  Analysis of brain activity in resting-state is of fundamental importance in
identifying functional characteristics of neuronal system. Although resting
brain has been extensively investigated for low frequency synchrony between
brain regions, namely Functional Connectivity (FC), the other main stream of
brain connectivity analysis that seeks causal interactions between brain
regions, Effective Connectivity (EC), has been little explored in spontaneous
brain oscillations. Inherent complexity of brain activities in resting-state,
as is observed in BOLD (Blood Oxygenation-Level Dependant) fluctuations, call
for exploratory methods for characterizing these causal networks. On the other
hand, the inevitable effects that hemodynamic system imposes on causal
inferences based on fMRI data, lead us toward the methods in which causal
inferences can take place in latent neuronal level, rather than observed BOLD
time-series. To simultaneously satisfy these two concerns, in this paper, we
introduce a novel state-space system identification approach for studying
causal interactions among brain regions in the absence of explicit cognitive
task. Using extensive simulations, we study the effects of network size and
signal to noise ratio (SNR) on the accuracy of our proposed method in EC
detection. Our simulations demonstrate that Subspace-based Identification
Algorithm (SIA) is sufficiently robust against above-mentioned factors, and can
reliably unravel the underlying causal interactions of resting-state BOLD fMRI
