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Stanslaski S, Summers RLS, Tonder L, Tan Y, Case M, Raike RS, Morelli N, Herrington TM, Beudel M, Ostrem JL, Little S, Almeida L, Ramirez-Zamora A, Fasano A, Hassell T, Mitchell KT, Moro E, Gostkowski M, Sarangmat N, Bronte-Stewart H. Sensing data and methodology from the Adaptive DBS Algorithm for Personalized Therapy in Parkinson's Disease (ADAPT-PD) clinical trial. NPJ Parkinsons Dis 2024; 10:174. [PMID: 39289373 PMCID: PMC11408616 DOI: 10.1038/s41531-024-00772-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 08/05/2024] [Indexed: 09/19/2024] Open
Abstract
Adaptive deep brain stimulation (aDBS) is an emerging advancement in DBS technology; however, local field potential (LFP) signal rate detection sufficient for aDBS algorithms and the methods to set-up aDBS have yet to be defined. Here we summarize sensing data and aDBS programming steps associated with the ongoing Adaptive DBS Algorithm for Personalized Therapy in Parkinson's Disease (ADAPT-PD) pivotal trial (NCT04547712). Sixty-eight patients were enrolled with either subthalamic nucleus or globus pallidus internus DBS leads connected to a Medtronic PerceptTM PC neurostimulator. During the enrollment and screening procedures, a LFP (8-30 Hz, ≥1.2 µVp) control signal was identified by clinicians in 84.8% of patients on medication (65% bilateral signal), and in 92% of patients off medication (78% bilateral signal). The ADAPT-PD trial sensing data indicate a high LFP signal presence in both on and off medication states of these patients, with bilateral signal in the majority, regardless of PD phenotype.
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Affiliation(s)
- Scott Stanslaski
- Medtronic Neuromodulation, Medtronic, Minneapolis, Minnesota, USA.
| | | | - Lisa Tonder
- Medtronic Neuromodulation, Medtronic, Minneapolis, Minnesota, USA
| | - Ye Tan
- Medtronic Neuromodulation, Medtronic, Minneapolis, Minnesota, USA
| | - Michelle Case
- Medtronic Neuromodulation, Medtronic, Minneapolis, Minnesota, USA
| | - Robert S Raike
- Medtronic Neuromodulation, Medtronic, Minneapolis, Minnesota, USA
| | - Nathan Morelli
- Medtronic Neuromodulation, Medtronic, Minneapolis, Minnesota, USA
| | | | - Martijn Beudel
- Department of Neurology, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Jill L Ostrem
- Department of Neurology, University of California San Francisco, San Francisco, USA
| | - Simon Little
- Department of Neurology, University of California San Francisco, San Francisco, USA
| | - Leonardo Almeida
- Department of Neurology, University of Minnesota, Minneapolis, USA
| | - Adolfo Ramirez-Zamora
- Department of Neurology, Shands at University of Florida, University of Florida, Gainesville, USA
| | - Alfonso Fasano
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, University of Toronto, Toronto, ON, Canada
- Krembil Brain Institute, University of Toronto, Toronto, ON, Canada
| | - Travis Hassell
- Department of Neurology, Vanderbilt University Medical Center, Nashville, USA
| | - Kyle T Mitchell
- Duke University Movement Disorders Center, Duke University, Durham, USA
| | - Elena Moro
- Grenoble Alpes University, Division of Neurology, Grenoble Institute of Neuroscience, CHU of Grenoble, Grenoble, France
| | - Michal Gostkowski
- Center for Neurological Restoration, Cleveland Clinic Foundation, Cleveland, USA
| | | | - Helen Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, USA
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2
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Sumarac S, Youn J, Fearon C, Zivkovic L, Keerthi P, Flouty O, Popovic M, Hodaie M, Kalia S, Lozano A, Hutchison W, Fasano A, Milosevic L. Clinico-physiological correlates of Parkinson's disease from multi-resolution basal ganglia recordings. NPJ Parkinsons Dis 2024; 10:175. [PMID: 39261476 PMCID: PMC11391063 DOI: 10.1038/s41531-024-00773-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 08/05/2024] [Indexed: 09/13/2024] Open
Abstract
Parkinson's disease (PD) has been associated with pathological neural activity within the basal ganglia. Herein, we analyzed resting-state single-neuron and local field potential (LFP) activities from people with PD who underwent awake deep brain stimulation surgery of the subthalamic nucleus (STN; n = 125) or globus pallidus internus (GPi; n = 44), and correlated rate-based and oscillatory features with UPDRSIII off-medication subscores. Rate-based single-neuron features did not correlate with PD symptoms. STN single-neuron and LFP low-beta (12-21 Hz) power and burst dynamics showed modest correlations with bradykinesia and rigidity severity, while STN spiketrain theta (4-8 Hz) power correlated modestly with tremor severity. GPi low- and high-beta (21-30 Hz) power and burst dynamics correlated moderately with bradykinesia and axial symptom severity. These findings suggest that elevated single-neuron and LFP oscillations may be linked to symptoms, though modest correlations imply that the pathophysiology of PD may extend beyond resting-state beta oscillations.
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Affiliation(s)
- Srdjan Sumarac
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Jinyoung Youn
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, Toronto, ON, Canada
- Department of Neurology, University of Toronto, Toronto, ON, Canada
| | - Conor Fearon
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, Toronto, ON, Canada
- Department of Neurology, University of Toronto, Toronto, ON, Canada
| | - Luka Zivkovic
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Prerana Keerthi
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Oliver Flouty
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL, USA
| | - Milos Popovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- KITE, University Health Network, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada
| | - Mojgan Hodaie
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada
| | - Suneil Kalia
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- KITE, University Health Network, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada
| | - Andres Lozano
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada
| | - William Hutchison
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Alfonso Fasano
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, Toronto, ON, Canada
- Department of Neurology, University of Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- KITE, University Health Network, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada
| | - Luka Milosevic
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada.
