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Schmidt SL, Chowdhury AH, Mitchell KT, Peters JJ, Gao Q, Lee HJ, Genty K, Chow SC, Grill WM, Pajic M, Turner DA. At home adaptive dual target deep brain stimulation in Parkinson's disease with proportional control. Brain 2024; 147:911-922. [PMID: 38128546 PMCID: PMC10907084 DOI: 10.1093/brain/awad429] [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: 05/22/2023] [Revised: 10/24/2023] [Accepted: 11/18/2023] [Indexed: 12/23/2023] Open
Abstract
Continuous deep brain stimulation (cDBS) of the subthalamic nucleus (STN) or globus pallidus is an effective treatment for the motor symptoms of Parkinson's disease. The relative benefit of one region over the other is of great interest but cannot usually be compared in the same patient. Simultaneous DBS of both regions may synergistically increase the therapeutic benefit. Continuous DBS is limited by a lack of responsiveness to dynamic, fluctuating symptoms intrinsic to the disease. Adaptive DBS (aDBS) adjusts stimulation in response to biomarkers to improve efficacy, side effects, and efficiency. We combined bilateral DBS of both STN and globus pallidus (dual target DBS) in a prospective within-participant, clinical trial in six patients with Parkinson's disease (n = 6, 55-65 years, n = 2 females). Dual target cDBS was tested for Parkinson's disease symptom control annually over 2 years, measured by motor rating scales, on time without dyskinesia, and medication reduction. Random amplitude experiments probed system dynamics to estimate parameters for aDBS. We then implemented proportional-plus-integral aDBS using a novel distributed (off-implant) architecture. In the home setting, we collected tremor and dyskinesia scores as well as individualized β and DBS amplitudes. Dual target cDBS reduced motor symptoms as measured by Unified Parkinson's Disease Rating Scale (UPDRS) to a greater degree than either region alone (P < 0.05, linear mixed model) in the cohort. The amplitude of β-oscillations in the STN correlated to the speed of hand grasp movements for five of six participants (P < 0.05, Pearson correlation). Random amplitude experiments provided insight into temporal windowing to avoid stimulation artefacts and demonstrated a correlation between STN β amplitude and DBS amplitude. Proportional plus integral control of aDBS reduced average power, while preserving UPDRS III scores in the clinic (P = 0.28, Wilcoxon signed rank), and tremor and dyskinesia scores during blinded testing at home (n = 3, P > 0.05, Wilcoxon ranked sum). In the home setting, DBS power reductions were slight but significant. Dual target cDBS may offer an improvement in treatment of motor symptoms of Parkinson's disease over DBS of either the STN or globus pallidus alone. When combined with proportional plus integral aDBS, stimulation power may be reduced, while preserving the increased benefit of dual target DBS.
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Affiliation(s)
- Stephen L Schmidt
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Afsana H Chowdhury
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
| | - Kyle T Mitchell
- Department of Neurology, Duke University Medical Center, Durham, NC 27710, USA
| | - Jennifer J Peters
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Qitong Gao
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
| | - Hui-Jie Lee
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA
| | - Katherine Genty
- Department of Neurosurgery, Duke University Medical Center, Durham, NC 27710, USA
| | - Shein-Chung Chow
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
- Department of Neurosurgery, Duke University Medical Center, Durham, NC 27710, USA
- Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Miroslav Pajic
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
| | - Dennis A Turner
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
- Department of Neurosurgery, Duke University Medical Center, Durham, NC 27710, USA
- Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA
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2
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Ahmadipour P, Sani OG, Pesaran B, Shanechi MM. Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity. J Neural Eng 2024; 21:026001. [PMID: 38016450 PMCID: PMC10913727 DOI: 10.1088/1741-2552/ad1053] [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: 06/02/2023] [Revised: 10/23/2023] [Accepted: 11/28/2023] [Indexed: 11/30/2023]
Abstract
Objective.Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain-machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales.Approach.Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient learning for modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical SID method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and with spiking and local field potential population activity recorded during a naturalistic reach and grasp behavior.Main results.We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson-Gaussian observations, multiscale SID had a much lower training time while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity and behavior.Significance.Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest, such as for online adaptive BMIs to track non-stationary dynamics or for reducing offline training time in neuroscience investigations.
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Affiliation(s)
- Parima Ahmadipour
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Omid G Sani
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Bijan Pesaran
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Maryam M Shanechi
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
- Thomas Lord Department of Computer Science, Alfred E. Mann Department of Biomedical Engineering, and the Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States of America
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Palopoli-Trojani K, Schmidt SL, Baringer KD, Slotkin TA, Peters JJ, Turner DA, Grill WM. Temporally non-regular patterns of deep brain stimulation (DBS) enhance assessment of evoked potentials while maintaining motor symptom management in Parkinson's disease (PD). Brain Stimul 2023; 16:1630-1642. [PMID: 37863388 PMCID: PMC10872419 DOI: 10.1016/j.brs.2023.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 09/25/2023] [Accepted: 10/11/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Traditional deep brain stimulation (DBS) at fixed regular frequencies (>100 Hz) is effective in treating motor symptoms of Parkinson's disease (PD). Temporally non-regular patterns of DBS are a new parameter space that may help increase efficacy and efficiency. OBJECTIVE To compare the effects of temporally non-regular patterns of DBS to traditional regularly-spaced pulses. METHODS We simultaneously recorded local field potentials (LFP) and monitored motor symptoms (tremor and bradykinesia) in persons with PD during DBS in subthalamic nucleus (STN). We quantified both oscillatory activity and DBS local evoked potentials (DLEPs) from the LFP. RESULTS Temporally non-regular patterns were as effective as traditional pulse patterns in modulating motor symptoms, oscillatory activity, and DLEPs. Moreover, one of our novel patterns enabled recording of longer duration DLEPs during clinically effective stimulation. CONCLUSIONS Stimulation gaps of 50 ms can be used to increase efficiency and to enable regular assessment of long-duration DLEPs while maintaining effective symptom management. This may be a promising paradigm for closed-loop DBS with biomarker assessment during the gaps.
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Affiliation(s)
| | - Stephen L Schmidt
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Karley D Baringer
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Theodore A Slotkin
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, USA
| | - Jennifer J Peters
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Dennis A Turner
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Neurobiology and Department of Neurosurgery, Duke University, Durham, NC, USA
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Neurobiology and Department of Neurosurgery, Duke University, Durham, NC, USA; Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
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Su F, Wang H, Zu L, Chen Y. Closed-loop modulation of model parkinsonian beta oscillations based on CAR-fuzzy control algorithm. Cogn Neurodyn 2023; 17:1185-1199. [PMID: 37786652 PMCID: PMC10542090 DOI: 10.1007/s11571-022-09820-3] [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: 10/19/2021] [Revised: 04/20/2022] [Accepted: 04/28/2022] [Indexed: 12/01/2022] Open
Abstract
Closed-loop deep brain stimulation (DBS) can apply on-demand stimulation based on the feedback signal (e.g. beta band oscillation), which is deemed to lower side effects of clinically used open-loop DBS. To facilitate the application of model-based closed-loop DBS in clinical, studies must consider state variations, e.g., variation of desired signal with different movement conditions and variation of model parameters with time. This paper proposes to use the controlled autoregressive (CAR)-fuzzy control algorithm to modulate the pathological beta band (13-35 Hz) oscillation of a basal ganglia-cortex-thalamus model. The CAR model is used to identify the relationship between DBS frequency parameter and beta oscillation power. Then the error between the one-step-ahead predicted beta power of CAR model and the desired value is innovatively used as the input of fuzzy controller to calculate the next step stimulation frequency. Compared with 130 Hz open-loop DBS, the proposed closed-loop DBS method could lower the mean stimulation frequency to 74.04 Hz with similar beta oscillation suppression performance. The Mamdani fuzzy controller is selected because which could establish fuzzy controller rules according to human operation experience. Adding prediction module to closed-loop control improves the accuracy of fuzzy control, compared with proportional-integral control and fuzzy control, the proposed CAR-fuzzy control algorithm has higher tracking reliability, response speed and robustness.
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Affiliation(s)
- Fei Su
- School of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, 271018 China
| | - Hong Wang
- School of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, 271018 China
| | - Linlu Zu
- School of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, 271018 China
| | - Yan Chen
- Department of Neurology, Shanghai Jiahui International Hospital, Shanghai, 200233 China
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Lange F, Soares C, Roothans J, Raimundo R, Eldebakey H, Weigl B, Peach R, Daniels C, Musacchio T, Volkmann J, Rosas MJ, Reich MM. Machine versus physician-based programming of deep brain stimulation in isolated dystonia: A feasibility study. Brain Stimul 2023; 16:1105-1111. [PMID: 37422109 DOI: 10.1016/j.brs.2023.06.018] [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: 01/09/2023] [Revised: 06/23/2023] [Accepted: 06/26/2023] [Indexed: 07/10/2023] Open
Abstract
BACKGROUND Deep brain stimulation of the internal globus pallidus effectively alleviates dystonia motor symptoms. However, delayed symptom control and a lack of therapeutic biomarkers and a single pallidal sweetspot region complicates optimal programming. Postoperative management is complex, typically requiring multiple, lengthy follow-ups with an experienced physician - an important barrier to widespread adoption in medication-refractory dystonia patients. OBJECTIVE Here we prospectively tested the best machine-predicted programming settings in a dystonia cohort treated with GPi-DBS against the settings derived from clinical long-term care in a specialised DBS centre. METHODS Previously, we reconstructed an anatomical map of motor improvement probability across the pallidal region using individual stimulation volumes and clinical outcomes in dystonia patients. We used this to develop an algorithm that tests in silico thousands of putative stimulation settings in de novo patients after reconstructing an individual, image-based anatomical model of electrode positions, and suggests stimulation parameters with the highest likelihood of optimal symptom control. To test real-life application, our prospective study compared results in 10 patients against programming settings derived from long-term care. RESULTS In this cohort, dystonia symptom reduction was observed at 74.9 ± 15.3% with C-SURF programming as compared to 66.3 ± 16.3% with clinical programming (p < 0.012). The average total electrical energy delivered (TEED) was similar for both the clinical and C-SURF programming (262.0 μJ/s vs. 306.1 μJ/s respectively). CONCLUSION Our findings highlight the clinical potential of machine-based programming in dystonia, which could markedly reduce the programming burden in postoperative management.
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Affiliation(s)
- Florian Lange
- Department of Neurology, University Hospital and Julius Maximilian University, 97080, Wuerzburg, Germany.
| | - Carolina Soares
- Department of Neurology, Centro Hospitalar Universitário de São João, EPE, 4200-319, Porto, Portugal; Department of Clinic Neurosciences and Mental Health, Faculty of Medicine of University of Porto, 4200-319, Porto, Portugal
| | - Jonas Roothans
- Department of Neurology, University Hospital and Julius Maximilian University, 97080, Wuerzburg, Germany
| | - Rita Raimundo
- Department of Neurology, Centro Hospitalar Trás-os-Montes e Alto Douro, EPE, Unidade Hospitalar de Vila Real, 5000-508, Vila Real, Portugal
| | - Hazem Eldebakey
- Department of Neurology, University Hospital and Julius Maximilian University, 97080, Wuerzburg, Germany
| | - Benedikt Weigl
- Department of Neurology, University Hospital and Julius Maximilian University, 97080, Wuerzburg, Germany
| | - Robert Peach
- Department of Neurology, University Hospital and Julius Maximilian University, 97080, Wuerzburg, Germany; Department of Brain Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Christine Daniels
- Department of Neurology, University Hospital and Julius Maximilian University, 97080, Wuerzburg, Germany
| | - Thomas Musacchio
- Department of Neurology, University Hospital and Julius Maximilian University, 97080, Wuerzburg, Germany
| | - Jens Volkmann
- Department of Neurology, University Hospital and Julius Maximilian University, 97080, Wuerzburg, Germany
| | - Maria José Rosas
- Department of Neurology, Centro Hospitalar Universitário de São João, EPE, 4200-319, Porto, Portugal
| | - Martin M Reich
- Department of Neurology, University Hospital and Julius Maximilian University, 97080, Wuerzburg, Germany
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6
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Degan S, Feng Y, Hoffmann U, Turner DA. Placement of Extracranial Stimulating Electrodes and Measurement of Cerebral Blood Flow and Intracranial Electrical Fields in Anesthetized Mice. J Vis Exp 2023:10.3791/65195. [PMID: 37335103 PMCID: PMC10476879 DOI: 10.3791/65195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2023] Open
Abstract
The detection of cerebral blood flow (CBF) responses to various forms of neuronal activation is critical for understanding dynamic brain function and variations in the substrate supply to the brain. This paper describes a protocol for measuring CBF responses to transcranial alternating current stimulation (tACS). Dose-response curves are estimated both from the CBF change occurring with tACS (mA) and from the intracranial electric field (mV/mm). We estimate the intracranial electrical field based on the different amplitudes measured by glass microelectrodes within each side of the brain. In this paper, we describe the experimental setup, which involves using either bilateral laser Doppler (LD) probes or laser speckle imaging (LSI) to measure the CBF; as a result, this setup requires anesthesia for the electrode placement and stability. We present a correlation between the CBF response and the current as a function of age, showing a significantly larger response at higher currents (1.5 mA and 2.0 mA) in young control animals (12-14 weeks) compared to older animals (28-32 weeks) (p < 0.005 difference). We also demonstrate a significant CBF response at electrical field strengths <5 mV/mm, which is an important consideration for eventual human studies. These CBF responses are also strongly influenced by the use of anesthesia compared to awake animals, the respiration control (i.e., intubated vs. spontaneous breathing), systemic factors (i.e., CO2), and local conduction within the blood vessels, which is mediated by pericytes and endothelial cells. Likewise, more detailed imaging/recording techniques may limit the field size from the entire brain to only a small region. We describe the use of extracranial electrodes for applying tACS stimulation, including both homemade and commercial electrode designs for rodents, the concurrent measurement of the CBF and intracranial electrical field using bilateral glass DC recording electrodes, and the imaging approaches. We are currently applying these techniques to implement a closed-loop format for augmenting the CBF in animal models of Alzheimer's disease and stroke.
