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首页> 外文学位 >Mapping the influence of genes on brain structure using tensor based morphometry (TBM) and diffusion tensor imaging (DTI).
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Mapping the influence of genes on brain structure using tensor based morphometry (TBM) and diffusion tensor imaging (DTI).

机译：使用基于张量的形态学（TBM）和扩散张量成像（DTI）来绘制基因对大脑结构的影响图。

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摘要

Genetic studies have been growing in popularity as a subfield of computational neuroimaging. Improvements in computational techniques for genetic studies may greatly further our understanding of the contribution of genes to brain structure and function. Here we use Tensor-based morphometry (TBM) to analyze both structural MRI (sMRI) and diffusion tensor images (DTI) to understand group differences in anatomy in populations with congenital disorders (Fragile X, Velo-cardio facial syndrome (VCFS), congenital blindness) compared to healthy control populations. We also look at the influence of genes on normal brain anatomy using sMRI and DTI images from healthy young adult twins.;TBM consists of registering all subjects from two groups to be compared to a common space, and analyzing the Jacobian matrices induced by the deformation to obtain group differences in local brain size and/or shape. Here we first show results of a standard TBM analysis on Fragile X and VCFS datasets. We extend TBM to DTI by performing group statistics on the diffusion tensors instead of the Jacobian matrices. We also improve on the more common univariate analyses by looking at several multivariate measures. Both uni- and multi-variate DT-derived measures are analyzed, including: the 6-dimensional full DT and the 3 eigenvalues, standard univariate measures such as the fractional anisotropy (FA) and the mean diffusivity (MD), and the more geometrically correct geodesic anisotropy. The method is applied to obtain anatomical differences from innately blind subjects, as well as to extract genetic information from the twins. Effect sizes are stronger with the multivariate statistics, compared to the more standard scalars ones.;Standard scalar twin statistics include the intraclass correlation and Falconer's heritability. More recently, structural equation models were used in twin statistics (ACE model) to better distinguish between the various genetic and environmental factors that may contribute to phenotypes. We extend and apply all of these statistical methods from univariate to DT-derived multivariate measures. Genetic analysis of the full DT leads to better-fitting statistical models with higher power to detect effects of gene and shared-environments. The multivariate intraclass correlation can be improved by using the restricted maximum-likelihood method.
机译：遗传学研究作为计算神经成像的一个子领域越来越受欢迎。遗传研究计算技术的改进可能极大地增进我们对基因对大脑结构和功能的贡献的理解。在这里，我们使用基于Tensor的形态计量学（TBM）来分析结构性MRI（sMRI）和扩散张量图像（DTI），以了解先天性疾病（脆性X，Velo-心脏面部综合征（VCFS），先天性失明）与健康对照人群相比。我们还使用来自健康的年轻成年双胞胎的sMRI和DTI图像来研究基因对正常大脑解剖结构的影响; TBM包括注册两组要比较的对象与一个公共空间，并分析由变形引起的雅可比矩阵以获得局部大脑大小和/或形状的群体差异。在这里，我们首先显示对脆弱X和VCFS数据集进行标准TBM分析的结果。通过对扩散张量而不是雅可比矩阵执行组统计，我们将TBM扩展到DTI。通过查看几种多元度量，我们还改进了更常见的单变量分析。分析了单变量和多变量DT衍生的测度，包括：6维完整DT和3个特征值，标准单变量测度（例如分数各向异性（FA）和平均扩散率（MD））以及更几何的正确的测地线各向异性。该方法适用于从先天盲目的受试者获得解剖学差异，以及从双胞胎中提取遗传信息。与更标准的标量相比，多元统计量的效应量更大。；标准的标量孪生统计量包括类内相关性和Falconer的遗传力。最近，在双胞胎统计（ACE模型）中使用了结构方程模型，以更好地区分可能影响表型的各种遗传和环境因素。我们将所有这些统计方法从单变量扩展到DT衍生的多变量，并将其应用。完整DT的遗传分析可产生更适合统计模型，并具有更高的检测基因和共享环境影响的能力。可以使用受限的最大似然法来改善多元类内相关性。

著录项

  • 作者

    Lee, Dong-Eun.;

  • 作者单位

    University of California, Los Angeles.;

  • 授予单位 University of California, Los Angeles.;
  • 学科 Engineering Biomedical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 209 p.
  • 总页数 209
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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