Real-Time Functional MRI with Automated Pattern Classification

描述（由申请人提供）：随着功能磁共振成像（fMRI）在神经科学领域的快速发展，以及越来越多的临床研究使用功能磁共振成像，需要自动化的方法来分析和解释数据。fMRI的高灵敏度使在线分析成为可能，这就要求对潜在的感觉、运动和认知过程的结果进行在线解释。这种能力可以帮助用户最大限度地利用从功能磁共振成像中获得的信息，并对数据质量做出决定。目标是在有限数量的样本数据集的情况下大幅提高非常高维功能磁共振数据的多类模式分类性能，并开发基于我们定制的实时功能磁共振分析平台（TurboFIRE）的集成工具，以在正在进行的实时功能磁共振扫描中执行动态变化的激活模式分类。目标是(a)开发用于实时模式识别的集成高性能fMRI分析链，(b)开发用于模式分类的新颖稀疏自适应聚合和PLS方法，以及(c)通过演示运动，视觉，听觉和心理计算任务的方法来表征用于分类空间分布激活模式的数据分析链的性能。这种自动、实时功能MRI方法的成功演示将为正在进行的扫描过程中动态变化的大脑激活模式的分类提供概念验证。这将为在临床环境中成功执行感觉和运动功能提供一个标准，并促进在研究环境中识别更高的认知过程。分类结果的在线显示也将帮助用户适应范式以提高特异性，并使被试的互动访谈能够进一步探索潜在的大脑过程。该项目的长期目标是开发一种自动化的功能磁共振成像分析工具，该工具将在临床和研究中具有重大的商业潜力。

DESCRIPTION (provided by applicant): With the rapid development of functional MRI (fMRI) in neuroscience and the increasing number of studies using fMRI in clinical research, there is a need for automated methods to analyze and interpret the data. The high sensitivity of fMRI enables online analysis, which calls for online interpretation of the results in terms of underlying sensory, motor and cognitive processes. This capability can help the user to maximize the information obtained from fMRI and to make decisions on the data quality. The goals are to substantially improve the performance of multi-class pattern classification of very high- dimensional fMRI data with limited number of sample data sets, and to develop an integrated tool based on our custom- designed real-time fMRI analysis platform (TurboFIRE) to perform pattern classification of dynamically changing activation patterns during an ongoing real-time fMRI scan. The aims are (a) Develop an integrated high-performance fMRI analysis chain for real-time pattern recognition, (b) Develop novel sparsity-adaptive aggregation and PLS methods for pattern classification, and (c) Characterize the performance of the data analysis chain for classifying spatially distributed activation patterns by demonstrating the methods on motor, visual, auditory and mental computation tasks. The successful demonstration of this automatic, real-time functional MRI methodology will provide a proof-of-concept of classifying dynamically changing brain activation patterns during the ongoing scan. This will provide a criterion for successfully performing a sensory and motor function in the clinical setting and facilitate the identification of higher cognitive processes in the research setting. Online display of the classification result will also assist the user in adapting the paradigm to improve specificity and enables an interactive interview of the subject to further explore underlying brain processes. The long-term goal of this project is to develop an automated fMRI analysis tool that will have significant commercial potential for clinical and research use.