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Affiliation(s)
- Simone Degan
- Department of Neurosurgery, Duke University Medical Center
| | - Yu Feng
- Department of Neurosurgery, Duke University Medical Center
| | - Ulrike Hoffmann
- Department of Anesthesiology and Pain Management, University Texas Southwestern Medical School
| | - Dennis A Turner
- Department of Neurosurgery, Duke University Medical Center; Department of Neurobiology, Duke University Medical Center; Department of Biomedical Engineering, Duke University; Research and Surgery Services, Durham VA Medical Center;
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Yeh CH, Zhang C, Shi W, Lo MT, Tinkhauser G, Oswal A. Cross-Frequency Coupling and Intelligent Neuromodulation. CYBORG AND BIONIC SYSTEMS 2023; 4:0034. [PMID: 37266026 PMCID: PMC10231647 DOI: 10.34133/cbsystems.0034] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 05/02/2023] [Indexed: 06/03/2023] Open
Abstract
Cross-frequency coupling (CFC) reflects (nonlinear) interactions between signals of different frequencies. Evidence from both patient and healthy participant studies suggests that CFC plays an essential role in neuronal computation, interregional interaction, and disease pathophysiology. The present review discusses methodological advances and challenges in the computation of CFC with particular emphasis on potential solutions to spurious coupling, inferring intrinsic rhythms in a targeted frequency band, and causal interferences. We specifically focus on the literature exploring CFC in the context of cognition/memory tasks, sleep, and neurological disorders, such as Alzheimer's disease, epilepsy, and Parkinson's disease. Furthermore, we highlight the implication of CFC in the context and for the optimization of invasive and noninvasive neuromodulation and rehabilitation. Mainly, CFC could support advancing the understanding of the neurophysiology of cognition and motor control, serve as a biomarker for disease symptoms, and leverage the optimization of therapeutic interventions, e.g., closed-loop brain stimulation. Despite the evident advantages of CFC as an investigative and translational tool in neuroscience, further methodological improvements are required to facilitate practical and correct use in cyborg and bionic systems in the field.
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Affiliation(s)
- Chien-Hung Yeh
- School of Information and Electronics,
Beijing Institute of Technology, Beijing, China
| | - Chuting Zhang
- School of Information and Electronics,
Beijing Institute of Technology, Beijing, China
| | - Wenbin Shi
- School of Information and Electronics,
Beijing Institute of Technology, Beijing, China
| | - Men-Tzung Lo
- Department of Biomedical Sciences and Engineering,
National Central University, Taoyuan, Taiwan
| | - Gerd Tinkhauser
- Department of Neurology,
Bern University Hospital and University of Bern, Bern, Switzerland
| | - Ashwini Oswal
- MRC Brain Network Dynamics Unit,
University of Oxford, Oxford, UK
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8
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Ahmadipour P, Sani OG, Pesaran B, Shanechi MM. Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.26.542509. [PMID: 37398400 PMCID: PMC10312539 DOI: 10.1101/2023.05.26.542509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain-machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales. Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical subspace identification method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and spike-LFP population activity recorded during a naturalistic reach and grasp behavior. We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson-Gaussian observations, multiscale SID had a much lower computational cost while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity. Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest.
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9
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Yang Y, Zhang F, Gao X, Feng L, Xu K. Progressive alterations in electrophysiological and epileptic network properties during the development of temporal lobe epilepsy in rats. Epilepsy Behav 2023; 141:109120. [PMID: 36868167 DOI: 10.1016/j.yebeh.2023.109120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 01/28/2023] [Accepted: 01/29/2023] [Indexed: 03/05/2023]
Abstract
OBJECTIVE Refractory temporal lobe epilepsy (TLE) with recurring seizures causing continuing pathological changes in neural reorganization. There is an incomplete understanding of how spatiotemporal electrophysiological characteristics changes during the development of TLE. Long-term multi-site epilepsy patients' data is hard to obtain. Thus, our study relied on animal models to reveal the changes in electrophysiological and epileptic network characteristics systematically. METHODS Long-term local field potentials (LFPs) were recorded over a period of 1 to 4 months from 6 pilocarpine-treated TLE rats. We compared variations of seizure onset zone (SOZ), seizure onset pattern (SOP), the latency of seizure onsets, and functional connectivity network from 10-channel LFPs between the early and late stages. Moreover, three machine learning classifiers trained by early-stage data were used to test seizure detection performance in the late stage. RESULTS Compared to the early stage, the earliest seizure onset was more frequently detected in hippocampus areas in the late stage. The latency of seizure onsets between electrodes became shorter. Low-voltage fast activity (LVFA) was the most common SOP and the proportion of it increased in the late stage. Different brain states were observed during seizures using Granger causality (GC). Moreover, seizure detection classifiers trained by early-stage data were less accurate when tested in late-stage data. SIGNIFICANCE Neuromodulation especially closed-loop deep brain stimulation (DBS) is effective in the treatment of refractory TLE. Although the frequency or amplitude of the stimulation is generally adjusted in existing closed-loop DBS devices in clinical usage, the adjustment rarely considers the pathological progression of chronic TLE. This suggests that an important factor affecting the therapeutic effect of neuromodulation may have been overlooked. The present study reveals time-varying electrophysiological and epileptic network properties in chronic TLE rats and indicates that classifiers of seizure detection and neuromodulation parameters might be designed to adapt to the current state dynamically with the progression of epilepsy.
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Affiliation(s)
- Yufang Yang
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.
| | - Fang Zhang
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.
| | - Xiang Gao
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China; Institute of Advanced Digital Technology and Instrument, Zhejiang University, Hangzhou, China.
| | | | - Kedi Xu
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China; The MOE Frontier Science Center for Brain Science and Brain-machine Integration, Hangzhou, China.
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10
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Madadi Asl M, Valizadeh A, Tass PA. Decoupling of interacting neuronal populations by time-shifted stimulation through spike-timing-dependent plasticity. PLoS Comput Biol 2023; 19:e1010853. [PMID: 36724144 PMCID: PMC9891531 DOI: 10.1371/journal.pcbi.1010853] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 01/05/2023] [Indexed: 02/02/2023] Open
Abstract
The synaptic organization of the brain is constantly modified by activity-dependent synaptic plasticity. In several neurological disorders, abnormal neuronal activity and pathological synaptic connectivity may significantly impair normal brain function. Reorganization of neuronal circuits by therapeutic stimulation has the potential to restore normal brain dynamics. Increasing evidence suggests that the temporal stimulation pattern crucially determines the long-lasting therapeutic effects of stimulation. Here, we tested whether a specific pattern of brain stimulation can enable the suppression of pathologically strong inter-population synaptic connectivity through spike-timing-dependent plasticity (STDP). More specifically, we tested how introducing a time shift between stimuli delivered to two interacting populations of neurons can effectively decouple them. To that end, we first used a tractable model, i.e., two bidirectionally coupled leaky integrate-and-fire (LIF) neurons, to theoretically analyze the optimal range of stimulation frequency and time shift for decoupling. We then extended our results to two reciprocally connected neuronal populations (modules) where inter-population delayed connections were modified by STDP. As predicted by the theoretical results, appropriately time-shifted stimulation causes a decoupling of the two-module system through STDP, i.e., by unlearning pathologically strong synaptic interactions between the two populations. Based on the overall topology of the connections, the decoupling of the two modules, in turn, causes a desynchronization of the populations that outlasts the cessation of stimulation. Decoupling effects of the time-shifted stimulation can be realized by time-shifted burst stimulation as well as time-shifted continuous simulation. Our results provide insight into the further optimization of a variety of multichannel stimulation protocols aiming at a therapeutic reshaping of diseased brain networks.
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Affiliation(s)
- Mojtaba Madadi Asl
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran
| | - Alireza Valizadeh
- Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Peter A. Tass
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States of America
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11
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Yu L, Noor MS, Kiss ZHT, Murari K. Monitoring stimulus-evoked hemodynamic response during deep brain stimulation with single fiber spectroscopy. JOURNAL OF BIOPHOTONICS 2022; 15:e202200076. [PMID: 36054592 DOI: 10.1002/jbio.202200076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 06/30/2022] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
Deep brain stimulation (DBS) is a revolutionary treatment for movement disorders. Measuring DBS-induced hemodynamic responses may be useful for surgical guidance of DBS electrode implantation as well as to study the mechanism and assess therapeutic effects of DBS. In this study, we evaluated the performance of a single fiber spectroscopic (SFS) system for measuring hemodynamic response in different cortical layers in a DBS animal model. We showed that SFS is capable of measuring minute relative changes in oxygen saturation and blood volume fraction in-vivo at a sampling rate of 22-33 Hz. During stimulation, blood volume fraction increased, while oxygen saturation showed both increases and decreases at different cortical depths across animals. In addition, we showed the potential of using SFS for measuring other physiological parameters, for example, heart rate, and respiratory rate.
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Affiliation(s)
- Linhui Yu
- Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - M Sohail Noor
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Zelma H T Kiss
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Kartikeya Murari
- Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada
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12
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Bove F, Genovese D, Moro E. Developments in the mechanistic understanding and clinical application of deep brain stimulation for Parkinson's disease. Expert Rev Neurother 2022; 22:789-803. [PMID: 36228575 DOI: 10.1080/14737175.2022.2136030] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION. Deep brain stimulation (DBS) is a life-changing treatment for patients with Parkinson's disease (PD) and gives the unique opportunity to directly explore how basal ganglia work. Despite the rapid technological innovation of the last years, the untapped potential of DBS is still high. AREAS COVERED. This review summarizes the developments in the mechanistic understanding of DBS and the potential clinical applications of cutting-edge technological advances. Rather than a univocal local mechanism, DBS exerts its therapeutic effects through several multimodal mechanisms and involving both local and network-wide structures, although crucial questions remain unexplained. Nonetheless, new insights in mechanistic understanding of DBS in PD have provided solid bases for advances in preoperative selection phase, prediction of motor and non-motor outcomes, leads placement and postoperative stimulation programming. EXPERT OPINION. DBS has not only strong evidence of clinical effectiveness in PD treatment, but technological advancements are revamping its role of neuromodulation of brain circuits and key to better understanding PD pathophysiology. In the next few years, the worldwide use of new technologies in clinical practice will provide large data to elucidate their role and to expand their applications for PD patients, providing useful insights to personalize DBS treatment and follow-up.
