Brain Structural Biomarkers of Risk and Resilience to Pain Chronification

项目摘要 慢性疼痛非常普遍，对个人和社会来说代价高昂。一旦慢性疼痛就很难治疗 或反过来使早期识别成为预防和早期治疗的关键，以改善长期结果。 我们实验室和其他实验室最近的脑成像数据提供了证据，表明大脑的结构特性 边缘系统的大脑，如体积和/或形状的核丘脑（NAc），杏仁核，海马 可以预测亚急性腰痛（SBP）发作后腰痛“慢性化”的可能性 （为期6-12周）。大脑边缘系统在预测慢性疼痛的脆弱性或恢复力方面的作用符合 一般认为慢性疼痛部分是由于外周伤害性输入之间的相互作用 和脆弱的边缘脑回路，这是已知的调解负面情感和厌恶学习 与慢性疼痛有关。因此，了解大脑边缘系统回路的结构特性 预测人类慢性疼痛的风险或恢复力是创新的，具有高度的转化意义。 然而，向慢性疼痛转变的脑成像研究在数量上仍然非常有限， 因此，我们仍然对确切的边缘系统回路和结构连接性缺乏了解 慢性疼痛的潜在风险或恢复力。迄今为止，只有一项小型研究报告说，当地的白色物质 可以预测对疼痛慢性化的恢复力。此外，没有关于疼痛慢性化的脑成像研究， 研究了重要的生物学变量的作用，如性别或已建立的临床表型，如 或不存在下背痛的神经性成分。因此，本提案的总体目标是 为了使用现有技术的结构映射技术来研究局部解剖特性（例如体积，形状， 神经突方向）和白色物质连接性来鉴定疼痛慢性化的生物标志物。我们将使用2 不同的方法，一种基于生物学上合理的假设，一种基于数据驱动的假设 在我们的初步数据中。此外，建议将探讨性与临床腰痛的效果 这些生物标志物上的疼痛表型。我们将招募SBP患者，并获得脑解剖和多壳 基线和6个月随访时的弥散加权成像数据。患者将被归类为已恢复 或在随访后持续存在。目标1将测试基线局部和全局灰色、白色物质差异 恢复期和持续期患者的大脑边缘系统区域，如NAc，杏仁核，海马体， 前扣带皮层，和已知在慢性疼痛中起作用的白色物质通路（例如，扣带 束和内囊），并评估这些措施是否在随访时发生变化。目标2将使用数据- 驱动的方法，以检查全球和当地的灰色，白色物质的属性，预测风险或对慢性 痛苦最后，目的3将探讨性别和有无神经根病症状对 目的1和2中鉴定的生物标志物。

PROJECT SUMMARY Chronic pain is highly prevalent and costly to individuals and society. Once chronic, pain is very hard to treat or reverse making early identification key for prevention and early treatment to improve long-term outcomes. Recent brain imaging data from our lab as well as others have provided evidence that the structural properties of the limbic brain, such as the volume and/or shape of nucleus accumbens (NAc), amygdala, and hippocampus can predict the likelihood of “chronification” of low-back pain after an episode of sub-acute low back pain (SBP) (duration 6-12 weeks). The role of the limbic brain in predicting vulnerability or resilience to chronic pain fits with the general understanding that chronic pain is partly due to an interaction between peripheral nociceptive input and vulnerable limbic brain circuitries, which are known to mediate the negative affective and aversive learning associated with chronic pain. Hence, understanding how the structural properties of the limbic brain circuitries predict risk or resilience to chronic pain in humans is innovative and has high translational significance. However, the brain imaging studies of the transition to chronic pain remain very limited in number and scope and, hence, we still have a poor understanding of the exact limbic circuitry and structural connectivity underlying risk of or resilience to chronic pain. To date, only one small study reported that local white matter properties can predict resilience to pain chronification. Furthermore, no brain imaging study of pain chronification investigated the role of important biological variables like sex or established clinical phenotypes like the presence or absence of a neuropathic component of the low back pain. Therefore, the overarching aim of this proposal is to use state of the art structural mapping techniques to study local anatomical properties (e.g. volume, shape, neurite orientation) and white matter connectivity to identify biomarkers for pain chronification. We will use 2 different approaches, one based on biologically plausible hypotheses, and one based on data driven hypotheses identified in our preliminary data. In addition, the proposal will explore the effect of sex and clinical low-back pain phenotypes on theses biomarkers. We will recruit SBP patients and obtain brain anatomical and multi-shell diffusion weighted imaging data at baseline and at 6 months follow-up. Patients will be classified as recovered or persistent after the follow-up visit. Aim 1 will test baseline local and global gray, white matter differences between recovered and persistent patients in areas of the limbic brains like NAc, amygdala, hippocampus, anterior cingulate cortex, and white matter pathways known for their role in chronic pain (e.g., the cingulum bundle and internal capsule) and assess whether these measures change at follow-up. Aim 2 will use a data- driven approach to examine global and local gray, white matter properties predicting risk or resilience to chronic pain. Finally, aim 3 will explore the effect of sex and presence or absence of radiculopathy symptoms on the biomarkers identified in Aims 1 and 2.