Inferring causality using timelag analysis of BOLD data

Brain region interactions could reveal much about the structure of cognitive systems, and might be measurable using time-lagged correlation of BOLD data collected using fMRI. Previous researchers have described methods for detecting time-lagged correlation between region of interest (ROI) activation, primarily variants on Granger causality (GC). With appropriate caveats, GC can draw inferences from temporal precedence about effective connectivity between ROIs in a way methods like SEM and DCM do not. Some studies examined circumstances where time-lagged correlation between ROI activation can help draw causal inferences from BOLD data, and tested the limits of poor temporal resolution of fMRI on this method. The current project examines whether time-lag analysis can estimate the direction of causation in frontal and parietal areas known to act together in spatial working memory tasks. Independent Component Analysis is used to identify components whose interactions are then examined using the GC method. The method successfully identified causal relationships on replicated, artificially simulated data, but has not yet significantly detected GC relationships between ROIs in the spatial working memory task. Changes under way to better detect GC relationships include multivariate Granger analysis, a frequency-domain approach, and optimizing selection of components.