A procedure to increase the power of Granger-causal analysis through temporal smoothing.
A procedure to increase the power of Granger-causal analysis through temporal smoothing.
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DOI：
10.1016/j.jneumeth.2018.07.010
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发表时间：
2018-10-01
影响因子：
3
通讯作者：
Kramer MA
中科院分区：
文献类型：
--
作者：
Spencer E;Martinet LE;Eskandar EN;Chu CJ;Kolaczyk ED;Cash SS;Eden UT;Kramer MA
人类大脑如何协调网络活动以支持认知和行为仍然知之甚少。新的高分辨率记录模式有助于更详细地了解人脑网络。已经提出了几种方法来推断功能网络，表明大脑区域之间的活动的瞬时协调，从神经时间序列。一类方法是基于从多个传感器记录的时间序列的统计建模（例如，多变量格兰杰因果关系）。然而，拟合这样的模型在计算上仍然具有挑战性，因为神经活动中的历史结构可能很长，需要许多模型参数来完全捕获动态。我们开发了一种基于格兰杰因果关系的方法，假设历史相关性平滑变化。我们拟合多元自回归模型，使得滞后历史项的系数是光滑函数。我们这样做，通过建模的历史条件与低维样条的基础上，这需要比标准的方法少得多的参数，并增加了模型的统计能力。我们表明，这种方法可以准确估计大脑动力学和功能网络的模拟和例子的大脑电压活动记录从患者的耐药性癫痫。所提出的方法具有更大的统计功率比格兰杰方法的信号网络，表现出扩展和平滑的历史依赖性。所提出的工具允许从许多具有扩展历史依赖性的脑区域进行功能网络的条件推断，进一步提高了格兰杰因果关系对脑网络科学的适用性。
How the human brain coordinates network activity to support cognition and behavior remains poorly understood. New high-resolution recording modalities facilitate a more detailed understanding of the human brain network. Several approaches have been proposed to infer functional networks, indicating the transient coordination of activity between brain regions, from neural time series. One category of approach is based on statistical modeling of time series recorded from multiple sensors (e.g., multivariate Granger causality). However, fitting such models remains computationally challenging as the history structure may be long in neural activity, requiring many model parameters to fully capture the dynamics. We develop a method based on Granger causality that makes the assumption that the history dependence varies smoothly. We fit multivariate autoregressive models such that the coefficients of the lagged history terms are smooth functions. We do so by modelling the history terms with a lower dimensional spline basis, which requires many fewer parameters than the standard approach and increases the statistical power of the model. We show that this procedure allows accurate estimation of brain dynamics and functional networks in simulations and examples of brain voltage activity recorded from a patient with pharmacoresistant epilepsy. ]The proposed method has more statistical power than the Granger method for networks of signals that exhibit extended and smooth history dependencies. The proposed tool permits conditional inference of functional networks from many brain regions with extended history dependence, furthering the applicability of Granger causality to brain network science.
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影响因子：
16.2
作者：
He, Biyu J.;Zempel, John M.;Snyder, Abraham Z.;Raichle, Marcus E.
通讯作者：
Raichle, Marcus E.
影响因子：
16.2
作者：
Fries P
通讯作者：
Fries P
影响因子：
2.9
作者：
Deng, Xinyi;Eskandar, Emad N.;Eden, Uri T.
通讯作者：
Eden, Uri T.
影响因子：
2.7
作者：
DiMatteo, I;Genovese, CR;Kass, RE
通讯作者：
Kass, RE
影响因子：
3.5
作者：
Eden UT;Gale JT;Amirnovin R;Eskandar EN
通讯作者：
Eskandar EN