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

描述（由申请人提供）：亨廷顿舞蹈病（HD）是一种毁灭性的无法治愈的遗传性神经退行性疾病，大多数患者在成年早期受到影响。通过基因检测，可以在临床发病前几年识别出最终会患上HD的人，从而提高了在临床前阶段开始治疗以延缓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存在一些挑战。一个主要的困难是确定在这类试验中使用的最佳结果指标。目前，亨廷顿舞蹈症的临床试验使用临床结果指标，如统一亨廷顿病评定量表，但这些指标在临床未受影响的个体中并不有用。测量表型转化（即从临床前HD进展到诊断HD）作为结果测量可能不切实际，因为临床试验的受试者可能需要很多年才能出现明确的HD症状。因此，人们一直在努力确定可靠的生物标志物来衡量临床前HD （pHD）受试者的进展。PREDICT- HD是一项由NINDS资助的多中心纵向研究，旨在测量临床前HD发生的早期临床和成像（MRI）变化，目的是识别此类生物标志物。利用一种专为纵向脑成像数据分析而设计的新网络建模策略，我们在HD突变的预先表现携带者的静息状态代谢扫描中确定并验证了HD相关的进展模式（HDPP）。我们的初步数据表明，通过捕捉整个大脑中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，我们建议使用FDG PET添加静息状态代谢成像（将在基线和1年后进行），以量化PREDICT-HD参与者在每个纵向时间点的个体受试者HDPP表达。我们计划解决以下具体目标：(1)在一组新的具有良好特征的博士受试者中验证HDPP，并测量其在1年内表达的变化；(2)比较1年内HDPP的变化率与其他PREDICT-HD测量的变化率，包括MRI（体积测量，MHDPP）和临床测量；(3)重现并验证与HD症状发作相关的新型大脑网络。这项工作的最终目标是确定最敏感和可靠的成像测量，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.