Lagged covariance structure models for studying functional connectivity in the brain

Most cognitive processes are supported by large networks of brain regions. To describe the operation of these networks, it is critical to understand how individual areas are functionally connected. Here, we establish a statistical framework for studying effective and functional brain connectivity, using data obtained with a relatively new neuroimaging method, the event-related optical signal (EROS). The novelty of our approach is the use of timing information (in the form of lagged cross-correlations) in interpreting the connections between areas. Interpretation of lagged cross-correlations exploits the combination of spatial and temporal resolution provided by EROS. In this paper, we apply dynamic factor analysis as a method for testing various structural models on the lagged covariance matrices derived from the EROS data. We first illustrate the approach by testing a simple path model of neural activity propagation from area V1 to V3 in a visual stimulation task. We then build more complex structural equation models with latent variables, describing both within-hemisphere integrity, and interactions between the two hemispheres, to interpret data from a second task involving inter-hemispheric competition. The results demonstrate how the integrity of anatomical connections between the two hemispheres explains different patterns of cross-hemispheric interactions. This approach allows for fitting brain imaging data to complex models that capture dynamic cognitive processes as they rapidly evolve over time.