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应用程序响应RFA-MH-18-611“改变的神经元回路，受体和 HIV诱导的中枢神经系统（CNS）功能障碍的网络（R21）。 尽管联合抗逆转录病毒疗法有效抑制了病毒，但高达50%的 患者继续患有HIV相关的神经认知障碍（HAND）。认知 HIV患者的缺陷或损害可能是由于早期阶段的遗留效应， 感染、残留病毒储库伴持续神经炎症和潜在神经毒性 一些抗逆转录病毒药物。此外，与以下疾病相关的共病性疾病 艾滋病毒阳性人群的老龄化、药物使用的高流行率和宿主特征，可能 进一步增加风险并加剧手的严重程度。为患有以下疾病的患者提供最佳护理 手需要有效和适当的诊断，可以指导有效的治疗。 然而，目前HAND的诊断方法需要长时间和专业的认知， 测试和涉及主观成分。我们的总体目标是开发一个无人监督的 机器学习（ML）算法来评估大脑病理，使用客观的措施，如 DTI上结构连接的改变，与其他神经成像和临床增强 变量最终，这可能会导致一个强大的方法来分类亚型和量化 HIV感染者的脑损伤。这一探索性项目有三个具体目标（SA）： SA 1：采用自动无监督ML算法检测HIV感染者的亚组 受试者，仅基于DTI纤维束成像（结构连接）。SA 2：添加目标 人口统计学、遗传学、临床和非DTI MR变量与训练DTI纤维束成像数据 设置，并确定它们对预测HAND和认知表现的影响。SA 3：评价 对欠采样稳定性的优化模型，并确定它是否可以推广 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研究） 研究）。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，以识别患有HAND或有HAND风险的个体，指导预防， 治疗HAND，从而减轻HIV感染和痴呆的负担。