PROFILING VULVODYNIA BASED ON NEUROBIOLOGICAL AND BEHAVIORAL ENDOPHENOTYPES

描述（由申请人提供）：外阴痛（VD）是一种慢性疼痛障碍，影响多达15%的女性，导致健康相关生活质量的严重损害。由于缺乏对其神经生物学基础的了解，这种疾病的治疗受到阻碍。该研究基于这样的假设：与其他持续性疼痛疾病一样，VD临床表型是由多种生物内表型组成的，并且可以确定有意义的亚群。在目前的提案中，我们计划使用功能和结构脑成像以及遗传，生理和生物学参数对大量VD患者样本进行广泛的表型分析。我们假设中枢机制（包括外部生殖器内感受信号处理/调节的改变）是临床表现的重要决定因素，这些大脑特征的差异可能在治疗反应性中发挥重要作用。这种表型对未来的药物开发具有相当大的意义。我们建议通过完成三个具体目标来检验这一假设。目的1将描述VD的多模态脑结构和连通性指数的变化。这将通过应用复杂的网络分析和机器学习算法来比较VD患者的静息状态[RS]功能和结构（灰质和白质）脑成像，与200名年龄匹配的女性健康对照（HC）， 200名肠易激综合征（IBS）患者和100名间质性膀胱炎/膀胱痛综合征患者进行比较，这些患者可从加州大学洛杉矶分校的大型脑扫描库中获得。目的2将表征VD的连通性指数，并确定结构（灰质和白质）和RS改变与临床、行为和遗传参数之间的关系。这将通过将Aim 1中确定的结构和RS功能异常与相关参数相关联来实现，包括：临床（症状严重程度、疾病持续时间、共病疼痛或精神诊断）、行为（压力疼痛阈值）和生物学（属于与下丘脑-垂体-肾上腺[HPA]轴功能、疼痛、炎症、儿茶酚胺和血清素信号系统相关的基因簇的候选基因多态性）。目标3将通过应用先进的数学分类技术对脑、生物、行为和临床内表型进行分类，根据内表型集群识别VD患者亚组。这将通过使用无监督机器学习算法结合成像和其他表型数据来完成，并将产生VD的不同机制亚群。

DESCRIPTION (provided by applicant): Vulvodynia (VD) is a chronic pain disorder affecting up to 15% of women and resulting in substantial impairment in health-related quality-of-life. The treatment of the disorder is hampered by a lack of knowledge regarding its neurobiological basis. The proposed study is based on the general hypothesis that like other persistent pain conditions, VD clinical phenotypes are composed of multiple biological endophenotypes, and that meaningful subgroups can be identified. In the current proposal, we plan to extensively phenotype a large sample of VD patients using functional and structural brain imaging together with genetic, physiological, and biological parameters. We hypothesize that central mechanisms (including alterations in the processing/modulation of interoceptive signals from the external genitals) are important determinants of the clinical presentation, and that differences in these brain signatures could play an important role in treatment responsiveness. Such phenotyping has considerable implications for future drug development. We propose to test this hypothesis by accomplishing three specific aims. Aim 1 will characterize alterations in multimodal structural brain and connectivity indices in VD. This will be accomplished by applying complex network analysis and machine learning algorithms to compare resting state [RS] functional and structural (grey and white matter) brain imaging in VD patients to 200 age-matched female healthy controls (HC), 200 patients with irritable bowel syndrome (IBS) and 100 patients with interstitial cystitis/painful bladder syndrome which are available from a large brain scan repository at UCLA. Aim 2 will characterize the connectivity indices in VD and identify the association between structural (grey and white matter) and RS alterations with clinical, behavioral and genetic parameters. This will be accomplished by associating structural and RS functional abnormalities identified in Aim 1 with relevant parameters including: clinical (symptom severity, disease duration, co-morbid pain or psychiatric diagnosis), behavioral (pressure pain thresholds), and biological (candidate gene polymorphisms belonging to clusters of genes related to hypothalamic-pituitary-adrenal [HPA] axis function, pain, inflammatory, catecholamine, and serotonin signaling systems). Aim 3 will identify VD patient subgroups based on endophenotype clusters by applying advanced mathematical classification techniques to brain, biological, behavioral and clinical endophenotypes. This will be accomplished by combining imaging and other phenotyping data using unsupervised machine learning algorithms and will yield distinct mechanistic subgroups of VD.