Machine Learning to Generate a Multivariate Model of Brain Injury in HIV Patients

机器学习生成 HIV 患者脑损伤的多变量模型

• 批准号: 9751995
• 负责人: LINDA CHANG
• 金额: $ 19.31万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9751995
• 关键词: AIDS dementia; Aging; Alcohol or Other Drugs use; Algorithms; Anisotropy; Anti-Retroviral Agents; Biological Markers; Brain; Brain Injuries; Brain Pathology; Brain imaging; CD4 Lymphocyte Count; Caring; Characteristics; Choline; Chronic Disease; Clinic; Clinical; Clinical Data; Cognitive; Cognitive deficits; Data; Data Set; Diagnosis; Diagnostic; Diffuse; Discipline of Nuclear Medicine; Disease; Economics; Functional disorder; Genetic; Glutamates; Glutamine; Goals; Guide prevention; HIV; HIV Infections; HIV-1; HIV-associated neurocognitive disorder; Hepatitis C; High Prevalence; Impaired cognition; Individual; Infection; Injury; Lead; Life Expectancy; Light; Machine Learning; Magnetic Resonance Imaging; Measures; Modality; Modeling; Monitor; Multicenter Studies; Neuraxis; Neuronal Dysfunction; Neuronal Injury; Outcome; Patient Care; Patients; Pattern; Pharmaceutical Preparations; Population; Process; Protocols documentation; Reproducibility; Research; Residual state; Risk; Severities; Standardization; Structure; Subgroup; Techniques; The Multicenter AIDS Cohort Study; Training; Viral; Viral reservoir; antiretroviral therapy; axon injury; base; brain abnormalities; cerebral atrophy; clinical phenotype; cognitive performance; cognitive testing; effective therapy; machine learning algorithm; morphometry; multimodality; myoinositol; neuroimaging; neuroinflammation; neuron loss; neuronal circuitry; neurotoxic; neurotoxicity; novel; receptor; tractography; unsupervised learning; white matter

This R21 application responds to RFA-MH-18-611 “Altered neuronal circuits, receptors and networks in HIV-induced Central Nervous System (CNS) dysfunction (R21)”. Despite effective viral suppression from combined antiretroviral therapy, up to 50% of the patients continue to have HIV-associated neurocognitive disorders (HAND). The cognitive deficits or impairment in HIV patients may be due to legacy effects from early stages of the infection, residual viral reservoirs with ongoing neuroinflammation, and potential neurotoxicity from some of the antiretroviral medications. Furthermore, co-morbid disorders associated with the aging HIV+ population, the high prevalence of substance use, and host characteristics, may further increase the risk and exacerbate the severity of HAND. Optimal care for patients with HAND requires efficient and appropriate diagnosis that can guide effective treatments. However, the current diagnostic approach for HAND requires lengthy and specialized cognitive tests and involves subjective components. Our overall goal is to develop an unsupervised machine learning (ML) algorithm to assess brain pathology, using objective measures such as alterations in structural connectivity on DTI, augmented with other neuroimaging and clinical variables. Ultimately, this may lead to a robust approach to classify subtypes and to quantify brain injury in HIV-infected individuals. This exploratory project has three specific aims (SA): SA1: Employ an automated unsupervised ML algorithm to detect subgroups of HIV-infected subjects, based solely on DTI tractography (structural connectivity). SA2: Add objective demographic, genetic, clinical, and non-DTI MR variables to the training DTI tractography data set, and determine their effects on predicting HAND and cognitive performance. SA3: Evaluate the optimized model for stability to undersampling, and determine whether it can be generalized to other data sets, including those from multi-center studies (i.e. Multicenter AIDS Cohort Study). Our optimized ML model has the potential to provide efficient, objective and reproducible biomarkers to identify individuals with or at risk for HAND, to guide prevention and treatment for HAND, and thereby ameliorate the burden of HIV infection and dementias.

此R21申请对RFA-MH-18-611的响应“更改神经元电路，接收器和 HIV引起的中枢神经系统（CNS）功能障碍（R21）的网络”。 尽管抗逆转录病毒疗法有效抑制病毒，但多达50％ 患者继续患有HIV相关的神经认知障碍（手）。认知 艾滋病毒患者的缺陷或损害可能是由于从早期阶段造成的。 感染，持续神经炎症的残留病毒储存和潜在的神经毒性 来自一些抗逆转录病毒药物。此外，与之相关的疾病 衰老的HIV+种群，物质使用的高流行和宿主特征，可能 进一步增加风险并加剧手的严重性。患者的最佳护理 手需要有效且适当的诊断，以指导有效的治疗方法。 但是，当前的手诊断方法需要冗长而专业的认知 测试并涉及主观成分。我们的总体目标是开发一个无监督的 使用客观措施（例如 DTI上结构连通性的改变，随着其他神经影像和临床的增强 变量。最终，这可能会导致一种强大的方法来对亚型进行分类和量化 艾滋病毒感染者的脑损伤。这个探索性项目具有三个特定目标（SA）： SA1：采用自动监督的ML算法来检测HIV感染的亚组 受试者，仅基于DTI拖拉术（结构连通性）。 SA2：添加目标 人口统计学，遗传，临床和非DTI MR变量DTI拖拉术数据 设置并确定它们对预测手和认知表现的影响。 SA3：评估 优化的模型，以稳定采样，并确定是否可以推广 到其他数据集，包括来自多中心研究的数据集（即多中心艾滋病队列 学习）。我们优化的ML模型有可能提供高效，客观和 可重复的生物标志物识别有手或有风险的人，指导预防和 手的治疗，从而改善HIV感染和痴呆症的伯嫩。