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设计神经动力学:从数字双胞胎建模到再生

Calin Petru Tataru^1,2,3, Adrian Vasile Dumitru⁴, Nicolaie Dobrin^2,5

¹Department of Opthamology, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania.

International journal of molecular sciences

|January 10, 2026

概括

认知衰退涉及神经网络中的快速状态过渡,而不是线性回归. 了解这些关键的转变为神经退行性疾病干预提供了新的途径.

科学领域:

系统神经科学系统神经科学
神经退行性疾病研究研究
计算神经科学是一种神经科学.

背景情况:

认知衰退的特点是神经网络中的快速,全球状态过渡,而不是线性回归.
了解和控制这些转变是神经科学的一个主要挑战.
这篇论文将认知衰退重新定义为由分子,血管和系统因素驱动的过渡阶段.

研究的目的:

将认知衰退作为复杂神经网络的过渡阶段进行概念重构.
整合最近关于破坏神经网络稳定,导致神经退行的因素的发现.
为调节神经退行性疾病进展提出新的范式和治疗干预措施.

主要方法:

结合了关于淋巴清除,aquaporin-4极性,静脉输出和神经免疫信号的发现.
系统神经科学范式的应用:连接学,网络神经科学和关键过渡理论.
利用人工智能,机器学习,多omics,放射基因组学和数字双胞胎建模用于预测生物标志物.

主要成果:

关键机制的干扰会破坏神经生理稳定,影响记忆,意识和弹性.
加剧因素包括表观遗传沉默,神经血管脱,ECM重塑和代谢崩.
新兴的预测生物标志物和系统崩的早期警告正在被确定.

结论:

认知衰退代表一种可翻译的状态变化,而不是不可逆转的退化.
新的范式提供了工具来描述和预测系统层面的状态变化.
精确的神经干预和再生医学提供了防止神经退行进展的机会,并考虑道德问题.

关键词:

人工智能驱动的大脑建模适应性神经控制适应性神经控制天体细胞信号传递.吸引人的景观景观.闭环神经调节的神经调节.数字双胞胎神经科学神经科学表观遗传的神经修复淋巴静脉合淋巴脉合非线性大脑动力学非线性大脑动力学系统神经工程神经工程

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