VISUALIZATION OF TEMPORAL PATTERNS OF COMPLEX BRAIN ACTIVITY

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。 该项目旨在开发可视化复杂大脑活动的空间和时间特征的最佳方法。大脑网络的混合性(即网络中的串联、并行、局部独立操作和层级依赖的组合)预计会导致心理过程潜在活动的非常复杂的时空模式。解剖学研究表明，大脑中的模块化组织具有与网络结构一致的广泛互连。神经生理学研究揭示了大脑区域内神经元集合中可能存在的巨大范围的时间活动模式，建模研究表明这种模式可以编码各种功能(例如，Steriade等人，1996；Samsonovich&McNaughton，1997)。当人们考虑大脑各区域的活动时，可以预料到更大范围的时间模式。对少数神经元(例如2个或3个)或大脑区域之间的时间模式的探索，已经显示出活动中有趣的相关性(例如，McClurkin&Optican，1996；Engel等人，1997)。当我们观察特定心理过程背后所有相互关联的区域之间的时空模式时，很可能会揭示出更多的东西(Simpson等人，1995)。检查不同脑区激活之间的相关性的方法最初已被应用于PET和fMRI数据(Friston等人，1996)。我们的小组将通过利用我们集成的多模式成像技术在活动区内和活动区之间提供的高分辨率时间模式来扩展这一方法。然而，时空模式将比目前用其他方法研究的模式复杂一个数量级，需要该中心提供新的可视化和分析工具。

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 is aimed at developing optimal methods for visualizing spatial and temporal features of complex brain activity. The hybrid nature of brain networks (i.e. combinations of serial, parallel, local independent operations and hierarchical dependencies within a network) is expected to result in very complicated spatial?temporal patterns of activity underlying mental processes. Anatomical studies have demonstrated modular organization in the brain with extensive interconnectivity consistent with network structures. Neurophysiological studies have uncovered the tremendous range of temporal activity patterns possible in ensembles of neurons within a brain area and modeling studies suggest that such patterns can encode a variety of functions (e.g. Steriade et al., 1996; Samsonovich & McNaughton, 1997). An even larger range of temporal patterns can be expected when one considers activity across brain areas. Explorations of temporal patterns between small numbers of neurons (e.g. 2 or 3), or brain areas, have shown interesting correlations in activity (e.g. McClurkin & Optican, 1996; Engel et al., 1997). It is likely that much more will be revealed as we look at the spatial temporal patterns between all the interconnected areas underlying a given mental process (Simpson et al., 1995). Methods that examine correlations between activation in different brain regions have been applied initially to PET and fMRI data (Friston et al., 1996). Our group will expand upon this approach by taking advantage of the high resolution temporal patterns within and between active regions provided by our integrated multimodal imaging techniques. However, the spatial temporal patterns will be an order of magnitude more complex than those currently being studied with other methods, requiring the new tools provided by this Center for visualization and analysis.