Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans.
Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans.
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DOI：
10.1016/j.neuroimage.2010.08.030
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发表时间：
2011-01-15
期刊：
影响因子：
5.7
通讯作者：
Keilholz, Sheila D.
中科院分区：
文献类型：
--
作者：
Majeed, Waqas;Magnuson, Matthew;Hasenkamp, Wendy;Schwarb, Hillary;Schumacher, Eric H.;Barsalou, Lawrence;Keilholz, Sheila D.
关键词：
大多数涉及BOLD信号自发波动的研究提取了连接模式，这些模式显示了在扫描过程中保持的大脑区域之间的关系。然而，在本研究中，我们研究了BOLD波动的时空动态，以确定扫描中常见的传播模式。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。将该算法应用于在没有任何任务或刺激的情况下获得的大鼠和人的高时间分辨率T2*加权多层图像。在大鼠中，主要模式由高信号强度的波组成，在皮层的外侧向内侧方向传播，重复了我们之前的发现。这些波主要在感觉运动皮层观察到，但也扩展到视觉和顶叶关联区。第二种模式，局限于皮层下区域，包括尾状壳核信号强度的初始增加和随后的减少。在人类中，最常见的时空模式包括由“默认模式”（例如，后扣带皮层和前额叶前部内侧皮层）和“任务积极”（例如，顶叶上部皮层和前运动皮层）网络组成的区域的激活之间的改变。信号从焦点起点传播也被观察到。模式发现算法对用户定义参数的变化不敏感，结果在受试者内部和受试者之间是一致的。这种探索大脑自发网络活动的新方法对传统功能连接研究的解释具有重要意义，并可能增加从神经成像数据中获得的信息量。
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. 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.
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DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
作者：
Fox, MD;Snyder, AZ;Raichle, ME
通讯作者：
Raichle, ME
影响因子：
5.7
作者：
Gonçalves, SI;de Munck, JC;da Silva, FHL
通讯作者：
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影响因子：
2.5
作者：
Cordes, D;Haughton, V;Maravilla, K
通讯作者：
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DOI：
10.1073/pnas.0705791104
发表时间：
2007-11-13
影响因子：
11.1
作者：
Lu, Hanbing;Zuo, Yantao;Stein, Elliot A.
通讯作者：
Stein, Elliot A.
影响因子：
2.5
作者：
Williams, Kathleen A.;Magnuson, Matthew;Majeed, Waqas;LaConte, Stephen M.;Peltier, Scott J.;Hu, Xiaoping;Keilholz, Sheila D.
通讯作者：
Keilholz, Sheila D.