PROFILING VULVODYNIA BASED ON NEUROBIOLOGICAL AND BEHAVIORAL ENDOPHENOTYPES

描述（由申请人提供）：外阴痛（VD）是一种慢性疼痛疾病，影响高达15%的女性，并导致健康相关生活质量的实质性损害。由于缺乏关于其神经生物学基础的知识，这种疾病的治疗受到阻碍。拟定的研究基于以下一般假设：与其他持续性疼痛疾病一样，VD临床表型由多种生物学内表型组成，并且可以确定有意义的亚组。在目前的提案中，我们计划广泛表型的VD患者的大样本使用功能和结构的脑成像与遗传，生理和生物参数。我们假设中枢机制（包括外生殖器内感受信号的处理/调节的改变）是临床表现的重要决定因素，并且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影响。我们建议通过实现三个具体目标来检验这一假设。目的1将表征VD中多模态结构脑和连接指数的改变。这将通过应用复杂网络分析和机器学习算法来比较静息状态[RS]功能和结构来实现（灰质和白色物质）脑成像，200例肠易激综合征（IBS）患者和100例间质性膀胱炎患者/疼痛性膀胱综合征，可从加州大学洛杉矶分校的大型脑扫描库获得。目的2将描述VD的连接性指数，并确定结构（灰质和白色物质）和RS改变与临床、行为和遗传参数之间的关联。这将通过将目标1中识别的结构和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、共病疼痛或精神病诊断）、行为（压力疼痛阈值）和生物学（候选基因多态性属于与下丘脑-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）。目的3将通过将先进的数学分类技术应用于脑、生物、行为和临床内表型，基于内表型聚类来识别VD患者亚组。这将通过使用无监督机器学习算法组合成像和其他表型数据来实现，并将产生VD的不同机制亚组。

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.