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Affiliation(s)
- Francesco Bove
- Neurology Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Danilo Genovese
- Fresco Institute for Parkinson's and Movement Disorders, Department of Neurology, New York University School of Medicine, New York, New York, USA
| | - Elena Moro
- Grenoble Alpes University, CHU of Grenoble, Division of Neurology, Grenoble, France.,Grenoble Institute of Neurosciences, INSERM, U1216, Grenoble, France
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13
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Mitchell KT, Schmidt SL, Cooney JW, Grill WM, Peters J, Rahimpour S, Lee HJ, Jung SH, Mantri S, Scott B, Lad SP, Turner DA. Initial Clinical Outcome With Bilateral, Dual-Target Deep Brain Stimulation Trial in Parkinson Disease Using Summit RC + S. Neurosurgery 2022; 91:132-138. [PMID: 35383660 PMCID: PMC9514741 DOI: 10.1227/neu.0000000000001957] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 01/16/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Deep brain stimulation (DBS) is an effective therapy in advanced Parkinson disease (PD). Although both subthalamic nucleus (STN) and globus pallidus (GP) DBS show equivalent efficacy in PD, combined stimulation may demonstrate synergism. OBJECTIVE To evaluate the clinical benefit of stimulating a combination of STN and GP DBS leads and to demonstrate biomarker discovery for adaptive DBS therapy in an observational study. METHODS We performed a pilot trial (n = 3) of implanting bilateral STN and GP DBS leads, connected to a bidirectional implantable pulse generator (Medtronic Summit RC + S; NCT03815656, IDE No. G180280). Initial 1-year outcome in 3 patients included Unified PD Rating Scale on and off medications, medication dosage, Hauser diary, and recorded beta frequency spectral power. RESULTS Combined DBS improved PD symptom control, allowing >80% levodopa medication reduction. There was a greater decrease in off-medication motor Unified PD Rating Scale with multiple electrodes activated (mean difference from off stimulation off medications -18.2, range -25.5 to -12.5) than either STN (-12.8, range -20.5 to 0) or GP alone (-9, range -11.5 to -4.5). Combined DBS resulted in a greater reduction of beta oscillations in STN in 5/6 hemispheres than either site alone. Adverse events occurred in 2 patients, including a small cortical hemorrhage and seizure at 24 hours postoperatively, which resolved spontaneously, and extension wire scarring requiring revision at 2 months postoperatively. CONCLUSION Patients with PD preferred combined DBS stimulation in this preliminary cohort. Future studies will address efficacy of adaptive DBS as we further define biomarkers and control policy.
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Affiliation(s)
- Kyle T. Mitchell
- Department of Neurology, Duke University Medical Center, Durham, North Carolina, USA
| | - Stephen L. Schmidt
- Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina, USA
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Jeffrey W. Cooney
- Department of Neurology, Duke University Medical Center, Durham, North Carolina, USA
| | - Warren M. Grill
- Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina, USA
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
- Department of Neurobiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Jennifer Peters
- Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina, USA
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Shervin Rahimpour
- Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina, USA
- Department of Neurosurgery, Clinical Neuroscience Center, University of Utah, Salt Lake City, Utah, USA;
| | - Hui-Jie Lee
- Duke University CTSI Biostatistics, Epidemiology and Research Design, Durham, North Carolina, USA
| | - Sin-Ho Jung
- Duke University CTSI Biostatistics, Epidemiology and Research Design, Durham, North Carolina, USA
| | - Sneha Mantri
- Department of Neurology, Duke University Medical Center, Durham, North Carolina, USA
| | - Burton Scott
- Department of Neurology, Duke University Medical Center, Durham, North Carolina, USA
| | - Shivanand P. Lad
- Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina, USA
| | - Dennis A. Turner
- Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina, USA
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
- Department of Neurobiology, Duke University Medical Center, Durham, North Carolina, USA
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14
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Dale J, Schmidt SL, Mitchell K, Turner DA, Grill WM. Evoked potentials generated by deep brain stimulation for Parkinson's disease. Brain Stimul 2022; 15:1040-1047. [DOI: 10.1016/j.brs.2022.07.048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 04/18/2022] [Accepted: 07/20/2022] [Indexed: 11/29/2022] Open
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15
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Darcy N, Lofredi R, Al-Fatly B, Neumann WJ, Hübl J, Brücke C, Krause P, Schneider GH, Kühn A. Spectral and spatial distribution of subthalamic beta peak activity in Parkinson's disease patients. Exp Neurol 2022; 356:114150. [PMID: 35732220 DOI: 10.1016/j.expneurol.2022.114150] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 05/27/2022] [Accepted: 06/15/2022] [Indexed: 11/17/2022]
Abstract
Current efforts to optimize subthalamic deep brain stimulation in Parkinson's disease patients aim to harness local oscillatory activity in the beta frequency range (13-35 Hz) as a feedback-signal for demand-based adaptive stimulation paradigms. A high prevalence of beta peak activity is prerequisite for this approach to become routine clinical practice. In a large dataset of postoperative rest recordings from 106 patients we quantified occurrence and identified determinants of spectral peaks in the alpha, low and high beta bands. At least one peak in beta band occurred in 92% of patients and 84% of hemispheres off medication, irrespective of demographic parameters, clinical subtype or motor symptom severity. Distance to previously described clinical sweet spot was significantly related both to beta peak occurrence and to spectral power (rho -0.21, p 0.006), particularly in the high beta band. Electrophysiological landscapes of our cohort's dataset in normalised space showed divergent heatmaps for alpha and beta but found similar regions for low and high beta frequency bands. We discuss potential ramifications for clinicians' programming decisions. In summary, this report provides robust evidence that spectral peaks in beta frequency range can be detected in the vast majority of Parkinsonian subthalamic nuclei, increasing confidence in the broad applicability of beta-guided deep brain stimulation.
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Affiliation(s)
- Natasha Darcy
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health (BIH), Berlin, Germany.
| | - Roxanne Lofredi
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health (BIH), Berlin, Germany
| | - Bassam Al-Fatly
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Wolf-Julian Neumann
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Humboldt-Universität, Berlin, Germany
| | - Julius Hübl
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christof Brücke
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Patricia Krause
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Gerd-Helge Schneider
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Andrea Kühn
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Humboldt-Universität, Berlin, Germany; NeuroCure, Exzellenzcluster, Charité - Universitätsmedizin Berlin, Berlin, Germany; DZNE, German center for neurodegenerative diseases, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Germany
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16
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Fang H, Yang Y. Designing and Validating a Robust Adaptive Neuromodulation Algorithm for Closed-Loop Control of Brain States. J Neural Eng 2022; 19. [PMID: 35576912 DOI: 10.1088/1741-2552/ac7005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/16/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Neuromodulation systems that use closed-loop brain stimulation to control brain states can provide new therapies for brain disorders. To date, closed-loop brain stimulation has largely used linear time-invariant controllers. However, nonlinear time-varying brain network dynamics and external disturbances can appear during real-time stimulation, collectively leading to real-time model uncertainty. Real-time model uncertainty can degrade the performance or even cause instability of time-invariant controllers. Three problems need to be resolved to enable accurate and stable control under model uncertainty. First, an adaptive controller is needed to track the model uncertainty. Second, the adaptive controller additionally needs to be robust to noise and disturbances. Third, theoretical analyses of stability and robustness are needed as prerequisites for stable operation of the controller in practical applications. APPROACH We develop a robust adaptive neuromodulation algorithm that solves the above three problems. First, we develop a state-space brain network model that explicitly includes nonlinear terms of real-time model uncertainty and design an adaptive controller to track and cancel the model uncertainty. Second, to improve the robustness of the adaptive controller, we design two linear filters to increase steady-state control accuracy and reduce sensitivity to high-frequency noise and disturbances. Third, we conduct theoretical analyses to prove the stability of the neuromodulation algorithm and establish a trade-off between stability and robustness, which we further use to optimize the algorithm design. Finally, we validate the algorithm using comprehensive Monte Carlo simulations that span a broad range of model nonlinearity, uncertainty, and complexity. MAIN RESULTS The robust adaptive neuromodulation algorithm accurately tracks various types of target brain state trajectories, enables stable and robust control, and significantly outperforms state-of-the-art neuromodulation algorithms. SIGNIFICANCE Our algorithm has implications for future designs of precise, stable, and robust closed-loop brain stimulation systems to treat brain disorders and facilitate brain functions.
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Affiliation(s)
- Hao Fang
- University of Central Florida, Research 1 Room 334, 313/316, University of Central Florida, 4353 Scorpius St., Orlando, Florida, 32816-2368, UNITED STATES
| | - Yuxiao Yang
- Department of Electrical and Computer Engineering, University of Central Florida, 4353 Scorpius St., Orlando, Florida, 32816-2368, UNITED STATES
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17
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Low-Noise Amplifier for Deep-Brain Stimulation (DBS). ELECTRONICS 2022. [DOI: 10.3390/electronics11060939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Deep-brain stimulation (DBS) is an emerging research topic aiming to improve the quality of life of patients with brain diseases, and a great deal of effort has been focused on the development of implantable devices. This paper presents a low-noise amplifier (LNA) for the acquisition of biopotentials on DBS. This electronic module was designed in a low-voltage/low-power CMOS process, targeting implantable applications. The measurement results showed a gain of 38.6 dB and a −3 dB bandwidth of 2.3 kHz. The measurements also showed a power consumption of 2.8 μW. Simulations showed an input-referred noise of 6.2 μVRMS. The LNA occupies a microdevice area of 122 μm × 283 μm, supporting its application in implanted systems.
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18
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Wang C, Pesaran B, Shanechi MM. Modeling multiscale causal interactions between spiking and field potential signals during behavior. J Neural Eng 2022; 19. [DOI: 10.1088/1741-2552/ac4e1c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 01/24/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. Brain recordings exhibit dynamics at multiple spatiotemporal scales, which are measured with spike trains and larger-scale field potential signals. To study neural processes, it is important to identify and model causal interactions not only at a single scale of activity, but also across multiple scales, i.e. between spike trains and field potential signals. Standard causality measures are not directly applicable here because spike trains are binary-valued but field potentials are continuous-valued. It is thus important to develop computational tools to recover multiscale neural causality during behavior, assess their performance on neural datasets, and study whether modeling multiscale causalities can improve the prediction of neural signals beyond what is possible with single-scale causality. Approach. We design a multiscale model-based Granger-like causality method based on directed information and evaluate its success both in realistic biophysical spike-field simulations and in motor cortical datasets from two non-human primates (NHP) performing a motor behavior. To compute multiscale causality, we learn point-process generalized linear models that predict the spike events at a given time based on the history of both spike trains and field potential signals. We also learn linear Gaussian models that predict the field potential signals at a given time based on their own history as well as either the history of binary spike events or that of latent firing rates. Main results. We find that our method reveals the true multiscale causality network structure in biophysical simulations despite the presence of model mismatch. Further, models with the identified multiscale causalities in the NHP neural datasets lead to better prediction of both spike trains and field potential signals compared to just modeling single-scale causalities. Finally, we find that latent firing rates are better predictors of field potential signals compared with the binary spike events in the NHP datasets. Significance. This multiscale causality method can reveal the directed functional interactions across spatiotemporal scales of brain activity to inform basic science investigations and neurotechnologies.
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19
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Cosentino G, Todisco M, Blandini F. Noninvasive neuromodulation in Parkinson's disease: Neuroplasticity implication and therapeutic perspectives. HANDBOOK OF CLINICAL NEUROLOGY 2022; 184:185-198. [PMID: 35034733 DOI: 10.1016/b978-0-12-819410-2.00010-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Noninvasive brain stimulation techniques can be used to study in vivo the changes of cortical activity and plasticity in subjects with Parkinson's disease (PD). Also, an increasing number of studies have suggested a potential therapeutic effect of these techniques. High-frequency repetitive transcranial magnetic stimulation (rTMS) and anodal transcranial direct current stimulation (tDCS) represent the most used stimulation paradigms to treat motor and nonmotor symptoms of PD. Both techniques can enhance cortical activity, compensating for its reduction related to subcortical dysfunction in PD. However, the use of suboptimal stimulation parameters can lead to therapeutic failure. Clinical studies are warranted to clarify in PD the additional effects of these stimulation techniques on pharmacologic and neurorehabilitation treatments.
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Affiliation(s)
- Giuseppe Cosentino
- Translational Neurophysiology Research Unit, IRCCS Mondino Foundation, Pavia, Italy; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Massimiliano Todisco
- Translational Neurophysiology Research Unit, IRCCS Mondino Foundation, Pavia, Italy; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy; Movement Disorders Research Center, IRCCS Mondino Foundation, Pavia, Italy.
| | - Fabio Blandini
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy; Movement Disorders Research Center, IRCCS Mondino Foundation, Pavia, Italy
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20
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Cagle JN, Wong JK, Johnson KA, Foote KD, Okun MS, de Hemptinne C. Suppression and Rebound of Pallidal Beta Power: Observation Using a Chronic Sensing DBS Device. Front Hum Neurosci 2021; 15:749567. [PMID: 34566612 PMCID: PMC8458625 DOI: 10.3389/fnhum.2021.749567] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 08/23/2021] [Indexed: 11/14/2022] Open
Abstract
Pallidal deep brain stimulation (DBS) is an increasingly used therapy for Parkinson’s disease (PD). Here, we study the effect of DBS on pallidal oscillatory activity as well as on symptom severity in an individual with PD implanted with a new pulse generator (Medtronic Percept system) which facilitates chronic recording of local field potentials (LFP) through implanted DBS lead. Pallidal LFPs were recorded while delivering stimulation in a monopolar configuration using stepwise increments (0.5 mA, every 20 s). At each stimulation amplitude, the power spectral density (PSD) was computed, and beta power (13–30 Hz) was calculated and correlated with the degree of bradykinesia. Pallidal beta power was reduced when therapeutic stimulation was delivered. Beta power correlated to the severity of bradykinesia. Worsening of parkinsonism when excessive stimulation was applied was associated with a rebound in the beta band power. These preliminary results suggest that pallidal beta power might be used as an objective marker of the disease state in PD. The use of brain sensing from implanted neural interfaces may in the future facilitate clinical programming. Detection of rebound could help to optimize benefits and minimize worsening from overstimulation.
