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Fang H, Berman SA, Wang Y, Yang Y. Robust adaptive deep brain stimulation control of in-silico non-stationary Parkinsonian neural oscillatory dynamics. J Neural Eng 2024; 21:036043. [PMID: 38834058 DOI: 10.1088/1741-2552/ad5406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 06/04/2024] [Indexed: 06/06/2024]
Abstract
Objective. Closed-loop deep brain stimulation (DBS) is a promising therapy for Parkinson's disease (PD) that works by adjusting DBS patterns in real time from the guidance of feedback neural activity. Current closed-loop DBS mainly uses threshold-crossing on-off controllers or linear time-invariant (LTI) controllers to regulate the basal ganglia (BG) Parkinsonian beta band oscillation power. However, the critical cortex-BG-thalamus network dynamics underlying PD are nonlinear, non-stationary, and noisy, hindering accurate and robust control of Parkinsonian neural oscillatory dynamics.Approach. Here, we develop a new robust adaptive closed-loop DBS method for regulating the Parkinsonian beta oscillatory dynamics of the cortex-BG-thalamus network. We first build an adaptive state-space model to quantify the dynamic, nonlinear, and non-stationary neural activity. We then construct an adaptive estimator to track the nonlinearity and non-stationarity in real time. We next design a robust controller to automatically determine the DBS frequency based on the estimated Parkinsonian neural state while reducing the system's sensitivity to high-frequency noise. We adopt and tune a biophysical cortex-BG-thalamus network model as an in-silico simulation testbed to generate nonlinear and non-stationary Parkinsonian neural dynamics for evaluating DBS methods.Main results. We find that under different nonlinear and non-stationary neural dynamics, our robust adaptive DBS method achieved accurate regulation of the BG Parkinsonian beta band oscillation power with small control error, bias, and deviation. Moreover, the accurate regulation generalizes across different therapeutic targets and consistently outperforms current on-off and LTI DBS methods.Significance. These results have implications for future designs of closed-loop DBS systems to treat PD.
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Affiliation(s)
- Hao Fang
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310058, People's Republic of China
- Nanhu Brain-computer Interface Institute, Hangzhou 311100, People's Republic of China
| | - Stephen A Berman
- College of Medicine, University of Central Florida, Orlando, FL 32816, United States of America
| | - Yueming Wang
- Nanhu Brain-computer Interface Institute, Hangzhou 311100, People's Republic of China
- Qiushi Academy for Advanced Studies, Hangzhou 310058, People's Republic of China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, People's Republic of China
- State Key Laboratory of Brain-machine Intelligence, Hangzhou 310058, People's Republic of China
| | - Yuxiao Yang
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310058, People's Republic of China
- Nanhu Brain-computer Interface Institute, Hangzhou 311100, People's Republic of China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, People's Republic of China
- State Key Laboratory of Brain-machine Intelligence, Hangzhou 310058, People's Republic of China
- Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Hangzhou 310058, People's Republic of China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, People's Republic of China
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Jia Q, Liu Y, Lv S, Wang Y, Jiao P, Xu W, Xu Z, Wang M, Cai X. Wireless closed-loop deep brain stimulation using microelectrode array probes. J Zhejiang Univ Sci B 2024:1-21. [PMID: 38423536 DOI: 10.1631/jzus.b2300400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 08/25/2023] [Indexed: 03/02/2024]
Abstract
Deep brain stimulation (DBS), including optical stimulation and electrical stimulation, has been demonstrated considerable value in exploring pathological brain activity and developing treatments for neural disorders. Advances in DBS microsystems based on implantable microelectrode array (MEA) probes have opened up new opportunities for closed-loop DBS (CL-DBS) in situ. This technology can be used to detect damaged brain circuits and test the therapeutic potential for modulating the output of these circuits in a variety of diseases simultaneously. Despite the success and rapid utilization of MEA probe-based CL-DBS microsystems, key challenges, including excessive wired communication, need to be urgently resolved. In this review, we considered recent advances in MEA probe-based wireless CL-DBS microsystems and outlined the major issues and promising prospects in this field. This technology has the potential to offer novel therapeutic options for psychiatric disorders in the future.
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Affiliation(s)
- Qianli Jia
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yaoyao Liu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shiya Lv
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiding Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peiyao Jiao
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhaojie Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mixia Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China.
