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