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Affiliation(s)
- Jackson N Cagle
- Department of Neurology, University of Florida, Gainesville, FL, United States.,Norman Fixel Institute for Neurological Diseases, Gainesville, FL, United States
| | - Joshua K Wong
- Department of Neurology, University of Florida, Gainesville, FL, United States.,Norman Fixel Institute for Neurological Diseases, Gainesville, FL, United States
| | - Kara A Johnson
- Department of Neurology, University of Florida, Gainesville, FL, United States.,Norman Fixel Institute for Neurological Diseases, Gainesville, FL, United States
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases, Gainesville, FL, United States.,Department of Neurosurgery, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Michael S Okun
- Department of Neurology, University of Florida, Gainesville, FL, United States.,Norman Fixel Institute for Neurological Diseases, Gainesville, FL, United States
| | - Coralie de Hemptinne
- Department of Neurology, University of Florida, Gainesville, FL, United States.,Norman Fixel Institute for Neurological Diseases, Gainesville, FL, United States
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21
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Zhu Y, Wang J, Li H, Liu C, Grill WM. Adaptive Parameter Modulation of Deep Brain Stimulation Based on Improved Supervisory Algorithm. Front Neurosci 2021; 15:750806. [PMID: 34602976 PMCID: PMC8481598 DOI: 10.3389/fnins.2021.750806] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 08/20/2021] [Indexed: 11/23/2022] Open
Abstract
Clinically deployed deep brain stimulation (DBS) for the treatment of Parkinson's disease operates in an open loop with fixed stimulation parameters, and this may result in high energy consumption and suboptimal therapy. The objective of this manuscript is to establish, through simulation in a computational model, a closed-loop control system that can automatically adjust the stimulation parameters to recover normal activity in model neurons. Exaggerated beta band activity is recognized as a hallmark of Parkinson's disease and beta band activity in model neurons of the globus pallidus internus (GPi) was used as the feedback signal to control DBS of the GPi. Traditional proportional controller and proportional-integral controller were not effective in eliminating the error between the target level of beta power and the beta power under Parkinsonian conditions. To overcome the difficulties in tuning the controller parameters and improve tracking performance in the case of changes in the plant, a supervisory control algorithm was implemented by introducing a Radial Basis Function (RBF) network to build the inverse model of the plant. Simulation results show the successful tracking of target beta power in the presence of changes in Parkinsonian state as well as during dynamic changes in the target level of beta power. Our computational study suggests the feasibility of the RBF network-driven supervisory control algorithm for real-time modulation of DBS parameters for the treatment of Parkinson's disease.
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Affiliation(s)
- Yulin Zhu
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Huiyan Li
- School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China
| | - Chen Liu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Warren M. Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
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22
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Mottaghi S, Kohl S, Biemann D, Liebana S, Montaño Crespo RE, Buchholz O, Wilson M, Klaus C, Uchenik M, Münkel C, Schmidt R, Hofmann UG. Bilateral Intracranial Beta Activity During Forced and Spontaneous Movements in a 6-OHDA Hemi-PD Rat Model. Front Neurosci 2021; 15:700672. [PMID: 34456673 PMCID: PMC8397450 DOI: 10.3389/fnins.2021.700672] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 07/20/2021] [Indexed: 11/26/2022] Open
Abstract
Cortico-basal ganglia beta oscillations (13–30 Hz) are assumed to be involved in motor impairments in Parkinson’s Disease (PD), especially in bradykinesia and rigidity. Various studies have utilized the unilateral 6-hydroxydopamine (6-OHDA) rat PD model to further investigate PD and test novel treatments. However, a detailed behavioral and electrophysiological characterization of the model, including analyses of popular PD treatments such as DBS, has not been documented in the literature. We hence challenged the 6-OHDA rat hemi-PD model with a series of experiments (i.e., cylinder test, open field test, and rotarod test) aimed at assessing the motor impairments, analyzing the effects of Deep Brain Stimulation (DBS), and identifying under which conditions excessive beta oscillations occur. We found that 6-OHDA hemi-PD rats presented an impaired performance in all experiments compared to the sham group, and DBS could improve their overall performance. Across all the experiments and behaviors, the power in the high beta band was observed to be an important biomarker for PD as it showed differences between healthy and lesioned hemispheres and between 6-OHDA-lesioned and sham rats. This all shows that the 6-OHDA hemi-PD model accurately represents many of the motor and electrophysiological symptoms of PD and makes it a useful tool for the pre-clinical testing of new treatments when low β (13–21 Hz) and high β (21–30 Hz) frequency bands are considered separately.
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Affiliation(s)
- Soheil Mottaghi
- Neuroelectronic Systems, Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Technical Faculty, University of Freiburg, Freiburg, Germany
| | - Sandra Kohl
- Neuroelectronic Systems, Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Dirk Biemann
- Neuroelectronic Systems, Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Samuel Liebana
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom.,Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - Ruth Eneida Montaño Crespo
- Neuroelectronic Systems, Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Oliver Buchholz
- Neuroelectronic Systems, Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Mareike Wilson
- Neuroelectronic Systems, Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Carolin Klaus
- Neuroelectronic Systems, Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Michelle Uchenik
- Biomedical Department, Faculty of Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Christian Münkel
- Neuroelectronic Systems, Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Robert Schmidt
- Department of Psychology, The University of Sheffield, Sheffield, United Kingdom
| | - Ulrich G Hofmann
- Neuroelectronic Systems, Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany.,Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Technical Faculty, University of Freiburg, Freiburg, Germany
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23
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Personalised Advanced Therapies in Parkinson's Disease: The Role of Non-Motor Symptoms Profile. J Pers Med 2021; 11:jpm11080773. [PMID: 34442417 PMCID: PMC8400869 DOI: 10.3390/jpm11080773] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 07/25/2021] [Accepted: 07/26/2021] [Indexed: 12/22/2022] Open
Abstract
Device-aided therapies, including levodopa-carbidopa intestinal gel infusion, apomorphine subcutaneous infusion, and deep brain stimulation, are available in many countries for the management of the advanced stage of Parkinson’s disease (PD). Currently, selection of device-aided therapies is mainly focused on patients’ motor profile while non-motor symptoms play a role limited to being regarded as possible exclusion criteria in the decision-making process for the delivery and sustenance of a successful treatment. Differential beneficial effects on specific non-motor symptoms of the currently available device-aided therapies for PD are emerging and these could hold relevant clinical implications. In this viewpoint, we suggest that specific non-motor symptoms could be used as an additional anchor to motor symptoms and not merely as exclusion criteria to deliver bespoke and patient-specific personalised therapy for advanced PD.
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Sand D, Rappel P, Marmor O, Bick AS, Arkadir D, Lu BL, Bergman H, Israel Z, Eitan R. Machine learning-based personalized subthalamic biomarkers predict ON-OFF levodopa states in Parkinson patients. J Neural Eng 2021; 18. [PMID: 33906182 DOI: 10.1088/1741-2552/abfc1d] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 04/27/2021] [Indexed: 01/20/2023]
Abstract
Objective.Adaptive deep brain stimulation (aDBS) based on subthalamic nucleus (STN) electrophysiology has recently been proposed to improve clinical outcomes of DBS for Parkinson's disease (PD) patients. Many current models for aDBS are based on one or two electrophysiological features of STN activity, such as beta or gamma activity. Although these models have shown interesting results, we hypothesized that an aDBS model that includes many STN activity parameters will yield better clinical results. The objective of this study was to investigate the most appropriate STN neurophysiological biomarkers, detectable over long periods of time, that can predict OFF and ON levodopa states in PD patients.Approach.Long-term local field potentials (LFPs) were recorded from eight STNs (four PD patients) during 92 recording sessions (44 OFF and 48 ON levodopa states), over a period of 3-12 months. Electrophysiological analysis included the power of frequency bands, band power ratio and burst features. A total of 140 engineered features was extracted for 20 040 epochs (each epoch lasting 5 s). Based on these engineered features, machine learning (ML) models classified LFPs as OFF vs ON levodopa states.Main results.Beta and gamma band activity alone poorly predicts OFF vs ON levodopa states, with an accuracy of 0.66 and 0.64, respectively. Group ML analysis slightly improved prediction rates, but personalized ML analysis, based on individualized engineered electrophysiological features, were markedly better, predicting OFF vs ON levodopa states with an accuracy of 0.8 for support vector machine learning models.Significance.We showed that individual patients have unique sets of STN neurophysiological biomarkers that can be detected over long periods of time. ML models revealed that personally classified engineered features most accurately predict OFF vs ON levodopa states. Future development of aDBS for PD patients might include personalized ML algorithms.
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Affiliation(s)
- Daniel Sand
- Department of Medical Neurobiology (Physiology), Institute of Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.,The Edmond and Lily Safra Center for Brain Research, The Hebrew University, Jerusalem, Israel
| | - Pnina Rappel
- Department of Medical Neurobiology (Physiology), Institute of Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.,The Edmond and Lily Safra Center for Brain Research, The Hebrew University, Jerusalem, Israel
| | - Odeya Marmor
- Department of Medical Neurobiology (Physiology), Institute of Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.,The Edmond and Lily Safra Center for Brain Research, The Hebrew University, Jerusalem, Israel
| | - Atira S Bick
- Department of Medical Neurobiology (Physiology), Institute of Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.,The Brain Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - David Arkadir
- The Brain Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Bao-Liang Lu
- Center for Brain-like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Hagai Bergman
- Department of Medical Neurobiology (Physiology), Institute of Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.,The Edmond and Lily Safra Center for Brain Research, The Hebrew University, Jerusalem, Israel.,Functional Neurosurgery Unit, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Zvi Israel
- The Brain Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.,Functional Neurosurgery Unit, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Renana Eitan
- Department of Medical Neurobiology (Physiology), Institute of Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.,The Brain Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.,Jerusalem Mental Health Center, Hebrew University-Hadassah Medical School, Jerusalem, Israel.,Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States of America
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25
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Boakye M, Ugiliweneza B, Madrigal F, Mesbah S, Ovechkin A, Angeli C, Bloom O, Wecht JW, Ditterline B, Harel NY, Kirshblum S, Forrest G, Wu S, Harkema S, Guest J. Clinical Trial Designs for Neuromodulation in Chronic Spinal Cord Injury Using Epidural Stimulation. Neuromodulation 2021; 24:405-415. [PMID: 33794042 DOI: 10.1111/ner.13381] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 01/11/2021] [Accepted: 02/09/2021] [Indexed: 12/17/2022]
Abstract
STUDY DESIGN This is a narrative review focused on specific challenges related to adequate controls that arise in neuromodulation clinical trials involving perceptible stimulation and physiological effects of stimulation activation. OBJECTIVES 1) To present the strengths and limitations of available clinical trial research designs for the testing of epidural stimulation to improve recovery after spinal cord injury. 2) To describe how studies can control for the placebo effects that arise due to surgical implantation, the physical presence of the battery, generator, control interfaces, and rehabilitative activity aimed to promote use-dependent plasticity. 3) To mitigate Hawthorne effects that may occur in clinical trials with intensive supervised participation, including rehabilitation. MATERIALS AND METHODS Focused literature review of neuromodulation clinical trials with integration to the specific context of epidural stimulation for persons with chronic spinal cord injury. CONCLUSIONS Standard of care control groups fail to control for the multiple effects of knowledge of having undergone surgical procedures, having implanted stimulation systems, and being observed in a clinical trial. The irreducible effects that have been identified as "placebo" require sham controls or comparison groups in which both are implanted with potentially active devices and undergo similar rehabilitative training.
