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Modeling learning under volatility in Borderline Personality Disorder

边缘性人格障碍波动下的学习建模

基本信息

批准号：
10636918
负责人：
Sarah K Fineberg
金额：
$ 19.17万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10636918
关键词：
Acute Affect Amygdaloid structure Anterior Behavior Behavioral Biological Response Modifier Therapy Borderline Personality Disorder Brain Classification Clinical Clinical Research Clinical Treatment Computer Models Control Groups Cues Data Data Analyses Data Collection Diagnostic Specificity Disease Distress Environment FDA approved Family Foundations Functional Magnetic Resonance Imaging Functional disorder Future Goals Hyperactivity Impairment Impulsivity Individual Individual Differences Intervention Learning Measures Medical Mental disorders Mentors Mentorship Methodology Methods Modeling Moods Neurobiology Participant Patient Self-Report Patients Pattern Perception Performance Persons Pharmaceutical Preparations Phenotype Physicians Population Post-Traumatic Stress Disorders Prevalence Probability Prognosis Psyche structure Psychiatry Psychotherapy Recovery of Function Research Research Design Resources Rest Reversal Learning Rewards Risk Risk Factors Role Scientist Signal Transduction Social Functioning Social Problems Specificity Suicide Suicide attempt Supervision Symptoms Testing Time Training Trauma Trust Volatilization Work adverse childhood events behavioral response career career development caregiving cingulate cortex clinical predictors comorbidity comparison group completed suicide defined contribution design distrust effective therapy expectation experience improved improved outcome neglect neural neurobiological mechanism neurofeedback neuroimaging neuromechanism neurotransmission novel novel therapeutic intervention novel therapeutics patient oriented response skills social social deficits social learning suicidal risk theories trauma exposure trustworthiness

项目摘要

Project Summary / Abstract This is a patient-oriented career development proposal designed to provide the candidate with advanced training, protected research time, and mentored research experience. The research aims focus on Borderline Personality Disorder (BPD), which is a serious and debilitating mental illness that affects 2-6% of the population and increases risk of suicide. Current treatments have limited efficacy and there are no FDA- approved medications for BPD. The candidate's long-term career goal is to improve the treatment of BPD and other disorders with significant social symptoms by using computational psychiatry to better predict prognosis and best treatment match. To continue her progress toward this goal, the candidate proposes a detailed plan for training, including expert mentorship and three supervised research aims. The training plan is designed for the candidate to gain expertise in neuroimaging, computational modelling, and learning theory. The candidate will also build on her strong foundation in clinical research and treatment of people with Borderline Personality Disorder and in statistical approaches to data analysis. The mentorship team has extensive expertise in clinical psychiatry research, fMRI study design and analysis, and computational modelling of learning. They will provide the candidate with the resources and supervision needed to advance toward her research goals. These studies leverage recent advances in computational psychiatry. Through three hypothesis-driven research aims, subject behavior and brain activation will be tested during an interactive social learning task. These aims test learning under volatility (when the environment is unpredictable) in BPD compared to BPD+PTSD, PTSD, and trauma-exposed healthy controls. Aim 1 tests the role of anterior cingulate cortex for signaling volatility, and interaction between amygdala and anterior cingulate in this setting. Aim 2 tests learning patterns in BPD versus comparator subjects to identify illness-specific phenotypes. Aim 3 tests how neural and behavioral markers of learning relate to social functioning. Next steps will be to refine a computational model of learning in BPD to predict prognosis, predict best treatment match for an individual, and test novel biological treatment targets. Improving our mechanistic understanding of interpersonal symptoms will improve our clinical treatments and significantly reduce suffering for millions of people with BPD and their families.

项目摘要/摘要