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极为普遍的问题，与重大的个人痛苦有关， 和残疾，每年都会产生数十亿美元的成本。尽管它具有很大的负面影响 问题仍然是临床诊断，依赖于患者的疼痛强度的报告和临床的身体 考试。可靠的生物标志物可用于慢性疼痛，不依赖临床诊断和 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对患者进行分类和关注的工具，改善护理 临床试验开发了新型镇痛药，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影像学结果开始揭示复制品脑签名 慢性疼痛，涉及结构和功能性皮质 - 纹状体可塑性特定方面的变化。作为 这样，我们和其他人表明，活性改变了，核之间的功能连通性 伏尊和中位前额叶皮层，伏伏及其副外皮层的损失是 在实验室之间可重现，并且可能构成慢性低背部 - 疼痛。但是，尚不清楚该神经特征是否可以推广到其他肌肉骨骼 慢性疼痛状况如关节炎和/或慢性神经性疼痛，例如三叉神经痛和/或慢性 情感状况，例如重度抑郁症或特定于CLBP。此外，患者年龄和性别的影响 关于CLBP的神经签名，这是慢性疼痛的临床经验的主要决定者 未知。该应用程序的总体目的是确定这两个未知数。 在AIM 1中，我们将通过测试神经特异性对肌肉骨骼CLBP的特异性 骨关节炎（KOA）的慢性膝盖疼痛的预测准确性，这是不同的肌肉骨骼疼痛 条件，在三叉神经瘤（TN）的慢性疼痛中，这是一种神经性疼痛状况，在主要疼痛中 抑郁症（MDD），这是一种慢性阴性情感状况。为此，我们将使用机器 学习技术来训练预测模型并对健康对照组进行CLBP患者分类（即训练 设置）使用根据我们的工作和CLBP从可再现发现中绘制的先验定义的大脑特征 神经影像学文献。接下来，该模型将在新的CLBP患者和健康样本中进行验证 对照（即测试集）并在KOA，TN和MDD患者中进行了测试，以评估其他慢性疼痛的概括性 或对CLBP的情感条件或特异性。在AIM 2中，我们将研究年龄如何影响 CLBP的神经特征并测试CLBP患者和健康的组差异 大脑中的控件特征构成了年轻人（18-30岁）及以上的神经特征（> 50 年龄组。在AIM 3中，我们将探索性别对CLBP神经特征的影响 AIM 2中使用的方法，而是根据性别使用分组。