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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要慢性疼痛是我们社会中一个非常普遍的问题，与严重的个人痛苦有关，尽管这种医疗产生了很大的负面影响，但每年仍会产生数十亿美元的费用。问题仍然是临床诊断，依赖于患者的疼痛强度报告和临床医生的身体状况慢性疼痛的可靠生物标志物的可用性，不依赖于临床诊断和疼痛报告，将增加我们对不同慢性疼痛病理生理学的主观理解通过向上级提供对患者进行分类和跟踪的工具来改善护理，并极大地帮助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影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特定方面的变化。因此，我们和其他人已经证明，细胞核内的活动和细胞核之间的功能连接伏隔核和内侧前额皮质，以及伏隔核和背外侧前额叶皮质体积的损失可在实验室中重现，并可能构成慢性腰背痛的客观神经特征然而，目前尚不清楚这种神经特征是否可以推广到其他肌肉骨骼。慢性疼痛，如关节炎和/或慢性神经性疼痛，如三叉神经痛和/或慢性疼痛诸如重度抑郁症或 CLBP 特有的情感状况此外，患者年龄和性别的影响。关于 CLBP 的神经特征，这是慢性疼痛临床经历的主要决定因素，仍然存在该应用程序的总体目标是确定这两个未知数。在目标 1 中，我们将通过测试其神经特征对肌肉骨骼 CLBP 的特异性骨关节炎 (KOA) 引起的慢性膝痛的预测准确性，骨关节炎是一种不同的肌肉骨骼疼痛三叉神经痛 (TN) 引起的慢性疼痛（一种神经性疼痛），以及主要症状抑郁症（MDD），这是一种慢性消极情感状态，为此，我们将使用机器。学习技术来训练预测模型并将 CLBP 患者与健康对照进行分类（即训练集）使用根据我们的工作和 CLBP 的可重复发现得出的先验定义的大脑特征接下来，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在目标 2 中，我们将研究年龄如何影响 CLBP 的预测能力。 CLBP 的神经特征并测试 CLBP 患者和健康人之间的群体差异有多稳健大脑中的控制特征构成了年轻人（18-30岁）和老年人（> 50岁）的神经特征在目标 3 中，我们将遵循类似的方法探索性别对 CLBP 神经特征的影响。类似于目标 2 中使用的方法，但使用基于性别的分组。