Adaptive Planning in Complex Physical Scenarios

This project will identify brain networks that allow humans to be sophisticated planners and will develop novel tools for modeling those neural systems. In the real world people use multiple strategies to plan an action.  They might guess what to do and through trial and error gain some success with repeated practice. Or, they might use a familiar plan drawn from memory. Between these extremes is the ability to simulate possible actions. We will use novel tasks to capture these different strategies and employ network control theory as a convenient framework to model brain data.

Project objectives:

• Identify functional brain networks that generalize known information to new planning contexts using fMRI. Use this as challenge data for developing subspace models.
• Characterize the role of scale over space and time.
• Develop nonlinear models capable of evolving subspaces and/or coefficients of subspaces.
• Use TMS and resting state fMRI as independent model validation.
• Predict changes to network dynamics during planning task and apply this to TMS targeting to either enhance or impair planning performance in collaboration with the ARL-HRED team.