Network for the Study of Brain Systems and Dynamics

脑系统和动力学研究网络

• 批准号: 6770056
• 负责人: GREGORY V SIMPSON
• 金额: $ 32.9万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-30 至 2008-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6770056
• 关键词: adult human (21+); attention; biological signal transduction; brain; clinical research; cognition; computational neuroscience; electroencephalography; functional magnetic resonance imaging; human subject; interdisciplinary collaboration; meeting /conference /symposium; memory; method development; neural information processing; neuropsychology; perception; statistics /biometry

DESCRIPTION (provided by applicant): This project establishes an interdisciplinary network of investigators and focuses their work on the common goal of studying the dynamic brain processes underlying deployment of attention and subsequent selective processing of target stimuli. The overall goals of this project are to devise and execute an interdisciplinary study of attention and to use this opportunity to evaluate and expand the state-of-the-art signal processing, computational and modeling procedures employed in this project. The research is based upon a theoretical framework for directing attention grounded in behavioral, lesion, animal and physiological information. We utilize multi-modality functional imaging with fMRI and EEG measures during cued attention experiments, combined with advanced analyses from many perspectives to determine the spatial and temporal interplay of brain regions underlying attention. Following standard analyses of fMRI and EEG data we perform brain source localization of EEG data in combination with fMRI data as may be appropriate. Applications of advanced signal analyses include methods focused on extracting task-specific activity patterns based on pre-specified statistical hypotheses (e.g., Partial Least Squares, PLS) and methods that characterize all dimensions of the data, identifying potentially functionally distinct activity components that were not otherwise obvious from the outset (Independent Component Analysis, ICA). Model-based and model free methods for examining functional and effective connectivity between brain regions will be applied to the rich spatial and temporal data obtained with the fMRI and EEG recordings. Another goal will be to compare the multiple tools employed and to develop methods for optimally integrating them for studies of attention and other cognitive neuroscience domains. The investigators will meet regularly and work with the consultants regarding extension of the network to include cognitive and human neuropsychological studies, large scale modeling, and animal studies.

描述（由申请人提供）：该项目建立了一个跨学科的研究人员网络，并将他们的工作重点放在研究注意力部署和随后目标刺激选择性处理的动态大脑过程的共同目标上。该项目的总体目标是设计和执行注意力的跨学科研究，并利用这个机会评估和扩展该项目中采用的最先进的信号处理、计算和建模程序。该研究基于一个基于行为、病变、动物和生理信息的注意力引导理论框架。我们在提示注意力实验中利用多模态功能成像与功能磁共振成像和脑电图测量，结合多个角度的高级分析，以确定潜在注意力的大脑区域的空间和时间相互作用。根据功能磁共振成像和脑电图数据的标准分析，我们结合功能磁共振成像数据对脑电图数据进行脑源定位。高级信号分析的应用包括侧重于基于预先指定的统计假设（例如偏最小二乘法，PLS）提取特定任务活动模式的方法，以及表征数据所有维度的方法，识别潜在的功能不同的活动成分否则从一开始就很明显（独立成分分析，ICA）。用于检查大脑区域之间的功能和有效连接的基于模型和无模型的方法将应用于通过功能磁共振成像和脑电图记录获得的丰富的空间和时间数据。另一个目标是比较所使用的多种工具，并开发将它们最佳地整合到注意力和其他认知神经科学领域的研究中的方法。研究人员将定期会面，并与顾问就网络扩展进行合作，以包括认知和人类神经心理学研究、大规模建模和动物研究。