PROFILING VULVODYNIA BASED ON NEUROBIOLOGICAL AND BEHAVIORAL ENDOPHENOTYPES

点击翻译按钮获取中文摘要

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.