Consistency of Regions of Interest as nodes of fMRI functional brain networks.
Consistency of Regions of Interest as nodes of fMRI functional brain networks.
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DOI：
10.1162/netn_a_00013
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发表时间：
2017-10-01
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影响因子：
--
通讯作者：
Saramäki J
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文献类型：
--
作者：
Korhonen O;Saarimäki H;Glerean E;Sams M;Saramäki J
功能网络方法将fMRI大胆的时间序列映射到描述大脑区域之间功能关系的网络，为人类大脑的功能打开了新的洞察力。在这种方法中，网络节点的选择至关重要。一种选择是将fMRI体素视为节点。这导致了大量的节点，使得网络分析和结果的解释具有挑战性。一种常见的替代方法是使用解剖上接近的体素的预定义簇，感兴趣区域(ROI)。该方法假设ROI内的体素在功能上相似。由于这两种方法会导致不同的网络结构，因此了解从体素级别移动到ROI级别时网络连接会发生什么是至关重要的。我们发现在静态实验数据中，感兴趣区的一致性变化很大。感兴趣区定义为体素时间序列之间的平均皮尔逊相关系数。因此，每个ROI内的相似体素动态的假设通常不成立。此外，低一致性ROI的时间序列可能高度相关，从而导致ROI级别网络中的虚假链接。基于这些结果，我们建议应该仔细考虑在解剖定义的感兴趣区上平均粗略信号。网络方法对人脑的结构和功能动力学有了新的见解。然而，构建具有功能的大脑网络绝非易事--神经科学界仍然缺乏对大脑网络节点的标准定义。在本文中，我们考虑了两种最常用的方法：使用成像体素或预定义的感兴趣区域(ROI)作为网络的节点。我们研究了当体素级别的信号被平均以获得ROI级别的网络时会发生什么。我们引入了ROI一致性的概念来刻画ROI中体素动态的相似性。在一致性的帮助下，我们表明，尽管ROI中的体素被假设为类似的行为，但这一假设并不适用于所有ROI。
The functional network approach, where fMRI BOLD time series are mapped to networks depicting functional relationships between brain areas, has opened new insights into the function of the human brain. In this approach, the choice of network nodes is of crucial importance. One option is to consider fMRI voxels as nodes. This results in a large number of nodes, making network analysis and interpretation of results challenging. A common alternative is to use predefined clusters of anatomically close voxels, Regions of Interest (ROIs). This approach assumes that voxels within ROIs are functionally similar. Because these two approaches result in different network structures, it is crucial to understand what happens to network connectivity when moving from the voxel level to the ROI level. We show that the consistency of ROIs, defined as the mean Pearson correlation coefficient between the time series of their voxels, varies widely in resting-state experimental data. Therefore the assumption of similar voxel dynamics within each ROI does not generally hold. Further, the time series of low-consistency ROIs may be highly correlated, resulting in spurious links in ROI-level networks. Based on these results, we recommend that averaging BOLD signals over anatomically defined ROIs should be carefully considered. Network methods have opened new insights on structure and functional dynamics of the human brain. However, constructing functional brain networks is far from trivial—the neuroscientific community still lacks a standard definition of the nodes of brain networks. In the present article, we consider the two most commonly used approaches: using either imaging voxels or predefined Regions of Interest (ROIs) as nodes of the network. We investigate what happens when voxel-level signals are averaged for obtaining ROI-level networks. We introduce the concept of ROI consistency to characterize the similarity of the dynamics of voxels in an ROI. With the help of consistency, we show that although voxels in an ROI are assumed to behave similarly, this assumption does not hold for all ROIs.