PREDICT-FTD: Multimodal Imaging Prediction of FTLD Subtypes.

PREDICT-FTD：FTLD 亚型的多模态成像预测。

• 批准号: 10915129
• 负责人: HOWARD J ROSEN
• 金额: $ 140.83万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2024-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10915129
• 关键词: Affect; Age; Age of Onset; Alzheimer' s Disease; Anatomy; Atrophic; Brain; C9ORF72; Cerebrospinal Fluid; Clinical; Clinical Trials; DNA Sequence Alteration; Data; Data Set; Dementia; Deterioration; Development; Diffuse Pattern; Diffusion; Digit structure; Disease; Early Intervention; Frontotemporal Lobar Degenerations; Grant; Image; Individual; International; Intervention; Light; Machine Learning; Measures; Methods; Modeling; Monitor; Multimodal Imaging; Mutation; Nerve Degeneration; Neuroanatomy; Neurodegenerative Disorders; Neuronal Injury; Patients; Pattern; Persons; Primary Progressive Aphasia; Process; Progressive Supranuclear Palsy; Risk; Sample Size; Sampling Studies; Semantics; Statistical Models; Surface; Symptoms; Syndrome; Teenagers; Testing; Time; Variant; accurate diagnosis; behavioral variant frontotemporal dementia; clinical heterogeneity; clinical predictors; corticobasal syndrome; effective therapy; gray matter; high risk population; improved; machine learning method; machine learning model; mild cognitive impairment; multimodal neuroimaging; multimodality; mutation carrier; neurofilament; neuroimaging; neuron loss; personalized predictions; predictive modeling; prevent; structural imaging; tool; white matter change

Project Summary Frontotemporal lobar degeneration (FTLD) is a devastating neurodegenerative disorder, and a common cause of dementia in people under the age of 65. Recent advances are offering hope for disease modifying interventions, but such treatments will only be effective if patients are accurately diagnosed, and likely to be most effective early in the course of disease. The ability to accurately predict the type of clinical syndrome is critical for selecting appropriate early interventions that may be targeted for specific symptoms at specific stages. The ability to accurately predict the age at which the symptoms will emerge is critical for clinical trials, which requires accurate measures that can indicate that the individual at risk is likely to develop symptoms within a specific timeframe. Unfortunately, the type of clinical syndrome and age of onset can vary dramatically even within the same mutation. An individual with a given mutation can eventually develop any of the FTLD syndrome subtypes, and the age of onset of clinical syndromes can also vary within the same mutation. This striking clinical heterogeneity in FTLD due to genetic mutation severely limits our ability to predict when and what specific symptoms will emerge. Neuroimaging studies of symptomatic and presymptomatic FTLD mutation carriers have shown detectable gray matter and white matter changes across all mutations. However, the variability in the clinical syndrome trajectories that exist in each mutation has led to mixed findings. A small number of studies of presymptomatic mutation carriers who went on to develop clinical symptoms suggest that presymptomatic structural changes can be used to predict time to dementia. However, their sample sizes were small, often are numbered in the single digits or low teens, thus leaving many unanswered questions, including whether prediction differs by mutation and whether specific syndromes can be predicted, and whether the choice of neuroimaging measures may depend on the stage of the presymptomatic progression. Our proposed study will use large-scale longitudinal multimodal neuroimaging datasets of presymptomatic FTLD mutation carriers to predict the type of syndrome that they will develop and the time of onset. 1) We will focus on the presymptomatic trajectory of eventual clinical syndromes and develop new models that incorporate longitudinal data. 2) We will leverage the largest international consortia studies that follow presymptomatic mutation carriers. 3) We will consider more sensitive imaging measures. 4) Finally, we will employ powerful machine- learning-based methods appropriate for our proposed sample size. If successful, the methods and measures we develop can inform clinical trials that require accurate predictors of timeframe before conversion. If and when effective treatments become available, reliable predictors of when and which syndrome an individual will develop can be used for selecting appropriate early interventions that may be targeted for specific symptoms at specific stages, as well as specific measures that should be monitored in each at-risk individual.

项目摘要 额颞叶变性（FTLD）是一种毁灭性的神经退行性疾病，是一种常见 65岁以下人群的痴呆症原因。最近的进步为疾病改造提供了希望 干预措施，但是这种治疗只有准确诊断出患者，并且可能是有效的 在疾病的早期最有效。准确预测临床综合征类型的能力为 对于选择可能针对特定症状的适当的早期干预措施至关重要 阶段。准确预测症状出现的年龄至关重要的能力对于临床试验至关重要， 这需要准确的措施，以表明处于危险的人可能会出现症状 在特定的时间范围内。不幸的是，临床综合征和发作年龄的类型可能会大不相同 即使在同一突变中。具有给定突变的个体最终可以发展出任何FTLD 综合征亚型和临床综合征发作的年龄在同一突变中也会有所不同。这 由于基因突变而引起的FTLD临床异质性严重限制了我们预测何时和 会出现什么特定症状。有症状和症状性FTLD的神经影像学研究 突变载体显示了所有突变中可检测到的灰质和白质的变化。然而， 每个突变中存在的临床综合征轨迹的变异性导致了混合发现。一个小 继续发展临床症状的预症状突变携带者的研究数量表明 预症状结构变化可用于预测痴呆时间。但是，它们的样本量是 小，通常以单位数字或低青少年编号，因此留下了许多未解决的问题，包括 预测是否因突变以及是否可以预测特定综合症以及是否可以预测 神经影像措施的选择可能取决于预症状进展的阶段。我们提出的 研究将使用大规模的纵向多模式神经影像学数据集 携带者可以预测他们将发展的综合征类型和发作时间。 1）我们将专注于 最终临床综合征的预症状轨迹，并开发出纵向的新模型 数据。 2）我们将利用遵循预症状突变的最大国际财团研究 载体。 3）我们将考虑更敏感的成像措施。 4）最后，我们将采用强大的机器 - 基于学习的方法适合我们建议的样本量。如果成功，方法和措施 我们开发可以为临床试验提供信息，这些试验需要在转换之前准确预测时间范围。如果和 当有效的治疗可用时，可靠的何时和哪个综合症的可靠预测因素 开发可用于选择适当的早期干预措施，这些干预措施可能针对特定症状 特定阶段以及应在每个高危个人中监视的特定措施。