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Quantitative analysis of activation patterns in fMRI images of language regions of interest in the human brain in healthy subjects and patients with epilepsy.

机译：定量分析健康受试者和癫痫患者人脑中感兴趣的语言区域的功能磁共振成像图像中的激活模式。

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摘要

This dissertation describes the development of novel methods that use computer aided analysis of functional magnetic resonance imaging (fMRI) data to quantify the language activation patterns in certain regions of interest (ROI) in patients with epilepsy, and distinguish them from normal activation patterns. Previous studies attempted to study activation development by mining simple features and tracking them through development. As a result, none of these studies were able to clearly quantify the numerous differences in brain activation between normal populations and epilepsy patients' populations at different ages. Activation is a complex, multifaceted topic. Consequently, complex non-linear features must be studied and examined in detail. Various analysis techniques were used to investigate the difference between normal and patient activation patterns when performing the same task. Functional MRI images of both normal individuals and patients of different epilepsy types contain a wealth of information that have yet to be explored. By extracting volumetric and topological activation features from normal images and tracking how those features develop through age, the functional development of language networks in the brain can be followed through age especially in the developing ages (4-12 years) of normal individuals, and this can provide information regarding how epilepsy perturbs the development of a normal language network. There were two goals for this project; the first goal of this work was to develop a set of tools to evaluate any set of fMRI data that has been normalized to the MNI brain template. The other major goal was to use these tools to test the synaptic consolidation in normal population through age, and to investigate delays in language development in epilepsy patients compared to the normal population. Achieving these goals was approached by developing new analysis techniques and algorithms capable of identifying a normal pattern of language activation and then comparing epilepsy subjects' trends to that pattern. Two datasets were used in this project, the first was the subjects' data that was thresholded for activation, and the second was the raw (unthresholded) dataset of the same subjects. Nine volumetric features were calculated as well as hundreds of topological features for each region of interest. Correlation Analysis was applied to reduce the high number of features used. One way analysis of variance was then used to investigate differences between different subject groups using features that resulted from correlation analysis. Finally, Euclidean distance comparisons were computed to draw distinction between subject groups in feature space. The work described in this dissertation offers a number of contributions. Two developmental theories were quantitatively inspected for the first time using these calculated features. The first was the synaptic consolidation theory. This hypothesis was tested by calculating differences between features' means of the normal population at different age groups. The outcome of this test was not supportive of the hypothesized statements. Patients' immaturity theory was also examined by computing differences in correlation between normals and patients of matching and of superior age groups. The results of these tests strongly supported this theory. The methods developed in this work provide novel analysis techniques that can be used to investigate complex activation patterns and characteristics. With additional data and further investigation, it will be possible to test neurological development of subjects with a wider age range and more variability to explore normal activation patterns as well as those of patients.
机译：本文介绍了使用计算机辅助功能磁共振成像（fMRI）数据分析来量化癫痫患者某些特定区域（ROI）的语言激活模式并将其与正常激活模式区分开的新方法的发展。先前的研究试图通过挖掘简单特征并通过开发跟踪它们来研究激活开发。结果，这些研究都无法清楚地量化正常人群和癫痫患者人群在不同年龄时大脑激活的众多差异。激活是一个复杂的，多方面的主题。因此，必须详细研究和检查复杂的非线性特征。在执行相同任务时，各种分析技术被用来调查正常和患者激活模式之间的差异。正常人和不同癫痫类型患者的功能性MRI图像均包含大量信息，尚待探索。通过从正常图像中提取体积和拓扑激活特征并跟踪这些特征随年龄的发展，大脑中语言网络的功能发展可以随年龄而变化，尤其是在正常个体的发育年龄（4-12岁）中，这可以提供有关癫痫如何干扰正常语言网络发展的信息。这个项目有两个目标；这项工作的首要目标是开发一套工具，以评估已对MNI脑模板进行标准化的任何fMRI数据。另一个主要目标是使用这些工具来测试正常人群中随着年龄增长的突触巩固，并调查与正常人群相比癫痫患者语言发展的延迟。通过开发新的分析技术和算法来实现这些目标，这些技术和算法能够识别语言激活的正常模式，然后将癫痫患者的趋势与该模式进行比较。此项目中使用了两个数据集，第一个是受激活阈值的受试者数据，第二个是相同受试者的原始（无阈值）数据集。计算了每个感兴趣区域的九个体积特征以及数百个拓扑特征。应用了相关性分析以减少使用的大量特征。然后使用方差分析的一种方法，使用相关分析得出的特征来调查不同受试者组之间的差异。最后，计算出欧几里得距离比较，以得出特征空间中主题组之间的区别。本文所描述的工作提供了许多贡献。使用这些计算出的特征，首次对两种发展理论进行了定量检验。首先是突触巩固理论。通过计算不同年龄组正常人群特征均值之间的差异来检验该假设。该测试的结果不支持假设的陈述。还通过计算正常人与匹配年龄组和更高年龄组的患者之间的相关差异来检查患者的不成熟理论。这些测试的结果强烈支持了这一理论。在这项工作中开发的方法提供了新颖的分析技术，可用于研究复杂的激活模式和特征。借助其他数据和进一步的研究，将有可能测试年龄范围更广，变异性更大的受试者的神经系统发育，以探索正常的激活模式以及患者的正常激活模式。

著录项

  • 作者

    Oweis, Khalid Jamil.;

  • 作者单位

    The George Washington University.;

  • 授予单位 The George Washington University.;
  • 学科 Biology Neuroscience.;Engineering Biomedical.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 252 p.
  • 总页数 252
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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