NETWORK FOR THE STUDY OF BRAIN SYSTEMS & DYNAMICS

该子项目是利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得主要资金， 因此可以在其他CRISP条目中表示。列出的机构是 中心，不一定是研究者的机构。 该项目建立了一个跨学科的研究人员网络，并将他们的工作集中在研究注意力的部署和随后的目标刺激的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，并利用这个机会来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。该研究是基于一个理论框架，用于指导基于行为，病变，动物和生理信息的注意力。我们利用多模态功能成像与功能磁共振成像和脑电图的措施，在线索注意力实验，结合先进的分析，从许多角度来确定空间和时间的相互作用的大脑区域的关注。在fMRI和EEG数据的标准分析之后，我们结合fMRI数据适当地执行EEG数据的脑源定位。高级信号分析的应用包括专注于基于预先指定的统计假设（例如，偏最小二乘法（PLS）和表征数据所有维度的方法，识别从一开始就不明显的潜在功能不同的活性成分（独立成分分析，伊卡）。基于模型和无模型的方法来检查功能和有效的大脑区域之间的连接将被应用到丰富的空间和时间的数据与功能磁共振成像和脑电图记录。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整合用于注意力和其他认知神经科学领域研究的方法。研究人员将定期开会，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.