Abstract
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 (ROIs). 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 ROIs 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.
Highlights
Resting-state functional MRI has been widely used to study the intrinsic functional architecture of the human brain based on spontaneous oscillations of the blood oxygen level dependent (BOLD) signals (Biswal et al, 1995; Power et al, 2011; Smith et al, 2011; Yeo et al, 2011)
Other methods for estimating the direction of a connection varied from chance (Granger) to greater than 50% accuracy [Patel’s Tau and pairwise LiNGAM(Linear, Non-Gaussian, Acyclic causal Models)]. These results suggest that novel methods are needed to estimate directed connectivity from rs-fMRI data, with a large number of regions of interest (ROIs), which are necessary for full coverage of cortical and subcortical areas in the human brain
Results on Simulated Data The methods described in this paper were implemented in Matlab software, which is available upon request, and were applied to four of Smith’s fMRI simulations (Smith et al, 2011)
Summary
Resting-state functional MRI (rs-fMRI) has been widely used to study the intrinsic functional architecture of the human brain based on spontaneous oscillations of the blood oxygen level dependent (BOLD) signals (Biswal et al, 1995; Power et al, 2011; Smith et al, 2011; Yeo et al, 2011). The correlation method, though simple, plays a fundamental role in evaluating functional connectivity in the human brain for both task-evoked networks (Cole et al, 2014; Sadaghiani et al, 2015) and resting-state networks (Power et al, 2013; Hipp and Siegel, 2015; Sadaghiani et al, 2015). Reliable insight into the direction and causality of functional connections in the brain from BOLD signals would provide substantial breakthroughs in characterizing large-scale brain network dynamics
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