Brain Network Imaging: A Novel Biomarker for Preclinical Huntington's Disease

脑网络成像：临床前亨廷顿病的新型生物标志物

• 批准号: 8885931
• 负责人: ANDREW FEIGIN
• 金额: $ 28.86万
• 依托单位: FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8885931
• 关键词: Address; Adult; Affect; Behavioral; Biological Markers; Brain; Brain imaging; Clinical; Clinical Trials; Cognitive; Collaborations; Data; Data Collection; Deterioration; Diagnosis; Disease; Disease Progression; Evaluation; Funding; Future; Gene Mutation; Genetic screening method; Goals; Huntington Disease; Image; Individual; Inherited; Intervention; Investigational Therapies; Life; Longitudinal Studies; Magnetic Resonance Imaging; Maps; Measures; Metabolic; Methods; Motor; Mutation; National Institute of Neurological Disorders and Stroke; Nerve Degeneration; Neurobiology; Neurodegenerative Disorders; Onset of illness; Outcome Measure; Participant; Patients; Pattern; Phase; Positron-Emission Tomography; Process; Rest; Risk; Scanning; Signs and Symptoms; Symptoms; Testing; Time; United States National Institutes of Health; Visit; Work; base; brain metabolism; cohort; design; disease diagnosis; emerging adult; fluorodeoxyglucose; fluorodeoxyglucose positron emission tomography; follow-up; imaging biomarker; longitudinal analysis; network models; neuroprotection; novel; pre-clinical; prevent; public health relevance; research study; statistics; trend

DESCRIPTION (provided by applicant): Huntington's disease (HD) is a devastating untreatable hereditary neurodegenerative disorder that affects most sufferers in early adult life. Through genetic testing, people who will ultimately develop HD can be identified years before clinical onset, raising the possibility of initiating therapy in this preclinical period to delay o prevent disease onset. Performing clinical trials in a group of clinically normal individuals, however, presents several challenges. One major difficulty is defining the best outcome measure for use in such trials. Currently, clinical trials in HD utilize clinical outcome measures such as the Unified Huntington's Disease Rating Scale, but these measures are not useful in clinically unaffected individuals. Measuring phenoconversion (i.e. progressing from preclinical HD to diagnosed HD) as an outcome measure may be impractical as subjects in clinical trials may be many years from developing unequivocal signs of HD. Therefore, there has been a concerted effort to identify reliable biomarkers for measuring progression in preclinical HD (pHD) subjects. PREDICT- HD is an NINDS funded multicenter longitudinal study to measure the earliest clinical and imaging (MRI) changes that occur in preclinical HD with the goal of identifying such biomarkers. Utilizing a new network modeling strategy designed for the analysis of longitudinal brain imaging data, we identified and validated an HD-related progression pattern (HDPP) in resting state metabolic scans of premanifest carriers of the HD mutation. Our preliminary data suggest that by capturing functional changes occurring in a specific pattern across the whole brain, HDPP is likely to be more sensitive to disease progression than other imaging biomarkers. In this study, we propose adding resting state metabolic imaging with FDG PET (to be conducted at baseline and after 1 year) to quantify individual subject HDPP expression at each longitudinal time point in PREDICT-HD participants. We plan to address the following Specific Aims: (1) To validate HDPP in a new cohort of well characterized pHD subjects and to measure the change in its expression over 1 year; (2) To compare the rate of change in HDPP over 1 year to changes in other PREDICT-HD measures including MRI (volumetrics, MHDPP), and clinical measures; and (3) To reproduce and validate a novel brain network associated with HD symptom onset. The ultimate goal of this work is to identify the most sensitive and reliable imaging measure for use in future clinical trials in individuals with preclinical HD.

描述（由申请人提供）：亨廷顿氏病（HD）是一种毁灭性的不可治疗的遗传神经退行性疾病，会影响成年早期的大多数患者。通过基因检测，最终将发展高清的人可以在临床发作前几年确定，从而增加了在这个临床前期间开始治疗的可能性，以延迟o防止疾病发作。然而，在一组临床正常人中进行临床试验，提出了一些挑战。一个主要的困难是定义在此类试验中使用的最佳结果指标。目前，HD中的临床试验利用统一的亨廷顿疾病评级量表等临床结果指标，但是这些措施在临床上未受影响的个体中没有用。测量表现转化（即从临床前HD到诊断的HD发展）作为结局指标可能是不切实际的，因为临床试验中的受试者可能已经有很多年了，而发展的HD的明确迹象。因此，已经进行了一致的努力，以确定可靠的生物标志物来测量临床前HD（PHD）受试者的进展。预测HD是一项由Ninds资助的多中心纵向研究，用于测量临床前HD中发生的最早的临床和成像（MRI）变化，目的是识别此类生物标志物。利用设计用于分析纵向脑成像数据的新网络建模策略，我们在HD突变的前命中率携带者的静止状态代谢扫描中确定并验证了与HD相关的进程模式（HDPP）。我们的初步数据表明，通过捕获整个大脑特定模式发生的功能变化，HDPP可能比其他成像生物标志物更敏感疾病进展。在这项研究中，我们建议使用FDG PET（在基线和1年后进行）添加静息状态代谢成像，以量化预测HD参与者中每个纵向时间点的单个受试者HDPP表达。我们计划解决以下特定目的：（1）在新的特征性博士学位受试者中验证HDPP，并在1年内衡量其表达的变化； （2）将HDPP在1年内的变化率与其他预测HD措施的变化进行比较，包括MRI（体积，MHDPP）和临床指标； （3）重现和验证与HD症状发作相关的新型脑网络。这项工作的最终目的是确定在临床前高清个体的未来临床试验中使用的最敏感和可靠的成像指标。