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Xinxia Cai
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China. ,
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China. ,
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Siddique MAB, Zhang Y, An H. Monitoring time domain characteristics of Parkinson's disease using 3D memristive neuromorphic system. Front Comput Neurosci 2023; 17:1274575. [PMID: 38162516 PMCID: PMC10754992 DOI: 10.3389/fncom.2023.1274575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/06/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction Parkinson's disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS systems need to be adjusted in real-time in accordance with the state of PD symptoms. Therefore, fast and precise monitoring of PD symptoms is a critical function for CL-DBS systems. However, the current CL-DBS techniques suffer from high computational demands for real-time PD symptom monitoring, which are not feasible for implanted and wearable medical devices. Methods In this paper, we present an energy-efficient neuromorphic PD symptom detector using memristive three-dimensional integrated circuits (3D-ICs). The excessive oscillation at beta frequencies (13-35 Hz) at the subthalamic nucleus (STN) is used as a biomarker of PD symptoms. Results Simulation results demonstrate that our neuromorphic PD detector, implemented with an 8-layer spiking Long Short-Term Memory (S-LSTM), excels in recognizing PD symptoms, achieving a training accuracy of 99.74% and a validation accuracy of 99.52% for a 75%-25% data split. Furthermore, we evaluated the improvement of our neuromorphic CL-DBS detector using NeuroSIM. The chip area, latency, energy, and power consumption of our CL-DBS detector were reduced by 47.4%, 66.63%, 65.6%, and 67.5%, respectively, for monolithic 3D-ICs. Similarly, for heterogeneous 3D-ICs, employing memristive synapses to replace traditional Static Random Access Memory (SRAM) resulted in reductions of 44.8%, 64.75%, 65.28%, and 67.7% in chip area, latency, and power usage. Discussion This study introduces a novel approach for PD symptom evaluation by directly utilizing spiking signals from neural activities in the time domain. This method significantly reduces the time and energy required for signal conversion compared to traditional frequency domain approaches. The study pioneers the use of neuromorphic computing and memristors in designing CL-DBS systems, surpassing SRAM-based designs in chip design area, latency, and energy efficiency. Lastly, the proposed neuromorphic PD detector demonstrates high resilience to timing variations in brain neural signals, as confirmed by robustness analysis.
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Affiliation(s)
- Md Abu Bakr Siddique
- Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, United States
| | - Yan Zhang
- Department of Biological Sciences, Michigan Technological University, Houghton, MI, United States
| | - Hongyu An
- Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, United States
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Sun Y, Lü J, Zhou Y, Liu Y, Chai Y. Suppression of beta oscillations by delayed feedback in a cortex-basal ganglia-thalamus-pedunculopontine nucleus neural loop model. J Biol Phys 2023; 49:463-482. [PMID: 37572243 PMCID: PMC10651615 DOI: 10.1007/s10867-023-09641-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/28/2023] [Indexed: 08/14/2023] Open
Abstract
Excessive neural synchronization of neural populations in the beta (β) frequency range (12-35 Hz) is intimately related to the symptoms of hypokinesia in Parkinson's disease (PD). Studies have shown that delayed feedback stimulation strategies can interrupt excessive neural synchronization and effectively alleviate symptoms associated with PD dyskinesia. Work on optimizing delayed feedback algorithms continues to progress, yet it remains challenging to further improve the inhibitory effect with reduced energy expenditure. Therefore, we first established a neural mass model of the cortex-basal ganglia-thalamus-pedunculopontine nucleus (CBGTh-PPN) closed-loop system, which can reflect the internal properties of cortical and basal ganglia neurons and their intrinsic connections with thalamic and pedunculopontine nucleus neurons. Second, the inhibitory effects of three delayed feedback schemes based on the external globus pallidum (GPe) on β oscillations were investigated separately and compared with those based on the subthalamic nucleus (STN) only. Our results show that all four delayed feedback schemes achieve effective suppression of pathological β oscillations when using the linear delayed feedback algorithm. The comparison revealed that the three GPe-based delayed feedback stimulation strategies were able to have a greater range of oscillation suppression with reduced energy consumption, thus improving control performance effectively, suggesting that they may be more effective for the relief of Parkinson's motor symptoms in practical applications.
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Affiliation(s)
- Yuqin Sun
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 201306, China
| | - Jiali Lü
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 201306, China
| | - Ye Zhou
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 201306, China
| | - Yingpeng Liu
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 201306, China
| | - Yuan Chai
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, 201306, China.
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Chen H, Zhao D, Luo Z, Shen L, Shu Y, Li L. A screening method based on analytic hierarchy process for closed-loop DBS strategies of Parkinson's disease. Technol Health Care 2023:THC220587. [PMID: 36872807 DOI: 10.3233/thc-220587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
BACKGROUND Closed-loop deep brain stimulation (DBS) is a research hotspot in the treatment of Parkinson's disease. However, a variety of stimulation strategies will increase the selection time and cost in animal experiments and clinical studies. Moreover, the stimulation effect is little difference between similar strategies, so the selection process will be redundant. OBJECTIVE The objective was to propose a comprehensive evaluation model based on analytic hierarchy process (AHP) to select the best one among similar strategies. METHODS Two similar strategies, namely, threshold stimulation (CDBS) and threshold stimulus after EMD feature extraction (EDBS), were used for analysis and screening. The values of Similar to Unified Parkinson's Disease Rating Scale estimates (SUE), β power and energy consumption were calculated and analysed. The stimulation threshold with the best improvement effect was selected. The weights of the indices were allocated by AHP. Finally, the weights and index values were combined, and the comprehensive scores of the two strategies were calculated using the evaluation model. RESULTS The optimal stimulation threshold for CDBS was 52% and for EDBS was 62%. The weights of the indices were 0.45, 0.45 and 0.1, respectively. According to comprehensive scores, different from the situation where either EDBS or CDBS can be called optimal stimulation strategies. But under the same threshold stimulation, the EDBS was better than the CDBS under the optimal level. CONCLUSION The evaluation model based on AHP under the optimal stimulation conditions satisfied the screening conditions between the two strategies.
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Affiliation(s)
- Huan Chen
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Dechun Zhao
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Zixin Luo
- School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Lihao Shen
- School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yang Shu
- School of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Ling Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
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