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
- KITE, University Health Network, Toronto, ON, Canada.
- Center for Advancing Neurotechnological Innovation to Application (CRANIA), Toronto, ON, Canada.
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3
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Simpson TG, Godfrey W, Torrecillos F, He S, Herz DM, Oswal A, Muthuraman M, Pogosyan A, Tan H. Cortical beta oscillations help synchronise muscles during static posture holding in healthy motor control. Neuroimage 2024; 298:120774. [PMID: 39103065 DOI: 10.1016/j.neuroimage.2024.120774] [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: 01/19/2024] [Revised: 07/31/2024] [Accepted: 08/02/2024] [Indexed: 08/07/2024] Open
Abstract
How cortical oscillations are involved in the coordination of functionally coupled muscles and how this is modulated by different movement contexts (static vs dynamic) remains unclear. Here, this is investigated by recording high-density electroencephalography (EEG) and electromyography (EMG) from different forearm muscles while healthy participants (n = 20) performed movement tasks (static and dynamic posture holding, and reaching) with their dominant hand. When dynamic perturbation was applied, beta band (15-35 Hz) activities in the motor cortex contralateral to the performing hand reduced during the holding phase, comparative to when there was no perturbation. During static posture holding, transient periods of increased cortical beta oscillations (beta bursts) were associated with greater corticomuscular coherence and increased phase synchrony between muscles (intermuscular coherence) in the beta frequency band compared to the no-burst period. This effect was not present when resisting dynamic perturbation. The results suggest that cortical beta bursts assist synchronisation of different muscles during static posture holding in healthy motor control, contributing to the maintenance and stabilisation of functional muscle groups. Theoretically, increased cortical beta oscillations could lead to exaggerated synchronisation in different muscles making the initialisation of movements more difficult, as observed in Parkinson's disease.
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Affiliation(s)
- Thomas G Simpson
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - William Godfrey
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Flavie Torrecillos
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Shenghong He
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Damian M Herz
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Ashwini Oswal
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Muthuraman Muthuraman
- Neural Engineering with Signal Analytics and Artificial Intelligence (NESA-AI), Department of Neurology, Universitätsklinikum Würzburg, Würzburg, Germany
| | - Alek Pogosyan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Huiling Tan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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Grennan I, Mallet N, Magill PJ, Cagnan H, Sharott A. Beta bursts in the parkinsonian cortico-basal ganglia network form spatially discrete ensembles. Neurobiol Dis 2024; 201:106652. [PMID: 39209070 DOI: 10.1016/j.nbd.2024.106652] [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: 02/14/2024] [Revised: 08/21/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024] Open
Abstract
Defining spatial synchronisation of pathological beta oscillations is important, given that many theories linking them to parkinsonian symptoms propose a reduction in the dimensionality of the coding space within and/or across cortico-basal ganglia structures. Such spatial synchronisation could arise from a single process, with widespread entrainment of neurons to the same oscillation. Alternatively, the partially segregated structure of cortico-basal ganglia loops could provide a substrate for multiple ensembles that are independently synchronized at beta frequencies. Addressing this question requires an analytical approach that identifies groups of signals with a statistical tendency for beta synchronisation, which is unachievable using standard pairwise measures. Here, we utilized such an approach on multichannel recordings of background unit activity (BUA) in the external globus pallidus (GP) and subthalamic nucleus (STN) in parkinsonian rats. We employed an adapted version of a principle and independent component analysis-based method commonly used to define assemblies of single neurons (i.e., neurons that are synchronized over short timescales). This analysis enabled us to define whether changes in the power of beta oscillations in local ensembles of neurons (i.e., the BUA recorded from single contacts) consistently covaried over time, forming a "beta ensemble". Multiple beta ensembles were often present in single recordings and could span brain structures. Membership of a beta ensemble predicted significantly higher levels of short latency (<5 ms) synchrony in the raw BUA signal and phase synchronisation with cortical beta oscillations, suggesting that they comprised clusters of neurons that are functionally connected at multiple levels, despite sometimes being non-contiguous in space. Overall, these findings suggest that beta oscillations do not comprise of a single synchronisation process, but rather multiple independent activities that can bind both spatially contiguous and non-contiguous pools of neurons within and across structures. As previously proposed, such ensembles provide a substrate for beta oscillations to constrain the coding space of cortico-basal ganglia circuits.
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Affiliation(s)
- Isaac Grennan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Deptartment of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, United Kingdom
| | - Nicolas Mallet
- Universite de Bordeaux, Institut des Maladies Neurodégénératives, 33076 Bordeaux, France; CNRS UMR 5293, Institut des Maladies Neurodégénératives, 33076 Bordeaux, France
| | - Peter J Magill
- Medical Research Council Brain Network Dynamics Unit, Nuffield Deptartment of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, United Kingdom
| | - Hayriye Cagnan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Deptartment of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, United Kingdom; Department of Bioengineering, Sir Michael Uren Hub, Imperial College London, London, W12 0BZ, UK
| | - Andrew Sharott
- Medical Research Council Brain Network Dynamics Unit, Nuffield Deptartment of Clinical Neurosciences, University of Oxford, Oxford OX1 3TH, United Kingdom.
