Dialogue mechanisms between astrocytic and neuronal networks: A whole-brain modelling approach.

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
PLoS Computational Biology Pub Date : 2025-01-13 eCollection Date: 2025-01-01 DOI:10.1371/journal.pcbi.1012683
Obaï Bin Ka'b Ali, Alexandre Vidal, Christophe Grova, Habib Benali
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引用次数: 0

Abstract

Astrocytes critically shape whole-brain structure and function by forming extensive gap junctional networks that intimately and actively interact with neurons. Despite their importance, existing computational models of whole-brain activity ignore the roles of astrocytes while primarily focusing on neurons. Addressing this oversight, we introduce a biophysical neural mass network model, designed to capture the dynamic interplay between astrocytes and neurons via glutamatergic and GABAergic transmission pathways. This network model proposes that neural dynamics are constrained by a two-layered structural network interconnecting both astrocytic and neuronal populations, allowing us to investigate astrocytes' modulatory influences on whole-brain activity and emerging functional connectivity patterns. By developing a simulation methodology, informed by bifurcation and multilayer network theories, we demonstrate that the dialogue between astrocytic and neuronal networks manifests over fast-slow fluctuation mechanisms as well as through phase-amplitude connectivity processes. The findings from our research represent a significant leap forward in the modeling of glial-neuronal collaboration, promising deeper insights into their collaborative roles across health and disease states.

星形胶质细胞和神经元网络之间的对话机制:全脑建模方法。
星形胶质细胞通过形成广泛的间隙连接网络,密切和积极地与神经元相互作用,从而关键地塑造了整个大脑的结构和功能。尽管星形胶质细胞很重要,但现有的全脑活动计算模型忽略了星形胶质细胞的作用,而主要关注神经元。为了解决这一问题,我们引入了一个生物物理神经质量网络模型,旨在通过谷氨酸能和gaba能传递途径捕捉星形胶质细胞和神经元之间的动态相互作用。该网络模型提出,神经动力学受到星形胶质细胞和神经元群体相互连接的双层结构网络的约束,使我们能够研究星形胶质细胞对全脑活动的调节影响和新兴的功能连接模式。通过开发一种模拟方法,根据分岔和多层网络理论,我们证明星形细胞和神经元网络之间的对话表现在快慢波动机制以及通过相位振幅连接过程。我们的研究结果代表了神经胶质-神经元协作建模的重大飞跃,有望更深入地了解它们在健康和疾病状态下的协作作用。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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