Consistent resting-state networks across healthy subjects
Consistent resting-state networks across healthy subjects
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DOI：
10.1073/pnas.0601417103
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发表时间：
2006-09-12
影响因子：
11.1
通讯作者：
Beckmann, C. F.
中科院分区：
文献类型：
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作者：
Damoiseaux, J. S.;Rombouts, S. A. R. B.;Beckmann, C. F.
功能磁共振成像(FMRI)可以应用于研究人脑的功能连接。有人认为，静息时血氧水平依赖(BOLD)信号的波动反映了大脑的神经元基线活动，代表了在缺乏目标定向神经元动作和外部输入的情况下人类大脑的状态，这些缓慢的波动对应于功能上相关的静息状态网络。已经进行了几项关于静息功能磁共振的研究，报告了识别出的模式之间的明显相似性。然而，这些休眠模式的空间一致性尚未得到评估和量化。在这项研究中，我们将一种称为张量概率独立分量分析的数据分析方法应用于静息状态的fMRI数据，以找到跨受试者和会话的一致性。我们使用自举方法来表征和量化这些影响的一致性，并且我们估计了大胆的幅度调制以及体素方向的跨对象变化。分析发现了10种与潜在功能相关的模式，包括已知涉及运动功能、视觉处理、执行功能、听觉处理、记忆和所谓的默认模式网络的区域，每种模式的大胆信号变化高达3%。一般来说，具有高平均百分比粗体信号的区域是一致的，并且在平均值附近表现出最小的变化。这些发现表明，大脑的基线活动在表现出显著的时间动力学的受试者中是一致的，大胆信号变化的百分比与任务相关实验中发现的信号变化相当。
Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3%. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.