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5
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Cho S, Han HB, Jung D, Kim J, Choi JH. Mouse Escape Behaviors and mPFC-BLA Activity Dataset: Understanding Flexible Defensive Strategies Under Threat. Sci Data 2024; 11:861. [PMID: 39127738 DOI: 10.1038/s41597-024-03688-0] [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: 03/05/2024] [Accepted: 07/26/2024] [Indexed: 08/12/2024] Open
Abstract
Responding to threats in the real world demands a sophisticated orchestration of freeze and flight behaviors dynamically modulated by the neural activity. While the medial prefrontal cortex-basolateral amygdala (mPFC-BLA) network is known to play a pivotal role in coordinating these responses, the mechanisms underlying its population dynamics remain vague. As traditional Pavlovian fear conditioning models fall short in encapsulating the breadth of natural escape behaviors, we introduce a novel dataset to bridge this gap, capturing the defensive strategies of mice against a spider robot in a natural-like environment. The adaptive escape behaviors and concurrent mPFC-BLA activity in eight mice were monitored using wireless local field potential (LFP) and video recordings, both individually and in groups. Our data offers a unique avenue to explore the neural dynamics that govern fear- and vigilance-induced threat responses in isolated and social contexts. Supplemented by detailed methodologies and validation, the dataset allows for the analysis of the transient neural oscillatory dynamics, with prospective implications for the fields of neuroscience, robotics, and artificial intelligence.
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Affiliation(s)
- SungJun Cho
- Computational Cognitive & Systems Neuroscience Laboratory, Brain Science Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Hio-Been Han
- Computational Cognitive & Systems Neuroscience Laboratory, Brain Science Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 43 Vassar Street, Cambridge, MA, 02139, USA
- School of Convergence, Seoul National University of Science and Technology, Seoul, 01811, Republic of Korea
| | - DaYoung Jung
- Computational Cognitive & Systems Neuroscience Laboratory, Brain Science Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
- Department of Biotechnology, Korea University, Seoul, 02841, Republic of Korea
| | - Jisoo Kim
- Computational Cognitive & Systems Neuroscience Laboratory, Brain Science Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, CB2 3EG, UK
| | - Jee Hyun Choi
- Computational Cognitive & Systems Neuroscience Laboratory, Brain Science Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.
- Division of Bio-Medical Science & Technology, Korea University of Science and Technology, Daejeon, 34113, Republic of Korea.
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6
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Baker SK, Radcliffe EM, Kramer DR, Ojemann S, Case M, Zarns C, Holt-Becker A, Raike RS, Baumgartner AJ, Kern DS, Thompson JA. Comparison of beta peak detection algorithms for data-driven deep brain stimulation programming strategies in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:150. [PMID: 39122725 PMCID: PMC11315991 DOI: 10.1038/s41531-024-00762-7] [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: 02/27/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024] Open
Abstract
Oscillatory activity within the beta frequency range (13-30 Hz) serves as a Parkinson's disease biomarker for tailoring deep brain stimulation (DBS) treatments. Currently, identifying clinically relevant beta signals, specifically frequencies of peak amplitudes within the beta spectral band, is a subjective process. To inform potential strategies for objective clinical decision making, we assessed algorithms for identifying beta peaks and devised a standardized approach for both research and clinical applications. Employing a novel monopolar referencing strategy, we utilized a brain sensing device to measure beta peak power across distinct contacts along each DBS electrode implanted in the subthalamic nucleus. We then evaluated the accuracy of ten beta peak detection algorithms against a benchmark established by expert consensus. The most accurate algorithms, all sharing similar underlying algebraic dynamic peak amplitude thresholding approaches, matched the expert consensus in performance and reliably predicted the clinical stimulation parameters during follow-up visits. These findings highlight the potential of algorithmic solutions to overcome the subjective bias in beta peak identification, presenting viable options for standardizing this process. Such advancements could lead to significant improvements in the efficiency and accuracy of patient-specific DBS therapy parameterization.
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Affiliation(s)
- Sunderland K Baker
- Pennsylvania State University, Department of Biobehavioral Health, University Park, PA, 16802, USA
| | - Erin M Radcliffe
- University of Colorado Anschutz Medical Campus, Department of Neurosurgery, Aurora, CO, 80045, USA
- University of Colorado Anschutz Medical Campus, Department of Bioengineering, Aurora, CO, 80045, USA
| | - Daniel R Kramer
- University of Colorado Anschutz Medical Campus, Department of Neurosurgery, Aurora, CO, 80045, USA
| | - Steven Ojemann
- University of Colorado Anschutz Medical Campus, Department of Neurosurgery, Aurora, CO, 80045, USA
- University of Colorado Anschutz Medical Campus, Department of Neurology, Aurora, CO, 80045, USA
| | - Michelle Case
- Medtronic PLC, Neuromodulation Operating Unit, Minneapolis, MN, 55432, USA
| | - Caleb Zarns
- Medtronic PLC, Neuromodulation Operating Unit, Minneapolis, MN, 55432, USA
| | - Abbey Holt-Becker
- Medtronic PLC, Neuromodulation Operating Unit, Minneapolis, MN, 55432, USA
| | - Robert S Raike
- Medtronic PLC, Neuromodulation Operating Unit, Minneapolis, MN, 55432, USA
| | - Alexander J Baumgartner
- University of Colorado Anschutz Medical Campus, Department of Neurosurgery, Aurora, CO, 80045, USA
- University of Colorado Anschutz Medical Campus, Department of Neurology, Aurora, CO, 80045, USA
| | - Drew S Kern
- University of Colorado Anschutz Medical Campus, Department of Neurosurgery, Aurora, CO, 80045, USA
- University of Colorado Anschutz Medical Campus, Department of Neurology, Aurora, CO, 80045, USA
| | - John A Thompson
- University of Colorado Anschutz Medical Campus, Department of Neurosurgery, Aurora, CO, 80045, USA.
