Bioinformatics Infrastructure for Large Scale Studies of Aphasia Recovery

描述(申请人提供)：结合解剖学、生理学和行为学数据的关于失语症康复的大型前瞻性研究几乎不存在。这对失语症的诊断、预后和治疗的几乎所有研究都有重大影响，因为我们不知道疾病的自然病程，因此不能充分告知患者和家人或评估治疗干预的效果。我们认为，数据管理的复杂性，特别是关于解剖学和生理学数据，是设计和执行此类研究的主要绊脚石。由于拥有如此多样化的信息来源，如人口统计和医学数据、认知和语言测试结果、电生理记录和许多类型的脑图像，进行试图将这些数据相互关联的单一案例研究已经足够困难，更不用说包括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，如功能磁共振数据，我们也没有能力将单个受试者使用的方法扩大到更大的群体。数据量大，数据处理复杂，都带来了困难。因此，我们建议建立计算基础设施(R21阶段)，以促进失语症恢复的前瞻性研究(R33阶段)。该基础设施的基础是使用(A)数据库技术在单个代表性框架内表示不同的数据类型；以及(B)使用联邦政府(NSF)资助的基础计算研究开发的软件，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。恢复的生理学将在单个患者成像数据的神经网络模型中进行量化，并将其与从年龄匹配的健康成年人的成像数据中得出的标准模板进行数学“匹配”。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相关。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，使得涉及多种数据类型(例如，大脑激活和语言能力)的复杂查询可以被容易地检索，并且由于网格计算，需要大量计算机处理(例如，成像时间序列中的峰值检测)的查询可以被快速回答。最后，这一基础设施和数据将被共享，该系统的用户几乎可以从任何地方使用关系数据库查询接口提出这样的问题。

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.