Tan Huiling, Fischer Petra, Shah Syed A, Vidaurre Diego, Woolrich Mark W, Brown Peter
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1384-1387. doi: 10.1109/EMBC.2018.8512545.
Gait disturbances are a prominent feature of Parkinson's disease (PD), often refractory to medication or continuous deep brain stimulation (DBS) on basal ganglia targets such as the subthalamic nucleus (STN). Here we sought to identify movement states during stepping cycles, such as left leg stance and right leg stance. To this end we analyzed local field potential (LFP) activity in STN using a combination of the multivariate autoregressive (MAR) model and the Hidden Markov model (HMM). Our results confirm that information is present in the STN related to movement states in stepping cycles, and that it is feasible to decode movement states based on STN LFPs recorded from DBS electrodes. This information can be used to implement temporally flexible stimulation strategies in order to facilitate patterns of neural modulation associated with better gait performance.
步态障碍是帕金森病（PD）的一个突出特征，通常对药物治疗或针对基底神经节靶点（如丘脑底核（STN））的持续深部脑刺激（DBS）具有抗性。在这里，我们试图识别步行周期中的运动状态，如左腿站立和右腿站立。为此，我们结合多元自回归（MAR）模型和隐马尔可夫模型（HMM）分析了STN中的局部场电位（LFP）活动。我们的结果证实，STN中存在与步行周期中运动状态相关的信息，并且基于从DBS电极记录的STN LFP来解码运动状态是可行的。该信息可用于实施时间上灵活的刺激策略，以促进与更好步态表现相关的神经调节模式。
