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.
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