Improved Methods for Single Subject FMRI Analysis

单受试者 FMRI 分析的改进方法

• 批准号: 7075928
• 负责人: PAUL K MAZAIKA
• 金额: $ 16.23万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-06-01 至 2011-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7075928
• 关键词: artificial intelligence; bioimaging /biomedical imaging; body movement; brain disorder diagnosis; clinical research; cognition; cognition disorders; computer assisted diagnosis; computer program /software; developmental disease /disorder; dyslexia; functional magnetic resonance imaging; human data; human subject; image enhancement; image processing; learning disorders; mental disorder diagnosis; mental retardation; patient oriented research

DESCRIPTION (provided by applicant): Mental illness is a great burden for the affected individual and economically costly for society. The annual cost of mental disorders has been estimated to be $150 billion, increasing every year, and this total does not include more than three million people receiving disability benefits due to mental disorders. It is imperative that we prioritize research efforts focused on understanding brain function in order to improve diagnostic strategies and discover more effective therapies. Functional Magnetic Resonance Imaging (fMRI) is a powerful tool to visualize and measure typical and atypical cognitive processing. However, many important cognitive processing systems, such as those associated with memory, language, emotion and executive control, only produce small BOLD signals and thus measurements are noisy and have low statistical confidence. Hence, fMRI has not been readily adopted for clinical diagnosis of individual patients. I propose to develop greatly improved methods to suppress the noise sources in fMRI data in order to transform fMRI from a research tool about populations to a consistent and accurate diagnostic tool to study individual cognitive functions. Using the strategy that every noise suppression algorithm must perform well to reliably detect single trial fMRI BOLD signals, I developed visualization methods to "see" deeply into fMRI data to evaluate the quality of the data at every step of fMRI data processing. The preliminary studies indicate that there are clear opportunities to improve fMRI image analysis techniques. The proposed research will first develop and test methods to improve suppression of errors from motion and physiological fluctuations. Then it will translate this research by combining these techniques with pattern recognition to characterize individual cognitive activation patterns in typical and atypical populations. My quantitative science expertise is in image processing, algorithm design, and pattern recognition. The research directly supports my interdisciplinary career development with hands-on experience in experiment planning, fMRI scanner operation, neuroscience coursework, and new software methods for application to severely brain disordered populations. In particular, the subjects for this research will include important clinical psychiatric populations with disorders such as fragile X syndrome, Turner syndrome, autism, Williams syndrome, depression, and bipolar disorder, so that all newly developed methods can be immediately put into practice.

描述（由申请人提供）：精神疾病对受影响的个人来说是巨大的负担，并且对社会造成经济损失。据估计，精神障碍每年造成的费用为 1500 亿美元，并且每年都在增加，而这一总额还不包括因精神障碍而领取残疾福利的超过 300 万人。我们必须优先考虑了解大脑功能的研究工作，以改进诊断策略并发现更有效的疗法。功能磁共振成像 (fMRI) 是可视化和测量典型和非典型认知过程的强大工具。然而，许多重要的认知处理系统，例如与记忆、语言、情感和执行控制相关的系统，仅产生小的 BOLD 信号，因此测量结果充满噪声且统计置信度较低。因此，功能磁共振成像尚未轻易用于个体患者的临床诊断。我建议开发大大改进的方法来抑制功能磁共振成像数据中的噪声源，以便将功能磁共振成像从有关人群的研究工具转变为研究个体认知功能的一致且准确的诊断工具。采用每种噪声抑制算法都必须表现良好才能可靠地检测单次试验 fMRI BOLD 信号的策略，我开发了可视化方法来深入“查看”fMRI 数据，以评估 fMRI 数据处理每一步的数据质量。初步研究表明，显然有机会改进功能磁共振成像图像分析技术。拟议的研究将首先开发和测试方法来改善对运动和生理波动造成的误差的抑制。然后，它将通过将这些技术与模式识别相结合来转化这项研究，以表征典型和非典型人群中的个体认知激活模式。我的定量科学专业知识是图像处理、算法设计和模式识别。这项研究通过实验计划、功能磁共振成像扫描仪操作、神经科学课程作业以及应用于严重脑部疾病人群的新软件方法方面的实践经验，直接支持我的跨学科职业发展。特别是，这项研究的对象将包括患有脆性X综合征、特纳综合征、自闭症、威廉姆斯综合征、抑郁症和双相情感障碍等疾病的重要临床精神病人群，以便所有新开发的方法可以立即付诸实践。