Bahuguna Jyotika, Schwey Antoine, Battaglia Demian, Malfait Nicole
Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA), Faculté de Psychologie, Université de Strasbourg, Strasbourg, France.
Aix-Marseille Université, CNRS, Institut de Neurosciences de la Timone (UMR 7289), 13005 Marseille, France.
Netw Neurosci. 2025 May 8;9(2):712-742. doi: 10.1162/netn_a_00440. eCollection 2025.
We show that sensorimotor behavior can be reliably predicted from single-trial EEG oscillations fluctuating in a coordinated manner across brain regions, frequency bands, and movement time epochs. We define high-dimensional oscillatory portraits to capture the interdependence between basic oscillatory elements, quantifying oscillations occurring in single trials at specific frequencies, locations, and time epochs. We find that the general structure of the element interdependence networks (effective connectivity) remains stable across task conditions, reflecting an intrinsic coordination architecture and responds to changes in task constraints by subtle but consistently distinct topological reorganizations. Trial categories are reliably and significantly better separated using oscillatory portraits than from the information contained in individual oscillatory elements, suggesting an interelement coordination-based encoding. Furthermore, single-trial oscillatory portrait fluctuations are predictive of fine trial-to-trial variations in movement kinematics. Remarkably, movement accuracy appears to be reflected in the capacity of the oscillatory coordination architecture to flexibly update as an effect of movement-error integration.
我们表明，感觉运动行为可以从跨脑区、频段和运动时间阶段以协调方式波动的单次试验脑电图振荡中可靠地预测出来。我们定义了高维振荡图谱，以捕捉基本振荡元素之间的相互依赖性，量化在特定频率、位置和时间阶段的单次试验中发生的振荡。我们发现，元素相互依赖网络（有效连接性）的总体结构在不同任务条件下保持稳定，反映了一种内在的协调架构，并通过微妙但始终不同的拓扑重组对任务约束的变化做出反应。与使用单个振荡元素中包含的信息相比，使用振荡图谱能更可靠且显著地更好分离试验类别，这表明基于元素间协调的编码。此外，单次试验振荡图谱的波动可预测运动运动学中精细的逐次试验变化。值得注意的是，运动准确性似乎反映在振荡协调架构作为运动误差整合效应而灵活更新的能力上。
