CPD-NSL: A Two-Stage Brain Effective Connectivity Network Construction Method Based on Dynamic Bayesian Network

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhiqiong Wang, Qi Chen, Zhongyang Wang, Xinlei Wang, Luxuan Qu, Junchang Xin
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Abstract

Current brain science reveals that the connectivity patterns of the human brain are constantly changing when performing different tasks. Thus, brain effective connectivity networks based on non-stationary assumption can describe such neurodynamics better than the ones based on stationary assumption. However, existing methods for inferring non-stationary brain effective connectivity networks are committed to estimating the change points and network structures simultaneously. It is even worse that these methods will inevitably focus on one part of the estimation process and lead to the deviation of the results obtained by the other part. Then, the construction results of non-stationary brain effective connectivity networks cannot accurately reflect the real brain dynamics. In this paper, a novel approach to constructing non-stationary brain effective connectivity networks is proposed, namely CPD-NSL. It involves two stages including change point detection and network structure learning. In the first stage, the latent block model is used, and then the improved forward-backward search method is used to construct the stationary networks between adjacent change points in the network structure learning part. Finally, the constructed stationary networks are arranged in chronological order to obtain the final time-varying brain effective connectivity network. CPD-NSL is validated using simulated data as well as real fMRI data from HCP public datasets. The results show that CPD-NSL can restore the real network more accurately and consume less time. Experimental results on both simulated and real data demonstrate the effectiveness of the proposed method in constructing non-stationary state brain effective connectivity networks.

Abstract Image

CPD-NSL:基于动态贝叶斯网络的两阶段大脑有效连接网络构建方法
当前的脑科学发现,在执行不同任务时,人脑的连接模式是不断变化的。因此,基于非稳态假设的大脑有效连接网络能比基于稳态假设的网络更好地描述这种神经动力学。然而,现有的推断非稳态大脑有效连接网络的方法致力于同时估计变化点和网络结构。更糟糕的是,这些方法在估算过程中不可避免地会只关注其中一部分,而导致另一部分的结果出现偏差。那么,非稳态大脑有效连接网络的构建结果就不能准确反映真实的大脑动态。本文提出了一种构建非稳态脑有效连接网络的新方法,即 CPD-NSL。它包括两个阶段,包括变化点检测和网络结构学习。第一阶段使用潜块模型,然后在网络结构学习部分使用改进的前向后向搜索法构建相邻变化点之间的静态网络。最后,将构建的静态网络按时间顺序排列,得到最终的时变大脑有效连接网络。CPD-NSL 利用模拟数据和来自 HCP 公共数据集的真实 fMRI 数据进行了验证。结果表明,CPD-NSL 能更准确地还原真实网络,且耗时更短。在模拟数据和真实数据上的实验结果证明了所提出的方法在构建非稳态大脑有效连接网络方面的有效性。
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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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