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

基本信息

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
项目类别：
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

项目摘要

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）：亨廷顿舞蹈症（HD）是一种毁灭性的、无法治疗的遗传性神经退行性疾病，影响大多数成年早期的患者。通过基因检测，可以在临床发病前几年识别出最终患有HD的人，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，在一组临床正常的个体中进行临床试验存在一些挑战。一个主要困难是确定此类试验中使用的最佳结果衡量标准。目前，亨廷顿病的临床试验使用统一亨廷顿病评定量表等临床结果衡量标准，但这些衡量标准对临床上未受影响的个体没有用处。将表型转化（即从临床前 HD 发展为诊断 HD）作为结果测量可能不切实际，因为临床试验中的受试者可能需要很多年才能出现明确的 HD 体征。因此，人们共同努力寻找可靠的生物标志物来测量临床前 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 中发生的最早的临床和成像 (MRI) 变化，目的是识别此类生物标志物。利用专为分析纵向脑成像数据而设计的新网络建模策略，我们在 HD 突变携带者的静息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，通过捕获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。在本研究中，我们建议添加 FDG PET 静息态代谢成像（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。我们计划实现以下具体目标：（1）在一组特征明确的 pHD 受试者中验证 HDPP，并测量其表达在一年内的变化； (2) 将一年内 HDPP 的变化率与其他 PREDICT-HD 测量值（包括 MRI（体积测量，MHDPP）和临床测量值）的变化进行比较； (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灵敏、最可靠的成像测量方法，用于未来临床前 HD 患者的临床试验。