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

项目总结 慢性疼痛在我们的社会中是一个非常普遍的问题，与重大的个人痛苦有关， 和残疾，每年产生数十亿美元的成本。尽管它带来了极大的负面影响，但这一医疗 这个问题仍然是一个临床诊断，取决于患者对疼痛强度的报告和临床医生的身体状况 考试。慢性疼痛的可靠生物标志物的可用性，这些生物标志物不依赖于临床诊断和 主观的疼痛报告，将增加我们对不同慢性疼痛的病理生理学的了解 条件，通过为临床医生提供分类和跟踪患者的工具来改善护理，并在 开发新型止痛药的临床试验，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显示出可重现的大脑信号 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。AS 这样，我们和其他人已经表明，核内活动和核之间的功能连接发生了变化 伏隔核和内侧前额叶皮质，伏隔核和背外侧前额叶皮质体积的丧失 可在实验室中重现，并可能构成慢性腰背痛的客观神经标志- 疼痛。然而，目前尚不清楚这种神经特征是否适用于其他肌肉骨骼。 慢性疼痛，如关节炎和/或慢性神经病理性疼痛，如三叉神经痛和/或慢性 情感状况，如严重的抑郁症或CLBP特有的。此外，患者的年龄和性别的影响 关于CLBP的神经特征，这是慢性疼痛临床经验的主要决定因素，仍然存在 未知。本应用程序的总体目标是确定这两个未知数。 在目标1中，我们将通过测试其对肌肉骨骼CLBP的特异性来测试神经信号的特异性 骨关节炎(KOA)是一种不同于肌肉骨骼的疼痛，对慢性膝关节疼痛的预测准确性 情况，在三叉神经痛(TN)的慢性疼痛中，这是一种神经病理性疼痛情况，以及在主要 抑郁障碍(MDD)，这是一种慢性负面情绪状态。为此，我们将使用机器 训练预测模型并将CLBP患者与健康对照进行分类的学习技术(即，训练 SET)使用从基于我们的工作和CLBP的可重现发现中提取的先验定义的大脑特征 神经影像文献。接下来，将在CLBP患者和健康人的新样本上验证该模型 对照组(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概括性 或情感条件或对CLBP的特异性。在目标2中，我们将研究年龄如何影响预测能力 CLBP的神经特征及其在CLBP患者和健康人群中的差异 大脑中的控制特征构成了年轻人(18-30岁)和老年人(&gt；50)的神经特征 年龄段)。在目标3中，我们将探索性别对CLBP神经特征的影响，遵循类似的 方法与目标2中使用的方法相同，但使用基于性别的分组。

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.