- University of Colorado Anschutz Medical Campus, Department of Neurology, Aurora, CO, 80045, USA.
- University of Colorado Anschutz Medical Campus, Department of Psychiatry, Aurora, CO, 80045, USA.
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7
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Williams D. Why so slow? Models of parkinsonian bradykinesia. Nat Rev Neurosci 2024; 25:573-586. [PMID: 38937655 DOI: 10.1038/s41583-024-00830-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2024] [Indexed: 06/29/2024]
Abstract
Bradykinesia, or slowness of movement, is a defining feature of Parkinson disease (PD) and a major contributor to the negative effects on quality of life associated with this disorder and related conditions. A dominant pathophysiological model of bradykinesia in PD has existed for approximately 30 years and has been the basis for the development of several therapeutic interventions, but accumulating evidence has made this model increasingly untenable. Although more recent models have been proposed, they also appear to be flawed. In this Perspective, I consider the leading prior models of bradykinesia in PD and argue that a more functionally related model is required, one that considers changes that disrupt the fundamental process of accurate information transmission. In doing so, I review emerging evidence of network level functional connectivity changes, information transfer dysfunction and potential motor code transmission error and present a novel model of bradykinesia in PD that incorporates this evidence. I hope that this model may reconcile inconsistencies in its predecessors and encourage further development of therapeutic interventions.
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Affiliation(s)
- David Williams
- Department of Internal Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
- Department of Neurology, Whipps Cross University Hospital, Barts Health NHS Trust, London, UK.
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8
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Hoy CW, de Hemptinne C, Wang SS, Harmer CJ, Apps MAJ, Husain M, Starr PA, Little S. Beta and theta oscillations track effort and previous reward in the human basal ganglia and prefrontal cortex during decision making. Proc Natl Acad Sci U S A 2024; 121:e2322869121. [PMID: 39047043 PMCID: PMC11295073 DOI: 10.1073/pnas.2322869121] [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: 12/29/2023] [Accepted: 06/18/2024] [Indexed: 07/27/2024] Open
Abstract
Choosing whether to exert effort to obtain rewards is fundamental to human motivated behavior. However, the neural dynamics underlying the evaluation of reward and effort in humans is poorly understood. Here, we report an exploratory investigation into this with chronic intracranial recordings from the prefrontal cortex (PFC) and basal ganglia (BG; subthalamic nuclei and globus pallidus) in people with Parkinson's disease performing a decision-making task with offers that varied in levels of reward and physical effort required. This revealed dissociable neural signatures of reward and effort, with BG beta (12 to 20 Hz) oscillations tracking effort on a single-trial basis and PFC theta (4 to 7 Hz) signaling previous trial reward, with no effects of net subjective value. Stimulation of PFC increased overall acceptance of offers and sensitivity to reward while decreasing the impact of effort on choices. This work uncovers oscillatory mechanisms that guide fundamental decisions to exert effort for reward across BG and PFC, supports a causal role of PFC for such choices, and seeds hypotheses for future studies.
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Affiliation(s)
- Colin W. Hoy
- Department of Neurology, University of California, San Francisco, CA94143
| | - Coralie de Hemptinne
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL32608
- Department of Neurology, University of Florida, Gainesville, FL32608
| | - Sarah S. Wang
- Department of Neurology, University of California, San Francisco, CA94143
| | - Catherine J. Harmer
- Department of Psychiatry, University of Oxford, OxfordOX3 7JX, United Kingdom
| | - Matthew A. J. Apps
- Department of Experimental Psychology, University of Oxford, OxfordOX2 6GG, United Kingdom
- Institute for Mental Health, School of Psychology, University of Birmingham, Birmingham UKB15 2TT, United Kingdom
- Centre for Human Brain Health, School of Psychology, University of Birmingham, BirminghamB15 2TT, United Kingdom
| | - Masud Husain
- Department of Experimental Psychology, University of Oxford, OxfordOX2 6GG, United Kingdom
- Nuffield Department of Clinical Neurosciences, University of Oxford, OxfordOX3 9DU, United Kingdom
| | - Philip A. Starr
- Department of Neurological Surgery, University of California, San Francisco, CA94143, United Kingdom
| | - Simon Little
- Department of Neurology, University of California, San Francisco, CA94143
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9
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Liao XY, Gao YX, Qian TT, Zhou LH, Li LQ, Gong Y, Ye TF. Bibliometric analysis of electroencephalogram research in Parkinson's disease from 2004 to 2023. Front Neurosci 2024; 18:1433583. [PMID: 39099632 PMCID: PMC11294212 DOI: 10.3389/fnins.2024.1433583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Accepted: 07/08/2024] [Indexed: 08/06/2024] Open
Abstract
Background Parkinson's disease (PD) is a prevalent neurodegenerative disorder affecting millions globally. It encompasses both motor and non-motor symptoms, with a notable impact on patients' quality of life. Electroencephalogram (EEG) is a non-invasive tool that is increasingly utilized to investigate neural mechanisms in PD, identify early diagnostic markers, and assess therapeutic responses. Methods The data were sourced from the Science Citation Index Expanded within the Web of Science Core Collection database, focusing on publications related to EEG research in PD from 2004 to 2023. A comprehensive bibliometric analysis was conducted using CiteSpace and VOSviewer software. The analysis began with an evaluation of the selected publications, identifying leading countries, institutions, authors, and journals, as well as co-cited references, to summarize the current state of EEG research in PD. Keywords are employed to identify research topics that are currently of interest in this field through the analysis of high-frequency keyword co-occurrence and cluster analysis. Finally, burst keywords were identified to uncover emerging trends and research frontiers in the field, highlighting shifts in interest and identifying future research directions. Results A total of 1,559 publications on EEG research in PD were identified. The United States, Germany, and England have made notable contributions to the field. The University of London is the leading institution in terms of publication output, with the University of California closely following. The most prolific authors are Brown P, Fuhr P, and Stam C In terms of total citations and per-article citations, Stam C has the highest number of citations, while Brown P has the highest H-index. In terms of the total number of publications, Clinical Neurophysiology is the leading journal, while Brain is the most highly cited. The most frequently cited articles pertain to software toolboxes for EEG analysis, neural oscillations, and PD pathophysiology. Through analyzing the keywords, four research hotspots were identified: research on the neural oscillations and connectivity, research on the innovations in EEG Analysis, impact of therapies on EEG, and research on cognitive and emotional assessments. Conclusion This bibliometric analysis demonstrates a growing global interest in EEG research in PD. The investigation of neural oscillations and connectivity remains a primary focus of research. The application of machine learning, deep learning, and task analysis techniques offers promising avenues for future research in EEG and PD, suggesting the potential for advancements in this field. This study offers valuable insights into the major research trends, influential contributors, and evolving themes in this field, providing a roadmap for future exploration.
