Spatially Oriented Database for Digital Brain Images

描述(由申请人提供)：该项目的总体目标是将先进的图像处理、数据分析和数据管理技术整合到一个脑图像数据库(Braid)中。这些组件的集成极大地帮助了我们的合作者管理和分析基于图像的临床试验(IBCT)，以阐明人脑结构与功能的关联。在前一个周期中，我们扩展了分割算法以纳入更复杂的空间和多光谱信号强度信息；扩展了Braid及其图像处理流水线以适应急性中风数据；实现了我们的贝叶斯形态测量方法；构建了急性中风病变的概率图谱；并使用开源组件重新实现了Braid，改进了用户界面和性能。尽管这些结果与Braid的可视化和其他统计工具相结合，使我们和我们的合作者能够为同行评审的临床和工程文献做出贡献，但我们的经验表明，有必要扩展Braid。首先，我们目前的数据挖掘方法被设计为生成贝叶斯网络，该网络对结构-功能关系进行建模；也就是说，这些模型是描述性的。越来越多的临床神经学家正在试图构建预测模型，他们可以将这些模型应用于新的受试者，甚至是患者，以基于图像数据预测群体成员，反之亦然。这种预测模型可以指导阿尔茨海默病或中风等疾病的早期治疗，因此具有巨大的潜力。其次，尽管我们的贝叶斯形态计量算法在挖掘横断面数据方面表现出了巨大的前景，但许多形态计量研究都集中在退行性疾病上，因此需要进行纵向分析，这考虑到随着疾病的进展或治疗的反应而发生的时间变化。第三，鉴于医疗模式开发的快速发展，以及我们开发成功的方法来挖掘体素样病变和体积数据的经验，我们相信推广我们的贝叶斯数据挖掘方法以适应任意的统计和空间模型将产生一个广泛适用的结构-功能分析库。第四，临床神经学家可用方法的快速扩展也导致了对大型多模式图像集的获取，包括扩散张量数据、功能磁共振图像、病变缺失数据和体素体积测量。这样的数据集正在成为确定神经退行性疾病病理生理机制的标准。例如，几个阿尔茨海默病研究小组正在收集Fmr和结构磁共振数据，以确定形态和激活之间的区域相互作用是否比单独使用这两种方式更好地预测阿尔茨海默病的发展。这样的研究人员将从允许他们分析多光谱数据的软件中受益匪浅。最后，尽管我们对Braid的开源重新实现已经极大地扩展了我们的合作者可以在线使用的统计模型的范围，但我们还可以通过扩展这个统计数据库来进一步扩展Braid的用途，以支持对上面列出的所有数据挖掘算法的基于SQL的访问。为此，我们提出了五个具体目标来进一步扩展Braid的功能：开发用于为脑图像数据生成健壮的、可伸缩的分类器的贝叶斯方法；实施贝叶斯纵向形态计量算法；开发用于独立于通道的结构-函数数据挖掘的类库；开发对多通道图像数据的数据挖掘支持；以及增强Braid的统计数据以集成这些数据挖掘算法，并提供对这些工具的在线访问。我们将使用6个IBCT的数据对这些图像分析、分割和统计扩展进行测试。与公共健康相关：该项目的目标是开发简化脑图像数据分析的软件。我们将从一个可通过网络访问的图像数据库中提供所有这些软件组件。我们预计，随着我们实现这个软件套件的更多组件，神经科学家将发现更容易对他们与人脑结构和功能相关的数据进行复杂的多变量分析。

DESCRIPTION (provided by applicant): The overall goal of this project is the integration of advanced image-processing, data-analysis, and data-management techniques into a brain-image database (BRAID). The integration of these components has greatly aided our collaborators' management and analysis of image-based clinical trials (IBCTs) for the elucidation of structure- function associations in the human brain. In the previous cycle, we extended our segmentation algorithm to incorporate more complex spatial and multispectral signal-intensity information; extended BRAID and its image-processing pipeline to accommodate acute-stroke data; implemented our Bayesian approach to morphometry; constructed a probabilistic atlas of acute-stroke lesions; and re-implemented BRAID using open-source components, improving the user interface and performance in the process. Although these results, in conjunction with BRAID's visualization and other statistical tools, have enabled our collaborators and us to contribute to the peer-reviewed clinical and engineering literature, our experience has demonstrated the need for extensions to BRAID. First, our current data-mining approaches are designed to generate Bayesian networks that model a structure-function relationship; that is, these models are descriptive. Increasingly, clinical neuroscientists are attempting to construct predictive models, which they could apply to new subjects, or even patients, to predict group membership based on image data, or vice versa. Such predictive models could guide early therapy for Alzheimer disease or stroke, among other diseases, and thus have immense potential. Second, although our Bayesian morphometry algorithm has shown great promise for mining cross-sectional data, many morphometry studies center on degenerative diseases, and therefore require longitudinal analysis, which takes into account temporal changes as the disease progresses, or responds to therapy. Third, given the rapid advances in modality development, and based on our experience developing successful approaches to mining voxel-wise lesion and volumetric data, we believe that generalizing our Bayesian data-mining approach to accommodate arbitrary statistical and spatial models would result in a widely applicable structure-function analysis library. Fourth, the rapid expansion of modalities available to clinical neuroscientists has also led to the acquisition of large, multimodality image sets, including diffusion-tensor data, functional MR images, lesion-deficit data, and voxel-wise volumetry. Such data sets are becoming the standard for determining the pathophysiologic mechanisms of neurodegenerative disease. For example, several Alzheimer research groups are collecting fMR and structural MR data, in order to determine whether regional interactions between morphology and activation predict the development of Alzheimer disease better than either modality alone. Such researchers would benefit greatly from software that would allow them to analyze multispectral data. Finally, although our open-source reimplementation of BRAID has already greatly extended the range of statistical models available on-line to our collaborators, we could further extend BRAID's utility by augmenting this statistics datablade to support SQL- based access to all of the data-mining algorithms listed above. Toward these ends, we propose five specific aims to further extend BRAID's functionality: development of a Bayesian method for generating robust, scalable classifiers for brain-image data; implementation of a Bayesian longitudinal morphometry algorithm; development of a class library for modality-independent structure-function data mining; develop data-mining support for multimodality image data; and augmentation of BRAID's statistics datablade to integrate these data-mining algorithms, and to provide on-line access to these tools. We will test these image-analysis, segmentation, and statistical extensions to BRAID, using data from 6 IBCTs. PUBLIC HEALTH RELEVANCE: The goal of this project is to develop software that simplifies the analysis of brain-image data. We will make all of these software components available from a web-accessible image database. We expect that, as we implement more components of this software suite, neuroscientists will find it much easier to perform complex multivariate analyses of their data relating structure and function of the human brain.