The effect of L-dopa and DBS on cortical oscillations in Parkinson's disease analyzed by hidden Markov model algorithm
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 essentia...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811924004890 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841546003740622848 |
|---|---|
| author | Kunzhou Wei Hang Ping Xiaochen Tang Dianyou Li Shikun Zhan Bomin Sun Xiangyan Kong Chunyan Cao |
| author_facet | Kunzhou Wei Hang Ping Xiaochen Tang Dianyou Li Shikun Zhan Bomin Sun Xiangyan Kong Chunyan Cao |
| author_sort | Kunzhou Wei |
| collection | DOAJ |
| description | 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); the motor state of high β in medoff were correlated with the Unified Parkinson's Disease Rating Scale III (UPDRS III). Conversely, DBS significantly diminishes the motor state of HMM and power in motor area of high β oscillations. The score changes of tremor and limb rigidity after DBS treatment were significantly correlated with the changes of motor state of high β. Conclusions: This study demonstrates that L-dopa and STN-DBS exert differing effects on β oscillations in the motor cortex of PD patients, primarily in high β band. Understanding these distinct neurophysiological impacts can provide valuable insights for refining therapeutic approaches in motor control for PD patients. |
| format | Article |
| id | doaj-art-a76943bdd3ee4ab98f175badb1baacf9 |
| institution | Kabale University |
| issn | 1095-9572 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj-art-a76943bdd3ee4ab98f175badb1baacf92025-01-11T06:38:35ZengElsevierNeuroImage1095-95722025-01-01305120992The effect of L-dopa and DBS on cortical oscillations in Parkinson's disease analyzed by hidden Markov model algorithmKunzhou Wei0Hang Ping1Xiaochen Tang2Dianyou Li3Shikun Zhan4Bomin Sun5Xiangyan Kong6Chunyan Cao7School of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China; The Institute for Future Wireless Research (iFWR), Ningbo University, Ningbo 315211, ChinaSchool of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China; The Institute for Future Wireless Research (iFWR), Ningbo University, Ningbo 315211, ChinaShanghai Mental Health Center, Shanghai, ChinaDepartment of Neurosurgery, Affiliated Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Neurosurgery, Affiliated Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Neurosurgery, Affiliated Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaSchool of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China; The Institute for Future Wireless Research (iFWR), Ningbo University, Ningbo 315211, China; Corresponding authors.Department of Neurosurgery, Affiliated Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Corresponding authors.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); the motor state of high β in medoff were correlated with the Unified Parkinson's Disease Rating Scale III (UPDRS III). Conversely, DBS significantly diminishes the motor state of HMM and power in motor area of high β oscillations. The score changes of tremor and limb rigidity after DBS treatment were significantly correlated with the changes of motor state of high β. Conclusions: This study demonstrates that L-dopa and STN-DBS exert differing effects on β oscillations in the motor cortex of PD patients, primarily in high β band. Understanding these distinct neurophysiological impacts can provide valuable insights for refining therapeutic approaches in motor control for PD patients.http://www.sciencedirect.com/science/article/pii/S1053811924004890PDL-dopaDBSMEGTDE-HMMMotor cortex |
| spellingShingle | Kunzhou Wei Hang Ping Xiaochen Tang Dianyou Li Shikun Zhan Bomin Sun Xiangyan Kong Chunyan Cao The effect of L-dopa and DBS on cortical oscillations in Parkinson's disease analyzed by hidden Markov model algorithm NeuroImage PD L-dopa DBS MEG TDE-HMM Motor cortex |
| title | The effect of L-dopa and DBS on cortical oscillations in Parkinson's disease analyzed by hidden Markov model algorithm |
| title_full | The effect of L-dopa and DBS on cortical oscillations in Parkinson's disease analyzed by hidden Markov model algorithm |
| title_fullStr | The effect of L-dopa and DBS on cortical oscillations in Parkinson's disease analyzed by hidden Markov model algorithm |
| title_full_unstemmed | The effect of L-dopa and DBS on cortical oscillations in Parkinson's disease analyzed by hidden Markov model algorithm |
| title_short | The effect of L-dopa and DBS on cortical oscillations in Parkinson's disease analyzed by hidden Markov model algorithm |
| title_sort | effect of l dopa and dbs on cortical oscillations in parkinson s disease analyzed by hidden markov model algorithm |
| topic | PD L-dopa DBS MEG TDE-HMM Motor cortex |
| url | http://www.sciencedirect.com/science/article/pii/S1053811924004890 |
| work_keys_str_mv | AT kunzhouwei theeffectofldopaanddbsoncorticaloscillationsinparkinsonsdiseaseanalyzedbyhiddenmarkovmodelalgorithm AT hangping theeffectofldopaanddbsoncorticaloscillationsinparkinsonsdiseaseanalyzedbyhiddenmarkovmodelalgorithm AT xiaochentang theeffectofldopaanddbsoncorticaloscillationsinparkinsonsdiseaseanalyzedbyhiddenmarkovmodelalgorithm AT dianyouli theeffectofldopaanddbsoncorticaloscillationsinparkinsonsdiseaseanalyzedbyhiddenmarkovmodelalgorithm AT shikunzhan theeffectofldopaanddbsoncorticaloscillationsinparkinsonsdiseaseanalyzedbyhiddenmarkovmodelalgorithm AT bominsun theeffectofldopaanddbsoncorticaloscillationsinparkinsonsdiseaseanalyzedbyhiddenmarkovmodelalgorithm AT xiangyankong theeffectofldopaanddbsoncorticaloscillationsinparkinsonsdiseaseanalyzedbyhiddenmarkovmodelalgorithm AT chunyancao theeffectofldopaanddbsoncorticaloscillationsinparkinsonsdiseaseanalyzedbyhiddenmarkovmodelalgorithm AT kunzhouwei effectofldopaanddbsoncorticaloscillationsinparkinsonsdiseaseanalyzedbyhiddenmarkovmodelalgorithm AT hangping effectofldopaanddbsoncorticaloscillationsinparkinsonsdiseaseanalyzedbyhiddenmarkovmodelalgorithm AT xiaochentang effectofldopaanddbsoncorticaloscillationsinparkinsonsdiseaseanalyzedbyhiddenmarkovmodelalgorithm AT dianyouli effectofldopaanddbsoncorticaloscillationsinparkinsonsdiseaseanalyzedbyhiddenmarkovmodelalgorithm AT shikunzhan effectofldopaanddbsoncorticaloscillationsinparkinsonsdiseaseanalyzedbyhiddenmarkovmodelalgorithm AT bominsun effectofldopaanddbsoncorticaloscillationsinparkinsonsdiseaseanalyzedbyhiddenmarkovmodelalgorithm AT xiangyankong effectofldopaanddbsoncorticaloscillationsinparkinsonsdiseaseanalyzedbyhiddenmarkovmodelalgorithm AT chunyancao effectofldopaanddbsoncorticaloscillationsinparkinsonsdiseaseanalyzedbyhiddenmarkovmodelalgorithm |