PROFILING VULVODYNIA BASED ON NEUROBIOLOGICAL AND BEHAVIORAL ENDOPHENOTYPES

基于神经生物学和行为内表型的外阴痛分析

基本信息

批准号：
8850714
负责人：
JENNIFER S LABUS
金额：
$ 35.88万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-01 至 2016-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8850714
关键词：
Affect Age Algorithms Arousal Behavioral Behavioral Genetics Biological Biopsy Brain Brain imaging Brain scan Candidate Disease Gene Catecholamines Chronic Classification Clinical Complex Data Development Diagnostic Disease Drug Targeting Emotional Female Functional disorder Future Gene Cluster Genetic Genetic Polymorphism Genital system Gossypium Histology Image Impairment Interstitial Cystitis Irritable Bowel Syndrome Knowledge Location Machine Learning Measures Medical Genetics Neurobiology Pain Pain Disorder Pain Threshold Pathway Analysis Patients Perception Peripheral Persistent pain Pharmaceutical Preparations Phenotype Physiological Play Population Control Process Psychiatric Diagnosis Research Rest Role Sampling Serotonin Severities Signal Transduction Structure Subgroup Swab Symptoms System Techniques Terminology Testing Therapeutic Tissues Validation Vestibulodynia Visual Vulva Vulvodynia Woman allodynia base central pain chronic pain clinical phenotype disorder control drug development effective therapy endophenotype functional disability gray matter health related quality of life hypothalamic-pituitary-adrenal axis illness length improved indexing inflammatory pain novel patient population pressure public health relevance repository response white matter

项目摘要

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.

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％的妇女，并导致与健康相关的生活质量造成重大损害。由于缺乏有关其神经生物学基础的知识，该疾病的治疗受到阻碍。拟议的研究基于以下一般假设：与其他持续性疼痛条件一样，VD临床表型由多种生物学内表型组成，并且可以鉴定出有意义的亚组。在当前的建议中，我们计划使用功能和结构脑成像以及遗传，生理和生物学参数的功能和结构脑成像广泛表型。我们假设中心机制（包括来自外部生殖器的互感信号的处理/调制的改变）是临床表现的重要决定因素，并且这些大脑签名的差异可能在治疗反应性中起重要作用。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特定目标来检验这一假设。 AIM 1将表征VD中多模式结构大脑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]功能和结构性和结构性（灰色和白质）脑成像对200名年龄匹配的女性健康对照组（HC），200名患有肠易激综合症（IBS）患者的患者在大脑症状群中，可在大脑症状上扫描肠炎/疼痛症状，并具有100例患者。 AIM 2将表征VD中的连通性指数，并确定结构（灰色和白质）与RS改变与临床，行为和遗传参数之间的关联。 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功能，疼痛，炎症，儿茶酚胺和5-羟色胺信号传导系统）。 AIM 3将通过将高级数学分类技术应用于大脑，生物学，行为和临床内表型来鉴定基于内表型簇的VD患者亚组。这将通过使用无监督的机器学习算法组合成像和其他表型数据来实现，并产生VD的不同机械子组。