Research on pain prediction based on functional responses and personality traits
Pain is an unpleasant sensory and emotional experience associated with actual or potential tissue damage. It does not only lead to somatic discomfort, but also adversely affect the health of humans in mental, psychological, physical, and many other aspects, thus directly reducing the quality of human life. Pain is one of the most common clinical symptoms, and has been recognized as a worldwide healthcare problem. 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. Jointly applying Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) techniques, we aim to provide an objective and reliable assessment of pain perception by developing effective pain prediction models and advanced pattern recognition algorithms. The pain prediction models and pattern recognition algorithms were achieved by utilizing (1) neural responses to transient and tonic nociceptive stimuli, (2) features of resting-state cortical activities, (3) other pain related physiological features, e.g., body temperature, blood pressure, and electromyographical signal, and (4) personality characteristics associated with subjects’ pain sensitivity. The proposed approach to objectively and reliably assess pain perception can not only be applied in analgesic related studies, but also in clinical diagnosis to assess the intensity of pain perception and to evaluate the efficacy of pain management, thus helping relieving patients from pain.