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Resting-state functional MRI (rs-fMRI) is widely used to noninvasively studyhuman brain networks. Network functional connectivity is often estimated bycalculating the timeseries correlation between blood-oxygen-level dependent(BOLD) signal from different regions of interest. However, standard correlationcannot characterize the direction of information flow between regions. In thispaper, we introduce and test a new concept, prediction correlation, to estimateeffective connectivity in functional brain networks from rs-fMRI. In thisapproach, the correlation between two BOLD signals is replaced by a correlationbetween one BOLD signal and a prediction of this signal via a causal systemdriven by another BOLD signal. Three validations are described: (1) Predictioncorrelation performed well on simulated data where the ground truth was known,and outperformed four other methods. (2) On simulated data designed to displaythe "common driver" problem, prediction correlation did not introduce falseconnections between non-interacting driven ROIs. (3) On experimental data,prediction correlation recovered the previously identified network organizationof human brain. Prediction correlation scales well to work with hundreds ofROIs, enabling it to assess whole brain interregional connectivity at thesingle subject level. These results provide an initial validation thatprediction correlation can capture the direction of information flow andestimate the duration of extended temporal delays in information flow betweenregions of interest based on BOLD signal. This approach not only maintains thehigh sensitivity to network connectivity provided by the correlation analysis,but also performs well in the estimation of causal information flow in thebrain.

Initial validation for the estimation of resting-state fMRI effective connectivity by a generalization of the correlation approach