Khan Furrukh, Novikov David, Dalm Brian, Xiaoxi Jessie, Flouty Oliver, Thomas Evan
Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, United States.
Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, United States.
Front Neurosci. 2025 Jun 18;19:1398834. doi: 10.3389/fnins.2025.1398834. eCollection 2025.
With the commercial availability of deep brain stimulation neurostimulators and sensing leads capable of recording deep brain Local Field Potentials, researchers now commonly study the spectral characteristics of Local Field Potentials recorded from the subthalamic nucleus of patients with Parkinson's disease. Correlating subthalamic synchronized oscillatory activity with motor impairment in Parkinson's disease patients has recently gained attention in the literature.
Based on the deep brain recordings of a Parkinson's disease patient our objective is to (i) Use actual measurements of the patient's tremor to support a hypothesis that connects the features of the Local Field Potential's beta-band spectrum (13-31 Hz), with the lower frequency (4-8 Hz) features of the patient's tremor, such as tremor frequency and tremor fluctuation time and (ii) Justify the hypothesis through theoretical reasoning based on communication theory in Electrical Engineering.
Tremor characteristics (i.e., tremor frequency and tremor fluctuation time) derived from limb coordinate time-series were obtained from a video of the patient by using Google's MediaPipe Artificial Intelligence Framework. Spectra of the deep brain recordings and measured tremor time-series were analyzed using the Fast Fourier Transform. Burst trains in the deep brain signals and tremor bursts in the measured tremor signal were investigated by using Continuous Wave Transform scalograms.
Support for the hypothesis is provided by a close agreement between the measured results of the tremor (from a patient's video) and the predictions of the hypothesis based on the Local Filed Potential deep brain spectrum. We show that the defining features in the scalogram obtained from the deep brain signal are directly related to the features in the scalogram of the measured tremor. We provide a theoretical justification of the hypothesis by relating features of the deep brain beta-bursts, seen in the Local Field Potential scalogram, to a pair of beta-band dominant peaks found in the spectrum of the deep brain signal by leveraging the phenomena of "beating" (amplitude modulation) from communications theory.
We conclude that tremor properties of a Parkinson's disease patient, like tremor frequency and tremor fluctuation duration, can be obtained from the patient's subthalamic nucleus beta-band spectrum.
随着能够记录深部脑局部场电位的深部脑刺激神经刺激器和传感导线的商业化可得,研究人员现在通常研究从帕金森病患者丘脑底核记录的局部场电位的频谱特征。将帕金森病患者的丘脑底核同步振荡活动与运动障碍相关联最近在文献中受到关注。
基于一名帕金森病患者的深部脑记录,我们的目标是:(i)使用患者震颤的实际测量结果来支持一个假设,该假设将局部场电位β波段频谱(13 - 31赫兹)的特征与患者震颤的较低频率(4 - 8赫兹)特征,如震颤频率和震颤波动时间联系起来;(ii)通过基于电气工程通信理论的理论推理来证明该假设。
通过使用谷歌的MediaPipe人工智能框架从患者的视频中获取从肢体坐标时间序列得出的震颤特征(即震颤频率和震颤波动时间)。使用快速傅里叶变换分析深部脑记录和测量的震颤时间序列的频谱。使用连续波变换小波图研究深部脑信号中的突发序列和测量的震颤信号中的震颤突发。
震颤(来自患者视频)的测量结果与基于局部场电位深部脑频谱的假设预测之间的密切一致性为该假设提供了支持。我们表明,从深部脑信号获得的小波图中的定义特征与测量的震颤小波图中的特征直接相关。通过利用通信理论中的“拍频”(幅度调制)现象,将局部场电位小波图中看到的深部脑β突发的特征与深部脑信号频谱中发现的一对β波段主导峰值相关联,我们为该假设提供了理论依据。
我们得出结论,帕金森病患者的震颤特性,如震颤频率和震颤波动持续时间,可以从患者的丘脑底核β波段频谱中获得。