Large-scale Granger causality (lsGC) analysis quantifies multivariate voxel-resolution connectivity in resting-state functional MRI (fMRI) unlike commonly used multivariate approaches that estimate connectivity at a coarse resolution. We investigate the effect noise and repetition time (TR) of fMRI signals have on the ability of lsGC to capture true connectivity and compare with traditionally used multivariate Granger causality analysis (mvGC). To this end, we use realistic fMRI simulations, generated with varying TR and noise levels, for fifty-node simulations. LsGC produces directed connectivity graphs, represented as connectivity matrices which we compare with the known ground truth of the simulations with the Area Under the receiver operating characteristic Curve (AUC) as a measure of agreement. The best AUC with lsGC was 0.957 while the least was 0.835 at TR = 3 s. Our results show that lsGC performs much better than mvGC approaches for both noise levels and different TR. An interesting finding with lsGC was that at higher sampling rate, corresponding to TR < 2 s increase in noise did not significantly reduce performance. However, as with increasing TR beyond 2 s, the effects of noise in the system is no longer negligible. Our results indicate that if the TR is sufficiently small, the performance of lsGC is not hindered greatly by noise levels. However, at higher TR, the deterioration of performance due to high TR is compounded by higher noise levels, indicating that improvements in TR may be more beneficial in extracting accurate lsGC connections.