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Affiliation(s)
- Maxwell Boakye
- Department of Neurological Surgery, University of Louisville, Louisville, KY, USA.,Kentucky Spinal Cord Injury Research Center, University of Louisville, Louisville, KY, USA
| | - Beatrice Ugiliweneza
- Department of Neurological Surgery, University of Louisville, Louisville, KY, USA.,Kentucky Spinal Cord Injury Research Center, University of Louisville, Louisville, KY, USA.,Department of Health Management and Systems Sciences, University of Louisville, Louisville, KY, USA
| | - Fabian Madrigal
- Department of Neurological Surgery, University of Louisville, Louisville, KY, USA
| | - Samineh Mesbah
- Kentucky Spinal Cord Injury Research Center, University of Louisville, Louisville, KY, USA
| | - Alexander Ovechkin
- Department of Neurological Surgery, University of Louisville, Louisville, KY, USA.,Kentucky Spinal Cord Injury Research Center, University of Louisville, Louisville, KY, USA
| | - Claudia Angeli
- Kentucky Spinal Cord Injury Research Center, University of Louisville, Louisville, KY, USA.,Department of Bioengineering, University of Louisville, Louisville, KY, USA.,Frazier Rehabilitation Institute, University of Louisville Health, Louisville, KY, USA
| | - Ona Bloom
- Feinstein Institute for Medical Research, Manhasset, NY, USA.,Department of Molecular Medicine, Zucker School of Medicine at Hofstra Northwell, Manhasset, NY, USA.,Department of Physical Medicine and Rehabilitation, Zucker School of Medicine at Hofstra Northwell, Manhasset, NY, USA.,James J Peters VA Medical Center, Bronx, NY, USA
| | - Jill W Wecht
- James J Peters VA Medical Center, Bronx, NY, USA.,The Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bonnie Ditterline
- Department of Neurological Surgery, University of Louisville, Louisville, KY, USA.,Kentucky Spinal Cord Injury Research Center, University of Louisville, Louisville, KY, USA
| | - Noam Y Harel
- James J Peters VA Medical Center, Bronx, NY, USA.,The Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steven Kirshblum
- Kessler Institute for Rehabilitation, Department of Physical Medicine and Rehabilitation, Rutgers New Jersey Medical School, Newark, NY, USA.,Human Performance and Engineering Research, Kessler Foundation, West Orange, NJ, USA
| | - Gail Forrest
- Human Performance and Engineering Research, Kessler Foundation, West Orange, NJ, USA.,Department of Physical Medicine and Rehabilitation, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Samuel Wu
- Department of Biostatistics, CTSI Data Coordinating Center, University of Florida, Gainesville, FL, USA
| | - Susan Harkema
- Department of Neurological Surgery, University of Louisville, Louisville, KY, USA.,Kentucky Spinal Cord Injury Research Center, University of Louisville, Louisville, KY, USA.,Frazier Rehabilitation Institute, University of Louisville Health, Louisville, KY, USA
| | - James Guest
- Neurological Surgery, and the Miami Project to Cure Paralysis, Miller School of Medicine, Miami, FL, USA
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26
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Rodriguez-Zurrunero R, Araujo A, Lowery MM. Methods for Lowering the Power Consumption of OS-Based Adaptive Deep Brain Stimulation Controllers. SENSORS (BASEL, SWITZERLAND) 2021; 21:2349. [PMID: 33800544 PMCID: PMC8036781 DOI: 10.3390/s21072349] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 03/11/2021] [Accepted: 03/24/2021] [Indexed: 12/16/2022]
Abstract
The identification of a new generation of adaptive strategies for deep brain stimulation (DBS) will require the development of mixed hardware-software systems for testing and implementing such controllers clinically. Towards this aim, introducing an operating system (OS) that provides high-level features (multitasking, hardware abstraction, and dynamic operation) as the core element of adaptive deep brain stimulation (aDBS) controllers could expand the capabilities and development speed of new control strategies. However, such software frameworks also introduce substantial power consumption overhead that could render this solution unfeasible for implantable devices. To address this, in this work four techniques to reduce this overhead are proposed and evaluated: a tick-less idle operation mode, reduced and dynamic sampling, buffered read mode, and duty cycling. A dual threshold adaptive deep brain stimulation algorithm for suppressing pathological oscillatory neural activity was implemented along with the proposed energy saving techniques on an energy-efficient OS, YetiOS, running on a STM32L476RE microcontroller. The system was then tested using an emulation environment coupled to a mean field model of the parkinsonian basal ganglia to simulate local field potential (LFPs) which acted as a biomarker for the controller. The OS-based controller alone introduced a power consumption overhead of 10.03 mW for a sampling rate of 1 kHz. This was reduced to 12 μW by applying the proposed tick-less idle mode, dynamic sampling, buffered read and duty cycling techniques. The OS-based controller using the proposed methods can facilitate rapid and flexible testing and implementation of new control methods. Furthermore, the approach has the potential to become a central element in future implantable devices to enable energy-efficient implementation of a wide range of control algorithms across different neurological conditions and hardware platforms.
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Affiliation(s)
| | - Alvaro Araujo
- B105 Electronic Systems Lab. ETSI Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain;
| | - Madeleine M. Lowery
- School of Electrical, Electronical and Communications Engineering, University College Dublin, Belfield, Dublin 4, Ireland;
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Phase-Dependent Deep Brain Stimulation: A Review. Brain Sci 2021; 11:brainsci11040414. [PMID: 33806170 PMCID: PMC8103241 DOI: 10.3390/brainsci11040414] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 02/28/2021] [Accepted: 03/23/2021] [Indexed: 02/06/2023] Open
Abstract
Neural oscillations are repetitive patterns of neural activity in the central nervous systems. Oscillations of the neurons in different frequency bands are evident in electroencephalograms and local field potential measurements. These oscillations are understood to be one of the key mechanisms for carrying out normal functioning of the brain. Abnormality in any of these frequency bands of oscillations can lead to impairments in different cognitive and memory functions leading to different pathological conditions of the nervous system. However, the exact role of these neural oscillations in establishing various brain functions is still under investigation. Closed loop deep brain stimulation paradigms with neural oscillations as biomarkers could be used as a mechanism to understand the function of these oscillations. For making use of the neural oscillations as biomarkers to manipulate the frequency band of the oscillation, phase of the oscillation, and stimulation signal are of importance. This paper reviews recent trends in deep brain stimulation systems and their non-invasive counterparts, in the use of phase specific stimulation to manipulate individual neural oscillations. In particular, the paper reviews the methods adopted in different brain stimulation systems and devices for stimulating at a definite phase to further optimize closed loop brain stimulation strategies.
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Su F, Chen M, Zu L, Li S, Li H. Model-Based Closed-Loop Suppression of Parkinsonian Beta Band Oscillations Through Origin Analysis. IEEE Trans Neural Syst Rehabil Eng 2021; 29:450-457. [PMID: 33531302 DOI: 10.1109/tnsre.2021.3056544] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Excessive beta band (13-30 Hz) oscillations have been observed in the basal ganglia (BG) of patients with Parkinson's disease (PD). Understanding the origin and transmission of beta band oscillations are important to improve treatments of PD, such as closed-loop deep brain stimulation (DBS). This paper proposed a model-based closed-loop GPi stimulation system to suppress pathological beta band oscillations of BG. The feedback nucleus was selected through the analysis of GPi oscillations variation when different synaptic currents were blocked, mainly projections from globus pallidus external (GPe), the subthalamic nucleus (STN) and striatum. Since simulation results proved the important role of synaptic current from GPe in shaping the excessive GPi beta band oscillations, the local field potential (LFP) of GPe was chosen as the feedback signal. That is to say, the feedback nucleus was selected based on the origin analysis of the pathological GPi beta band oscillation. The closed-loop algorithm was the multiplication of linear delayed feedback of the filtered GPe-LFP and modeled synaptic dynamics from GPe to GPi. Thus, the formed stimulation waveform was synaptic current like shape, which was proved to be more energy efficient than open-loop continuous DBS in suppressing GPi beta band oscillation. With the development of DBS devices, the efficiency of this closed-loop stimulation could be testified in animal model and clinical.
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29
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Modelling and prediction of the dynamic responses of large-scale brain networks during direct electrical stimulation. Nat Biomed Eng 2021; 5:324-345. [PMID: 33526909 DOI: 10.1038/s41551-020-00666-w] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 11/24/2020] [Indexed: 01/19/2023]
Abstract
Direct electrical stimulation can modulate the activity of brain networks for the treatment of several neurological and neuropsychiatric disorders and for restoring lost function. However, precise neuromodulation in an individual requires the accurate modelling and prediction of the effects of stimulation on the activity of their large-scale brain networks. Here, we report the development of dynamic input-output models that predict multiregional dynamics of brain networks in response to temporally varying patterns of ongoing microstimulation. In experiments with two awake rhesus macaques, we show that the activities of brain networks are modulated by changes in both stimulation amplitude and frequency, that they exhibit damping and oscillatory response dynamics, and that variabilities in prediction accuracy and in estimated response strength across brain regions can be explained by an at-rest functional connectivity measure computed without stimulation. Input-output models of brain dynamics may enable precise neuromodulation for the treatment of disease and facilitate the investigation of the functional organization of large-scale brain networks.
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30
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Turner DA, Degan S, Galeffi F, Schmidt S, Peterchev AV. Rapid, Dose-Dependent Enhancement of Cerebral Blood Flow by transcranial AC Stimulation in Mouse. Brain Stimul 2020; 14:80-87. [PMID: 33217607 DOI: 10.1016/j.brs.2020.11.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 10/18/2020] [Accepted: 11/12/2020] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Transcranial electrical stimulation at an appropriate dose may demonstrate intracranial effects, including neuronal stimulation and cerebral blood flow responses. OBJECTIVE We performed in vivo experiments on mouse cortex using transcranial alternating current [AC] stimulation to assess whether cerebral blood flow can be reliably altered by extracranial stimulation. METHODS We performed transcranial AC electrical stimulation transversely across the closed skull in anesthetized mice, measuring transcranial cerebral blood flow with a laser Doppler probe and intracranial electrical responses as endpoint biomarkers. We calculated a stimulation dose-response function between intracranial electric field and cerebral blood flow. RESULTS Stimulation at electric field amplitudes of 5-20 mV/mm at 10-20 Hz rapidly increased cerebral blood flow (within 100 ms), which then quickly decreased with no residual effects. The time to peak and blood flow shape varied with stimulation intensity and duration, showing a linear correlation between stimulation dose and peak blood flow increase. Neither afterdischarges nor spreading depression occurred from this level of stimulation. CONCLUSIONS Extracranial stimulation amplitudes sufficient to evoke reliable blood flow changes require electric field strengths higher than what is tolerable in unanesthetized humans (<1 mV/mm), but less than electroconvulsive therapy levels (>40 mV/mm). However, anesthesia effects, spontaneous blood flow fluctuations, and sampling error may accentuate the apparent field strength needed for enhanced blood flow. The translation to a human dose-response function to augment cerebral blood flow (i.e., in stroke recovery) will require significant modification, potentially to pericranial, focused, multi-electrode application or intracranial stimulation.
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Affiliation(s)
- Dennis A Turner
- Neurosurgery, Duke University, USA; Neurobiology, Duke University, USA; Biomedical Engineering, Duke University, USA; Surgery and Research Branches, Durham VAMC, Durham, NC, 27710, USA.
| | - Simone Degan
- Neurosurgery, Duke University, USA; Surgery and Research Branches, Durham VAMC, Durham, NC, 27710, USA
| | - Francesca Galeffi
- Neurosurgery, Duke University, USA; Surgery and Research Branches, Durham VAMC, Durham, NC, 27710, USA
| | - Stephen Schmidt
- Neurosurgery, Duke University, USA; Biomedical Engineering, Duke University, USA
| | - Angel V Peterchev
- Neurosurgery, Duke University, USA; Psychiatry & Behavioral Sciences, Duke University, USA; Biomedical Engineering, Duke University, USA; Electrical & Computer Engineering, Duke University, USA
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31
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Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification. Nat Neurosci 2020; 24:140-149. [PMID: 33169030 DOI: 10.1038/s41593-020-00733-0] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 10/02/2020] [Indexed: 11/09/2022]
Abstract
Neural activity exhibits complex dynamics related to various brain functions, internal states and behaviors. Understanding how neural dynamics explain specific measured behaviors requires dissociating behaviorally relevant and irrelevant dynamics, which is not achieved with current neural dynamic models as they are learned without considering behavior. We develop preferential subspace identification (PSID), which is an algorithm that models neural activity while dissociating and prioritizing its behaviorally relevant dynamics. Modeling data in two monkeys performing three-dimensional reach and grasp tasks, PSID revealed that the behaviorally relevant dynamics are significantly lower-dimensional than otherwise implied. Moreover, PSID discovered distinct rotational dynamics that were more predictive of behavior. Furthermore, PSID more accurately learned behaviorally relevant dynamics for each joint and recording channel. Finally, modeling data in two monkeys performing saccades demonstrated the generalization of PSID across behaviors, brain regions and neural signal types. PSID provides a general new tool to reveal behaviorally relevant neural dynamics that can otherwise go unnoticed.
