Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30322, USA.
Neuroimage. 2011 Jan 15;54(2):1043-52. doi: 10.1016/j.neuroimage.2010.09.024. Epub 2010 Sep 17.
Most neuroimaging studies of resting state networks have concentrated on functional connectivity (FC) based on instantaneous correlation in a single network. In this study we investigated both FC and effective connectivity (EC) based on Granger causality of four important networks at resting state derived from functional magnetic resonance imaging data - default mode network (DMN), hippocampal cortical memory network (HCMN), dorsal attention network (DAN) and fronto-parietal control network (FPCN).
METHODOLOGY/PRINCIPLE FINDINGS: A method called correlation-purged Granger causality analysis was used, not only enabling the simultaneous evaluation of FC and EC of all networks using a single multivariate model, but also accounting for the interaction between them resulting from the smoothing of neuronal activity by hemodynamics. FC was visualized using a force-directed layout upon which causal interactions were overlaid. FC results revealed that DAN is very tightly coupled compared to the other networks while the DMN forms the backbone around which the other networks amalgamate. The pattern of bidirectional causal interactions indicates that posterior cingulate and posterior inferior parietal lobule of DMN act as major hubs. The pattern of unidirectional causal paths revealed that hippocampus and anterior prefrontal cortex (aPFC) receive major inputs, likely reflecting memory encoding/retrieval and cognitive integration, respectively. Major outputs emanating from anterior insula and middle temporal area, which are directed at aPFC, may carry information about interoceptive awareness and external environment, respectively, into aPFC for integration, supporting the hypothesis that aPFC-seeded FPCN acts as a control network.
CONCLUSIONS/SIGNIFICANCE: Our findings indicate the following. First, regions whose activities are not synchronized interact via time-delayed causal influences. Second, the causal interactions are organized such that cingulo-parietal regions act as hubs. Finally, segregation of different resting state networks is not clear cut but only by soft boundaries.
大多数静息态网络的神经影像学研究都集中在基于单个网络中瞬时相关的功能连接(FC)上。在这项研究中,我们使用功能磁共振成像数据从四个重要网络中静息态下的 Granger 因果关系研究了 FC 和有效连接(EC),这四个网络分别是默认模式网络(DMN)、海马皮质记忆网络(HCMN)、背侧注意网络(DAN)和额顶控制网络(FPCN)。
方法/原理发现:我们使用了一种称为相关清除 Granger 因果分析的方法,该方法不仅能够使用单个多元模型同时评估所有网络的 FC 和 EC,还能够解释由于神经元活动被血液动力学平滑而导致的它们之间的相互作用。FC 是通过力导向布局可视化的,因果相互作用叠加在该布局上。FC 的结果表明,与其他网络相比,DAN 非常紧密地耦合,而 DMN 则形成了其他网络融合的主干。双向因果相互作用的模式表明,DMN 的后扣带回和后下顶叶作为主要枢纽。单向因果路径的模式表明,海马体和前额叶前皮质(aPFC)接收主要输入,这可能分别反映了记忆编码/检索和认知整合。从前脑岛和颞中区域发出的主要输出,其方向指向 aPFC,可能分别携带关于内脏感知和外部环境的信息,以便在 aPFC 中进行整合,这支持了 aPFC 启动的 FPCN 作为控制网络的假说。
结论/意义:我们的发现表明:首先,活动不同步的区域通过时滞因果影响相互作用。其次,因果相互作用是组织化的,扣带回和顶叶区域作为枢纽。最后,不同静息态网络的分离不是明确的,而只是通过软边界。