Control and Intelligence Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran 14395-515, Iran.
Neuroimage. 2012 Apr 2;60(2):1236-49. doi: 10.1016/j.neuroimage.2011.12.075. Epub 2012 Jan 8.
The resting brain has been extensively investigated for low frequency synchrony between brain regions, namely Functional Connectivity (FC). However the other main stream of brain connectivity analysis that seeks causal interactions between brain regions, Effective Connectivity (EC), has been little explored. Inherent complexity of brain activities in resting-state, as observed in BOLD (Blood Oxygenation-Level Dependant) fluctuations, calls for exploratory methods for characterizing these causal networks. On the other hand, the inevitable effects that hemodynamic system imposes on causal inferences in 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. This algorithm is a geometrically inspired method for identification of stochastic systems, purely based on output observations. Using extensive simulations, three aspects of our proposed method are investigated: ability in discriminating existent interactions from non-existent ones, the effect of observation noise, and downsampling on algorithm performance. Our simulations demonstrate that Subspace-based Identification Algorithm (SIA) is sufficiently robust against above-mentioned factors, and can reliably uncover the underlying causal interactions of resting-state fMRI. Furthermore, in contrast to previously established state-space approaches in Effective Connectivity studies, this method is able to characterize causal networks with large number of brain regions. In addition, we utilized the proposed algorithm for identification of causal relationships underlying anti-correlation of default-mode and Dorsal Attention Networks during the rest, using fMRI. We observed that Default-Mode Network places in a higher order in hierarchical structure of brain functional networks compared to Dorsal Attention Networks.
静息态大脑的低频同步（即功能连接）已得到广泛研究。然而，寻求大脑区域之间因果相互作用的另一个主要的大脑连通性分析流派，即有效连通性（EC），则很少被探索。静息态大脑活动的固有复杂性，如 BOLD（血氧水平依赖）波动所观察到的，需要探索性方法来描述这些因果网络。另一方面，在 fMRI 数据中，血流动力学系统对因果推断的不可避免影响，促使我们寻求可以在潜在神经元水平而不是观察到的 BOLD 时间序列中进行因果推断的方法。为了同时满足这两个关注点，在本文中，我们提出了一种新的状态空间系统识别方法，用于在没有明确认知任务的情况下研究大脑区域之间的因果相互作用。该算法是一种基于输出观测的几何启发式随机系统识别方法。通过广泛的模拟，研究了我们提出的方法的三个方面：从不存在的交互中区分存在的交互的能力、观测噪声的影响以及对算法性能的降采样影响。我们的模拟表明，基于子空间的识别算法（SIA）对上述因素具有足够的鲁棒性，可以可靠地揭示静息态 fMRI 的潜在因果相互作用。此外，与之前在有效连通性研究中建立的状态空间方法相比，该方法能够描述具有大量大脑区域的因果网络。此外，我们利用所提出的算法，通过 fMRI 识别默认模式和背侧注意网络在静息时反相关的因果关系。我们观察到，与背侧注意网络相比，默认模式网络在大脑功能网络的层次结构中处于更高的层次。
