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Estimating causal interactions in the brain from functional magneticresonance imaging (fMRI) data remains a challenging task. Multiple studies havedemonstrated that all current approaches to determine direction of connectivityperform poorly even when applied to synthetic fMRI datasets. Recent advances inthis field include methods for pairwise inference, which involve creating asparse connectome in the first step, and then using a classifier in order todetermine the directionality of connection between of every pair of nodes inthe second step. In this work, we introduce an advance to the second step ofthis procedure, by building a classifier based on fractional moments of theBOLD distribution combined into cumulants. The classifier is trained ondatasets generated under the Dynamic Causal Modeling (DCM) generative model.The directionality is inferred based upon statistical dependencies between thetwo node time series, e.g. assigning a causal link from time series of lowvariance to time series of high variance. Our approach outperforms or performsas well as other methods for effective connectivity when applied to thebenchmark datasets. Crucially, it is also more resilient to confounding effectssuch as differential noise level across different areas of the connectome.

Increasing robustness of pairwise methods for effective connectivity in  Magnetic Resonance Imaging by using fractional moment series of BOLD signal  distributions