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Predicting Brain Changes in HIV/AIDS

预测艾滋病毒/艾滋病患者的大脑变化

基本信息

批准号：
8889732
负责人：
BRADFORD NAVIA
金额：
$ 52.84万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-08-01 至 2017-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8889732
关键词：
AIDS/HIV problem Affect Alcohol or Other Drugs use Algorithms Anti-Retroviral Agents Atrophic Basal Ganglia Biological Markers Brain Brain Diseases CD4 Positive T Lymphocytes Cell Count Chronic Clinical Clinical Trials Clinical Trials Design Cognition Cognitive Country Data Data Analyses Data Set Diffusion Magnetic Resonance Imaging Disease Disease Progression Engineering Epidemic Ethnic group Fiber Functional disorder Future Grant HIV HIV Infections Hepatitis C Image Image Analysis Impaired cognition Letters Life Life Expectancy MRI Scans Machine Learning Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Maps Measures Medicine Methods Modeling Neurocognitive Deficit Neurologic Neurosciences Patients Pattern Persons Pharmaceutical Preparations Plasma Protocols documentation Public Health RNA Research Risk Sample Size Sampling Scanning Science Shock Source Structure of genu of corpus callosum System Testing Therapeutic Trials Time Tissues Viral Load result Virus Work aged antiretroviral therapy base brain tissue chemokine cohort design empowered follow-up frontal lobe immune activation improved innovation morphometry neuroimaging novel outcome forecast predictive modeling tool white matter

项目摘要

DESCRIPTION (provided by applicant): In affluent countries where combination anti-retroviral therapy (CART) is widely used, life expectancy with HIV has increased well beyond 10 years, but this has greatly increased the number of people living with HIV/AIDS. 40 million people are now at risk for progressive HIV-related damage to the brain (Navia 2011), and 40% show some neurological or cognitive impairment. Building on our recent discoveries and innovations in MRI and diffusion tensor imaging, our project charts the dynamics of HIV disease in the brain, revealing factors that predict clinical decline and brain decline. By analyzing data from the HIVNC (HIV Neuroimaging Consortium) cohort (218 HIV+ subjects scanned longitudinally with MRI every 26-32 weeks for 3 years; 900 scans), we will use our sensitive image analysis method, tensor-based morphometry to (1) map rates of brain tissue loss over time, determining which brain systems lose tissue fastest; (2) relate these loss patterns to neurocognitive impairment (NCI); and (3) determine which host and disease-related factors, at baseline, predict higher atrophic rates over the 3-year follow-up interval. To deeply probe the causes of dysfunction, we will use whole-brain tractography based on diffusion tensor imaging in 267 subjects to determine where HIV+ patients have reduced fiber integrity. We will use each patient's DTI scan to compute a whole-brain connection matrix, based on a state-of-the-art whole-brain tractography method we developed. We will combine the best neuroimaging measures from Aims 1 and 2 into our support vector machine method to predict (1) future rates of atrophy, and (2) cognitive decline over the 3-year follow-up. With our best predictors of decline from Aims 1 and 2, we will predict which HIV+ patients will show imminent decline. We will estimate the sample sizes needed for a neuroimaging-based drug trial to detect a 5%, 10%, or 25% slowing in the rate of atrophy, and the same percents of slowing in the rate of cognitive decline. We will test whether our predictors generalize to the large, independent Charter and Miriam datasets beyond HIVNC (see Support Letters from Drs. Igor Grant and Ron Cohen), and when used by our many HIV research collaborators now using our methods; see Pilot Data). As shown in new pilot data, we assess how imaging protocol differences affect the measures; our innovations to reduce scan protocol confounds (e.g., adjusted FA) will guide selection of the most robust predictors. We will evaluate how useful these new measures are for predicting cognitive decline, and boosting power in an antiretroviral drug trial. In other words, to what extent can a DTI scan, and an MRI-based map of atrophy, help to make clinical predictions of imminent cognitive impairment? Can they help select a sample for a drug trial? Our activities will make clinical trial design more efficient by selecting subjects with greater potential to respond t future therapies. As always, we will disseminate our methods to both experts and trainees in medicine, neuroscience, engineering, and to our network of over 100 collaborating labs (including two national HIV consortia: HIVNC, CHARTER, and the Miriam HIV cohort), to accelerate their work.

描述（由申请人提供）：在广泛使用抗逆转录病毒联合疗法（CART）的富裕国家，艾滋病毒感染者的预期寿命已大大超过10年，但这也大大增加了艾滋病毒/艾滋病感染者的人数。目前有4000万人面临与艾滋病毒相关的大脑进行性损伤的风险（Navia 2011）， 40%的人表现出某种神经或认知障碍。基于我们最近在MRI和弥散张量成像方面的发现和创新，我们的项目绘制了艾滋病毒在大脑中的动态图，揭示了预测临床衰退和大脑衰退的因素。通过分析HIVNC （HIV神经成像联盟）队列的数据（218名HIV阳性受试者每26-32周进行MRI纵向扫描，持续3年；900次扫描），我们将使用我们的敏感图像分析方法，基于张量的形态测定法来(1)绘制脑组织随时间的损失率，确定哪些脑系统组织损失最快；(2)将这些损失模式与神经认知障碍（NCI）联系起来；(3)确定哪些宿主和疾病相关因素在基线时预测3年随访期间较高的萎缩率。为了深入探讨功能障碍的原因，我们将在267名受试者中使用基于弥散张量成像的全脑束造影来确定HIV+患者纤维完整性降低的部位。我们将使用每个病人的DTI扫描来计算全脑连接矩阵，基于我们开发的最先进的全脑束造影方法。我们将把目标1和目标2中最好的神经成像测量结合到我们的支持向量机方法中，以预测(1)未来的萎缩率，以及(2)3年随访期间的认知能力下降。根据目标1和目标2的最佳预测指标，我们将预测哪些HIV+患者即将出现衰退。我们将估计以神经成像为基础的药物试验所需的样本量，以检测萎缩速度减慢5%，10%或25%，以及认知能力下降速度减慢的相同百分比。我们将测试我们的预测是否可以推广到大型的，独立的Charter和Miriam数据集，而不是HIVNC(见支持信博士。Igor Grant和Ron Cohen)，当我们的许多HIV研究合作者使用我们的方法时；参见试点数据)。正如新的试点数据所示，我们评估了成像方案差异如何影响测量；我们在减少扫描协议混淆（例如，调整FA）方面的创新将指导选择最稳健的预测因子。我们将评估这些新措施在预测认知能力下降和增强抗逆转录病毒药物试验中的作用。换句话说，DTI扫描和基于mri的萎缩图在多大程度上有助于对即将发生的认知障碍进行临床预测？他们能帮助选择药物试验的样本吗？我们的活动将通过选择对未来治疗有更大反应潜力的受试者，使临床试验设计更有效。一如既往，我们将把我们的方法传播给医学、神经科学、工程领域的专家和学员，以及我们的100多个合作实验室网络（包括两个国家艾滋病毒联盟：HIVNC、CHARTER和Miriam艾滋病毒队列），以加快他们的工作。