Ouchi Tomohiro, Scholl Leo R, Rajeswaran Pavithra, Canfield Ryan A, Smith Lydia I, Orsborn Amy L
Electrical and Computer Engineering, University of Washington, Seattle, Washington 98115.
Department of Bioengineering, University of Washington, Seattle, Washington 98115.
J Neurosci. 2025 Mar 19;45(12):e1536242025. doi: 10.1523/JNEUROSCI.1536-24.2025.
Goal-directed reaches give rise to dynamic neural activity across the brain as we move our eyes and arms and process outcomes. High spatiotemporal resolution mapping of multiple cortical areas will improve our understanding of how these neural computations are spatially and temporally distributed across the brain. In this study, we used micro-electrocorticography (µECoG) recordings in two male monkeys performing visually guided reaches to map information related to eye movements, arm movements, and receiving rewards over primary motor cortex, premotor cortex, frontal eye field, and dorsolateral prefrontal cortex. Time-frequency and decoding analyses revealed that eye and arm movement information shifts across brain regions during a reach, likely reflecting shifts from planning to execution. Although eye and arm movement temporally overlapped, phase clustering analyses enabled us to resolve differences in eye and arm information across brain regions. This analysis revealed that eye and arm information spatially overlapped in motor cortex, which we further confirmed by demonstrating that arm movement decoding performance from motor cortex activity was impacted by task-irrelevant eye movements. Phase clustering analyses also identified reward-related activity in the prefrontal and premotor cortex. Our results demonstrate µECoG's strengths for functional mapping and provide further detail on the spatial distribution of eye, arm, and reward information processing distributed across frontal cortices during reaching. These insights advance our understanding of the overlapping neural computations underlying coordinated movements and reveal opportunities to leverage these signals to enhance future brain-computer interfaces.
当我们移动眼睛和手臂并处理结果时,目标导向的伸手动作会在整个大脑中引发动态神经活动。对多个皮质区域进行高时空分辨率映射,将增进我们对这些神经计算如何在大脑中进行空间和时间分布的理解。在这项研究中,我们对两只雄性猴子进行了微电极皮质电图(µECoG)记录,它们执行视觉引导的伸手动作,以绘制与眼动、手臂运动以及在初级运动皮层、运动前皮层、额叶眼区和背外侧前额叶皮层接收奖励相关的信息。时频和解码分析表明,在伸手过程中,眼动和手臂运动信息在不同脑区之间转移,这可能反映了从计划到执行的转变。尽管眼动和手臂运动在时间上重叠,但相位聚类分析使我们能够分辨不同脑区中眼动和手臂信息的差异。该分析表明,眼动和手臂信息在运动皮层在空间上重叠,我们通过证明运动皮层活动对手臂运动的解码性能受到与任务无关的眼动影响进一步证实了这一点。相位聚类分析还在前额叶和运动前皮层中确定了与奖励相关的活动。我们的结果证明了µECoG在功能映射方面的优势,并进一步详细说明了在伸手过程中分布在额叶皮质的眼动、手臂运动和奖励信息处理的空间分布。这些见解推进了我们对协调运动背后重叠神经计算的理解,并揭示了利用这些信号增强未来脑机接口的机会。