Assessing Brain Interactivity: Model Specification, Causality and Dynamics

Science often advances when new technology facilitates the acquisition of new or more precise types of measurements. However, fully exploiting the power of new technology demands new models and analytical tools. Currently, this is the case with brain imaging technology. For example, one powerful aspect of functional magnetic resonance imaging (fMRI) is its ability to capture the dynamic responses of the brain over time. For the most part, however, existing experimental models and statistical techniques in cognitive neuroscience restrict fMRI studies to identifying brain areas that are active at a given time, while subjects are performing a specific task. Other brain areas that are active before or during such tasks are considered “background,” “secondary” or often just irrelevant and are consequently ignored.

Thus, current methods and assumptions prevent cognitive neuroscience from systematically and rigorously testing causal hypotheses about how networks of brain areas might function collectively in the performance of cognitive or motor tasks. This limitation prevents researchers from exploring at least two novel insights into how the brain might function: 1) higher order cognitive functions may require that a sequence of brain regions are activated jointly in a particular dynamical