Evaluating frequency-wise directed connectivity of BOLD signals applying relative power contribution with the linear multivariate time-series models
Evaluating frequency-wise directed connectivity of BOLD signals applying relative power contribution with the linear multivariate time-series models
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DOI：
10.1016/j.neuroimage.2004.11.042
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发表时间：
2005-04
期刊：
影响因子：
5.7
通讯作者：
O. Yamashita;N. Sadato;T. Okada;T. Ozaki
中科院分区：
文献类型：
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作者：
O. Yamashita;N. Sadato;T. Okada;T. Ozaki
在本文中，我们提出了一种统计方法来评估功能磁共振成像（fMRI）数据的定向相互作用。本分析采用多元自回归（MAR）模型与相对功率贡献（RPC）相结合。通过对数据进行MAR模型拟合来确定连接的方向，通过RPC来量化连接的强度。由于RPC是在频域中计算的，因此我们可以评估每个频率分量的连通性。由此，我们可以确定指定的连接是否代表低频或高频连接，这不能单独使用估计的MAR系数来检查。我们将这种分析方法应用于在视觉运动任务中获得的fMRI数据，证实了先前报道的在与块实验设计相对应的频率周围自下而上的连接。此外，我们使用带有外生变量的MAR模型（MARX）来扩展我们对这些数据的理解，并显示V1的输入如何转移到更高的皮质区域。
In this article, we propose a statistical method to evaluate directed interactions of functional magnetic-resonance imaging (fMRI) data. The multivariate autoregressive (MAR) model was combined with the relative power contribution (RPC) in this analysis. The MAR model was fitted to the data to specify the direction of connections, and the RPC quantifies the strength of connections. As the RPC is computed in the frequency domain, we can evaluate the connectivity for each frequency component. From this, we can establish whether the specified connections represent low- or high-frequency connectivity, which cannot be examined solely using the estimated MAR coefficients. We applied this analysis method to fMRI data obtained during visual motion tasks, confirming previous reports of bottom-up connectivity around the frequency corresponding to the block experimental design. Furthermore, we used the MAR model with exogenous variables (MARX) to extend our understanding of these data, and to show how the input to V1 transfers to higher cortical areas.