Nonparametric Dynamic Granger Causality based on Multi-Space Spectrum Fusion for Time-varying Directed Brain Network Construction.

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Chanlin Yi, Jiamin Zhang, Zihan Weng, Wanjun Chen, Dezhong Yao, Fali Li, Zehong Cao, Peiyang Li, Peng Xu
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引用次数: 0

Abstract

Nonparametric estimation of time-varying directed networks can unveil the intricate transient organization of directed brain communication while circumventing constraints imposed by prescribed model-driven methods. A robust time-frequency representation - the foundation of its causality inference - is critical for enhancing its reliability. This study proposed a novel method, i.e., nonparametric dynamic Granger causality based on Multi-space Spectrum Fusion (ndGCMSF), which integrates complementary spectrum information from different spaces to generate reliable spectral representations to estimate dynamic causalities across brain regions. Systematic simulations and validations demonstrate that ndGCMSF exhibits superior noise resistance and a powerful ability to capture subtle dynamic changes in directed brain networks. Particularly, ndGCMSF revealed that during instruction response movements, the laterality in the hemisphere ipsilateral to the hemiplegic limb emerges upon instruction onset and diminishes upon task accomplishment. These intrinsic variations further provide reliable features for distinguishing two types of hemiplegia (left vs. right) and assessing motor functions. The ndGCMSF offers powerful functional patterns to derive effective brain networks in dynamically changing operational settings and contributes to extensive areas involving dynamical and directed communications.

基于多空间频谱融合的非参数动态格兰杰因果关系用于时变定向脑网络构建
对时变有向网络的非参数估计可以揭示有向大脑通信的复杂瞬态组织,同时规避规定的模型驱动方法所带来的限制。稳健的时频表示是其因果推断的基础,对于提高其可靠性至关重要。本研究提出了一种新方法,即基于多空间频谱融合的非参数动态格兰杰因果关系(ndGCMSF),它整合了来自不同空间的互补频谱信息,生成可靠的频谱表示,以估计跨脑区的动态因果关系。系统模拟和验证表明,ndGCMSF 具有卓越的抗噪能力和捕捉定向脑网络中微妙动态变化的强大能力。特别是,ndGCMSF 发现,在指令响应运动过程中,偏瘫肢体同侧半球的侧向性在指令开始时出现,并在任务完成时减弱。这些内在变化进一步为区分两种偏瘫类型(左侧偏瘫和右侧偏瘫)和评估运动功能提供了可靠的特征。ndGCMSF提供了强大的功能模式,可在动态变化的操作环境中推导出有效的大脑网络,并有助于涉及动态和定向通信的广泛领域。
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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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