Rogers Baxter P, Morgan Victoria L, Newton Allen T, Gore John C
Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, USA.
Magn Reson Imaging. 2007 Dec;25(10):1347-57. doi: 10.1016/j.mri.2007.03.007. Epub 2007 May 11.
Functional magnetic resonance imaging (fMRI) is widely used to detect and delineate regions of the brain that change their level of activation in response to specific stimuli and tasks. Simple activation maps depict only the average level of engagement of different regions within distributed systems. FMRI potentially can reveal additional information about the degree to which components of large-scale neural systems are functionally coupled together to achieve specific tasks. In order to better understand how brain regions contribute to functionally connected circuits, it is necessary to record activation maps either as a function of different conditions, at different times or in different subjects. Data obtained under different conditions may then be analyzed by a variety of techniques to infer correlations and couplings between nodes in networks. Several multivariate statistical methods have been adapted and applied to analyze variations within such data. An approach of particular interest that is suited to studies of connectivity within single subjects makes use of acquisitions of runs of MRI images obtained while the brain is in a so-called steady state, either at rest (i.e., without any specific stimulus or task) or in a condition of continuous activation. Interregional correlations between fluctuations of MRI signal potentially reveal functional connectivity. Recent studies have established that interregional correlations between different components of circuits in each of the visual, language, motor and working memory systems can be detected in the resting state. Correlations at baseline are changed during the performance of a continuous task. In this review, various methods available for assessing connectivity are described and evaluated.
功能磁共振成像（fMRI）被广泛用于检测和描绘大脑中因特定刺激和任务而改变激活水平的区域。简单的激活图仅描绘了分布式系统中不同区域的平均参与水平。fMRI有可能揭示关于大规模神经系统组件在功能上耦合在一起以完成特定任务的程度的额外信息。为了更好地理解大脑区域如何对功能连接的回路做出贡献，有必要将激活图记录为不同条件、不同时间或不同受试者的函数。然后可以通过各种技术分析在不同条件下获得的数据，以推断网络中节点之间的相关性和耦合。几种多元统计方法已经被改编并应用于分析此类数据中的变化。一种特别有趣的适用于研究单受试者内连通性的方法利用了在大脑处于所谓的稳态时（无论是在休息状态，即没有任何特定刺激或任务，还是在持续激活状态）获取的一系列MRI图像。MRI信号波动之间的区域间相关性可能揭示功能连通性。最近的研究已经证实，在静息状态下可以检测到视觉、语言、运动和工作记忆系统中每个系统的回路不同组件之间的区域间相关性。在执行连续任务期间，基线时的相关性会发生变化。在这篇综述中，描述并评估了可用于评估连通性的各种方法。
