Kerezoudis Panagiotis, Jensen Michael A, Huang Harvey, Ojemann Jeffrey G, Klassen Bryan T, Ince Nuri F, Hermes Dora, Miller Kai J
Division of Neuroscience, Mayo Graduate School of Biomedical Sciences, Rochester, MN, USA; Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA.
Medical Scientist Training Program, Mayo Clinic, Rochester, MN, USA.
Neurosci Lett. 2025 Jan 10;845:138062. doi: 10.1016/j.neulet.2024.138062. Epub 2024 Nov 26.
Electrocorticographic (ECoG) signals provide high-fidelity representations of sensorimotor cortex activation during contralateral hand movements. Understanding the relationship between independent and coordinated finger movements along with their corresponding ECoG signals is crucial for precise brain mapping and neural prosthetic development. We analyzed subdural ECoG signals from three adult epilepsy patients with subdural electrode arrays implanted for seizure foci identification. Patients performed a cue-based task consisting of thumb flexion, index finger flexion or a pinching movement of both fingers together. Broadband power changes were estimated using principal component analysis of the power spectrum. All patients showed significant increases in broadband power during each movement compared to rest. We created topological maps for each movement type on brain renderings and quantified spatial overlap between movement types using a resampling metric. Pinching exhibited the highest spatial overlap with index flexion, followed by superimposed index and thumb flexion, with the least overlap observed for thumb flexion alone. This analysis provides practical insights into the complex overlap of finger representations in the motor cortex during various movement types and may help guide more nuanced approaches to brain-computer interfaces and neural prosthetics.
皮层脑电图(ECoG)信号可提供对侧手部运动期间感觉运动皮层激活的高保真表示。了解独立和协调的手指运动与其相应的ECoG信号之间的关系对于精确的脑图谱绘制和神经假体开发至关重要。我们分析了三名成年癫痫患者的硬膜下ECoG信号,这些患者植入了硬膜下电极阵列以识别癫痫病灶。患者执行了一项基于提示的任务,包括拇指屈曲、食指屈曲或双手手指捏合动作。使用功率谱的主成分分析估计宽带功率变化。与静息状态相比,所有患者在每次运动期间的宽带功率均显著增加。我们在脑图谱上为每种运动类型创建了拓扑图,并使用重采样指标量化了运动类型之间的空间重叠。捏合动作与食指屈曲的空间重叠最高,其次是叠加的食指和拇指屈曲,单独的拇指屈曲观察到的重叠最少。该分析为各种运动类型期间运动皮层中手指表征的复杂重叠提供了实际见解,并可能有助于指导脑机接口和神经假体更细微的方法。