The Effects of Computational Method, Data Modeling, and TR on Effective Connectivity Results

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Abstract
As the use of effective connectivity has become more popular, it is important to understand how the results from different analyses compare with each other, as the results from studies employing differing methods for determining connectivity may not reach the same conclusion. Simulated fMRI time series data were used to compare the results from four of the more commonly used computational methods, structural equation modeling, autoregressive analysis, Granger causality, and dynamic causal modeling to determine which may be better suited to the task. The results show that all three methods are able to detect changes in system dynamics. Structural equation modeling appeared to be the least sensitive to changes in TR or source of variance, and Granger causality the most sensitive. The results also suggest that improved reporting on data analyses is necessary, and employing an effect statistic to depict results may remove some of the ambiguity in comparing results across studies using differing methods to determine connectivity.