Choi Jin Woo, Cui Chuyi, Wilkins Kevin, Bronte-Stewart Helen
Stanford University School of Medicine.
Res Sq. 2024 Oct 31:rs.3.rs-5112726. doi: 10.21203/rs.3.rs-5112726/v1.
Adaptive deep brain stimulation (DBS) provides individualized therapy for people with Parkinson's disease (PWP) by adjusting the stimulation in real-time using neural signals that reflect their motor state. Current algorithms, however, utilize condensed and manually selected neural features which may result in a less robust and biased therapy. In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performance from subthalamic nucleus local field potentials (STN LFPs). The LFP data were acquired when eighteen PWP performed stepping in place, and the ground reaction forces were measured to track their weight shifts representing gait performance. By exhibiting a stronger correlation with weight shifts compared to the higher-correlation beta power from the two leads and outperforming other evaluated model designs, N2GNet effectively leverages a comprehensive frequency band, not limited to the beta range, to track gait performance solely from STN LFPs.
自适应深部脑刺激（DBS）通过使用反映帕金森病患者（PWP）运动状态的神经信号实时调整刺激，为他们提供个性化治疗。然而，当前的算法利用的是经过压缩和人工选择的神经特征，这可能会导致治疗效果不够稳健且存在偏差。在本研究中，我们提出了神经到步态神经网络（N2GNet），这是一种基于深度学习的新型回归模型，能够根据丘脑底核局部场电位（STN LFP）跟踪实时步态表现。在18名帕金森病患者进行原地踏步时采集LFP数据，并测量地面反作用力以跟踪他们代表步态表现的体重转移。与来自两根导联的相关性更高的β功率相比，N2GNet与体重转移表现出更强的相关性，并且优于其他评估的模型设计，它有效地利用了一个全面的频段，而不仅限于β范围，仅从STN LFP跟踪步态表现。
