Identification of the General Anesthesia Induced Loss of Consciousness by Cross Fuzzy Entropy-Based Brain Network

Abstract

Although the spatiotemporal complexity and network connectivity are clarified to be disrupted during the general anesthesia (GA) induced unconsciousness, it remains to be difficult to exactly monitor the fluctuation of consciousness clinically. In this study, to track the loss of consciousness (LOC) induced by GA, we first developed the multi-channel cross fuzzy entropy method to construct the time-varying networks, whose temporal fluctuations were then explored and quantitatively evaluated. Thereafter, an algorithm was further proposed to detect the time onset at which patients lost their consciousness. The results clarified during the resting state, relatively stable fuzzy fluctuations in multi-channel network architectures and properties were found; by contrast, during the LOC period, the disrupted frontal-occipital connectivity occurred at the early stage, while at the later stage, the inner-frontal connectivity was identified. When specifically exploring the early LOC stage, the uphill of the clustering coefficients and the downhill of the characteristic path length were found, which might help resolve the propofol-induced consciousness fluctuation in patients. Moreover, the developed detection algorithm was validated to have great capacity in exactly capturing the time point (in seconds) at which patients lost consciousness. The findings demonstrated that the time-varying cross-fuzzy networks help decode the GA and are of great significance for developing anesthesia depth monitoring technology clinically.

Frequency-dependent alterations in the default mode network during general anesthesia: a study in the rat

“…However, the effects of anesthesia on DMN architecture within the state itself is unclear. The time-series uctuations in FC potentially modulate the brain network 38,39 and re ects the effects of anesthesia 40 . Thus, we calculated the top 20% of connections based on fuzzy entropy to measure exibility in the time-series uctuations in FC across the different states (Fig.…”

“…Thereafter, the top 20% of network edges with the largest fuzzy entropy were used to identify the exible network architectures of the DMN. Information regarding fuzzy entropy has been described in the literature 40,72 .…”

The EEG complexity, information integration and brain network changes in minimally conscious state patients during general anesthesia

“…Many studies have confirmed that complexity and brain networks are effective indicators for evaluating the level of consciousness during GA in normal subjects [17][18][19]. Complexity indices and network indices are related but represent different aspects of a system.…”

Recovery of Brain Network Integration and Segregation During the Loss and Recovery of Consciousness Induced by Sevoflurane

“…Using intracortical recordings in macaque monkeys, Schroeder et al [12] showed that ketamine anesthesia inhibits communication among structurally linked cortical regions. By investigating functional connectivity and constructing time-varying networks, Li et al [13], [14] monitored the fluctuation of consciousness during anesthesia. Using network nodes (brain regions) and the FC between nodes, FNA provides a standard method to quantify and study functional integration and segregation of the brain.…”