Identifying neural pathophysiology in juvenile fibromyalgia

摘要 青少年起病纤维肌痛(JFM)是一种使人衰弱的慢性疼痛状况，主要影响青少年， 女性，在大脑发育的关键时期，大多数人都会持续到成年期 病人。由于缺乏明确的物理或实验室结果，JFM作为一种临床应用受到了质疑。 实体，有时被认为仅仅是焦虑或抑郁的表现。这会导致贫穷 了解、污名化和适当的疾病管理，强调有必要确定 客观病理生理学。我们之前曾将机器学习应用于fMRI数据，以产生 分布式脑活动的多变量模式，加在一起可以将测试对象识别为成年FM患者 与健康成年人进行交叉验证，准确率高(93%)。然而，推算成人调频大脑 JFM的异常是有问题的，因为影响青少年大脑发育的因素很多，而且 成人和青少年形式的疾病的临床差异。这项提案的目标是确定 JFM在量身定制症状激发任务中的脑病理生理学特征。目前有一个 完全缺乏对患有广泛性疼痛/JFM的儿童的大脑与疼痛相关的研究。这项研究将 为了解JFM的神经生理学基础的一系列研究奠定基础， 发现JFM的脑病理生理学与成人FM是否不同，并评估治疗效果 脑部病理生理学的特异性标记物。这项研究是NIH/NIAMS资助的试验的R01辅助研究 (R01 AR070474；Kashikar-Zuck)，“纤维肌痛综合训练的多部位随机临床试验 青少年纤维肌痛(适合青少年)“。来自美国的JFM患者的队列特征非常好 父母试验为研究JFM神经相关性提供了一个独特的机会。我们时间紧迫的研究将 通过确定疼痛的神经生理学关联来转化母项目的科学成果， JFM这一具有广泛代表性的大样本的心理和身体症状 治疗前和治疗后的患者。我们假设机器学习在定制过程中应用于fMRI数据 症状激发任务将识别预测JFM状态(与健康状态)的神经活动模式， 将与JFM症状维度(疼痛、非痛性感觉过敏、疲劳和 抑郁症状)。这项辅助研究将利用心理和生理上的综合 已在父母R01试验中捕获的功能配置文件，以确定具有临床意义的神经学 JFM中的措施，并探索这些措施随治疗而改变的可能性。这条研究路线 有可能对理解和识别JFM病理生理产生深远影响，并提供 神经生理知情的治疗建议。

ABSTRACT Juvenile-onset fibromyalgia (JFM) is a debilitating, chronic pain condition affecting adolescents, primarily females, during a critical period for brain development, and that persists into adulthood for the majority of patients. Due to the lack of definite physical or laboratory findings, JFM has been questioned as a clinical entity, and sometimes regarded as merely an expression of anxiety or depression. This leads to poor understanding, stigmatization, and appropriate disease management, underscoring the need for identifying objective pathophysiology. We have previously used machine learning applied to fMRI data to yield multivariate patterns of distributed brain activity that, together, can identify test subjects as adult FM patients vs. healthy adults with high cross-validated accuracy (93%). However, extrapolating adult FM brain abnormalities to JFM is problematic, given the many factors impacting the developing adolescent brain and the clinical differences between adult and juvenile forms of the disease. The goal of this proposal is to identify brain pathophysiology characteristic of JFM during tailored symptom provocation tasks. There is currently a complete lack of research into the brain correlates of pain in children with widespread pain/JFM. This study will lay the foundation for a line of research in understanding the neurophysiologic underpinnings of JFM, discovering whether brain pathophysiology in JFM differs from adult FM, and assessing treatment effects on specific markers of brain pathophysiology. This study is an R01 ancillary study to the NIH/NIAMS-funded trial (R01 AR070474; Kashikar-Zuck), “Multi-site randomized clinical trial of Fibromyalgia Integrative Training for Juvenile Fibromyalgia (FIT Teens)”. The exceptionally well characterized cohort of JFM patients from the parent trial presents a unique opportunity to study JFM neural correlates. Our time-sensitive study will transform the scientific output of the parent project by identifying neurophysiological correlates of pain, psychological and physical symptoms in this large, representative, extensively-characterized sample of JFM patients before and after treatment. We hypothesize that machine learning applied to fMRI data during tailored symptom-provocation tasks will identify patterns of neural activity predictive of JFM status (vs. healthy), which will correlate with JFM symptom dimensions (pain, non-painful sensory hypersensitivity, fatigue, and depressive symptoms). This ancillary study will utilize the comprehensive psychological and physical functioning profiles already being captured in the parent R01 trial to identify clinically meaningful neurologic measures in JFM and explore the potential for these measures to change with treatment. This line of research has the potential for a profound impact on understanding and identifying JFM pathophysiology and providing neuro-physiologically informed treatment recommendations.