Improved Methods for Single Subject FMRI Analysis for Clinical Application

用于临床应用的单受试者 FMRI 分析的改进方法

• 批准号: 7624371
• 负责人: PAUL K MAZAIKA
• 金额: $ 16.09万
• 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/7624371
• 关键词: Adopted; Affect; Algorithms; Autistic Disorder; Biological Markers; Bipolar Disorder; Brain; Brain Diseases; Clinical; Cognitive; Computer software; Data; Data Analyses; Data Quality; Databases; Diagnostic; Discrimination; Disease; Dyslexia; Emotions; Error Sources; Experimental Designs; Fragile X Syndrome; Functional Magnetic Resonance Imaging; Goals; Hand; Human; Image; Image Analysis; Imagery; Individual; Language; Magnetism; Maps; Measurement; Measures; Memory; Mental disorders; Methods; Mining; Modeling; Motion; Movement; Neurodevelopmental Disorder; Neurosciences; Noise; Operative Surgical Procedures; Patients; Pattern; Pattern Recognition; Physiological; Physiology; Population; Predisposition; Process; Research; Research Personnel; Research Subjects; Residual state; Rest; Science; Signal Transduction; Societies; Source; Specificity; System; Techniques; Testing; Translating; Turner' s Syndrome; Williams Syndrome; base; career development; clinical Diagnosis; clinical application; cognitive function; computerized data processing; cost; depression; design; disability; effective therapy; executive function; experience; image processing; improved; neuropsychiatry; prevent; research study; tool

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(申请人提供)：精神疾病对受影响的个人来说是一个巨大的负担，对社会来说也是经济上的代价。据估计，精神障碍每年的成本为1500亿美元，而且每年都在增加，这一总数不包括因精神障碍而领取残疾救济金的300多万人。为了改进诊断策略和发现更有效的治疗方法，我们必须将重点放在了解大脑功能的研究工作上。功能磁共振成像(FMRI)是可视化和测量典型和非典型认知加工的有力工具。然而，许多重要的认知处理系统，如与记忆、语言、情绪和执行控制相关的系统，只产生微小的粗略信号，因此测量结果噪声大，统计置信度低。因此，功能磁共振成像并不容易被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fMRI数据中的噪声源，以便将fMRI从关于人群的研究工具转变为研究个体认知功能的一致和准确的诊断工具。使用每种噪声抑制算法都必须很好地执行才能可靠地检测单次试验fMRI粗放信号的策略，我开发了可视化方法来深入了解fMRI数据，以评估fMRI数据处理的每一步的数据质量。初步研究表明，fMRI图像分析技术存在明显的改进机会。这项拟议的研究将首先开发和测试改进运动和生理波动误差抑制的方法。然后，它将通过将这些技术与模式识别相结合来翻译这项研究，以表征典型和非典型人群中的个体认知激活模式。我的量化科学专长是图像处理、算法设计和模式识别。这项研究直接支持了我的跨学科职业发展，在实验规划、功能磁共振扫描仪操作、神经科学课程工作以及应用于严重大脑紊乱人群的新软件方法方面具有实践经验。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疾病人群，以便所有新开发的方法都可以立即付诸实施。