Electromyography Biomarkers for Quantifying the Intraoperative Efficacy of Deep Brain Stimulation in Parkinson's Patients With Resting Tremor

Wang, Kailiang; Burns, Mathew R; Xu, Dan; Hu, Wei; Fan, Shiying; Han, Chunlei; Wang, Qiao; Shimabukuro, Michio; Xia, Xiaotong; Zhang, Jianguo; Wang, Feng; Meng, Fangang

doi:10.3389/fneur.2020.00142

Cited by 11 publications

(5 citation statements)

References 49 publications

(65 reference statements)

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“…These features have been fed as input into the LDA, CNN, and SVM, where the SVM performed the best ( Ruonala, 2022 ). In a few investigations, EMG features, encompassing frequency, amplitude, and regularity, were scrutinized ( Khobragade et al, 2018 ; Wang et al, 2020 ). The signal mean and power of the peak frequency performed well as features when using a random forest model and a deep learning network for adaptive DBS ( Khobragade et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Model-based closed-loop control of thalamic deep brain stimulation

Tian,

Saradhi,

Bello

et al. 2024

Front. Netw. Physiol.

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Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson’s disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists’ expertise and patients’ experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients’ symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity.Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim–DBS are linked to symptomatic changes in EMG signals. By using a proportional–integral–derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim–DBS so that the power of EMG reaches a desired control target.Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.

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Section: Discussionmentioning

confidence: 99%

Model-based closed-loop control of thalamic deep brain stimulation

Tian,

Saradhi,

Bello

et al. 2024

Front. Netw. Physiol.

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“…The surgical procedure was described in detail in previous reports of studies conducted in our center. 18 , 24 , 25 DBS electrode (model 3389, Medtronic, Minneapolis, USA, or model L301, Pins Medical, China; both electrodes share the same parameters) implantations were performed under local anesthesia, using a Leksell stereotactic system (Elekta Instrument AB, Stockholm, Sweden). Electrodes were then connected to an IPG which was implanted in the subclavicular area under general anesthesia.…”

Section: Methodsmentioning

confidence: 99%

Sleep outcomes and related factors in Parkinson’s disease after subthalamic deep brain electrode implantation: a retrospective cohort study

Yin

Chen

et al. 2023

Ther Adv Neurol Disord

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Background: Subthalamic nucleus deep brain stimulation (STN-DBS) improves sleep qualities in Parkinson’s disease (PD) patients; however, it remains elusive whether STN-DBS improves sleep by directly influencing the sleep circuit or alleviates other cardinal symptoms such as motor functions, other confounding factors including stimulation intensity may also involve. Studying the effect of microlesion effect (MLE) on sleep after STN-DBS electrode implantation may address this issue. Objective: To examine the influence of MLE on sleep quality and related factors in PD, as well as the effects of regional and lateral specific correlations with sleep outcomes after STN-DBS electrode implantation. Study Design: Case–control study; Level of evidence, 3. Data Sources and Methods: In 78 PD patients who underwent bilateral STN-DBS surgery in our center, we compared the sleep qualities, motor performances, anti-Parkinsonian drug dosage, and emotional conditions at preoperative baseline and postoperative 1-month follow-up. We determined the related factors of sleep outcomes and visualized the electrodes position, simulated the MLE-engendered volume of tissue lesioned (VTL), and investigated sleep-related sweet/sour spots and laterality in STN. Results: MLE improves sleep quality with Pittsburgh Sleep Quality Index (PSQI) by 13.36% and Parkinson’s Disease Sleep Scale-2 (PDSS-2) by 17.95%. Motor ( P = 0.014) and emotional ( P = 0.001) improvements were both positively correlated with sleep improvements. However, MLE in STN associative subregions, as an independent factor, may cause sleep deterioration ( r = 0.348, P = 0.002), and only the left STN showed significance ( r = 0.327, P = 0.004). Sweet spot analysis also indicated part of the left STN associative subregion is the sour spot indicative of sleep deterioration. Conclusion: The MLE of STN-DBS can overall improve sleep quality in PD patients, with a positive correlation between motor and emotional improvements. However, independent of all other factors, the MLE in the STN associative subregion, particularly the left side, may cause sleep deterioration.

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“…These features have been fed as input into LDA, CNN, and SVM, where SVM performed the best [80]. In a few investigations, EMG features, encompassing frequency, amplitude, and regularity, were scrutinized [81] [82]. The signal mean and power of the peak frequency performed well as features when using a random forest model and a deep learning network for adaptive DBS [81].…”

Section: The Use Of Machine Learning Methods In Closed-loop Dbsmentioning

confidence: 99%

Model-Based Closed-Loop Control of Thalamic Deep Brain Stimulation

Tian,

Saradhi,

Bello

et al. 2023

Preprint

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Closed-loop control of deep brain stimulation (DBS) is crucial for effective and automatic treatments of various neurological disorders like Parkinson’s disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists’ expertise and patients’ experience. The continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system utilizes a feedback biomarker/signal to track worsening (or improving) patient’s symptoms and offers several advantages compared to open-loop DBS. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying the DBS or symptoms, for example how DBS modulates dynamics of synaptic plasticity. In this work, we proposed a computational framework for development of a model-based DBS controller where a biophysically-reasonable model can describe the relationship between DBS and neural activity, and a polynomial-based approximation can estimate the relationship between the neural and behavioral activity. A controller is utilized in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. These DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given EMG recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed a biophysically-reasonable simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By utilizing a PID controller, we showed that a closed-loop system can track EMG signals and adjusts the stimulation frequency of Vim-DBS so that the power of EMG in [2, 200] Hz reaches a desired target. We demonstrated that our model-based closed-loop control system of Vim-DBS finds an appropriate DBS frequency that aligns well with clinical studies. Our model-based closed-loop system is adaptable to different control targets, highlighting its potential usability for different diseases and personalized systems.

show abstract

Electromyography Biomarkers for Quantifying the Intraoperative Efficacy of Deep Brain Stimulation in Parkinson's Patients With Resting Tremor

Cited by 11 publications

References 49 publications

Model-based closed-loop control of thalamic deep brain stimulation

Model-based closed-loop control of thalamic deep brain stimulation

Sleep outcomes and related factors in Parkinson’s disease after subthalamic deep brain electrode implantation: a retrospective cohort study

Model-Based Closed-Loop Control of Thalamic Deep Brain Stimulation

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