The frontiers of cognitive brain imaging are shifting from examinations of isolated individual brain regions to examinations of interactions between multiple regions in distributed brain networks. Empirical evidence consistently shows that identification of the structure and dynamics of brain networks is the key to understanding cognitive information processing as functions of development, aging, and clinical conditions, among other factors. With funding from the National Science Foundation, Dr. Peter C. Molenaar and his colleagues, Drs. Frank Hillary, Ping Li, and Michael Rovine of the Pennsylvania State University University Park are developing new methods, known as 'effective connectivity mapping,' to identify how different brain regions causally influence each other's activity. Currently available methods have several known shortcomings, and the proposed project is addressing these shortcomings by developing new methods to explicitly model both contemporaneous and time-lagged relationships among the activities of brain regions. The new method will be highly important for functional brain imaging research. It is designed to efficiently accommodate individual differences in connectivity maps and to carry out automatic data-driven search for the optimal network solutions. The researchers are giving special emphasis to integrating the methodology with dimension-reduction techniques in order to identify brain regions of interest and to application of new estimation techniques allowing for arbitrarily time-varying strengths of effective connections among brain regions. The new methodology is based on bilinear vector-autoregressive models, with or without external input, as well as extension of this model into stochastic state-space models.
Application of brain imaging is widespread and increasingly focused on understanding the ways in which activities of brain regions are integrated into interconnected networks. Individual humans differ substantially in brain structure and function, and connections among their brain regions can vary across time due to multiple factors. Therefore, robust statistical methods are required to reliably identify the networks of brain regions of interest. This project is aimed at accomplishing this goal by means of the application of innovative statistical methodology that is extensively validated with simulated and empirical data and implemented in software tools that can be applied in both theory-driven and data-driven ways. The investigators are validating the methodology with large-scale simulation studies and with applications to existing data sets. The new analysis tools will be implemented in commonly used computational platforms. For the benefit of the greater scientific community, the methods will also be made publicly available through the development of user-friendly interfaces, courseware, and consultation.
|Effective start/end date||7/1/12 → 6/30/16|
- National Science Foundation: $509,873.00