Robust brain causality network construction based on Bayesian multivariate autoregression
Robust brain causality network construction based on Bayesian multivariate autoregression
复制标题
基于贝叶斯多元自回归的鲁棒脑因果网络构建
DOI：
10.1016/j.bspc.2020.101864
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发表时间：
2020-04
影响因子：
5.1
通讯作者：
Xu Peng
中科院分区：
文献类型：
--
作者：
Li Peiyang;Huang Xiaoye;Zhu Xuyang;Li Cunbo;Liu Huan;Zhou Weiwei;Bore Joyce Chelangat;Zhang Tao;Zhang Yangsong;Yao Dezhong;Xu Peng
背景认知过程涉及多个脑区之间的信息整合，可以用因果网络来衡量。针对传统的最小二乘法估计因果网络时，由于异常噪声的存在以及实际应用中信号样本数量较少而导致网络失真的问题，采用贝叶斯推理对多元自回归模型(MVAR)中的参数进行估计，以抑制异常值的影响。结果通过仿真研究，我们发现该方法能够有效地抑制异常点的影响，并且在样本量变小时表现出稳定的性能。在实际运动图像功能磁共振成像(FMRI)中的应用也表明，该方法即使在少量fMRI样本的情况下也能捕捉到运动图像固有的半球偏侧化。与现有方法相比，我们提出的基于贝叶斯的Granger分析方法与传统的Granger因果分析方法进行了比较。结论本文所进行的分析表明，基于贝叶斯的Granger分析方法对小样本情况下的异常情况或生理信号具有良好的稳健性。
BackgroundCognitive processes involve information integration among multiple encephalic regions, which can be measured by causal networks. However, the estimation of causal networks by means of some traditional methods with the least square will lead to distorted networks because of the unexpected outlier noise and the small number of signal samples in real applications.New methodIn this work, we adopted Bayesian inference to estimate parameters in a multivariate autoregression model (MVAR), to restrain the influence of outliers.ResultsThrough the simulation study, we observed that our proposed method can efficiently suppress outlier influence and shows stable performance when sample sizes become small. Application to real motor imagery functional magnetic resonance imaging (fMRI) also revealed that the proposed approach can capture the inherent hemispheric lateralization of motor imagery even with a small number of fMRI samples.Comparison with existing methodsWe compared our proposed Bayesian-based Granger analysis with traditional Granger causality analysis.ConclusionsThe analyses conducted in the current work demonstrate the robustness of Bayesian-based Granger analysis to outlier conditions or physiological signals with small sample sizes.
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影响因子：
5.7
作者：
Nam KW;Castellanos N;Simmons A;Froudist-Walsh S;Allin MP;Walshe M;Murray RM;Evans A;Muehlboeck JS;Nosarti C
通讯作者：
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影响因子：
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影响因子：
5.7
作者：
Zhang Tao;Liu Tiejun;Li Fali;Li Mengchen;Liu Dongbo;Zhang Rui;He Hui;Li Peiyang;Gong Jinnan;Luo Cheng;Yao Dezhong;Xu Peng
通讯作者：
Xu Peng
影响因子：
3.7
作者：
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通讯作者：
T. Burr
影响因子：
3.3
作者：
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通讯作者：
Babiloni F