The power of spectral density analysis for mapping endogenous BOLD signal fluctuations
The power of spectral density analysis for mapping endogenous BOLD signal fluctuations
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DOI：
10.1002/hbm.20601
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发表时间：
2008-07-01
影响因子：
4.8
通讯作者：
Egan, Gary F.
中科院分区：
文献类型：
--
作者：
Duff, Eugene P.;Johnston, Leigh A.;Egan, Gary F.
FMRI揭示了功能性脑系统中相关的低频脑血管振荡的存在，这被认为反映了大规模神经活动的内在特征。这些波动所显示的空间相关性是它们的识别特征，使它们区别于与其他过程相关的波动。主要的分析方法表征这些相关性，识别网络及其与各种因素的相互作用。然而，需要其他分析方法来充分表征区域信号动力学，从而促进区域之间的这些相关性。在这项研究中，我们表明，区域信号的功率谱密度（PSD）分析可以识别不同条件下振荡动力学的变化，并能够表征信号变化的性质和空间范围，这些变化是连通性测量变化的基础。我们分析了由静息状态扫描和记录2分钟连续单侧手指敲打和休息的扫描组成的会话的频谱密度变化。我们通过额外跟踪选定运动区域之间的相关性来评估PSD和连接性测量的关系。在实验过程中，脑灰质和白质的光谱密度逐渐增加。手指敲击产生广泛的低频频谱密度下降。这种变化在整个皮层中是对称的，并且超出了与任务相关的侧化信号增加和已建立的“静息状态”运动网络。运动区域之间的相关性也随着任务表现而降低。综上所述，PSD分析是一种检测和表征BOLD信号振荡的灵敏方法，可以增强对网络连通性的分析。
FMRI has revealed the presence of correlated low-frequency cerebro-vascular oscillations within functional brain systems, which are thought to reflect an intrinsic feature of large-scale neural activity. The spatial correlations shown by these fluctuations has been their identifying feature, distinguishing them from fluctuations associated with other processes. Major analysis methods characterize these correlations, identifying networks and their interactions with various factors. However, other analysis approaches are required to fully characterize the regional signal dynamics contributing to these correlations between regions. In this study we show that analysis of the power spectral density (PSD) of regional signals can identify changes in oscillatory dynamics across conditions, and is able to characterize the nature and spatial extent of signal changes underlying changes in measures of connectivity. We analyzed spectral density changes in sessions consisting of both resting-state scans and scans recording 2 min blocks of continuous unilateral finger tapping and rest. We assessed the relationship of PSD and connectivity measures by additionally tracking correlations between selected motor regions. Spectral density gradually increased in gray and white matter during the experiment. Finger tapping produced widespread decreases in low-frequency spectral density. This change was symmetric across the cortex, and extended beyond both the lateralized task-related signal increases, and the established "resting-state" motor network. Correlations between motor regions also reduced with task performance. In conclusion, analysis of PSD is a sensitive method for detecting and characterizing BOLD signal oscillations that can enhance the analysis of network connectivity.