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

描述（由申请人提供）：亨廷顿氏病（HD）是一种毁灭性的不可治愈的遗传性神经退行性疾病，影响大多数成年早期患者。通过基因检测，最终将发展为HD的人可以在临床发病前几年被识别，从而提高了在临床前阶段开始治疗以延迟或预防疾病发作的可能性。然而，在一组临床正常个体中进行临床试验存在一些挑战。一个主要的困难是定义在此类试验中使用的最佳结果测量。目前，HD的临床试验使用临床结果测量，例如统一亨廷顿氏疾病评定量表，但这些测量在临床未受影响的个体中没有用。测量表型转化（即从临床前HD进展为诊断的HD）作为结局指标可能不切实际，因为临床试验中的受试者可能需要多年才能出现明确的HD体征。因此，一直在共同努力，以确定可靠的生物标志物，用于测量临床前HD（pHD）受试者的进展。PREDICT- HD是一项NINDS资助的多中心纵向研究，旨在测量临床前HD中发生的最早临床和成像（MRI）变化，目的是识别此类生物标志物。利用一种新的网络建模策略，设计用于纵向脑成像数据的分析，我们确定并验证了HD相关的进展模式（HDPP）在静息状态下的代谢扫描的预显携带者的HD突变。我们的初步数据表明，通过捕获整个大脑中特定模式下发生的功能变化，HDPP可能比其他成像生物标志物对疾病进展更敏感。在这项研究中，我们建议增加静息态代谢成像与FDG PET（在基线和1年后进行），以量化个体受试者HDPP表达在每个纵向时间点的预测-HD参与者。我们计划解决以下具体目标：（1）在一个新的充分表征的pHD受试者队列中验证HDPP，并测量其表达在1年内的变化;（2）比较HDPP在1年内的变化率与其他PREDICT-HD指标（包括MRI）的变化（容积测量，MHDPP）和临床测量;和（3）再现和验证与HD症状发作相关的新型脑网络。这项工作的最终目标是确定最敏感和可靠的成像措施，用于未来的临床试验中的个人与临床前HD。

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.