Frequency-Dependent Relationship Between Resting-State Functional Magnetic Resonance Imaging Signal Power and Head Motion Is Localized Within Distributed Association Networks
Frequency-Dependent Relationship Between Resting-State Functional Magnetic Resonance Imaging Signal Power and Head Motion Is Localized Within Distributed Association Networks
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DOI：
10.1089/brain.2013.0153
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发表时间：
2014-02-01
影响因子：
3.4
通讯作者：
Napadow, Vitaly
中科院分区：
文献类型：
--
作者：
Kim, Jieun;Van Dijk, Koene R. A.;Napadow, Vitaly
最近的研究强调了静息状态功能磁共振成像（rs-fMRI）数据中光谱功率分析的重要性。权力的显著调节被归因于认知任务的表现，并被归因于临床意义。然而，诸如头部运动等混杂因素对光谱功率的作用尚未完全了解。具体来说，rs-fMRI功率和运动之间的频率相关的空间分布是未知的。我们利用大型rs-fMRI数据集（n = 1000）来量化头部运动对不同频段频谱功率的影响。我们(1)对整个样本进行了回归分析，(2)计算了高运动组和低运动组之间的差异图，与常见的实验设计更一致，两种分析都给出了相似的结果。较大的头部运动导致较低频率（0.007-0.05 Hz）的频谱功率降低，但在较高频率（0.12-0.167 Hz）的频谱功率增加。重要的是，我们的全脑体素分析表明，分布式关联网络中的大脑区域（例如，默认模式和额顶叶控制网络）最容易受到头部运动的影响。这些结果与全局信号回归（GSR）一致或不一致。此外，在没有GSR的情况下，我们注意到与中央前回和中央后回（S1 /M1）、中扣带皮层和脑岛的低频功率呈正相关，而与S1 /M1、视觉和外侧颞叶皮层的中频（0.05-0.12 Hz）功率负相关。因此，头部运动显著影响rs-fMRI功率，在为已知有较大头部运动的临床人群分配诊断标记时必须非常小心。
Recent studies have highlighted the importance of analyzing spectral power in resting-state functional magnetic resonance imaging (rs-fMRI) data. Significant modulation of power has been ascribed to the performance of cognitive tasks and has been ascribed clinical significance. However, the role of confounding factors such as head motion on spectral power is not fully understood. Specifically, the spatial distribution of frequency-dependent associations between rs-fMRI power and motion is unknown. We utilized a large rs-fMRI dataset (n = 1000) to quantify the influence of head motion on spectral power in different frequency bands. We (1) performed regression analyses across the entire sample and (2) computed difference maps between high- and low-motion groups, more consistent with common experimental designs, and both analyses gave similar results. Greater head motion led to reduced spectral power at lower frequencies (0.007-0.05 Hz), but increased power at higher frequencies (0.12-0.167 Hz). Importantly, our whole-brain voxel-wise analysis showed that brain areas in distributed association networks (e.g., default mode and frontoparietal control networks) were most susceptible to head motion. These results were consistent with or without global signal regression (GSR). Additionally, without GSR, we noted a positive correlation with low-frequency power in the pre- and postcentral gyrus (S1 /M1), mid-cingulate cortex, and insula and a negative correlation with mid-frequency (0.05-0.12 Hz) power in S1 /M1, visual, and lateral temporal cortices. Hence, head motion significantly affects rs-fMRI power and great care must be taken when assigning a diagnostic marker for clinical populations known to present with greater head motion.