| 其他摘要 | Pain, especially chronic pain, has become a public health problem that has been concerned by the whole society. It not only lead to somatic discomfort, but also adversely affect the health of humans in psychological, physical, and many other aspects, thus directly reducing the people's quality of life. Research on the mechanism of pain and analgesic strategy would not only help relieving patients from pain, but also help solving the serious, pain induced, social issues. However, at the current stage, the diagnosis and treatment of pain heavily rely on the subjective and inaccurate report of pain from patients. Since an objective and reliable measurement of pain is highly needed in various basic and clinical applications, several neuroimaging techniques have been adopted to reveal neurologic signature of pain perception and to predict pain based on nociceptive-evoked brain responses. This is becoming one of the worldwide hot topics of pain research.
Laser stimulators currently represent the most accurate tool for experimental studies of pain,6 as they activate Ad- and C nociceptors selectively and elicit sensations of "pure" pain without touch. For these reasons, laser stimulation is considered the gold standard to investigate pain psychophysically and electrophysiologically, and it has been used in hundreds of human and rat studies. However, our previous study demonstrated that the so-called "Aδ-LEPs", instead of reflecting the activation of the Aδ-nociceptive system, is actually consequent to the activation of the auditory system by laser-generated ultrasounds that can be detected by rats, but not by humans. This auditory response has been so far mistakenly interpreted as reflecting the A8-somatosensory input, thus undermining the conclusions of several previous investigations. As demonstrated in our previous study, recording LEPs in freely-moving rats with white noise masking is a valid model to improve the translation of animal results to human physiology and pathophysiology.
By using the optimized animal models, several further studies are performed in this paper. By using various methods of EEG signal processing, diverse features of LEPs are extracted from both humans and rats in different ways, and are trained in a support vector machine(SVM) model to predict the intensity of pain. Specifically, traditional EEG signal processing methods containing time domain analysis and time frequency analysis are performed to extract features from 58 human subjects in study 1 .The results shows that human A s -ERP responses in time domain and A δ –ERP responses and y oscillation in time frequency domain play an important role in predicting pain intensity. Furthermore, traditional EEG signal processing methods containing time domain analysis and time frequency analysis are performed to extract features from 12 rats in study 2. The result shows that rat C-ERP responses in time domain and C-ERP responses in time frequency domain and y oscillations in time frequency domain play an important role in predicting pain intensity. In study 3, several methods of complexity analysis and entropy analysis are used to extract features from 58 human subjects, and some of them are important in predicting pain intensity. In study 4, some of features extracted from 12 rats by using diverse methods of complexity analysis and entropy analysis, also make great contributions to predicting pain intensity in rats. In study 5, the result shows that the use of multi-dimension features can improve the prediction accuracy of pain in both humans and rats.
In summary, by using various methods of EEG signal processing, this study systematically investigated diverse features of LEPs extracted from both humans and rats, and the role of these features in predicting the intensity of pain. Several methods of complexity analysis and entropy analysis are provided to be important in predicting pain intensity, and they should be more widely used in future studies. |
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