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Affiliation(s)
- Xiao-Yu Liao
- Department of Rehabilitation Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Ya-Xin Gao
- Department of Rehabilitation Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Ting-Ting Qian
- Department of Rehabilitation Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Lu-Han Zhou
- The Fourth Rehabilitation Hospital of Shanghai, Shanghai, China
| | - Li-Qin Li
- Department of Rehabilitation Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Yan Gong
- Department of Rehabilitation Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Tian-Fen Ye
- Department of Rehabilitation Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
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10
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da Silva Castanheira J, Wiesman AI, Hansen JY, Misic B, Baillet S. The neurophysiological brain-fingerprint of Parkinson's disease. EBioMedicine 2024; 105:105201. [PMID: 38908100 PMCID: PMC11253223 DOI: 10.1016/j.ebiom.2024.105201] [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: 12/04/2023] [Revised: 05/30/2024] [Accepted: 05/30/2024] [Indexed: 06/24/2024] Open
Abstract
BACKGROUND Research in healthy young adults shows that characteristic patterns of brain activity define individual "brain-fingerprints" that are unique to each person. However, variability in these brain-fingerprints increases in individuals with neurological conditions, challenging the clinical relevance and potential impact of the approach. Our study shows that brain-fingerprints derived from neurophysiological brain activity are associated with pathophysiological and clinical traits of individual patients with Parkinson's disease (PD). METHODS We created brain-fingerprints from task-free brain activity recorded through magnetoencephalography in 79 PD patients and compared them with those from two independent samples of age-matched healthy controls (N = 424 total). We decomposed brain activity into arrhythmic and rhythmic components, defining distinct brain-fingerprints for each type from recording durations of up to 4 min and as short as 30 s. FINDINGS The arrhythmic spectral components of cortical activity in patients with Parkinson's disease are more variable over short periods, challenging the definition of a reliable brain-fingerprint. However, by isolating the rhythmic components of cortical activity, we derived brain-fingerprints that distinguished between patients and healthy controls with about 90% accuracy. The most prominent cortical features of the resulting Parkinson's brain-fingerprint are mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also demonstrate that Parkinson's symptom laterality can be decoded directly from cortical neurophysiological activity. Furthermore, our study reveals that the cortical topography of the Parkinson's brain-fingerprint aligns with that of neurotransmitter systems affected by the disease's pathophysiology. INTERPRETATION The increased moment-to-moment variability of arrhythmic brain-fingerprints challenges patient differentiation and explains previously published results. We outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and symptom laterality of Parkinson's disease. Thus, the proposed definition of a rhythmic brain-fingerprint of Parkinson's disease may contribute to novel, refined approaches to patient stratification. Symmetrically, we discuss how rhythmic brain-fingerprints may contribute to the improved identification and testing of therapeutic neurostimulation targets. FUNDING Data collection and sharing for this project was provided by the Quebec Parkinson Network (QPN), the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD; release 6.0) program, the Cambridge Centre for Aging Neuroscience (Cam-CAN), and the Open MEG Archives (OMEGA). The QPN is funded by a grant from Fonds de Recherche du Québec - Santé (FRQS). PREVENT-AD was launched in 2011 as a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the FRQS, an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, and the Canada Fund for Innovation. The Brainstorm project is supported by funding to SB from the NIH (R01-EB026299-05). Further funding to SB for this study included a Discovery grant from the Natural Sciences and Engineering Research Council of Canada of Canada (436355-13), and the CIHR Canada research Chair in Neural Dynamics of Brain Systems (CRC-2017-00311).
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Affiliation(s)
| | - Alex I Wiesman
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Justine Y Hansen
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Bratislav Misic
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sylvain Baillet
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
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11
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Lundqvist M, Miller EK, Nordmark J, Liljefors J, Herman P. Beta: bursts of cognition. Trends Cogn Sci 2024; 28:662-676. [PMID: 38658218 DOI: 10.1016/j.tics.2024.03.010] [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: 09/11/2023] [Revised: 03/11/2024] [Accepted: 03/20/2024] [Indexed: 04/26/2024]
Abstract
Beta oscillations are linked to the control of goal-directed processing of sensory information and the timing of motor output. Recent evidence demonstrates they are not sustained but organized into intermittent high-power bursts mediating timely functional inhibition. This implies there is a considerable moment-to-moment variation in the neural dynamics supporting cognition. Beta bursts thus offer new opportunities for studying how sensory inputs are selectively processed, reshaped by inhibitory cognitive operations and ultimately result in motor actions. Recent method advances reveal diversity in beta bursts that provide deeper insights into their function and the underlying neural circuit activity motifs. We propose that brain-wide, spatiotemporal patterns of beta bursting reflect various cognitive operations and that their dynamics reveal nonlinear aspects of cortical processing.
