Estimating direction in brain-behavior interactions: Proactive and reactive brain states in driving

Conventional neuroimaging analyses have revealed the computational specificity of localized brain regions, exploiting the power of the subtraction technique in fMRI and event-related potential analyses in EEG. Moving beyond this convention, many researchers have begun exploring network-based neurodynamics and coordination between brain regions as a function of behavioral parameters or environmental statistics; however, most approaches average evoked activity across the experimental session to study task-dependent networks. Here, we examined on-going oscillatory activity and use a methodology to estimate directionality in brain-behavior interactions. After source reconstruction, activity within specific frequency bands in a priori regions of interest was linked to continuous behavioral measurements, and we used a predictive filtering scheme to estimate the asymmetry between brain-to-behavior and behavior-to-brain prediction. We applied this approach to a simulated driving task and examine directed relationships between brain activity and continuous driving behavior (steering or heading error). Our results indicated that two neuro-behavioral states emerge in this naturalistic environment: a Proactive brain state that actively plans the response to the sensory information, and a Reactive brain state that processes incoming information and reacts to environmental statistics.