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: 7904169
• 负责人: Steven L Small
• 金额: $ 6.26万
• 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 2010-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7904169
• 关键词: Adult; Age; Algorithms; Aphasia; Astronomy; Behavioral; Bioinformatics; Biological; Brain; Brain imaging; Case Study; Cognitive; Communities; Complex; Computational Biology; Computer Storage Devices; Computers; Data; Data Set; Databases; Detection; Development; Diagnosis; Diffusion; Disease; Distributed Systems; Documentation; Family; Functional Magnetic Resonance Imaging; Funding; Goals; Hospitals; Image; Individual; Interview; Investigation; Knowledge; Language; Linguistics; Longitudinal Studies; Magnetic Resonance Imaging; Manufacturer Name; Measures; Medical; Metabolic; Metadata; Methods; Modality; Modeling; Neural Network Simulation; Ontology; Paper; Participant; Patients; Performance; Phase; Physics; Physiological; Physiology; Preparation; Privacy; Procedures; Process; Prospective Studies; Protocols documentation; Recovery; Research; Research Design; Research Infrastructure; Research Personnel; Resources; Security; Series; Source; Specific qualifier value; Structure; System; Techniques; Technology; Test Result; Testing; Therapeutic Intervention; Time; Translating; Work; abstracting; base; cluster computing; computer infrastructure; computerized data processing; data management; data modeling; data sharing; design; distributed data; improved; longitudinal analysis; medical information system; novel; outcome forecast; performance tests; programs; prospective; relational database; scale up; software development; task analysis; theories; tool; usability; virtual

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、生理学和行为数据的失语症恢复的大型前瞻性研究实际上不存在。这对几乎所有有关失语症的诊断、预后和治疗的研究都产生了重大影响，因为我们不知道该疾病的自然病程，因此无法充分告知患者和家属或评估治疗干预措施的效果。我们认为，数据管理的复杂性，特别是解剖和生理数据的复杂性，是此类研究设计和执行的主要障碍。由于信息来源多种多样，例如人口统计和医学数据、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个案例研究，更不用说包含具有统计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（例如功能性 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、数据处理复杂，给数据处理带来困难。因此，我们建议建立计算基础设施（R21阶段）以促进失语症恢复的前瞻性研究（R33阶段）。该基础设施基于 (a) 数据库技术的使用，以在单一表示框架内表示不同的数据类型； (b)“网格”计算，使用联邦（NSF）资助的基础计算研究开发的软件，在许多存储设备和计算机上分配数据和数据处理，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磁共振成像和扩散张量成像，以及语言和认知测量，来描述失语症生理和行为恢复的自然过程。恢复的生理学将在个体​​患者成像数据的神经网络模型中进行量化，并且它们与从健康年龄匹配的成年人的成像数据导出的规范模板的数学“拟合”。这些模型随时间的变化将与行为变化相关，从而构建恢复理论。计算基础设施将提供对失语症恢复研究所需的不同类型数据进行编码的方法，以便可以轻松检索涉及多种数据类型（例如，大脑激活和语言表现）的复杂查询，并且由于网格计算，可以快速回答需要大量计算机处理（例如，成像时间序列中的峰值检测）的查询。最后，该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此类问题。