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利用多模态生物标志物评估中风的运动功能能力及对侧运动区的作用。

Estimation of Stroke's Motor Function Ability Using Multimodal Biomarkers and the Role of Contralesional Motor Area.

作者信息

Kim Da-Hye, Kwon Gyu Hyun, Lee Seong-Whan, Kim Laehyun

机构信息

Bionics Research Center, Korea Institute of Science and Technology, Seoul, Republic of Korea.

Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.

出版信息

Brain Behav. 2025 May;15(5):e70492. doi: 10.1002/brb3.70492.

Abstract

PURPOSE

In the chronic phase, many stroke survivors did not regain their pre-stroke upper limb movement capabilities. This emphasizes the crucial role of assessing motor function in patients with stroke, as it provides valuable insights into setting effective rehabilitation goals. Accordingly, this study aimed to investigate the electroencephalography (EEG)-based functional brain network properties in stroke patients during motor tasks and assess their utility in predicting the upper limb Fugl-Meyer Assessment (UL-FMA) scores.

METHODS

We performed a comparative analysis of brain properties, including EEG power and network characteristics, in stroke patients and a healthy control (HC) group. Also, we selected prognostic factors of brain properties during voluntary movement for patients' motor function ability using stepwise regression analysis.

FINDINGS

Stroke patients manifested reduced global efficiency relative to the HC group, signifying impaired information processing attributed to brain injury. Local analyses highlighted pronounced disparities in the contralesional motor area (MA) between stroke patients and the HC group, revealing patterns indicative of compensatory mechanisms. Leveraging a multimodal approach incorporating EEG power and network features within the contralesional MA yielded a robust model for motor function estimation, outperforming unimodal models (adjusted R 0.99, RMSE 0.13). The findings of this study outperformed other models for estimating the motor abilities of chronic stroke patients. Another chronic stroke dataset was used to externally validate this study, and it had an adjusted R of 0.95. This suggests that the results of this study can be generalized.

CONCLUSION

Our findings provide insight into the brain properties of stroke-related motor impairment. These results underscore the pivotal role of the contralesional MA in assessing UL-FMA scores and represent how a multimodal approach to this area can suggest the possibility of using it as a meaningful biomarker for motor function. They also have potential implications for the development of individualized rehabilitation strategies, particularly during the chronic phase of recovery.

摘要

目的

在慢性期,许多中风幸存者未能恢复中风前的上肢运动能力。这凸显了评估中风患者运动功能的关键作用,因为它为设定有效的康复目标提供了有价值的见解。因此,本研究旨在调查中风患者在运动任务期间基于脑电图(EEG)的功能性脑网络特性,并评估其在预测上肢Fugl-Meyer评估(UL-FMA)分数方面的效用。

方法

我们对中风患者和健康对照组(HC)的脑特性进行了比较分析,包括EEG功率和网络特征。此外,我们使用逐步回归分析为患者的运动功能能力选择了自愿运动期间脑特性的预后因素。

结果

与HC组相比,中风患者的全局效率降低,这表明脑损伤导致信息处理受损。局部分析突出了中风患者与HC组之间对侧运动区(MA)的明显差异,揭示了补偿机制的模式。利用一种多模态方法,将对侧MA内的EEG功率和网络特征结合起来,产生了一个强大的运动功能估计模型,优于单模态模型(调整后R为0.99,均方根误差为0.13)。本研究的结果优于其他用于估计慢性中风患者运动能力的模型。另一个慢性中风数据集用于对本研究进行外部验证,其调整后R为0.95。这表明本研究的结果可以推广。

结论

我们的研究结果为中风相关运动障碍的脑特性提供了见解。这些结果强调了对侧MA在评估UL-FMA分数中的关键作用,并展示了针对该区域的多模态方法如何表明将其用作运动功能有意义生物标志物的可能性。它们还对个性化康复策略的制定具有潜在意义,特别是在恢复的慢性期。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fdeb/12060223/d2c33c03f049/BRB3-15-e70492-g004.jpg

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