Psychological and neurological predictors of acupuncture effect in patients with chronic pain: a randomized controlled neuroimaging trial

针灸科 医学 神经影像学 物理疗法 随机对照试验 物理医学与康复 楔前 扣带回前部 功能磁共振成像 认知 精神科 内科学 替代医学 病理 放射科
作者
Xu Wang,Jinling Li,Xiao‐Ya Wei,Guang‐Xia Shi,Na Zhang,Jian‐Feng Tu,Chao-Qun Yan,Yanan Zhang,Yueying Hong,Jing‐Wen Yang,Yu Wang,Cun‐Zhi Liu
出处
期刊:Pain [Lippincott Williams & Wilkins]
卷期号:164 (7): 1578-1592 被引量:10
标识
DOI:10.1097/j.pain.0000000000002859
摘要

Abstract Chronic pain has been one of the leading causes of disability. Acupuncture is globally used in chronic pain management. However, the efficacy of acupuncture treatment varies across patients. Identifying individual factors and developing approaches that predict medical benefits may promise important scientific and clinical applications. Here, we investigated the psychological and neurological factors collected before treatment that would determine acupuncture efficacy in knee osteoarthritis. In this neuroimaging-based randomized controlled trial, 52 patients completed a baseline assessment, 4-week acupuncture or sham-acupuncture treatment, and an assessment after treatment. The patients, magnetic resonance imaging operators, and outcome evaluators were blinded to treatment group assignment. First, we found that patients receiving acupuncture treatment showed larger pain intensity improvements compared with patients in the sham-acupuncture arm. Second, positive expectation, extraversion, and emotional attention were correlated with the magnitude of clinical improvements in the acupuncture group. Third, the identified neurological metrics encompassed striatal volumes, posterior cingulate cortex (PCC) cortical thickness, PCC/precuneus fractional amplitude of low-frequency fluctuation (fALFF), striatal fALFF, and graph-based small-worldness of the default mode network and striatum. Specifically, functional metrics predisposing patients to acupuncture improvement changed as a consequence of acupuncture treatment, whereas structural metrics remained stable. Furthermore, support vector machine models applied to the questionnaire and brain features could jointly predict acupuncture improvement with an accuracy of 81.48%. Besides, the correlations and models were not significant in the sham-acupuncture group. These results demonstrate the specific psychological, brain functional, and structural predictors of acupuncture improvement and may offer opportunities to aid clinical practices.
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