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

描述(申请人提供)：亨廷顿病(HD)是一种毁灭性的无法治愈的遗传性神经退行性疾病，影响大多数患者的早期成年生活。通过基因测试，最终将发展为HD的人可以在临床发病前几年被识别出来，这增加了在这一临床前阶段启动治疗以延迟或预防疾病发病的可能性。然而，在一组临床正常的个体中进行临床试验面临着几个挑战。一个主要困难是确定在此类试验中使用的最佳结果衡量标准。目前，HD的临床试验使用临床结果衡量标准，如统一亨廷顿氏病评定量表，但这些衡量标准对临床未受影响的个体无效。测量表型转化(即从临床前HD到诊断HD的进展)作为结果衡量标准可能是不切实际的，因为临床试验中的受试者可能需要多年才能发展出明确的HD迹象。因此，一直在共同努力寻找可靠的生物标记物来测量临床前HD(PHD)受试者的进展。Forecast-HD是NINDS资助的一项多中心纵向研究，旨在测量临床前HD发生的最早临床和成像(MRI)变化，目的是识别此类生物标记物。利用一种新的用于分析纵向脑成像数据的网络建模策略，我们在HD突变前显性携带者的静息状态代谢扫描中识别并验证了HD相关进展模式(HDPP)。我们的初步数据表明，通过捕捉整个大脑以特定模式发生的功能变化，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FDGPET的静息状态代谢成像(在基线和一年后进行)来量化Predicate-HD参与者在每个纵向时间点的个体HDPP表达。我们计划解决以下具体目标：(1)在具有良好特征的PHD受试者的新队列中验证HDPP，并测量其表达在一年内的变化；(2)将HDPP在一年内的变化率与包括MRI(体积测量学，MHDPP)和临床测量在内的其他预测高清指标的变化进行比较；以及(3)复制和验证与HD症状出现相关的新的大脑网络。这项工作的最终目标是确定最敏感和最可靠的成像方法，用于临床前HD患者的未来临床试验。

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.