| Structural equation modeling (SEM) is often used to investigate effective connectivity in functional neuroimaging studies. Modeling effective connectivity refers to an approach in which a number of specific brain regions, called regions of interest (ROIs), are selected according to some prior knowledge about the regions, and directional (causal) relationships between them are hypothesized and tested. Existing methods for SEM, however, have serious limitations in terms of their computational capacity and the range of models that can be specified. To alleviate these difficulties, I propose a new method of SEM for analysis of effective connectivity, called Dynamic GSCA (Generalized Structured Component Analysis). This method is a component-based method that combines the original GSCA and a multivariate autoregressive model to account for the dynamic nature of data taken over time. Dynamic GSCA can accommodate more elaborate structural models that describe relationships among ROIs and is less prone to computational difficulties, such as improper solutions and the lack of model identification, than the conventional methods of SEM. To illustrate the use of the proposed method, results of empirical studies based on synthetic and real data are reported. Further extensions of Dynamic GSCA are also discussed, including higher order components, multi-sample comparison, multilevel analysis, and latent interactions. |
