Year-end Sale % OFF 
Abstract
Chronic pain is a pathological developing course of pain. In clinic, an objective indicator is needed for diagnosing and better controlling chronic pain. The abnormal neural responses in chronic pain are reflected by multiple event-related potentials (ERPs) in time, frequency, and location domain, respectively. However, multiple changes in ERPs are not applicable in clinic. So, the principal feature covered the most informative changes extracted from these three domains of ERP during the development of chronic pain is needed. In the present study, a parallel factor analysis method was employed to extract time–frequency–channel features of laser-evoked potential (LEP) simultaneously from rats with chronic inflammatory pain. Results showed that the main feature of LEP in channel domain locates in the frontal brain region in rats with chronic inflammatory pain while in the parietal brain region in control rats. In the frequency domain, the main frequency of LEP was significantly higher in chronic inflammatory pain rats than that in control rats. These findings indicate that the frontal region with higher frequency response to nociceptive information is the principal feature in the chronic pain state. Our study provided not only a principal feature of LEP but also a promising strategy for chronic pain, which is potential for clinic application.
Highlights
Chronic pain is a pathological pain state characterized by pain persistence [1]
In the present study, in order to explore the principal feature of laser-evoked potential (LEP) during the development of chronic inflammatory pain in three domains, we recorded LEP obtained from the electrocorticogram (ECoG) of rats with chronic pain model and applied the parallel factor analysis (PARAFAC) method to decompose multichannel LEP data
In order to test the accuracy of PARAFAC method, the feature extracted by PARAFAC was compared with original LEP
Summary
Chronic pain is a pathological pain state characterized by pain persistence [1]. It is believed that chronic pain is not a simple condition of persistent pain perception, but a course of pain chronification that involves sensation, emotion, and cognition [2, 3]. The PCA method only describes the time and frequency characteristics within single recording area It cannot extract components for multichannel ERPs [14]. The parallel factor analysis (PARAFAC) is a method that could extract features in the time–frequency–channel domain simultaneously from original multichannel EEG data [15] It takes into account the frequency of oscillations in certain time periods among all the recording channels [16] and has been successfully applied to detect abnormal oscillatory activity in epilepsy and Alzheimer’s disease [17]. In the present study, in order to explore the principal feature of LEP during the development of chronic inflammatory pain in three domains, we recorded LEP obtained from the electrocorticogram (ECoG) of rats with chronic pain model and applied the PARAFAC method to decompose multichannel LEP data. Two-way analysis of variance was used to compare the frequency and time difference between the two groups
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