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Affiliation(s)
- Mikael Lundqvist
- Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden; The Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Earl K Miller
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jonatan Nordmark
- Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
| | - Johan Liljefors
- Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
| | - Pawel Herman
- School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden; Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden
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12
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Guo X, He S, Geng X, Yao P, Wiest C, Nie Y, Tan H, Wang S. Quantifying local field potential dynamics with amplitude and frequency stability between ON and OFF medication and stimulation in Parkinson's disease. Neurobiol Dis 2024; 197:106519. [PMID: 38685358 PMCID: PMC7616028 DOI: 10.1016/j.nbd.2024.106519] [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: 01/29/2024] [Revised: 03/26/2024] [Accepted: 04/25/2024] [Indexed: 05/02/2024] Open
Abstract
Neural oscillations are critical to understanding the synchronisation of neural activities and their relevance to neurological disorders. For instance, the amplitude of beta oscillations in the subthalamic nucleus has gained extensive attention, as it has been found to correlate with medication status and the therapeutic effects of continuous deep brain stimulation in people with Parkinson's disease. However, the frequency stability of subthalamic nucleus beta oscillations, which has been suggested to be associated with dopaminergic information in brain states, has not been well explored. Moreover, the administration of medicine can have inverse effects on changes in frequency and amplitude. In this study, we proposed a method based on the stationary wavelet transform to quantify the amplitude and frequency stability of subthalamic nucleus beta oscillations and evaluated the method using simulation and real data for Parkinson's disease patients. The results suggest that the amplitude and frequency stability quantification has enhanced sensitivity in distinguishing pathological conditions in Parkinson's disease patients. Our quantification shows the benefit of combining frequency stability information with amplitude and provides a new potential feedback signal for adaptive deep brain stimulation.
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Affiliation(s)
- Xuanjun Guo
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Shenghong He
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Xinyi Geng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Pan Yao
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom; State Key Laboratory of Transducer Technology, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, 100094 Beijing, China; School of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), 100049 Beijing, China
| | - Christoph Wiest
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Yingnan Nie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Huiling Tan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
| | - Shouyan Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, Shanghai, China; Shanghai Engineering Research Center of AI & Robotics, Fudan University, Shanghai, China; Engineering Research Center of AI & Robotics, Ministry of Education, Fudan University, Shanghai, China.
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13
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Zhang G, Yu H, Chen Y, Gong C, Hao H, Guo Y, Xu S, Zhang Y, Yuan X, Yin G, Zhang JG, Tan H, Li L. Neurophysiological features of STN LFP underlying sleep fragmentation in Parkinson's disease. J Neurol Neurosurg Psychiatry 2024:jnnp-2023-331979. [PMID: 38724231 DOI: 10.1136/jnnp-2023-331979] [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: 06/07/2023] [Accepted: 04/17/2024] [Indexed: 09/21/2024]
Abstract
BACKGROUND Sleep fragmentation is a persistent problem throughout the course of Parkinson's disease (PD). However, the related neurophysiological patterns and the underlying mechanisms remained unclear. METHOD We recorded subthalamic nucleus (STN) local field potentials (LFPs) using deep brain stimulation (DBS) with real-time wireless recording capacity from 13 patients with PD undergoing a one-night polysomnography recording, 1 month after DBS surgery before initial programming and when the patients were off-medication. The STN LFP features that characterised different sleep stages, correlated with arousal and sleep fragmentation index, and preceded stage transitions during N2 and REM sleep were analysed. RESULTS Both beta and low gamma oscillations in non-rapid eye movement (NREM) sleep increased with the severity of sleep disturbance (arousal index (ArI)-betaNREM: r=0.9, p=0.0001, sleep fragmentation index (SFI)-betaNREM: r=0.6, p=0.0301; SFI-gammaNREM: r=0.6, p=0.0324). We next examined the low-to-high power ratio (LHPR), which was the power ratio of theta oscillations to beta and low gamma oscillations, and found it to be an indicator of sleep fragmentation (ArI-LHPRNREM: r=-0.8, p=0.0053; ArI-LHPRREM: r=-0.6, p=0.0373; SFI-LHPRNREM: r=-0.7, p=0.0204; SFI-LHPRREM: r=-0.6, p=0.0428). In addition, long beta bursts (>0.25 s) during NREM stage 2 were found preceding the completion of transition to stages with more cortical activities (towards Wake/N1/REM compared with towards N3 (p<0.01)) and negatively correlated with STN spindles, which were detected in STN LFPs with peak frequency distinguishable from long beta bursts (STN spindle: 11.5 Hz, STN long beta bursts: 23.8 Hz), in occupation during NREM sleep (β=-0.24, p<0.001). CONCLUSION Features of STN LFPs help explain neurophysiological mechanisms underlying sleep fragmentations in PD, which can inform new intervention for sleep dysfunction. TRIAL REGISTRATION NUMBER NCT02937727.
