hot topic in recent years. However, systematic research is still lacking.<i>Approach.</i>We analyzed electrocorticography (ECoG) data recorded from nine rats during isoflurane-induced unconsciousness. To characterize the complexity and connectivity changes, we investigated ECoG power, symbolic dynamic-based entropy (i.e. permutation entropy (PE)), complexity (i.e. permutation Lempel-Ziv complexity (PLZC)), information integration (i.e. permutation cross mutual information (PCMI)), and PCMI-based cortical brain networks in the frontal, parietal, and occipital cortical regions.<i>Main results.</i>Firstly, LOC was accompanied by a raised power in the ECoG beta (12-30 Hz) but a decreased power in the high gamma (55-95 Hz) frequency band in all three brain regions. Secondly, PE and PLZC showed similar change trends in the lower frequency band (0.1-45 Hz), declining after LOC (<i>p</i>< 0.05) and increasing after recovery of consciousness (<i>p</i>< 0.001). Thirdly, intra-frontal and inter-frontal-parietal PCMI declined after LOC, in both lower (0.1-45 Hz) and higher frequency bands (55-95 Hz) (<i>p</i>< 0.001). Finally, the local network parameters of the nodal clustering coefficient and nodal efficiency in the frontal region decreased after LOC, in both the lower and higher frequency bands (<i>p</i>< 0.05). Moreover, global network parameters of the normalized average clustering coefficient and small world index increased slightly after LOC in the lower frequency band. However, this increase was not statistically significant.<i>Significance</i>. The PE, PLZC, PCMI and PCMI-based brain networks are effective metrics for qualifying the effects of isoflurane.
Medical subject headings
- Isoflurane