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首页> 外文学位 >Multimodal analysis in normal aging, mild cognitive impairment, and Alzheimer's disease: Group differentiation, baseline cognition, and predicition of future cognitive decline.
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Multimodal analysis in normal aging, mild cognitive impairment, and Alzheimer's disease: Group differentiation, baseline cognition, and predicition of future cognitive decline.

机译：正常衰老，轻度认知障碍和阿尔茨海默氏病的多模式分析：组别分化，基线认知和未来认知能力下降的预测。

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摘要

Alzheimer's disease (AD) is a progressive neurodegenerative disease with an insidious onset that makes it difficult to distinguish from normal aging. It begins with an impairment of memory that develops into amnestic mild cognitive impairment (aMCI) and later to dementia as deficits become apparent in other cognitive domains. Effective biomarkers that differentiate normal aging, MCI, and AD and predict future cognitive decline are needed. Potential biomarkers have been studied in isolation, but their impact when combined is not understood. The goal of this project is to determine the optimal combination of CSF biomarkers, MRI morphometry, FDG PET metabolism, and neuropsychological test scores to differentiate between normal aging subjects and those with MCI and AD. This study addresses: 1) the optimal normalization region and partial volume correction method to quantify FDG PET analysis, 2) the effects of adjusting MRI-based cortical thickness measures for differences in gray/white matter tissue contrast in normal aging and disease, 3) whether multimodal multivariate stepwise logistic regression models can predict group membership, and 4) whether multimodal multivariate stepwise linear regression models can determine which imaging and CSF biomarker variables best predict future cognitive decline. The results indicate that normalizing FDG PET to the cerebellum along with using a gray matter mask for partial volume correction provides optimal prediction. In contrast, age-associated changes in gray/white matter intensity ratio did not differentiate between the groups and only slightly improved the efficacy of cortical thickness as a biomarker. MRI morphometry of the gray matter and neuropsychological test scores were better able to discriminate between the groups than FDG PET or CSF biomarker concentrations. Combining all modalities significantly improved the index of discrimination, especially at the earliest stages of the disease. MRI gray matter morphometry variables were more highly associated with baseline cognitive function and best predicted future cognitive decline compared to other variables. Overall these findings demonstrate that a multimodal approach using MRI morphometry, FDG PET metabolism, neuropsychological test scores, and CSF biomarkers provides significantly better discrimination than any modality alone. Hence, the variables important for discriminating between the groups may be candidates for biomarkers in human clinical interventional trials.
机译：阿尔茨海默氏病（AD）是一种进行性神经退行性疾病，起病隐匿，难以与正常衰老区分开。它以记忆障碍开始，发展为记忆轻度认知障碍（aMCI），后来随着其他认知领域的缺陷变得明显而发展为痴呆。需要区分正常衰老，MCI和AD并预测未来认知能力下降的有效生物标志物。潜在的生物标志物已被单独研究，但结合使用时其影响尚不清楚。该项目的目标是确定CSF生物标志物，MRI形态学，FDG PET代谢和神经心理学测试成绩的最佳组合，以区分正常衰老受试者与MCI和AD受试者。这项研究的目的是：1）最佳的归一化区域和部分体积校正方法，以定量FDG PET分析； 2）调整基于MRI的皮质厚度测量对正常衰老和疾病中灰/白质组织对比度差异的影响，3）多峰多元逐步逻辑回归模型是否可以预测组成员身份，以及4）多峰多元逐步线性回归模型是否可以确定哪些影像学和CSF生物标志物变量最能预测未来的认知能力下降。结果表明，将FDG PET标准化至小脑以及使用灰质掩膜进行部分体积校正可提供最佳预测。相反，与年龄相关的灰色/白质强度比变化在两组之间没有区别，而仅稍微改善了皮质厚度作为生物标志物的功效。与FDG PET或CSF生物标志物浓度相比，灰质的MRI形态计量学和神经心理学测试得分能够更好地区分两组。结合所有方式，尤其是在疾病的早期阶段，显着改善了歧视指数。与其他变量相比，MRI灰质形态测量变量与基线认知功能和预测未来认知能力下降的关联性更高。总体而言，这些发现表明，使用MRI形态计量学，FDG PET代谢，神经心理学测验分数和CSF生物标志物的多模式方法比单独使用任何模式都具有更好的辨别力。因此，对于区分人群而言重要的变量可能是人类临床干预试验中生物标志物的候选者。

著录项

  • 作者

    Bauer, Corinna Mae.;

  • 作者单位

    Boston University.;

  • 授予单位 Boston University.;
  • 学科 Health Sciences Radiology.;Biology Neuroscience.;Health Sciences Aging.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 246 p.
  • 总页数 246
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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