Clinical Assessment in Neuropsychiatric Image Databases

描述（由申请人提供）：本项目旨在实现一个数据库 沿着用户友好的网络集成工具， 支持神经精神临床诊断和知识发现（KDD）。 在第一阶段，我们寻求为三个中间和令人信服的问题提供解决方案。 需求首先，为了做出充分知情的诊断和检查，临床医生现在 需要快速方便地访问规范数据库来解释， 常规视觉检查，个体患者的功能性脑图像。 第二，敏感和有效的搜索，有能力控制 人口统计学变量，需要正常受试者和 患者神经影像学数据库通常不包含筛选的受试者， 神经精神疾病的详细信息这种筛查是必不可少的，因为 精神疾病的终生患病率--导致长期的-- 术语结构和功能序列。第三，改进更有力 数据库管理系统（DBMS）和专用计算机接口必须 支持这些数据集及其查询。 我们提出了以下具体目标：1）实现一个关系， 包含PET图像数据、程序 和结果，沿着用于可视化和分析的通用工具。2)到 将我们现有的数据（约6，000次PET扫描）纳入数据库 用于归档、存储、策展和KDD;以及获取（用于临床 应用）50次正常葡萄糖代谢扫描（FDG）。3)开发一个 临床医生？4）评估第一阶段的结果a）通过具体的 应用于阿尔茨海默病的诊断疾病（AD）;通过测试 跨设施的参考数据集的通用性;以及 在临床随访和尸检方面进行交叉验证。 该项目应提供必要的基础设施和验证， 在第二阶段中，我们将朝着网络分布式数据库联盟的方向前进 包含控制和神经精神病学数据，以支持临床护理， research.

DESCRIPTION (provided by applicant): This project aims to implement a database of neuropsychiatric PET images along with user-friendly, web-integrated tools to support neuropsychiatric clinical diagnosis and knowledge discovery (KDD). In Phase I, we seek to provide solutions to three mediate and compelling needs. First, to make fully informed diagnoses and prognoses, clinicians now require fast and easy access to normative databases to interpret, beyond routine visual inspection, functional brain images of individual patients. Second, sensitive and efficient searches, that have the power to control for demographic variables, call for large datasets of both normal subjects and patients. Neuroimaging databases do not typically contain subjects screened in detail for neuropsychiatric disease. Such screening is essential given the 32% lifetime prevalence of mental illnesses -- resulting in long-\ term structural and functional cnsequences. Third, improved and more powerful database management systems (DBMS) and specialized computer interface must support such datasets and their query. We propose the following Specific Aims: 1) To implement a relational, interoperable, web-distributed database containing PET image data, procedures, and results, along with general tools for visualization and analysis. 2) To incorporate into the database our extant data (approximately 6,000 PET scans) for archiving, storage, curation, and KDD; and to acquire (for clinical application) fifty normative glucose metabolic scans (FDG). 3) To develop a clinician?s interface; and 4) To evaluate Phase I results a) by specific application to the diagnosis of Alzheimer?s disease (AD); by testing the generalizability of reference datasets across facilities; and by crossvalidating with respect to clinical follow-up and autopsy. This project should provide the necessary infrastructure and validation to proceed in Phase II toward a web-distributed federation of databases containing control and neuropsychiatric data to support clinical care and research.