Gelormini Carmine, Guerrini Lorena, Pescaglia Federica, Aubonnet Romain, Jónsson Halldór, Petersen Hannes, Di Lorenzo Giorgio, Gargiulo Paolo
Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.
Department of Engineering, University of Campania Luigi, Aversa, Italy.
Brain Topogr. 2025 Jun 3;38(4):47. doi: 10.1007/s10548-025-01119-w.
The ability to maintain our body's balance and stability in space is crucial for performing daily activities. Effective postural control (PC) strategies rely on integrating visual, vestibular, and proprioceptive sensory inputs. While neuroimaging has revealed key areas involved in PC-including brainstem, cerebellum, and cortical networks-the rapid neural mechanisms underlying dynamic postural tasks remain less understood. Therefore, we used EEG microstate analysis within the BioVRSea experiment to explore the temporal brain dynamics that support PC. This complex paradigm simulates maintaining an upright posture on a moving platform, integrated with virtual reality (VR), to replicate the sensation of balancing on a boat. Data were acquired from 266 healthy subjects using a 64-channel EEG system. Using a modified k-means method, five EEG microstate maps were identified to best model the paradigm. Differences in each microstate maps feature (occurrence, duration, and coverage) between experimental phases were analyzed using a linear mixed model, revealing significant differences between microstates within the experiment phases. The temporal parameters of microstate C showed significantly higher levels in all experimental phases compared to other microstate maps, whereas microstate B displayed an opposite pattern, consistently showing lower levels. This study marks the first attempt to use microstate analysis during a dynamic task, demonstrating the decisive role of microstate C and, conversely, microstate B in differentiating the PC phases. These results demonstrate the utility of microstate technique in studying temporal brain dynamics during PC, with potential applications in the early detection of neurodegenerative diseases.
在空间中维持身体平衡和稳定的能力对于进行日常活动至关重要。有效的姿势控制（PC）策略依赖于整合视觉、前庭和本体感觉输入。虽然神经影像学已经揭示了参与PC的关键区域，包括脑干、小脑和皮层网络，但动态姿势任务背后的快速神经机制仍不太清楚。因此，我们在BioVRSea实验中使用脑电图微状态分析来探索支持PC的大脑时间动态。这个复杂的范式模拟了在与虚拟现实（VR）集成的移动平台上保持直立姿势，以复制在船上平衡的感觉。使用64通道脑电图系统从266名健康受试者获取数据。使用改进的k均值方法，识别出五个脑电图微状态图以最佳地模拟该范式。使用线性混合模型分析实验阶段之间每个微状态图特征（出现、持续时间和覆盖范围）的差异，揭示实验阶段内微状态之间的显著差异。与其他微状态图相比，微状态C的时间参数在所有实验阶段均显示出显著更高的水平，而微状态B则呈现相反的模式，始终显示较低的水平。这项研究标志着首次在动态任务中使用微状态分析，证明了微状态C以及相反地微状态B在区分PC阶段中的决定性作用。这些结果证明了微状态技术在研究PC期间大脑时间动态方面的实用性，在神经退行性疾病的早期检测中具有潜在应用。
