How well does structural equation modeling reveal abnormal brain anatomical connections? An fMRI simulation study

Many brain disorders result from alterations in the strength of anatomical connectivity between different brain regions. This study investigates whether such alterations can be revealed by examining differences in interregional effective connectivity between patient and normal subjects. We applied one prominent effective connectivity method - Structural Equation Modeling (SEM) - to simulated functional MRI (fMRI) timeseries from a neurobiologically realistic network model in which the anatomical connectivity is known and can be manipulated. These timeseries were simulated for two task conditions, a delayed match-to-sample (DMS) task and passive-viewing, and for "normal subjects" and "patients" who had one weakened anatomical connection in the neural network model. SEM results were compared between task conditions as well as between groups. A significantly reduced effective connectivity corresponding to the weakened anatomical connection during the DMS task was found. We also obtained a significantly reduced set of effective connections in the patient networks for anatomical connections "downstream" from the weakened linkage. However, some "upstream" effective connections were significantly larger in the patient group relative to normals. Finally, we found that of the SEM model measures we examined, the total error variance was the best at distinguishing a patient network from a normal network. These results suggest that caution is necessary in applying effective connectivity methods to fMRI data obtained from non-normal populations, and emphasize that functional interactions among network elements can appear as abnormal even if only part of a network is damaged.