个体化小鼠脑网络模型在模拟创伤损伤后产生不对称的功能连接模式。

IF 3.6 3区 医学 Q2 NEUROSCIENCES
Network Neuroscience Pub Date : 2025-03-20 eCollection Date: 2025-01-01 DOI:10.1162/netn_a_00431
Adam C Rayfield, Taotao Wu, Jared A Rifkin, David F Meaney
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引用次数: 0

摘要

人们对创伤性脑损伤(TBI)的功能和认知影响知之甚少,因为即使是轻微的损伤(脑震荡)也可能导致持久的、无法治疗的症状。简化的脑动力学模型可以帮助研究人员更好地理解脑损伤模式和功能结果之间的关系。适当发展,这些计算模型提供了一种方法来研究计算和体内损伤分别对模型生物的模拟动力学和认知功能的影响。在这项研究中,我们应用Kuramoto模型和现有的中尺度小鼠脑结构网络来建立一个简化的小鼠脑动力学计算模型。我们探索如何优化我们的初始模型来预测在不同麻醉方案下收集的现有小鼠脑功能连接。最后,为了确定优化模型的动态变化在多大程度上可以预测脑损伤的程度,我们研究了我们的模拟如何响应不同程度的结构网络损伤。研究结果预测了实验性脑损伤后的低连接和高连接的混合,类似于脑损伤幸存者的结果,也表明代偿性连接重塑可能对脑损伤后的功能结果有影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Individualized mouse brain network models produce asymmetric patterns of functional connectivity after simulated traumatic injury.

The functional and cognitive effects of traumatic brain injury (TBI) are poorly understood, as even mild injuries (concussion) can lead to long-lasting, untreatable symptoms. Simplified brain dynamics models may help researchers better understand the relationship between brain injury patterns and functional outcomes. Properly developed, these computational models provide an approach to investigate the effects of both computational and in vivo injury on simulated dynamics and cognitive function, respectively, for model organisms. In this study, we apply the Kuramoto model and an existing mesoscale mouse brain structural network to develop a simplified computational model of mouse brain dynamics. We explore how to optimize our initial model to predict existing mouse brain functional connectivity collected from mice under various anesthetic protocols. Finally, to determine how strongly the changes in our optimized models' dynamics can predict the extent of a brain injury, we investigate how our simulations respond to varying levels of structural network damage. Results predict a mixture of hypo- and hyperconnectivity after experimental TBI, similar to results in TBI survivors, and also suggest a compensatory remodeling of connections that may have an impact on functional outcomes after TBI.

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来源期刊
Network Neuroscience
Network Neuroscience NEUROSCIENCES-
CiteScore
6.40
自引率
6.40%
发文量
68
审稿时长
16 weeks
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