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Castaño-Candamil S, Ferleger BI, Haddock A, Cooper SS, Herron J, Ko A, Chizeck HJ, Tangermann M. A Pilot Study on Data-Driven Adaptive Deep Brain Stimulation in Chronically Implanted Essential Tremor Patients. Front Hum Neurosci 2020; 14:541625. [PMID: 33250727 PMCID: PMC7674800 DOI: 10.3389/fnhum.2020.541625] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 10/15/2020] [Indexed: 11/13/2022] Open
Abstract
Deep brain stimulation (DBS) is an established therapy for Parkinson's disease (PD) and essential-tremor (ET). In adaptive DBS (aDBS) systems, online tuning of stimulation parameters as a function of neural signals may improve treatment efficacy and reduce side-effects. State-of-the-art aDBS systems use symptom surrogates derived from neural signals-so-called neural markers (NMs)-defined on the patient-group level, and control strategies assuming stationarity of symptoms and NMs. We aim at improving these aDBS systems with (1) a data-driven approach for identifying patient- and session-specific NMs and (2) a control strategy coping with short-term non-stationary dynamics. The two building blocks are implemented as follows: (1) The data-driven NMs are based on a machine learning model estimating tremor intensity from electrocorticographic signals. (2) The control strategy accounts for local variability of tremor statistics. Our study with three chronically implanted ET patients amounted to five online sessions. Tremor quantified from accelerometer data shows that symptom suppression is at least equivalent to that of a continuous DBS strategy in 3 out-of 4 online tests, while considerably reducing net stimulation (at least 24%). In the remaining online test, symptom suppression was not significantly different from either the continuous strategy or the no treatment condition. We introduce a novel aDBS system for ET. It is the first aDBS system based on (1) a machine learning model to identify session-specific NMs, and (2) a control strategy coping with short-term non-stationary dynamics. We show the suitability of our aDBS approach for ET, which opens the door to its further study in a larger patient population.
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Affiliation(s)
- Sebastián Castaño-Candamil
- Brain State Decoding Lab, Department of Computer Science, BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg im Breisgau, Germany
| | - Benjamin I Ferleger
- BioRobotics Lab, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Andrew Haddock
- BioRobotics Lab, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Sarah S Cooper
- BioRobotics Lab, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Jeffrey Herron
- Department of Neurological Surgery, University of Washington Medical Center, Seattle, WA, United States
| | - Andrew Ko
- Department of Neurological Surgery, University of Washington Medical Center, Seattle, WA, United States
| | - Howard J Chizeck
- BioRobotics Lab, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Michael Tangermann
- Brain State Decoding Lab, Department of Computer Science, BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg im Breisgau, Germany.,Autonomous Intelligent Systems, Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany.,Artificial Cognitive Systems Lab, Artificial Intelligence Department, Faculty of Social Sciences, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
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33
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Schmidt SL, Brocker DT, Swan BD, Turner DA, Grill WM. Evoked potentials reveal neural circuits engaged by human deep brain stimulation. Brain Stimul 2020; 13:1706-1718. [PMID: 33035726 DOI: 10.1016/j.brs.2020.09.028] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 09/29/2020] [Accepted: 09/30/2020] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Deep brain stimulation (DBS) is an effective therapy for reducing the motor symptoms of Parkinson's disease, but the mechanisms of action of DBS and neural correlates of symptoms remain unknown. OBJECTIVE To use the neural response to DBS to reveal connectivity of neural circuits and interactions between groups of neurons as potential mechanisms for DBS. METHODS We recorded activity evoked by DBS of the subthalamic nucleus (STN) in humans with Parkinson's disease. In follow up experiments we also simultaneously recorded activity in the contralateral STN or the ipsilateral globus pallidus from both internal (GPi) and external (GPe) segments. RESULTS DBS local evoked potentials (DLEPs) were stereotyped across subjects, and a biophysical model of reciprocal connections between the STN and the GPe recreated DLEPs. Simultaneous STN and GP recordings during STN DBS demonstrate that DBS evoked potentials were present throughout the basal ganglia and confirmed that DLEPs arose from the reciprocal connections between the STN and GPe. The shape and amplitude of the DLEPs were dependent on the frequency and duration of DBS and were correlated with resting beta band oscillations. In the frequency domain, DLEPs appeared as a 350 Hz high frequency oscillation (HFO) independent of the frequency of DBS. CONCLUSIONS DBS evoked potentials suggest that the intrinsic dynamics of the STN and GP are highly interlinked and may provide a promising new biomarker for adaptive DBS.
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Affiliation(s)
- Stephen L Schmidt
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - David T Brocker
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Brandon D Swan
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Dennis A Turner
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Neurobiology, Duke University Medical Center, Durham, NC, USA; Department of Neurosurgery, Duke University Medical Center, Durham, NC, USA
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Neurobiology, Duke University Medical Center, Durham, NC, USA; Department of Neurosurgery, Duke University Medical Center, Durham, NC, USA.
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34
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Ozturk M, Kaku H, Jimenez-Shahed J, Viswanathan A, Sheth SA, Kumar S, Ince NF. Subthalamic Single Cell and Oscillatory Neural Dynamics of a Dyskinetic Medicated Patient With Parkinson's Disease. Front Neurosci 2020; 14:391. [PMID: 32390796 PMCID: PMC7193777 DOI: 10.3389/fnins.2020.00391] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 03/30/2020] [Indexed: 02/01/2023] Open
Abstract
Single cell neuronal activity (SUA) and local field potentials (LFP) in the subthalamic nucleus (STN) of unmedicated Parkinson's disease (PD) patients undergoing deep brain stimulation (DBS) surgery have been well-characterized during microelectrode recordings (MER). However, there is limited knowledge about the changes in the firing patterns and oscillations above and within the territories of STN after the intake of dopaminergic medication. Here, for the first time, we report the STN single cell and oscillatory neural dynamics in a medicated patient with idiopathic PD using intraoperative MER. We recorded LFP and SUA with microelectrodes at various depths during bilateral STN-DBS electrode implantation. We isolated 26 neurons in total and observed that tonic and irregular firing patterns of individual neurons predominated throughout the territories of STN. While burst-type firings have been well-characterized in the dorsal territories of STN in unmedicated patients, interestingly, this activity was not observed in our medicated subject. LFP recordings lacked the excessive beta (8-30 Hz) activity, characteristic of the unmedicated state and signal energy was mainly dominated by slow oscillations below 8 Hz. We observed sharp gamma oscillations between 70 and 90 Hz within and above the STN. Despite the presence of a broadband high frequency activity in 200-400 Hz range, no cross-frequency interaction in the form of phase-amplitude coupling was noted between low and high frequency oscillations of LFPs. While our results are in agreement with the previously reported LFP recordings from the DBS lead in medicated PD patients, the sharp gamma peak present throughout the depth recordings and the lack of bursting firings after levodopa intake have not been reported before. The lack of bursting in SUA, the lack of excessive beta activity and cross frequency coupling between HFOs and lower rhythms further validate the link between bursting firing regime of neurons and pathological oscillatory neural activity in PD-STN. Overall, these observations not only validate the existing literature on the PD electrophysiology in healthy/medicated animal models but also provide insights regarding the underlying electro-pathophysiology of levodopa-induced dyskinesias in PD patients through demonstration of multiscale relationships between single cell firings and field potentials.
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Affiliation(s)
- Musa Ozturk
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
| | - Heet Kaku
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
| | - Joohi Jimenez-Shahed
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Ashwin Viswanathan
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States
| | - Suneel Kumar
- Department of Neurology, Baylor College of Medicine, Houston, TX, United States
| | - Nuri F. Ince
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
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35
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Hoang KB, Turner DA. The Emerging Role of Biomarkers in Adaptive Modulation of Clinical Brain Stimulation. Neurosurgery 2020; 85:E430-E439. [PMID: 30957145 DOI: 10.1093/neuros/nyz096] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 03/01/2019] [Indexed: 11/14/2022] Open
Abstract
Therapeutic brain stimulation has proven efficacious for treatment of nervous system diseases, exerting widespread influence via disease-specific neural networks. Activation or suppression of neural networks could theoretically be assessed by either clinical symptom modification (ie, tremor, rigidity, seizures) or development of specific biomarkers linked to treatment of symptomatic disease states. For example, biomarkers indicative of disease state could aid improved intraoperative localization of electrode position, optimize device efficacy or efficiency through dynamic control, and eventually serve to guide automatic adjustment of stimulation settings. Biomarkers to control either extracranial or intracranial stimulation span from continuous physiological brain activity, intermittent pathological activity, and triggered local phenomena or potentials, to wearable devices, blood flow, biochemical or cardiac signals, temperature perturbations, optical or magnetic resonance imaging changes, or optogenetic signals. The goal of this review is to update new approaches to implement control of stimulation through relevant biomarkers. Critical questions include whether adaptive systems adjusted through biomarkers can optimize efficiency and eventually efficacy, serve as inputs for stimulation adjustment, and consequently broaden our fundamental understanding of abnormal neural networks in pathologic states. Neurosurgeons are at the forefront of translating and developing biomarkers embedded within improved brain stimulation systems. Thus, criteria for developing and validating biomarkers for clinical use are important for the adaptation of device approaches into clinical practice.
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Affiliation(s)
- Kimberly B Hoang
- Department of Neurosurgery, MD Anderson Cancer Center, Houston, Texas
| | - Dennis A Turner
- Departments of Neurosurgery, Duke University Medical Center, Durham, North Carolina.,Department of Neurobiology, Duke University Medical Center, Durham, North Carolina.,Department of Biomedical Engineering, Duke University, Durham, North Carolina
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36
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Basal ganglia oscillations as biomarkers for targeting circuit dysfunction in Parkinson's disease. PROGRESS IN BRAIN RESEARCH 2020; 252:525-557. [PMID: 32247374 DOI: 10.1016/bs.pbr.2020.02.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Oscillations are a naturally occurring phenomenon in highly interconnected dynamical systems. However, it is thought that excessive synchronized oscillations in brain circuits can be detrimental for many brain functions by disrupting neuronal information processing. Because synchronized basal ganglia oscillations are a hallmark of Parkinson's disease (PD), it has been suggested that aberrant rhythmic activity associated with symptoms of the disease could be used as a physiological biomarker to guide pharmacological and electrical neuromodulatory interventions. We here briefly review the various manifestations of basal ganglia oscillations observed in human subjects and in animal models of PD. In this context, we also review the evidence supporting a pathophysiological role of different oscillations for the suppression of voluntary movements as well as for the induction of excessive motor activity. In light of these findings, it is discussed how oscillations could be used to guide a more precise targeting of dysfunctional circuits to obtain improved symptomatic treatment of PD.
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Schmidt SL, Peters JJ, Turner DA, Grill WM. Continuous deep brain stimulation of the subthalamic nucleus may not modulate beta bursts in patients with Parkinson's disease. Brain Stimul 2020; 13:433-443. [PMID: 31884188 PMCID: PMC6961347 DOI: 10.1016/j.brs.2019.12.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 11/19/2019] [Accepted: 12/10/2019] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Neural oscillations represent synchronous neuronal activation and are ubiquitous throughout the brain. Oscillatory activity often includes brief high-amplitude bursts in addition to background oscillations, and burst activity may predict performance on working memory, motor, and comprehension tasks. OBJECTIVE We evaluated beta burst activity as a possible biomarker for motor symptoms in Parkinson's disease (PD). The relationship between beta amplitude dynamics and motor symptoms is critical for adaptive DBS for treatment of PD. METHODS We applied threshold-based and support vector machine (SVM) analyses of burst parameters to a defined on/off oscillator and to intraoperative recordings of local field potentials from the subthalamic nucleus of 16 awake patients with PD. RESULTS Filtering and time-frequency analysis techniques critically influenced the accuracy of identifying burst activity. Threshold-based analysis lead to biased results in the presence of changes in long-term beta amplitude and accurate quantification of bursts with thresholds required unknowable a priori knowledge of the time in bursts. We therefore implemented an SVM analysis, and we did not observe changes in burst fraction, rate, or duration with the application of cDBS in the participant data, even though SVM analysis was able to correctly identify bursts of the defined on/off oscillator. CONCLUSION Our results suggest that cDBS of the STN may not change beta burst activity. Additionally, threshold-based analysis can bias the fraction of time spent in bursts. Improved analysis strategies for continuous and adaptive DBS may achieve improved symptom control and reduce side-effects.
