QQWeiboFeedback 疼痛相關(guān)高頻振蕩信號(hào): 進(jìn)展與展望 Alternative TitlePain-related gamma band oscillations: Progress and prospect 李鎮(zhèn)江1; 張立波2,3; 張會(huì)娟2,3; 岳路鵬2,3; 胡理2,3 First Author李鎮(zhèn)江 Correspondent Emailhuli@psych.ac.cn Contribution Rank2 Abstract

慢性疼痛不僅喪失了急性疼痛所具有的警示作用,還嚴(yán)重危害患者的身心健康,大幅增加家庭和社會(huì)的醫(yī)療成本.慢性疼痛的有效治療依賴于對(duì)疼痛的準(zhǔn)確評(píng)估.然而,臨床診斷中仍缺乏疼痛的客觀評(píng)估方法,大大增加了疼痛管理和鎮(zhèn)痛效果評(píng)估的難度.最近一系列電生理研究發(fā)現(xiàn),疼痛誘發(fā)的γ頻帶高頻振蕩信號(hào)和疼痛有密切關(guān)聯(lián),能在一定程度上表征疼痛強(qiáng)度,具體表現(xiàn)為:(1)在短時(shí)疼痛情境下,初級(jí)軀體感覺皮層γ振蕩可特異性地編碼疼痛的主觀強(qiáng)度和疼痛敏感性的個(gè)體差異,并能反映心理因素對(duì)疼痛的調(diào)節(jié)作用.(2)在長時(shí)疼痛情境下,前額葉γ振蕩與主觀疼痛評(píng)分顯著相關(guān).(3)在慢性疼痛情境下,前額區(qū)域γ振蕩能反映不同類型慢性疼痛患者的自發(fā)疼痛強(qiáng)度.然而,目前研究仍存在γ振蕩信噪比低、與其他神經(jīng)振蕩關(guān)系不明、產(chǎn)生機(jī)制復(fù)雜以及與疼痛的因果證據(jù)不足等問題.未來研究有必要采用更加敏感和標(biāo)準(zhǔn)化的分析方法,整合跨物種研究、神經(jīng)調(diào)節(jié)等技術(shù),以系統(tǒng)、全面地闡明γ振蕩在疼痛信息處理中的作用機(jī)制,進(jìn)而促進(jìn)疼痛評(píng)估標(biāo)準(zhǔn)的客觀化,幫助改善疼痛管理現(xiàn)狀.

Other Abstract

Pain is vital for survival as it signals bodily threats and promotes recovery from tissue damage. Chronic pain, however, is considered maladaptive since it provides no apparent protective or recuperative benefits. Currently, approximately 20% of adults around the world suffer from chronic pain, which seriously affects their quality of life. Besides, the extent of the chronic pain problem poses a significant economic burden for both patients and the whole society (in China, the annual cost of pain exceeds $100 billion). Unfortunately, most of the patients had not received adequate treatment for their pain. Therefore, effective treatment of chronic pain is highly needed, which necessitates reliable and valid pain assessment. Since pain a subjective first-person experience, self-report using a visual analog scale or a numeric rating scale is the gold standard to determine the intensity of pain in clinical practice. However, self-report of pain is well known to be easily contaminated with reporting biases, and could not be used in some vulnerable populations, e.g., infants and patients with disorders of consciousness. As a result, the lack of an accurate assessment of pain could lead to an inadequate or suboptimal treatment of pain in these vulnerable populations. To solve this issue, it would be necessary to explore the possibility of assessing pain objectively using neural indicators that could complement the self-report of pain. Recent studies showed that pain-induced gamma band oscillations are one of the most promising biomarkers of the perceived intensity of both stimulus-evoked and spontaneous pain across different populations. First, being elicited by nociceptive stimuli, gamma band oscillations in the primary somatosensory cortex encode subjective pain perception reliably and selectively: Reliably, because they consistently reflect pain at both within-subject and between-subject levels; selectively, because they always track the intensity of pain, even when saliency of nociceptive stimuli is modulated. Second, gamma band oscillations in the prefrontal cortex encode selectively subjective ratings of tonic pain. Third, gamma band oscillations in the prefrontal area are positively correlated with subjective ratings of ongoing pain in chronic pain patients. All these findings demonstrated that gamma band oscillations could be identified as an objective neural indicator of pain perception, which could be useful for the assessment of pain in the future. However, the neural origin and neural mechanisms of pain-induced gamma band oscillations are largely unknown due to a series of technical issues, e.g., the low signal-to-noise ratio of gamma band oscillations, the low spatial resolution of the sampling techniques to record gamma band oscillations, and the intrinsic limitation of correlation analysis in current studies (unable to reveal causal relationship). To achieve a better understanding of the functional significance of pain-induced gamma band oscillations, novel analytical strategies are required to process neural responses that are collected using advanced experimental techniques on different species. With these efforts, we will understand better the neural mechanisms of pain-induced gamma band oscillations, which will increase the accuracy of the diagnosis of pain and the prediction of treatment outcome in various clinical conditions. In addition, the development of approaches to modulate pain-induced gamma band oscillations, e.g., via transcranial alternating current stimulation coupled with neurofeedback, could provide a promising avenue for effective pain treatment in the future.

