英文摘要 | Propofol has been widely used clinically as an intravenous anesthetics. But we still do not know the precise mechanism by which anesthetic agents affect the brain activity. The study of general anesthesia mechanism is hot in anesthesiology. Due to technical limitations, previous research was concentrated on molecular and cellular level and studied the effects of general anesthetics on the entire range of ion channels and neurotransmitters. These traditional methods are difficult to locate the exact target of general anesthetics. Over the past 20 years, the development of functional brain imaging techniques provided a new way to study the mechanisms of anesthesia. In the present study, we will apply methodologies of brain network and machine learning to explore the mechanism of propofol based on functional magnetic resonance imaging data from healthy volunteers. The main contributions of this thesis include following issues:
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Investigating the changes of the functional connectivity pattern of insular subdivisions induced by increasing doses of propofol. We parcellated the structurally and functionally complex insula into three subregions based on the data at awake state, and used these parcellated subregions as seeds for functional connectivity analysis. The dosal anterior insula (dAI) is more involved in high-level cognitive processes, the ventral anterior insula (vAI) is associated with affective processes and the posterior insula (PI) is associated with sensorimotor processes. In present work, we found the insular cognitive network and insular sensorimotor network were propofol-sensitive and progressively inhibited with propofol in a dose-dependent manner. However, the insular affective network under the influence of propofol was small.
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Observe the changes in default mode netwok (DMN) induced by increasing doses of propofol and progressively deepening sedation. Applying graph theoretical analysis, we provided detailed observation and comprehensive investigation of global and local changes (degree, clustering coefficient, shortest path length, global efficiency and local efficiency) of DMN during wakefulness, light sedation and deep sedation. We found that DMN is propofol-sensitive. A small dose of propofol can significantly inhibit the DMN, affecting the level of consciousness. As the dose was increased, the inhibitory effect was enhanced. Specially, propofol had a dose-dependent inhibitive effects on the anterior medial prefrontal cortex (aMPFC), right superior frontal gyrus, left inferior temporal gyrus and left lateral parietal lobe. Ventral medial prefrontal cortex (vMPFC) and parahippocampus (PHC) were less sensitive to propofol, and could be significantly inhibited by a higher concentration of propofol.
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Studying how propofol affects the whole-brain functional network. Applying graph theoretical analysis, we investigated functional connectivity and topological properties of whole-brain network during wakefulness and light sedation and deep sedation. We found no significant changes of the global network topology properties(degree, clustering coefficient, shortest path length, global efficiency and local efficiency). However, we found that hub distributions were drastically affected by propofol. In normal awake state, hubs were in frontal and parietal areas. As the dose was increased, hubs transferred from heteromodal association cortex in the anterior part of the brain to unimodal association and primary cortices in the posterior part. In addition, we observed systemic decreases in functional connectivity, particular inter-network connections. Most of the functional connectivity was hub-related. this study provides imaging evidence that propofol may suppress consciousness through reconfiguring hub structures and disrupting the functional integration.
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Identifying intrinsic connectivity patterns during propofol-induced loss of consciousness. In this study, we applied the support vector machine with forward-back search strategy (SVM-FoBa) algorithm to classify different states of consciousness (wakefulness, light sedation and deep sedation) using intrinsic network patterns. Besides, with the feature subset directly extracted from the original features, through model understanding (e.g., weight analysis) we can readily identify the informative features. Classification accuracy of wakefulness and deep sedation achieved 96.4% based on only 18 features (total 1128). Most of these informative features were functional connectivity within and between DMN, executive control network (ECN), and salience network (SN). The most discriminative brain regions were cerebellum, supplementary motor area (SMA) and inferior temporal gyrus. The findings suggest that these networks and regions may play an important role in the neurobiological basis of consciousness and propofol.
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