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Affiliation(s)
- Stephen L Schmidt
- Biomedical Engineering Department, Duke University, Durham, NC, USA.
| | | | - Dennis A Turner
- Biomedical Engineering Department, Duke University, Durham, NC, USA; Neurobiology and Neurosurgery Departments, Duke University Medical Center, Durham, NC, USA
| | - Warren M Grill
- Biomedical Engineering Department, Duke University, Durham, NC, USA; Neurobiology and Neurosurgery Departments, Duke University Medical Center, Durham, NC, USA; Electrical and Computer Engineering Department, Duke University, Durham, NC, USA.
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Beuter A, Balossier A, Vassal F, Hemm S, Volpert V. Cortical stimulation in aphasia following ischemic stroke: toward model-guided electrical neuromodulation. BIOLOGICAL CYBERNETICS 2020; 114:5-21. [PMID: 32020368 DOI: 10.1007/s00422-020-00818-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Accepted: 01/28/2020] [Indexed: 06/10/2023]
Abstract
The aim of this paper is to integrate different bodies of research including brain traveling waves, brain neuromodulation, neural field modeling and post-stroke language disorders in order to explore the opportunity of implementing model-guided, cortical neuromodulation for the treatment of post-stroke aphasia. Worldwide according to WHO, strokes are the second leading cause of death and the third leading cause of disability. In ischemic stroke, there is not enough blood supply to provide enough oxygen and nutrients to parts of the brain, while in hemorrhagic stroke, there is bleeding within the enclosed cranial cavity. The present paper focuses on ischemic stroke. We first review accumulating observations of traveling waves occurring spontaneously or triggered by external stimuli in healthy subjects as well as in patients with brain disorders. We examine the putative functions of these waves and focus on post-stroke aphasia observed when brain language networks become fragmented and/or partly silent, thus perturbing the progression of traveling waves across perilesional areas. Secondly, we focus on a simplified model based on the current literature in the field and describe cortical traveling wave dynamics and their modulation. This model uses a biophysically realistic integro-differential equation describing spatially distributed and synaptically coupled neural networks producing traveling wave solutions. The model is used to calculate wave parameters (speed, amplitude and/or frequency) and to guide the reconstruction of the perturbed wave. A stimulation term is included in the model to restore wave propagation to a reasonably good level. Thirdly, we examine various issues related to the implementation model-guided neuromodulation in the treatment of post-stroke aphasia given that closed-loop invasive brain stimulation studies have recently produced encouraging results. Finally, we suggest that modulating traveling waves by acting selectively and dynamically across space and time to facilitate wave propagation is a promising therapeutic strategy especially at a time when a new generation of closed-loop cortical stimulation systems is about to arrive on the market.
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Affiliation(s)
- Anne Beuter
- Bordeaux INP, University of Bordeaux, Bordeaux, France.
| | - Anne Balossier
- Service de neurochirurgie fonctionnelle et stéréotaxique, AP-HM La Timone, Aix-Marseille University, Marseille, France
| | - François Vassal
- INSERM U1028 Neuropain, UMR 5292, Centre de Recherche en Neurosciences, Universités Lyon 1 et Saint-Etienne, Saint-Étienne, France
- Service de Neurochirurgie, Hôpital Nord, Centre Hospitalier Universitaire de Saint-Etienne, Saint-Étienne, France
| | - Simone Hemm
- School of Life Sciences, Institute for Medical Engineering and Medical Informatics, University of Applied Sciences and Arts Northwestern Switzerland, 4132, Muttenz, Switzerland
| | - Vitaly Volpert
- Institut Camille Jordan, UMR 5208 CNRS, University Lyon 1, 69622, Villeurbanne, France
- INRIA Team Dracula, INRIA Lyon La Doua, 69603, Villeurbanne, France
- People's Friendship University of Russia (RUDN University), Miklukho-Maklaya St, Moscow, Russian Federation, 117198
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Takeuchi Y, Berényi A. Oscillotherapeutics - Time-targeted interventions in epilepsy and beyond. Neurosci Res 2020; 152:87-107. [PMID: 31954733 DOI: 10.1016/j.neures.2020.01.002] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 12/18/2019] [Accepted: 12/19/2019] [Indexed: 02/09/2023]
Abstract
Oscillatory brain activities support many physiological functions from motor control to cognition. Disruptions of the normal oscillatory brain activities are commonly observed in neurological and psychiatric disorders including epilepsy, Parkinson's disease, Alzheimer's disease, schizophrenia, anxiety/trauma-related disorders, major depressive disorders, and drug addiction. Therefore, these disorders can be considered as common oscillation defects despite having distinct behavioral manifestations and genetic causes. Recent technical advances of neuronal activity recording and analysis have allowed us to study the pathological oscillations of each disorder as a possible biomarker of symptoms. Furthermore, recent advances in brain stimulation technologies enable time- and space-targeted interventions of the pathological oscillations of both neurological disorders and psychiatric disorders as possible targets for regulating their symptoms.
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Affiliation(s)
- Yuichi Takeuchi
- MTA-SZTE 'Momentum' Oscillatory Neuronal Networks Research Group, Department of Physiology, University of Szeged, Szeged, 6720, Hungary; Department of Neuropharmacology, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya, 467-8603, Japan.
| | - Antal Berényi
- MTA-SZTE 'Momentum' Oscillatory Neuronal Networks Research Group, Department of Physiology, University of Szeged, Szeged, 6720, Hungary; HCEMM-SZTE Magnetotherapeutics Research Group, University of Szeged, Szeged, 6720, Hungary; Neuroscience Institute, New York University, New York, NY 10016, USA.
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40
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Shanechi MM. Brain–machine interfaces from motor to mood. Nat Neurosci 2019; 22:1554-1564. [DOI: 10.1038/s41593-019-0488-y] [Citation(s) in RCA: 82] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 08/06/2019] [Indexed: 12/22/2022]
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41
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Su F, Kumaravelu K, Wang J, Grill WM. Model-Based Evaluation of Closed-Loop Deep Brain Stimulation Controller to Adapt to Dynamic Changes in Reference Signal. Front Neurosci 2019; 13:956. [PMID: 31551704 PMCID: PMC6746932 DOI: 10.3389/fnins.2019.00956] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 08/26/2019] [Indexed: 12/19/2022] Open
Abstract
High-frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN) is effective in suppressing the motor symptoms of Parkinson's disease (PD). Current clinically-deployed DBS technology operates in an open-loop fashion, i.e., fixed parameter high-frequency stimulation is delivered continuously, invariant to the needs or status of the patient. This poses two major challenges: (1) depletion of the stimulator battery due to the energy demands of continuous high-frequency stimulation, (2) high-frequency stimulation-induced side-effects. Closed-loop deep brain stimulation (CL DBS) may be effective in suppressing parkinsonian symptoms with stimulation parameters that require less energy and evoke fewer side effects than open loop DBS. However, the design of CL DBS comes with several challenges including the selection of an appropriate biomarker reflecting the symptoms of PD, setting a suitable reference signal, and implementing a controller to adapt to dynamic changes in the reference signal. Dynamic changes in beta oscillatory activity occur during the course of voluntary movement, and thus there may be a performance advantage to tracking such dynamic activity. We addressed these challenges by studying the performance of a closed-loop controller using a biophysically-based network model of the basal ganglia. The model-based evaluation consisted of two parts: (1) we implemented a Proportional-Integral (PI) controller to compute optimal DBS frequencies based on the magnitude of a dynamic reference signal, the oscillatory power in the beta band (13-35 Hz) recorded from model globus pallidus internus (GPi) neurons. (2) We coupled a linear auto-regressive model based mapping function with the Routh-Hurwitz stability analysis method to compute the parameters of the PI controller to track dynamic changes in the reference signal. The simulation results demonstrated successful tracking of both constant and dynamic beta oscillatory activity by the PI controller, and the PI controller followed dynamic changes in the reference signal, something that cannot be accomplished by constant open-loop DBS.
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Affiliation(s)
- Fei Su
- Department of Biomedical Engineering, Duke University, Durham, NC, United States.,School of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai'an, China.,School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Karthik Kumaravelu
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
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Mirza KB, Golden CT, Nikolic K, Toumazou C. Closed-Loop Implantable Therapeutic Neuromodulation Systems Based on Neurochemical Monitoring. Front Neurosci 2019; 13:808. [PMID: 31481864 PMCID: PMC6710388 DOI: 10.3389/fnins.2019.00808] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 07/19/2019] [Indexed: 12/29/2022] Open
Abstract
Closed-loop or intelligent neuromodulation allows adjustable, personalized neuromodulation which usually incorporates the recording of a biomarker, followed by implementation of an algorithm which decides the timing (when?) and strength (how much?) of stimulation. Closed-loop neuromodulation has been shown to have greater benefits compared to open-loop neuromodulation, particularly for therapeutic applications such as pharmacoresistant epilepsy, movement disorders and potentially for psychological disorders such as depression or drug addiction. However, an important aspect of the technique is selection of an appropriate, preferably neural biomarker. Neurochemical sensing can provide high resolution biomarker monitoring for various neurological disorders as well as offer deeper insight into neurological mechanisms. The chemicals of interest being measured, could be ions such as potassium (K+), sodium (Na+), calcium (Ca2+), chloride (Cl−), hydrogen (H+) or neurotransmitters such as dopamine, serotonin and glutamate. This review focusses on the different building blocks necessary for a neurochemical, closed-loop neuromodulation system including biomarkers, sensors and data processing algorithms. Furthermore, it also highlights the merits and drawbacks of using this biomarker modality.
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Affiliation(s)
- Khalid B Mirza
- Department of Electrical and Electronic Engineering, Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Caroline T Golden
- Department of Electrical and Electronic Engineering, Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Konstantin Nikolic
- Department of Electrical and Electronic Engineering, Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Christofer Toumazou
- Department of Electrical and Electronic Engineering, Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
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43
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Piña-Fuentes D, Beudel M, Little S, Brown P, Oterdoom DLM, van Dijk JMC. Adaptive deep brain stimulation as advanced Parkinson's disease treatment (ADAPT study): protocol for a pseudo-randomised clinical study. BMJ Open 2019; 9:e029652. [PMID: 31201193 PMCID: PMC6575861 DOI: 10.1136/bmjopen-2019-029652] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
INTRODUCTION Adaptive deep brain stimulation (aDBS), based on the detection of increased beta oscillations in the subthalamic nucleus (STN), has been assessed in patients with Parkinson's disease (PD) during the immediate postoperative setting. In these studies, aDBS was shown to be at least as effective as conventional DBS (cDBS), while stimulation time and side effects were reduced. However, the effect of aDBS on motor symptoms and stimulation-induced side effects during the chronically implanted phase (after the stun effect of DBS placement has disappeared) has not yet been determined. METHODS AND ANALYSIS This protocol describes a single-centre clinical study in which aDBS will be tested in 12 patients with PD undergoing battery replacement, with electrodes implanted in the STN, and as a proof of concept in the internal globus pallidus. Patients included will be allocated in a pseudo-randomised fashion to a three-condition (no stimulation/cDBS/ aDBS), cross-over design. A battery of tests will be conducted and recorded during each condition, which aim to measure the severity of motor symptoms and side effects. These tests include a tablet-based tapping test, a subscale of the Movement Disorder Society-unified Parkinson's disease rating scale (subMDS-UPDRS), the Speech Intelligibility Test (SIT) and a tablet-based version of the Stroop test. SubMDS-UPDRS and SIT recordings will be blindly assessed by independent raters. Data will be analysed using a linear mixed-effects model. ETHICS AND DISSEMINATION This protocol was approved by the Ethical Committee of the University Medical Centre Groningen, where the study will be carried out. Data management and compliance to research policies and standards of our centre, including data privacy, storage and veracity, will be controlled by an independent monitor. All the scientific findings derived from this protocol are aimed to be made public through publication of articles in international journals. TRIAL REGISTRATION NUMBER NTR 5456; Pre-results.
