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Spatially Oriented Database for Digital Brain Images

数字脑图像空间定向数据库

基本信息

批准号：
8693082
负责人：
Edward H Herskovits
金额：
$ 31.13万
依托单位：
UNIVERSITY OF MARYLAND BALTIMORE
依托单位国家：
美国
项目类别：
财政年份：
1995
资助国家：
美国
起止时间：
1995-09-30 至 2013-09-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8693082
关键词：
Spatially Oriented Database Digital Brain

项目摘要

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.

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集成到脑图像数据库（BRAID）中。这些组件的整合极大地帮助了我们的合作者对基于图像的临床试验（IBCT）的管理和分析，以阐明人脑中的结构-功能关联。在上一个周期中，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及其图像处理管道，以适应急性卒中数据;实施了我们的贝叶斯形态测量方法;构建了急性卒中病变的概率图谱;并使用开源组件重新实施了BRAID，改善了用户界面和过程中的性能。尽管这些结果与BRAID的可视化和其他统计工具相结合，使我们的合作者和我们能够为同行评审的临床和工程文献做出贡献，但我们的经验表明需要扩展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，模型的结构功能关系，也就是说，这些模型是描述性的。越来越多的临床神经科学家正在尝试构建预测模型，他们可以将其应用于新的受试者，甚至患者，以根据图像数据预测群体成员资格，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，虽然我们的贝叶斯形态测量算法在挖掘横截面数据方面表现出了很大的潜力，但许多形态测量研究都集中在退行性疾病上，因此需要纵向分析，这需要考虑到疾病进展或治疗反应的时间变化。第三，鉴于模态开发的快速进展，并根据我们开发成功的方法来挖掘体素病变和体积数据的经验，我们相信，概括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导致广泛适用的结构功能分析库。第四，临床神经科学家可用模式的快速扩展也导致了大型多模式图像集的采集，包括扩散张量数据、功能MR图像、病变缺陷数据和体素体积测定。这些数据集正在成为确定神经退行性疾病的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MR数据，以确定形态学和激活之间的区域相互作用是否比单独的模式更好地预测阿尔茨海默病的发展。这些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，尽管我们的BRAID的开源重新实现已经极大地扩展了我们的合作者在线可用的统计模型的范围，但我们可以通过增强此统计数据库来进一步扩展BRAID的实用性，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的目标，以进一步扩展BRAID的功能：贝叶斯方法的发展，产生强大的，可扩展的分类器的脑图像数据的贝叶斯纵向形态学算法的实施，开发一个类库的模态无关的结构功能数据挖掘，开发数据挖掘支持多模态图像数据，和扩充BRAID的统计数据库，以整合这些数据挖掘算法，并提供对这些工具的在线访问。我们将使用6个IBCT的数据测试这些图像分析、分割和BRAID的统计扩展。公共卫生相关性：该项目的目标是开发简化大脑图像数据分析的软件。我们将使所有这些软件组件可从网络访问的图像数据库。我们希望，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与人脑结构和功能相关的数据进行复杂的多变量分析。

项目成果

期刊论文数量（29）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Predictive models for subtypes of autism spectrum disorder based on single-nucleotide polymorphisms and magnetic resonance imaging.

基于单核苷酸多态性和磁共振成像的自闭症谱系障碍亚型的预测模型。

DOI：
10.2478/v10039-011-0042-y
发表时间：
2011
期刊：
Advances in medical sciences
影响因子：
2.7
作者：
Jiao,Y;Chen,R;Ke,X;Cheng,L;Chu,K;Lu,Z;Herskovits,EH
通讯作者：
Herskovits,EH

Predictive models of autism spectrum disorder based on brain regional cortical thickness.

DOI：
10.1016/j.neuroimage.2009.12.047
发表时间：
2010-04-01
期刊：
NEUROIMAGE
影响因子：
5.7
作者：
Jiao, Yun;Chen, Rong;Ke, Xiaoyan;Chu, Kangkang;Lu, Zuhong;Herskovits, Edward H.
通讯作者：
Herskovits, Edward H.

Structural MRI in autism spectrum disorder.