Brain Mechanisms of Chronic Low-Back Pain: Specificity and Effects of Aging and Sex

慢性腰痛的脑机制：衰老和性别的特异性和影响

• 批准号: 10657958
• 负责人: Paul Geha
• 金额: $ 40.79万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10657958
• 关键词: Address; Affect; Affective; Age; Aging; Agreement; Analgesics; Arthritis; Back Pain; Biological Markers; Brain; Brain Pathology; Brain imaging; Caring; Chronic; Chronic low back pain; Classification; Clinical; Clinical Trials; Corpus striatum structure; Data; Decision Making; Diagnosis; Diagnostic; Emotional; Exhibits; Expert Opinion; Female; Frequencies; Functional Magnetic Resonance Imaging; Functional disorder; Grouping; Knee; Knee Osteoarthritis; Laboratories; Learning; Limbic System; Literature; Low Back Pain; Machine Learning; Major Depressive Disorder; Measurement; Measures; Medial; Mediating; Medical; Memory; Modeling; Moods; Musculoskeletal; Musculoskeletal Pain; Nature; Nucleus Accumbens; Pain; Pain Research; Pain intensity; Pathology; Patients; Population; Prefrontal Cortex; Prognosis; Reporting; Reproducibility; Reproducibility of Results; Sampling; Sensitivity and Specificity; Societies; Specificity; Structure; Substance Use Disorder; Syndrome; Techniques; Testing; Thick; Training; Trigeminal Neuralgia; Work; age effect; age group; aged; arthritic pain; chronic musculoskeletal pain; chronic neuropathic pain; chronic pain; chronic pain patient; chronic painful condition; clinical care; clinical diagnosis; cost; design; disability; executive function; experience; improved; inter-individual variation; knee pain; male; negative affect; neural; neuroimaging; novel; older patient; osteoarthritis pain; pain patient; painful neuropathy; predictive modeling; sex; tool

PROJECT SUMMARY Chronic pain is a highly prevalent problem in our society, is associated with significant personal suffering, and disability, and incurs billions of dollars in cost annually. Despite its highly negative impact this medical problem remains a clinical diagnosis relying on patients’ reports of pain intensity and the clinician’s physical exam. The availability of reliable biomarkers for chronic pain, which do not rely on clinical diagnosis and subjective pain reports, will increase our understanding of the pathophysiology of different chronic pain conditions, improve care by providing clinicians with tools to classify and follow patients, and help greatly in clinical trials developing novel analgesics where subjective pain reports remain the primary tool. Neuroimaging results from our lab as well as others are starting to reveal reproducible brain signatures of chronic pain involving changes in specific aspects of structural and functional cortico-striatal plasticity. As such, we and others have shown that altered activity in, and functional connectivity between the nucleus accumbens and medial prefrontal cortex, and loss of accumbens and dorso-lateral prefrontal cortex volume are reproducible across laboratories and can potentially constitute an objective neural signature of chronic low back- pain. However, it remains unknown whether this neural signature is generalizable to other musculoskeletal chronic pain conditions like arthritis and/or chronic neuropathic pain like trigeminal neuralgia and/or chronic affective conditions like major depression or is specific to CLBP. In addition, the effects of patients’ age and sex on the neural signature of CLBP, which are major determinants of the clinical experience of chronic pain, remain unknown. The overall aim of this application is to determine these two unknowns. In Aim 1 we will test the specificity of the neural signature to musculoskeletal CLBP by testing its predictive accuracy in chronic knee pain from osteoarthritis (KOA), which is a different musculoskeletal pain condition, in chronic pain from trigeminal neuralgia (TN), which is a neuropathic pain condition, and in major depressive disorder (MDD), which is a chronic negative affective condition. To that end, we will use machine learning techniques to train a predictive model and classify CLBP patients from healthy controls (i.e., training set) using a priori defined brain features drawn from reproducible findings based on our work and on the CLBP neuroimaging literature. Next, this model will be validated on a new sample of CLBP patients and healthy controls (i.e., test set) and tested in KOA, TN, and MDD patients to assess generalizability to other chronic pain or affective conditions or specificity to CLBP. In Aim 2 we will study how age affects the predictive power of the neural signature of CLBP and test how robust are the group differences between CLBP patients and healthy controls in the brain features making up the neural signature across young (18-30 years old) and older (> 50 years) age groups. In Aim 3 we will explore the effects of sex on the neural signature of CLBP following a similar approach to the one used in Aim 2 but using instead a grouping based on sex.

项目摘要 慢性疼痛是我们社会中一个非常普遍的问题，与重大的个人痛苦有关， 和残疾，每年花费数十亿美元。尽管这一医疗事故造成了严重的负面影响， 问题仍然是临床诊断依赖于患者对疼痛强度的报告和临床医生的身体检查。 考试可靠的慢性疼痛生物标志物的可用性，不依赖于临床诊断， 主观疼痛报告，将增加我们对不同慢性疼痛的病理生理学的理解 条件，通过为临床医生提供分类和随访患者的工具来改善护理， 开发新型镇痛药的临床试验，其中主观疼痛报告仍然是主要工具。 我们实验室和其他实验室的神经成像结果开始揭示可重复的大脑特征 慢性疼痛涉及结构和功能皮质-纹状体可塑性的特定方面的变化。作为 因此，我们和其他人已经表明，改变的活动和功能之间的连接， 额叶皮层和内侧前额叶皮层的体积，以及额叶皮层和背外侧前额叶皮层体积的损失， 在实验室中可重复，并可能构成慢性下背部的客观神经特征， 痛苦然而，目前尚不清楚这种神经信号是否可推广到其他肌肉骨骼系统。 慢性疼痛病症，如关节炎和/或慢性神经性疼痛，如三叉神经痛和/或慢性神经性疼痛， 情感状况如重度抑郁症或特定于CLBP。此外，患者的年龄和性别 CLBP的神经信号，这是慢性疼痛的临床经验的主要决定因素，仍然存在 未知本申请的总体目标是确定这两个未知数。 在目标1中，我们将通过测试神经信号对肌肉骨骼CLBP的特异性， 骨关节炎（KOA）慢性膝关节疼痛的预测准确性，这是一种不同的肌肉骨骼疼痛 条件，在慢性疼痛三叉神经痛（TN），这是一种神经性疼痛的条件，并在主要 抑郁症（MDD），这是一种慢性负性情感状况。为此，我们将使用机器 学习技术以训练预测模型并将CLBP患者与健康对照分类（即，培训 集）使用先验定义的大脑特征，这些特征来自基于我们的工作和CLBP的可重复发现 神经影像学文献接下来，该模型将在CLBP患者和健康人的新样本上进行验证。 对照（即，测试集），并在KOA、TN和MDD患者中进行测试，以评估对其他慢性疼痛的普遍性 或情感状况或对CLBP的特异性。在目标2中，我们将研究年龄如何影响 CLBP的神经特征，并测试CLBP患者和健康人之间的组差异有多稳健 在年轻人（18 - 30岁）和老年人（> 50岁）中， 年龄组。在目标3中，我们将探索性别对CLBP神经特征的影响， 目标2采用的方法，但采用的是基于性别的分组。