The effect of L-dopa and DBS on cortical oscillations in Parkinson's disease analyzed by hidden Markov model algorithm

作者：K. Wei, Hang Ping, Xiaochen Tang, Dianyou Li, Shikun Zhan, Bomin Sun, Xiangyan Kong, Chunyan Cao · 发表于：NeuroImage · 年份：2024 · DOI：10.1016/j.neuroimage.2024.120992 · 被引用次数：7 · 研究领域：Neurological disorders and treatments、Parkinson's Disease Mechanisms and Treatments、Transcranial Magnetic Stimulation Studies

BACKGROUND: Parkinson's disease (PD) is a movement disorder caused by dopaminergic neurodegeneration. Both Levodopa (L-dopa) and Subthalamic Deep Brain Stimulation (STN-DBS) effectively alleviate symptoms, yet their cerebral effects remain under-explored. Understanding these effects is essential for optimizing treatment strategies and assessing disease severity. Magnetoencephalogram (MEG) data provide a continuous time series signal that reflects the dynamic changes in brain activity. The hidden Markov model (HMM) can capture and model the temporal features and underlying states of the MEG signal to extract potential brain states and monitor dynamic changes. In this study, we employed HMM to investigate the cortical mechanism underlying the treatment of PD patients using MEG recordings. METHODS: 21 PD patients treated with medication underwent MEG recording in both L-dopa medoff and medon conditions. Additionally, 11 PD patients receiving STN-DBS treatment underwent MEG recording in both dbsoff and dbson conditions. The MEG data were segmented into four states by Time-delay embedded Hidden Markov Model (TDE-HMM) algorithm. The state parameters including Fractional Occupancy (FO), Interval Times (IT), and Life Time (LT) for each state and power spectrum of β band were analyzed to study the effects of L-dopa and STN-DBS treatment respectively. RESULTS: L-dopa significantly increased the motor state of HMM and power in the motor area of both high β (21-35 Hz) and low β (13-20 Hz...