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Affiliation(s)
- Dan Piña-Fuentes
- Department of Neurosurgery, University Medical Centre Groningen, Groningen, The Netherlands
| | - Martijn Beudel
- Department of Neurology, University Medical Centre Groningen, Groningen, The Netherlands
- Department of Neurology, Amsterdam University Medical Centre, The Netherlands
| | - Simon Little
- Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, Queen Square, London, UK
| | - Peter Brown
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - D L Marinus Oterdoom
- Department of Neurosurgery, University Medical Centre Groningen, Groningen, The Netherlands
| | - J Marc C van Dijk
- Department of Neurosurgery, University Medical Centre Groningen, Groningen, The Netherlands
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44
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Biomarkers for closed-loop deep brain stimulation in Parkinson disease and beyond. Nat Rev Neurol 2019; 15:343-352. [DOI: 10.1038/s41582-019-0166-4] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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45
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Jakobs M, Fomenko A, Lozano AM, Kiening KL. Cellular, molecular, and clinical mechanisms of action of deep brain stimulation-a systematic review on established indications and outlook on future developments. EMBO Mol Med 2019; 11:e9575. [PMID: 30862663 PMCID: PMC6460356 DOI: 10.15252/emmm.201809575] [Citation(s) in RCA: 112] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 12/23/2018] [Accepted: 02/20/2019] [Indexed: 12/31/2022] Open
Abstract
Deep brain stimulation (DBS) has been successfully used to treat movement disorders, such as Parkinson's disease, for more than 25 years and heralded the advent of electrical neuromodulation to treat diseases with dysregulated neuronal circuits. DBS is now superseding ablative techniques, such as stereotactic radiofrequency lesions. While serendipity has played a role in developing DBS as a therapy, research during the past two decades has shown that electrical neuromodulation is far more than a functional lesion that can be switched on and off. This understanding broadens the field to enable new types of stimulation, clinical indications, and research. This review highlights the complex effects of DBS from the single cell to the neuronal network. Specifically, we examine the electrical, cellular, molecular, and neurochemical mechanisms of DBS as applied to Parkinson's disease and other emerging applications.
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Affiliation(s)
- Martin Jakobs
- Department of Neurosurgery, University Hospital Heidelberg, Heidelberg, Germany
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Anton Fomenko
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Andres M Lozano
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Karl L Kiening
- Department of Neurosurgery, University Hospital Heidelberg, Heidelberg, Germany
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Neumann WJ, Turner RS, Blankertz B, Mitchell T, Kühn AA, Richardson RM. Toward Electrophysiology-Based Intelligent Adaptive Deep Brain Stimulation for Movement Disorders. Neurotherapeutics 2019; 16:105-118. [PMID: 30607748 PMCID: PMC6361070 DOI: 10.1007/s13311-018-00705-0] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Deep brain stimulation (DBS) represents one of the major clinical breakthroughs in the age of translational neuroscience. In 1987, Benabid and colleagues demonstrated that high-frequency stimulation can mimic the effects of ablative neurosurgery in Parkinson's disease (PD), while offering two key advantages to previous procedures: adjustability and reversibility. Deep brain stimulation is now an established therapeutic approach that robustly alleviates symptoms in patients with movement disorders, such as Parkinson's disease, essential tremor, and dystonia, who present with inadequate or adverse responses to medication. Currently, stimulation electrodes are implanted in specific target regions of the basal ganglia-thalamic circuit and stimulation pulses are delivered chronically. To achieve optimal therapeutic effect, stimulation frequency, amplitude, and pulse width must be adjusted on a patient-specific basis by a movement disorders specialist. The finding that pathological neural activity can be sampled directly from the target region using the DBS electrode has inspired a novel DBS paradigm: closed-loop adaptive DBS (aDBS). The goal of this strategy is to identify pathological and physiologically normal patterns of neuronal activity that can be used to adapt stimulation parameters to the concurrent therapeutic demand. This review will give detailed insight into potential biomarkers and discuss next-generation strategies, implementing advances in artificial intelligence, to further elevate the therapeutic potential of DBS by capitalizing on its modifiable nature. Development of intelligent aDBS, with an ability to deliver highly personalized treatment regimens and to create symptom-specific therapeutic strategies in real-time, could allow for significant further improvements in the quality of life for movement disorders patients with DBS that ultimately could outperform traditional drug treatment.
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Affiliation(s)
- Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Campus Charite Mitte, Chariteplatz 1, 10117, Berlin, Germany.
| | - Robert S Turner
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Benjamin Blankertz
- Department of Computer Science, Technische Universität Berlin, Berlin, Germany
| | - Tom Mitchell
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Andrea A Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Campus Charite Mitte, Chariteplatz 1, 10117, Berlin, Germany
- Berlin School of Mind and Brain, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Neurocure, Centre of Excellence, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - R Mark Richardson
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
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Detecting Bladder Biomarkers for Closed-Loop Neuromodulation: A Technological Review. Int Neurourol J 2018; 22:228-236. [PMID: 30599493 PMCID: PMC6312967 DOI: 10.5213/inj.1836246.123] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 12/11/2018] [Indexed: 12/19/2022] Open
Abstract
Neuromodulation was introduced for patients with poor outcomes from the existing traditional treatment approaches. It is well-established as an alternative, novel treatment option for voiding dysfunction. The current system of neuromodulation uses an open-loop system that only delivers continuous stimulation without considering the patient's state changes. Though the conventional open-loop system has shown positive clinical results, it can cause problems such as decreased efficacy over time due to neural habituation, higher risk of tissue damage, and lower battery life. Therefore, there is a need for a closed-loop system to overcome the disadvantages of existing systems. The closed-loop neuromodulation includes a system to monitor and stimulate micturition reflex pathways from the lower urinary tract, as well as the central nervous system. In this paper, we reviewed the current technological status to measure biomarker for closed-loop neuromodulation systems for voiding dysfunction.
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Basu I, Crocker B, Farnes K, Robertson MM, Paulk AC, Vallejo DI, Dougherty DD, Cash SS, Eskandar EN, Kramer MM, Widge AS. A neural mass model to predict electrical stimulation evoked responses in human and non-human primate brain. J Neural Eng 2018; 15:066012. [PMID: 30211694 PMCID: PMC6757338 DOI: 10.1088/1741-2552/aae136] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Deep brain stimulation (DBS) is a valuable tool for ameliorating drug resistant pathologies such as movement disorders and epilepsy. DBS is also being considered for complex neuro-psychiatric disorders, which are characterized by high variability in symptoms and slow responses that hinder DBS setting optimization. The objective of this work was to develop an in silico platform to examine the effects of electrical stimulation in regions neighboring a stimulated brain region. APPROACH We used the Jansen-Rit neural mass model of single and coupled nodes to simulate the response to a train of electrical current pulses at different frequencies (10-160 Hz) of the local field potential recorded in the amygdala and cortical structures in human subjects and a non-human primate. RESULTS We found that using a single node model, the evoked responses could be accurately modeled following a narrow range of stimulation frequencies. Including a second coupled node increased the range of stimulation frequencies whose evoked responses could be efficiently modeled. Furthermore, in a chronic recording from a non-human primate, features of the in vivo evoked response remained consistent for several weeks, suggesting that model re-parameterization for chronic stimulation protocols would be infrequent. SIGNIFICANCE Using a model of neural population activity, we reproduced the evoked response to cortical and subcortical stimulation in human and non-human primate. This modeling framework provides an environment to explore, safely and rapidly, a wide range of stimulation settings not possible in human brain stimulation studies. The model can be trained on a limited dataset of stimulation responses to develop an optimal stimulation strategy for an individual patient.
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Affiliation(s)
- Ishita Basu
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, United States of America. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States of America
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Milchenko M, Snyder AZ, Campbell MC, Dowling JL, Rich KM, Brier LM, Perlmutter JS, Norris SA. ESM-CT: a precise method for localization of DBS electrodes in CT images. J Neurosci Methods 2018; 308:366-376. [PMID: 30201271 PMCID: PMC6205293 DOI: 10.1016/j.jneumeth.2018.09.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 09/05/2018] [Accepted: 09/05/2018] [Indexed: 10/28/2022]
Abstract
BACKGROUND Deep brain stimulation (DBS) of the subthalamic nucleus produces variable effects in Parkinson disease. Variation may result from different electrode positions relative to target. Thus, precise electrode localization is crucial when investigating DBS effects. NEW METHOD We developed a semi-automated method, Electrode Shaft Modeling in CT images (ESM-CT) to reconstruct DBS lead trajectories and contact locations. We evaluated methodological sensitivity to operator-dependent steps, robustness to image resampling, and test-retest replicability. ESM-CT was applied in 56 patients to study electrode position change (and relation to time between scans, postoperative subdural air volume, and head tilt during acquisition) between images acquired immediately post-implantation (DBS-CT) and months later (DEL-CT). RESULTS Electrode tip localization was robust to image resampling and replicable to within ∼ 0.2 mm on test-retest comparisons. Systematic electrode displacement occurred rostral-ventral-lateral between DBS-CT and DEL-CT scans. Head angle was a major explanatory factor (p < 0.001,Pearson's r = 0.46, both sides) and volume of subdural air weakly predicted electrode displacement (p = 0.02,r = 0.29:p = 0.1,r = 0.25 for left:right). Modeled shaft curvature was slightly greater in DEL-CT. Magnitude of displacement and degree of curvature were independent of elapsed time between scans. COMPARISON WITH EXISTING METHODS Comparison of ESM-CT against two existing methods revealed systematic differences in one coordinate (1 ± 0.3 mm,p < 0.001) for one method and in three coordinates for another method (x:0.1 ± 0.1 mm, y:0.4 ± 0.2 mm, z:0.4 ± 0.2 mm, p < 10-10). Within-method coordinate variability across participants is similar. CONCLUSION We describe a robust and precise method for CT DBS contact localization. Application revealed that acquisition head angle significantly impacts electrode position. DBS localization schemes should account for head angle.
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Affiliation(s)
- Mikhail Milchenko
- Mallinckrodt Institute of Radiology, Department of Radiology, Washington University School of Medicine, (CB 8225), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA
| | - Abraham Z Snyder
- Mallinckrodt Institute of Radiology, Department of Radiology, Washington University School of Medicine, (CB 8225), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA; Department of Neurology, Washington University School of Medicine, (CB 8111), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA
| | - Meghan C Campbell
- Mallinckrodt Institute of Radiology, Department of Radiology, Washington University School of Medicine, (CB 8225), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA; Department of Neurology, Washington University School of Medicine, (CB 8111), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA
| | - Joshua L Dowling
- Department of Neurosurgical Surgery, Washington University School of Medicine, (CB 8057), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA
| | - Keith M Rich
- Department of Neurosurgical Surgery, Washington University School of Medicine, (CB 8057), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA
| | - Lindsey M Brier
- Mallinckrodt Institute of Radiology, Department of Radiology, Washington University School of Medicine, (CB 8225), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA
| | - Joel S Perlmutter
- Mallinckrodt Institute of Radiology, Department of Radiology, Washington University School of Medicine, (CB 8225), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA; Department of Neurology, Washington University School of Medicine, (CB 8111), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA; Department of Neurosurgical Surgery, Washington University School of Medicine, (CB 8057), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA; Department of Neuroscience, Washington University School of Medicine, (CB 8108), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA; Department of Occupational Therapy, CB 8505, 4444 Forest Park Ave, St. Louis, MO 63108, USA; Department of Physical Therapy, CB 8502, 4444 Forest Park Ave, St. Louis, MO, 63108, USA
| | - Scott A Norris
- Department of Neurology, Washington University School of Medicine, (CB 8111), 660 S. Euclid Avenue, St. Louis, MO, 63110, USA.
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Peh WYX, Raczkowska MN, Teh Y, Alam M, Thakor NV, Yen SC. Closed-loop stimulation of the pelvic nerve for optimal micturition. J Neural Eng 2018; 15:066009. [PMID: 30181427 DOI: 10.1088/1741-2552/aadee9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Neural stimulation to restore bladder function has traditionally relied on open-loop approaches that used pre-set parameters, which do not adapt to suboptimal outcomes. The goal of this study was to examine the effectiveness of a novel closed-loop stimulation paradigm for improving micturition or bladder voiding. APPROACH We compared the voiding efficiency obtained with this closed-loop framework against open-loop stimulation paradigms in anesthetized rats. The bladder pressures that preceded voiding, and the minimum current amplitudes for stimulating the pelvic nerves to evoke bladder contractions, were first calibrated for each animal. An automated closed-loop system was used to initiate voiding upon bladder fullness, adapt the stimulation current by using real-time bladder pressure changes to classify voiding outcomes, and halt stimulation when the bladder had been emptied or when the safe stimulation limit was reached. MAIN RESULTS In vivo testing demonstrated that the closed-loop system achieved high voiding efficiency or VE (75.7% ± 3.07%, mean ± standard error of the mean) and outperformed open-loop systems with either conserved number of stimulation epochs (63.2% ± 4.90% VE) or conserved charge injected (32.0% ± 1.70% VE). Post-hoc analyses suggest that the classification algorithm can be further improved with data from additional closed-loop experiments. SIGNIFICANCE This novel approach may be applied to an implantable device for treating underactive bladder (<60% VE), especially in cases where under- or over-stimulation of the nerve is a concern.
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Affiliation(s)
- Wendy Yen Xian Peh
- Singapore Institute for Neurotechnology, National University of Singapore, 28 Medical Drive, #05-02, Singapore 117456, Singapore
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