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Affiliation(s)
- Guokun Zhang
- National Engineering Research Center of Neuromodulation, Tsinghua University School of Aerospace Engineering, Beijing, China
| | - Huiling Yu
- National Engineering Research Center of Neuromodulation, Tsinghua University School of Aerospace Engineering, Beijing, China
| | - Yue Chen
- National Engineering Research Center of Neuromodulation, Tsinghua University School of Aerospace Engineering, Beijing, China
| | - Chen Gong
- National Engineering Research Center of Neuromodulation, Tsinghua University School of Aerospace Engineering, Beijing, China
| | - Hongwei Hao
- National Engineering Research Center of Neuromodulation, Tsinghua University School of Aerospace Engineering, Beijing, China
| | - Yi Guo
- Peking Union Medical College Hospital, Beijing, China
| | - Shujun Xu
- Department of Neurosurgery, Qilu Hospital of Shandong University Qingdao, Qingdao, Shandong, China
| | - Yuhuan Zhang
- Department of Otolaryngology Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Xuemei Yuan
- Department of Otolaryngology Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Guoping Yin
- Department of Otolaryngology Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, Beijing, China
| | | | - Huiling Tan
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Luming Li
- National Engineering Research Center of Neuromodulation, Tsinghua University School of Aerospace Engineering, Beijing, China
- IDG/McGovern Institute for Brain Research at Tsinghua University, Beijing, China
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14
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Yeh CH, Xu Y, Shi W, Fitzgerald JJ, Green AL, Fischer P, Tan H, Oswal A. Auditory cues modulate the short timescale dynamics of STN activity during stepping in Parkinson's disease. Brain Stimul 2024; 17:501-509. [PMID: 38636820 PMCID: PMC7616027 DOI: 10.1016/j.brs.2024.04.006] [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: 01/16/2024] [Revised: 03/26/2024] [Accepted: 04/10/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Gait impairment has a major impact on quality of life in patients with Parkinson's disease (PD). It is believed that basal ganglia oscillatory activity at β frequencies (15-30 Hz) may contribute to gait impairment, but the precise dynamics of this oscillatory activity during gait remain unclear. Additionally, auditory cues are known to lead to improvements in gait kinematics in PD. If the neurophysiological mechanisms of this cueing effect were better understood they could be leveraged to treat gait impairments using adaptive Deep Brain Stimulation (aDBS) technologies. OBJECTIVE We aimed to characterize the dynamics of subthalamic nucleus (STN) oscillatory activity during stepping movements in PD and to establish the neurophysiological mechanisms by which auditory cues modulate gait. METHODS We studied STN local field potentials (LFPs) in eight PD patients while they performed stepping movements. Hidden Markov Models (HMMs) were used to discover transient states of spectral activity that occurred during stepping with and without auditory cues. RESULTS The occurrence of low and high β bursts was suppressed during and after auditory cues. This manifested as a decrease in their fractional occupancy and state lifetimes. Interestingly, α transients showed the opposite effect, with fractional occupancy and state lifetimes increasing during and after auditory cues. CONCLUSIONS We show that STN oscillatory activity in the α and β frequency bands are differentially modulated by gait-promoting oscillatory cues. These findings suggest that the enhancement of α rhythms may be an approach for ameliorating gait impairments in PD.
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Affiliation(s)
- Chien-Hung Yeh
- School of Information and Electronics, Beijing Institute of Technology, Beijing, China; Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Ministry of Education (Beijing Institute of Technology), Beijing, China.
| | - Yifan Xu
- School of Information and Electronics, Beijing Institute of Technology, Beijing, China
| | - Wenbin Shi
- School of Information and Electronics, Beijing Institute of Technology, Beijing, China; Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Ministry of Education (Beijing Institute of Technology), Beijing, China.
| | - James J Fitzgerald
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom; Oxford Functional Neurosurgery, John Radcliffe Hospital, Oxford, United Kingdom
| | - Alexander L Green
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom; Oxford Functional Neurosurgery, John Radcliffe Hospital, Oxford, United Kingdom
| | - Petra Fischer
- School of Physiology, Pharmacology & Neuroscience, University of Bristol, Bristol, United Kingdom
| | - Huiling Tan
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
| | - Ashwini Oswal
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
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15
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Coleman SC, Seedat ZA, Pakenham DO, Quinn AJ, Brookes MJ, Woolrich MW, Mullinger KJ. Post-task responses following working memory and movement are driven by transient spectral bursts with similar characteristics. Hum Brain Mapp 2024; 45:e26700. [PMID: 38726799 PMCID: PMC11082833 DOI: 10.1002/hbm.26700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 03/09/2024] [Accepted: 04/14/2024] [Indexed: 05/13/2024] Open
Abstract
The post-movement beta rebound has been studied extensively using magnetoencephalography (MEG) and is reliably modulated by various task parameters as well as illness. Our recent study showed that rebounds, which we generalise as "post-task responses" (PTRs), are a ubiquitous phenomenon in the brain, occurring across the cortex in theta, alpha, and beta bands. Currently, it is unknown whether PTRs following working memory are driven by transient bursts, which are moments of short-lived high amplitude activity, similar to those that drive the post-movement beta rebound. Here, we use three-state univariate hidden Markov models (HMMs), which can identify bursts without a priori knowledge of frequency content or response timings, to compare bursts that drive PTRs in working memory and visuomotor MEG datasets. Our results show that PTRs across working memory and visuomotor tasks are driven by pan-spectral transient bursts. These bursts have very similar spectral content variation over the cortex, correlating strongly between the two tasks in the alpha (R2 = .89) and beta (R2 = .53) bands. Bursts also have similar variation in duration over the cortex (e.g., long duration bursts occur in the motor cortex for both tasks), strongly correlating over cortical regions between tasks (R2 = .56), with a mean over all regions of around 300 ms in both datasets. Finally, we demonstrate the ability of HMMs to isolate signals of interest in MEG data, such that the HMM probability timecourse correlates more strongly with reaction times than frequency filtered power envelopes from the same brain regions. Overall, we show that induced PTRs across different tasks are driven by bursts with similar characteristics, which can be identified using HMMs. Given the similarity between bursts across tasks, we suggest that PTRs across the cortex may be driven by a common underlying neural phenomenon.
