Georgia Institute of Technology and Emory University, Biomedical Engineering, Atlanta, GA 30322, USA.
Neuroimage. 2011 Jan 15;54(2):1140-50. doi: 10.1016/j.neuroimage.2010.08.030. Epub 2010 Aug 20.
Most studies involving spontaneous fluctuations in the BOLD signal extract connectivity patterns that show relationships between brain areas that are maintained over the length of the scanning session. In this study, however, we examine the spatiotemporal dynamics of the BOLD fluctuations to identify common patterns of propagation within a scan. A novel pattern finding algorithm was developed for detecting repeated spatiotemporal patterns in BOLD fMRI data. The algorithm was applied to high temporal resolution T2*-weighted multislice images obtained from rats and humans in the absence of any task or stimulation. In rats, the primary pattern consisted of waves of high signal intensity, propagating in a lateral to medial direction across the cortex, replicating our previous findings (Majeed et al., 2009a). These waves were observed primarily in sensorimotor cortex, but also extended to visual and parietal association areas. A secondary pattern, confined to subcortical regions consisted of an initial increase and subsequent decrease in signal intensity in the caudate-putamen. In humans, the most common spatiotemporal pattern consisted of an alteration between activation of areas comprising the "default-mode" (e.g., posterior cingulate and anterior medial prefrontal cortices) and the "task-positive" (e.g., superior parietal and premotor cortices) networks. Signal propagation from focal starting points was also observed. The pattern finding algorithm was shown to be reasonably insensitive to the variation in user-defined parameters, and the results were consistent within and between subjects. This novel approach for probing the spontaneous network activity of the brain has implications for the interpretation of conventional functional connectivity studies, and may increase the amount of information that can be obtained from neuroimaging data.
大多数涉及 BOLD 信号自发波动的研究都提取了连接模式，这些模式显示了大脑区域之间在扫描过程中保持的关系。然而，在这项研究中，我们研究了 BOLD 波动的时空动力学，以识别扫描过程中常见的传播模式。开发了一种新的模式发现算法，用于检测 BOLD fMRI 数据中的重复时空模式。该算法应用于没有任何任务或刺激的大鼠和人类的高时间分辨率 T2*-加权多切片图像。在大鼠中，主要模式由高强度信号的波组成，在皮层中从外侧到内侧传播，复制了我们以前的发现（Majeed 等人，2009a）。这些波主要观察到在感觉运动皮层中，但也扩展到视觉和顶叶联合区。次要模式仅限于皮层下区域，由尾状核和壳核中的信号强度初始增加和随后减少组成。在人类中，最常见的时空模式包括组成“默认模式”（例如后扣带回和前内侧前额叶皮层）和“任务正性”（例如顶叶和运动前皮层）网络的激活之间的变化。还观察到从焦点起始点的信号传播。模式发现算法对用户定义参数的变化具有相当的不敏感性，并且结果在受试者内和受试者间是一致的。这种探测大脑自发网络活动的新方法对解释传统功能连接研究具有重要意义，并可能增加从神经影像学数据中获得的信息量。
