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）：纳入解剖学，生理和行为数据的大量前瞻性研究实际上是不存在的。这实际上对所有对诊断，预后和治疗失语症的研究都有重大影响，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庭或评估治疗性干预的影响。我们认为，数据管理的复杂性，特别是在解剖学和生理数据方面，代表了此类研究的设计和执行的主要绊脚石。凭借人口统计学和医学数据等各种信息来源，认知和语言测试结果，电生理记录以及许多类型的大脑图像，很难进行单个案例研究，这些案例研究试图将这些数据相互联系，更不用说包括统计学上有意义的参与者的研究。即使该问题仅限于单个数据类型，例如功能性MRI数据，我们也无法扩大针对大型组的单个受试者中使用的方法。大量数据和数据处理的复杂性都会引起困难。因此，我们建议建立计算基础设施（R21阶段），以促进对失语症恢复（R33阶段）的前瞻性研究。基础架构基于使用（a）数据库技术来表示单个表示框架内的多种数据类型的使用； （b）使用Federally（NSF）资助的基本计算研究中开发的软件，可以使研究人员以方便的方式表达复杂的数据处理算法，以在许多存储设备和计算机上分发数据和数据处理以在许多存储设备和计算机上分发数据处理。纵向失语症研究将使用结构和功能性MRI和扩散张量成像以及语言和认知度量，以表征失语症的生理和行为恢复的自然过程。恢复的生理学将在单个患者成像数据的神经网络模型中进行量化，并将其数学上的“拟合”与健康年龄匹配的成年人成像数据得出的规范模板。随着时间的推移，这些模型的变化将与构建恢复理论的行为变化有关。计算基础架构将提供手段来编码失语症恢复研究所需的多种数据类型的方式，即可以轻松地检索涉及多种数据类型的复杂查询（例如，大脑激活和语言性能），并且可以快速回答需要大量的计算机处理（例如，成像时间序列中的峰值检测）的查询。最后，将共享此基础架构和数据，并且系统的用户几乎可以使用关系数据库查询接口提出此类问题。