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）：该项目建立了一个研究人员的跨学科网络，并将其工作重点放在研究注意力部署的动态大脑过程和随后选择性处理目标刺激的共同目标上。该项目的总体目标是设计和执行对注意力的跨学科研究，并利用这一机会评估和扩展该项目中采用的最新信号处理，计算和建模程序。该研究基于一个理论框架，用于指导以行为，病变，动物和生理信息为基础的注意力。在提示注意力实验期间，我们使用fMRI和脑电图措施利用多模式功能成像，并从许多角度使用高级分析，以确定注意力集中的大脑区域的空间和时间相互作用。遵循fMRI和EEG数据的标准分析，我们可以将脑电图数据与fMRI数据结合使用，这可能是适当的。高级信号分析的应用包括针对提取任务特异性活动模式的方法，基于预先指定的统计假设（例如，部分最小二乘，PLS）和方法，这些方法表征了数据的所有维度，从而识别潜在功能上不同的活动成分，而这些活动在opteet中并非明显地从发明（独立组成部分分析，ICA）。基于模型和模型的无需模型检查大脑区域之间功能和有效连通性的方法将应用于通过fMRI和EEG记录获得的丰富空间和时间数据。另一个目标是比较使用的多种工具，并开发方法，以最佳的方式整合它们，以研究注意力和其他认知神经科学领域。研究人员将定期开会，并与顾问合作，以扩展网络，包括认知和人类神经心理学研究，大规模建模和动物研究。