...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Human brain mapping >Investigating causality between interacting brain areas with multivariate autoregressive models of MEG sensor data
【24h】

Investigating causality between interacting brain areas with multivariate autoregressive models of MEG sensor data

机译：使用MEG传感器数据的多元自回归模型研究相互作用的大脑区域之间的因果关系

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this work, we investigate the feasibility to estimating causal interactions between brain regions based on multivariate autoregressive models (MAR models) fitted to magnetoencephalographic (MEG) sensor measurements. We first demonstrate the theoretical feasibility of estimating source level causal interactions after projection of the sensor-level model coefficients onto the locations of the neural sources. Next, we show with simulated MEG data that causality, as measured by partial directed coherence (PDC), can be correctly reconstructed if the locations of the interacting brain areas are known. We further demonstrate, if a very large number of brain voxels is considered as potential activation sources, that PDC as a measure to reconstruct causal interactions is less accurate. In such case the MAR model coefficients alone contain meaningful causality information. The proposed method overcomes the problems of model nonrobustness and large computation times encountered during causality analysis by existing methods. These methods first project MEG sensor time-series onto a large number of brain locations after which the MAR model is built on this large number of source-level time-series. Instead, through this work, we demonstrate that by building the MAR model on the sensor-level and then projecting only the MAR coefficients in source space, the true casual pathways are recovered even when a very large number of locations are considered as sources. The main contribution of this work is that by this methodology entire brain causality maps can be efficiently derived without any a priori selection of regions of interest. Hum Brain Mapp, 2013.
机译：在这项工作中，我们调查了基于适合于脑磁图（MEG）传感器测量的多元自回归模型（MAR模型）估计大脑区域之间因果关系的可行性。我们首先证明了在将传感器级别的模型系数投影到神经源的位置之后估计源级别的因果相互作用的理论可行性。接下来，我们通过模拟的MEG数据显示，如果知道了相互作用的大脑区域的位置，则可以正确地重建因部分定向相干性（PDC）测得的因果关系。我们进一步证明，如果将大量的大脑体素视为潜在的激活源，则PDC作为重建因果关系的一种手段就不太准确。在这种情况下，MAR模型系数仅包含有意义的因果关系信息。所提出的方法克服了现有方法在因果分析中模型不稳健和计算时间长的问题。这些方法首先将MEG传感器的时间序列投影到大量的大脑位置，然后在大量的源级时间序列上建立MAR模型。相反，通过这项工作，我们证明了通过在传感器级别构建MAR模型，然后仅在源空间中投影MAR系数，即使在将大量位置视为源的情况下，也可以恢复真正的偶然路径。这项工作的主要贡献在于，通过这种方法，可以有效地得出整个脑因果图，而无需先验选择感兴趣的区域。嗡嗡声大脑Mapp，2013年。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号