VISUALIZATION OF TEMPORAL PATTERNS OF COMPLEX BRAIN ACTIVITY

这个子项目是许多研究子项目中的一个 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来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者的研究机构。 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。 混合性质的大脑网络（即组合的串行，并行，本地独立的操作和网络内的层次依赖关系）预计将导致非常复杂的空间？心理过程中活动的时间模式。 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性。 神经生理学研究已经揭示了在脑区域内的神经元集合中可能的巨大范围的时间活动模式，并且建模研究表明这样的模式可以编码各种功能（例如Steriade等人，1996; Samsonovich & McNaughton，1997）。 当我们考虑到大脑各区域的活动时，我们可以预期更大范围的时间模式。对少量神经元（例如2或3个）或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年）。 当我们研究给定心理过程背后所有相互关联的区域之间的时空模式时，很可能会揭示更多信息（Simpson等人，1995年）。检查不同大脑区域中激活之间的相关性的方法最初已应用于PET和fMRI数据（Friston等人，1996年）。 我们的小组将通过利用我们的综合多模态成像技术提供的活动区域内和活动区域之间的高分辨率时间模式来扩展这种方法。 然而，时空模式将比目前正在研究的其他方法复杂一个数量级，需要该中心提供的新工具进行可视化和分析。

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.