Identifying Patterns of Cognitive, Motor, and Brain Structural Abnormalities Differentiating Alcohol Use Disorder with and without HIV Infection Comorbidity

识别认知、运动和脑结构异常的模式区分有或没有 HIV 感染合并症的酒精使用障碍

• 批准号: 9768139
• 负责人: Ehsan Adeli
• 金额: $ 2.29万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-24 至 2019-11-16
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9768139
• 关键词: Affect; Alcohol dependence; Awareness; Brain; Caregivers; Clinical; Cognitive; Comorbidity; Complex; Computer Simulation; Conflict (Psychology); Cross-Sectional Studies; Data; Data Set; Descriptor; Diagnostic; Disease; General Population; Goals; Grouping; HIV; HIV Infections; HIV Seronegativity; HIV Seropositivity; Impaired cognition; Individual; Knowledge; Label; Machine Learning; Magnetic Resonance Imaging; Measurement; Mentors; Methods; Modeling; Motor; Neuropsychological Tests; Neuropsychology; Outcome; Participant; Pattern; Physicians; Process; Research Design; Research Personnel; Research Project Grants; Structural defect; Structure; System; Technology; Testing; Time; Training; Validation; Virus Diseases; alcohol comorbidity; alcohol use disorder; base; brain morphology; cohort; experience; improved; learning strategy; longitudinal analysis; motor impairment; neuroimaging; neuropsychiatry; novel; predictive modeling; problem drinker; prospective; relating to nervous system; research clinical testing; targeted treatment

SUMMARY Alcohol Use Disorder (AUD) occurs in the largest segment of individuals in the U.S. who are dependent on a substance. The co-occurrence of AUD in individuals with human immunodeficiency virus (HIV) infection is high, occurring at twice the rate as occurs in the general population. AUD and HIV infection each are responsible for disruption of brain structural integrity and cognitive and motor impairments, affect some different and some overlapping neural systems, but can also exacerbate the untoward effects on selective systems through synergistic or additive processes. The goal of this research project is to develop novel machine learning methods to differentiate the compounding factors and effects of these two disorders involving brain to improve the mechanistic and dynamic understanding of HIV/AUD comorbidity effects on the brain. Efficient study of the untoward effects associated with the comorbidity of AUD and HIV on brain morphology requires identifying differences across multiple diagnostic groupings, i.e., healthy controls (CTRL), HIV-negative alcoholics (AUD), HIV-positive without alcohol dependency (HIV), and HIV-positive with alcohol dependency (HIV/AUD). Testing inferences across multiple diagnostic groupings of complex disorders commonly yields inconclusive or conflicting findings when done by conventional, univariate, cross-sectional study designs, which are constructed to model two cohorts at a time and hold "nuisance" variables constant with little power to include multiple factors comprising the complexity and dynamism that may well be relevant for distinguishing the primary disorders. Herein, I propose to develop robust and reliable machine learning technology that can identify patterns from longitudinal MRI data unique to each cohort. This type of data-driven method is able to analyze all data points concurrently and thus provide alternative approaches to conventional methods, which focus on correlating subsets of data guided by expert domain knowledge. I envision that this project will expand my understanding of the neuropsychiatry of AUD and HIV and will also result in extending a mechanistic understanding of neurofactors relevant to HIV/AUD comorbidity. Improving the mechanistic understanding of HIV/AUD comorbidity and diagnostic differences may enhance physician and caregiver awareness and aid clinicians in developing targeted therapeutic options for sustained symptomatic benefits.

总结 酒精使用障碍（AUD）发生在美国最大的一部分人中， 依赖于某种物质。AUD在个体中与人类共现 免疫缺陷病毒（艾滋病毒）感染率很高，发生率是发生率的两倍。 一般人口。AUD和HIV感染都是导致大脑结构破坏的原因。 完整性与认知和运动障碍，影响有些不同，有些重叠 神经系统，但也可以加剧对选择性系统的不利影响， 协同或加成方法。这项研究计划的目标是开发新的机器 学习区分这两种疾病的复合因素和影响的方法 涉及大脑以提高对HIV/AUD共病的机制和动态理解 对大脑的影响。 有效研究与AUD和HIV共病相关的不良反应 需要识别多个诊断分组之间的差异，即， 健康对照组（CTRL）、HIV阴性酗酒者（AUD）、HIV阳性无酒精者 依赖（HIV）和HIV阳性酒精依赖（HIV/AUD）。测试推论 在复杂疾病的多个诊断分组中， 当采用传统的、单变量的、横断面研究设计时， 被构造为一次对两个队列进行建模，并保持“讨厌”变量不变， 包括多种因素的能力，这些因素包括可能是 与区分原发性疾病有关。 在这里，我建议开发强大而可靠的机器学习技术， 从每个队列特有的纵向MRI数据中识别模式。这种数据驱动的 方法能够同时分析所有数据点，从而提供替代方法 传统的方法，其关注于由专家领域指导的数据子集的相关性 知识我设想这个项目将扩大我对神经精神病学的理解， AUD和HIV，也将导致扩大对神经因素的机械理解 与HIV/AUD合并症相关。 提高对HIV/AUD共病和诊断差异的机制理解 可以提高医生和护理人员的意识，并帮助临床医生制定有针对性的 治疗选择持续的症状的好处。