Yang Lan, Lu Jiayu, Li Dandan, Xiang Jie, Yan Ting, Sun Jie, Wang Bin
College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China.
Teranslational Medicine Research Center, Shanxi Medical University, Taiyuan 030001, China.
Brain Sci. 2023 Jul 28;13(8):1133. doi: 10.3390/brainsci13081133.
Alzheimer's disease (AD) is a degenerative brain disease, and the condition is difficult to assess. In the past, numerous brain dynamics models have made remarkable contributions to neuroscience and the brain from the microcosmic to the macroscopic scale. Recently, large-scale brain dynamics models have been developed based on dual-driven multimodal neuroimaging data and neurodynamics theory. These models bridge the gap between anatomical structure and functional dynamics and have played an important role in assisting the understanding of the brain mechanism. Large-scale brain dynamics have been widely used to explain how macroscale neuroimaging biomarkers emerge from potential neuronal population level disturbances associated with AD. In this review, we describe this emerging approach to studying AD that utilizes a biophysically large-scale brain dynamics model. In particular, we focus on the application of the model to AD and discuss important directions for the future development and analysis of AD models. This will facilitate the development of virtual brain models in the field of AD diagnosis and treatment and add new opportunities for advancing clinical neuroscience.
阿尔茨海默病（AD）是一种退行性脑疾病，其病情难以评估。过去，众多脑动力学模型在从微观到宏观尺度的神经科学和大脑研究方面做出了卓越贡献。最近，基于双驱动多模态神经成像数据和神经动力学理论开发了大规模脑动力学模型。这些模型弥合了解剖结构与功能动力学之间的差距，并在协助理解脑机制方面发挥了重要作用。大规模脑动力学已被广泛用于解释宏观神经成像生物标志物如何从与AD相关的潜在神经元群体水平干扰中产生。在本综述中，我们描述了这种利用生物物理大规模脑动力学模型研究AD的新兴方法。特别是，我们重点关注该模型在AD中的应用，并讨论AD模型未来发展和分析的重要方向。这将促进AD诊断和治疗领域虚拟脑模型的发展，并为推进临床神经科学增添新机遇。
