NETWORK FOR THE STUDY OF BRAIN SYSTEMS & DYNAMICS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 这个项目建立了一个跨学科的研究人员网络，并将他们的工作集中在共同的目标上，即研究潜在的注意部署和随后的目标刺激的选择性处理的动态脑过程。这个项目的总体目标是设计和执行一项关于注意力的跨学科研究，并利用这个机会来评估和扩展在这个项目中采用的最先进的信号处理、计算和建模程序。这项研究是基于一种基于行为、损伤、动物和生理信息的注意力引导理论框架。在线索注意实验中，我们利用多通道功能成像和fMRI和EEG测量，结合从多个角度进行的高级分析，来确定潜在注意力的大脑区域的空间和时间相互作用。在对fMRI和EEG数据进行标准分析之后，我们结合合适的fMRI数据对EEG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)提取特定于任务的活动模式的方法，以及表征数据的所有维度的方法(独立成分分析(ICA))，识别在其他方面从一开始就不明显的潜在的功能上不同的活动成分。基于模型和无模型的方法用于检查大脑区域之间的功能和有效连接，将应用于通过fMRI和EEG记录获得的丰富的空间和时间数据。另一个目标将是比较所使用的多种工具，并开发出将它们最佳地整合在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，并与顾问合作，将网络扩展到包括认知和人类神经心理学研究、大规模建模和动物研究。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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 combina tion 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.