Individual differences in brain morphology and connectivity to identify the mechanisms of comorbidities and treatment-refractory disease in females with endometriosis-associated pelvic pain

/ABSTRACT Endometriosis associated pelvic pain (EAPP) affects millions of females in the United States. Roughly 40% of females using gonadotropin-releasing hormone (GnRH) agonist therapy do not obtain relief for non-cyclic pelvic pain, and up to 20% of females undergoing hysterectomy experience persistent pelvic pain symptoms. This heterogeneity in treatment outcomes is further complicated by heterogeneity in clinical presentations, with enormous variability in frequency, intensity, and anatomic location of pain, as well as comorbid mental health issues. We are cross-disciplinary researchers with extensive experience exploring clinical subtypes (e.g., pelvic pain with and without comorbid pain or depression) using cutting-edge neuroimaging techniques. Our work suggests that central dysregulation of pain processing is an underappreciated culprit in EAPP. Our preliminary data suggests that heterogeneity in brain morphology is associated with refractory disease, and that these unique vulnerabilities may hold the key to understanding the wide range of clinical presentations and treatment outcomes seen in EAPP. Our overarching scientific premise is that robust computational modeling of the brain's morphology and connectivity in EAPP can reveal novel patient subtypes and treatment outcomes. We propose to synthesize data from three R01s focused on EAPP at the University of Michigan (UM), two focused on response to hysterectomy, and one focused on response to GnRH agonist therapy. These will be supplemented by population-based samples (UK Biobank, Adolescent Brain Cognitive Development [ABCD]) and additional pelvic pain samples (MAPP), to develop, refine, and validate brain-based models. Within each Aim, separate training and validation samples will be examined to ensure validity. Aim 1a. Use individual differences in brain morphology and connectivity to create, refine and validate neural models of two critical patient phenotypes: EAPP + widespread pain, and EAPP + depression and/or anxiety (UK Biobank; n = 456). Aim1b. Determine EAPP-specific and generic-CPP neural correlates by testing the performance of the models in Aim1a in a cognate CPP condition (IC/BPS; n= 315). Aim 2. Use individual differences in brain morphology and connectivity to create, refine, and validate neural models of refractory disease in EAPP, using both surgical and GnRH agonist therapy outcomes (UM endometriosis studies; n=332). Aim 3a: Contextualize brain morphology and connectivity in the lifespan by creating neural-based models of risk for dysmenorrhea, widespread pain, and depression in adolescent girls (ABCD dataset), and comparing the overlap with adult neural models. Exploratory Aim 3b: We will also use Subtype and Stage Inference (SuStaIn) computational techniques to conduct analyses that combine images across cohorts, including adolescent data from ABCD, to derive a data-driven taxonomy of novel patient subtypes. Impact: These analyses have the potential to fundamentally change the approach to EAPP by elucidating the CNS contributions to treatment-refractory disease and the development of EAPP. Project Number: 1R01HD117775-01 | Fiscal Year: 2025 | NIH Institute/Center: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) | Principal Investigator: Andrew Schrepf (+1 co-PI) | Institution: UNIVERSITY OF MICHIGAN AT ANN ARBOR, ANN ARBOR, MI | Award Amount: $2,969,745 | Activity Code: R01 | Study Section: Special Emphasis Panel[ZRG1 CCHI-W (57)] View on NIH RePORTER: https://reporter.nih.gov/project-details/1R01HD11777501