Bioinformatics Infrastructure for Large Scale Studies of Aphasia Recovery

用于失语症恢复大规模研究的生物信息学基础设施

• 批准号: 7669780
• 负责人: Steven L Small
• 金额: $ 62.4万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-20 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7669780
• 关键词: Adult; Algorithms; Aphasia; Behavioral; Bioinformatics; Brain; Brain imaging; Case Study; Cognitive; Complex; Computer Storage Devices; Computers; Data; Databases; Detection; Diagnosis; Diffusion Magnetic Resonance Imaging; Disease; Family; Functional Magnetic Resonance Imaging; Funding; Image; Individual; Investigation; Language; Linguistics; Measures; Medical; Methods; Modeling; Neural Network Simulation; Numbers; Participant; Patients; Performance; Phase; Physiological; Physiology; Process; Prospective Studies; Recovery; Research; Research Infrastructure; Research Personnel; Series; Source; System; Technology; Test Result; Therapeutic Intervention; Time; base; cluster computing; computerized data processing; data management; design; distributed data; healthy aging; outcome forecast; prospective; scale up; software development; theories

DESCRIPTION (provided by applicant): Large prospective studies of aphasia recovery that incorporate anatomical, physiological, and behavioral data are virtually non-existent. This has a significant impact on virtually all research into the diagnosis, prognosis, and treatment of aphasia, since we do not know the natural course of the disease, and thus cannot adequately inform patients and families or assess the effects of therapeutic interventions. We believe that the complexities of data management, particularly regarding anatomical and physiological data, represent a major stumbling block to the design and execution of such studies. With such diverse sources of information as demographic and medical data, cognitive and linguistic test results, electrophysiological recordings, and many types of brain images, it is hard enough to perform single case studies that attempt to relate these data to each other, let alone studies that include statistically meaningful numbers of participants. Even when the problem is restricted to a single data type, such as functional MRI data, we do not have the ability to scale up the methods used in individual subjects to larger groups. Both the large volume of data and the complexity of data processing cause difficulties. We thus propose to build computational infrastructure (R21 phase) to facilitate the prospective investigation of aphasia recovery (R33 phase). The infrastructure is based on the use of (a) database technology to represent diverse data types within a single representational framework; and (b) "grid" computing to distribute data and data processing over many storage devices and computers, using software developed in federally (NSF) funded basic computational research that allows investigators to express complex data processing algorithms in a convenient manner. The longitudinal aphasia study will use structural and functional MRI and diffusion tensor imaging, along with language and cognitive measures, to characterize the natural course of physiological and behavioral recovery from aphasia. The physiology of recovery will be quantified in neural network models of individual patient imaging data and their mathematical "fit" to normative templates derived from imaging data on healthy age-matched adults. The changes in these models over time will be related to the behavioral changes to construct a theory of recovery. The computational infrastructure will provide the means to encode the diverse types of data needed for aphasia recovery research in such a way that complex queries involving multiple data types (e.g., brain activation and language performance) can be retrieved easily, and that queries requiring significant computer processing (e.g., peak detection in imaging time series) can be answered quickly due to grid computing. Finally, this infrastructure and data will be shared, and a user of the system from virtually anywhere could pose such questions using the relational database query interface.

描述（由申请人提供）：结合解剖学、生理学和行为学数据的失语症恢复的大型前瞻性研究几乎不存在。这对几乎所有关于失语症的诊断、预后和治疗的研究都有重大影响，因为我们不知道疾病的自然病程，因此无法充分告知患者和家属或评估治疗干预的效果。我们认为，数据管理的复杂性，特别是关于解剖和生理数据，是设计和执行此类研究的主要绊脚石。由于信息来源多种多样，如人口统计学和医学数据、认知和语言测试结果、电生理记录以及多种类型的大脑图像，很难进行试图将这些数据相互关联的单一案例研究，更不用说包括统计学上有意义的参与者数量的研究了。即使问题仅限于单一数据类型，如功能性MRI数据，我们也没有能力将个体受试者使用的方法扩展到更大的群体。数据量大和数据处理的复杂性都造成了困难。因此，我们建议建立计算基础设施（R21阶段），以促进失语症恢复（R33阶段）的前瞻性研究。基础设施的基础是使用（a）数据库技术，在一个单一的代表性框架内代表不同的数据类型;（B）“网格”计算，利用在联邦资助的基础计算研究中开发的软件，将数据和数据处理分布在许多存储设备和计算机上，使研究人员能够以方便的方式表达复杂的数据处理算法。纵向失语症研究将使用结构和功能MRI和扩散张量成像，沿着语言和认知测量，以表征失语症生理和行为恢复的自然过程。将在个体患者成像数据的神经网络模型中量化恢复的生理学，并将其数学“拟合”到从健康年龄匹配成人的成像数据中获得的规范模板。这些模型随时间的变化将与行为的变化相联系，从而构建出一个恢复理论。计算基础设施将提供对失语症恢复研究所需的各种类型的数据进行编码的手段，这种方式使得涉及多种数据类型的复杂查询（例如，大脑激活和语言表现）可以被容易地检索，并且需要大量计算机处理的查询（例如，成像时间序列中的峰值检测）可以由于网格计算而被快速回答。最后，这一基础设施和数据将被共享，几乎任何地方的系统用户都可以使用关系数据库查询接口提出这样的问题。