Schwock Felix, Bloch Julien, Khateeb Karam, Zhou Jasmine, Atlas Les, Yazdan-Shahmorad Azadeh
Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.
Primate Research Center, Seattle, WA, USA.
bioRxiv. 2025 Jun 25:2024.02.26.582177. doi: 10.1101/2024.02.26.582177.
Estimating dynamic network communication is attracting increased attention, spurred by rapid advancements in multi-site neural recording technologies and efforts to better understand cognitive processes. Yet, traditional methods, which infer communication from statistical dependencies among distributed neural recordings, face core limitations: they do not incorporate possible mechanisms of neural communication, neglect spatial information from the recording setup, and yield predominantly static estimates that cannot capture rapid changes in the brain. To address these issues, we introduce the graph diffusion autoregressive model. Designed for distributed field potential recordings, our model combines vector autoregression with a network communication process to produce a high-resolution communication signal. We successfully validated the model on simulated neural activity and recordings from subdural and intracortical micro-electrode arrays placed in macaque sensorimotor cortex demonstrating its ability to describe rapid communication dynamics induced by optogenetic stimulation, changes in resting state communication, and neural correlates of behavior during a reach task.
随着多站点神经记录技术的迅速发展以及人们为更好地理解认知过程所做的努力，动态网络通信估计正日益受到关注。然而，传统方法通过分布式神经记录之间的统计相关性来推断通信，面临着核心局限性：它们没有纳入神经通信的可能机制，忽略了记录设置中的空间信息，并且主要产生静态估计，无法捕捉大脑中的快速变化。为了解决这些问题，我们引入了图扩散自回归模型。我们的模型专为分布式场电位记录而设计，将向量自回归与网络通信过程相结合，以产生高分辨率的通信信号。我们在模拟神经活动以及放置在猕猴感觉运动皮层的硬膜下和皮层内微电极阵列记录上成功验证了该模型，证明了其描述光遗传学刺激引起的快速通信动态、静息状态通信变化以及伸手任务期间行为的神经相关性的能力。