Keyword疼痛 疼痛評(píng)估 疼痛管理 高頻振蕩信號(hào) 2020 Language中文 DOI10.1360/TB-2019-0749 Source Publication科學(xué)通報(bào) ISSN0023-074X Volume65Issue:20Pages:2752-2762Subtype實(shí)證研究 Indexed ByCSCD ; 中文核心期刊要目總覽 Project Intro.

國家自然科學(xué)基金(31822025,31671141);; 中國科學(xué)院心理研究所科研啟動(dòng)項(xiàng)目(Y6CX021008)資助

CSCD IDCSCD:6818533 Citation statistics

Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]

Cited Times:3[CSCD]   [CSCD Record]

Document Type期刊論文 Identifierhttp://ir.psych.ac.cn/handle/311026/32559 Collection中國科學(xué)院心理健康重點(diǎn)實(shí)驗(yàn)室
Affiliation1.江西師范大學(xué)心理學(xué)院
2.中國科學(xué)院心理健康重點(diǎn)實(shí)驗(yàn)室(中國科學(xué)院心理研究所)
3.中國科學(xué)院大學(xué)心理學(xué)系
Recommended Citation
GB/T 7714 李鎮(zhèn)江,張立波,張會(huì)娟,等. 疼痛相關(guān)高頻振蕩信號(hào): 進(jìn)展與展望[J]. 科學(xué)通報(bào),2020,65(20):2752-2762. APA 李鎮(zhèn)江,張立波,張會(huì)娟,岳路鵬,&胡理.(2020).疼痛相關(guān)高頻振蕩信號(hào): 進(jìn)展與展望.科學(xué)通報(bào),65(20),2752-2762. MLA 李鎮(zhèn)江,et al."疼痛相關(guān)高頻振蕩信號(hào): 進(jìn)展與展望".科學(xué)通報(bào) 65.20(2020):2752-2762. Files in This Item: File Name/Size DocType Version Access License 疼痛相關(guān)高頻振蕩信號(hào)_ 進(jìn)展與展望.pd（2335KB）期刊論文出版稿限制開放CC BY-NC-SAApplication Full Text Related ServicesRecommend this itemBookmarkUsage statisticsExport to EndnoteGoogle ScholarSimilar articles in Google Scholar[李鎮(zhèn)江]'s Articles[張立波]'s Articles[張會(huì)娟]'s ArticlesBaidu academicSimilar articles in Baidu academic[李鎮(zhèn)江]'s Articles[張立波]'s Articles[張會(huì)娟]'s ArticlesBing ScholarSimilar articles in Bing Scholar[李鎮(zhèn)江]'s Articles[張立波]'s Articles[張會(huì)娟]'s ArticlesTerms of UseNo data!Social Bookmark/Share

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