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Affiliation(s)
- Sebastian C. Coleman
- Sir Peter Mansfield Imaging Centre, School of Physics and AstronomyUniversity of NottinghamNottinghamUK
| | - Zelekha A. Seedat
- Sir Peter Mansfield Imaging Centre, School of Physics and AstronomyUniversity of NottinghamNottinghamUK
- Young EpilepsyLingfieldUK
| | - Daisie O. Pakenham
- Sir Peter Mansfield Imaging Centre, School of Physics and AstronomyUniversity of NottinghamNottinghamUK
- Clinical NeurophysiologyQueen's Medical Centre, Nottingham University Hospitals NHS TrustNottinghamUK
| | - Andrew J. Quinn
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of PsychiatryUniversity of OxfordOxfordUK
- Centre for Human Brain Health, School of PsychologyUniversity of BirminghamBirminghamUK
| | - Matthew J. Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and AstronomyUniversity of NottinghamNottinghamUK
| | - Mark W. Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of PsychiatryUniversity of OxfordOxfordUK
| | - Karen J. Mullinger
- Sir Peter Mansfield Imaging Centre, School of Physics and AstronomyUniversity of NottinghamNottinghamUK
- Centre for Human Brain Health, School of PsychologyUniversity of BirminghamBirminghamUK
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16
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Lu J, Zhang X, Shu Z, Han J, Yu N. A dynamic brain network decomposition method discovers effective brain hemodynamic sub-networks for Parkinson's disease. J Neural Eng 2024; 21:026047. [PMID: 38621377 DOI: 10.1088/1741-2552/ad3eb6] [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: 01/17/2024] [Accepted: 04/15/2024] [Indexed: 04/17/2024]
Abstract
Objective.Dopaminergic treatment is effective for Parkinson's disease (PD). Nevertheless, the conventional treatment assessment mainly focuses on human-administered behavior examination while the underlying functional improvements have not been well explored. This paper aims to investigate brain functional variations of PD patients after dopaminergic therapy.Approach.This paper proposed a dynamic brain network decomposition method and discovered brain hemodynamic sub-networks that well characterized the efficacy of dopaminergic treatment in PD. Firstly, a clinical walking procedure with functional near-infrared spectroscopy was developed, and brain activations during the procedure from fifty PD patients under the OFF and ON states (without and with dopaminergic medication) were captured. Then, dynamic brain networks were constructed with sliding-window analysis of phase lag index and integrated time-varying functional networks across all patients. Afterwards, an aggregated network decomposition algorithm was formulated based on aggregated effectiveness optimization of functional networks in spanning network topology and cross-validation network variations, and utilized to unveil effective brain hemodynamic sub-networks for PD patients. Further, dynamic sub-network features were constructed to characterize the brain flexibility and dynamics according to the temporal switching and activation variations of discovered sub-networks, and their correlations with differential treatment-induced gait alterations were analyzed.Results.The results demonstrated that PD patients exhibited significantly enhanced flexibility after dopaminergic therapy within a sub-network related to the improvement of motor functions. Other sub-networks were significantly correlated with trunk-related axial symptoms and exhibited no significant treatment-induced dynamic interactions.Significance.The proposed method promises a quantified and objective approach for dopaminergic treatment evaluation. Moreover, the findings suggest that the gait of PD patients comprises distinct motor domains, and the corresponding neural controls are selectively responsive to dopaminergic treatment.
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Affiliation(s)
- Jiewei Lu
- College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China
| | - Xinyuan Zhang
- College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China
| | - Zhilin Shu
- College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China
| | - Jianda Han
- College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China
- Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin, People's Republic of China
- Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen, People's Republic of China
| | - Ningbo Yu
- College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China
- Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin, People's Republic of China
- Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen, People's Republic of China
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17
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Brown G, De Jesus S. Bridging the Gap: Local Field Potentials Offer a Peek Into the Brain of a Person With Parkinson Disease. Neurology 2024; 102:e209283. [PMID: 38513164 DOI: 10.1212/wnl.0000000000209283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 01/20/2024] [Indexed: 03/23/2024] Open
Abstract
Clinically available deep brain recordings in patients with Parkinson disease (PD) offer insights into disease mechanisms and create a pathway for personalized treatment strategies. This case illustrates the transformative potential of recordings of neuronal firing in the form of local field potentials (LFPs) by detailing a patient's clinical trajectory for 6 months after deep brain stimulation (DBS) surgery to treat their PD symptoms. LFPs, obtained easily in clinic with a tablet interface to measure and track brain rhythms across the disease course, enriched the patient's clinical picture. Specifically, strong beta peaks were captured at initial programming, and, as the beta peaks diminished over the course of optimizing settings, symptoms improved. These signals may also reveal insights into the neural dynamics of PD such as hypersynchrony in basal ganglia circuitry. Furthermore, the ability to record chronically may unlock new understanding of neuronal dysfunction in PD, possibly enabling future adaptive DBS. In conclusion, identification, tracking, and modulation of LFPs correlated with subjective and objective clinical improvement in the case presented. The use of neurophysiologic signals in the future may lead to therapeutic innovations for our patients with PD.
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Affiliation(s)
- Gregory Brown
- From the Department of Neurology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey, PA
| | - Sol De Jesus
- From the Department of Neurology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey, PA
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18
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Bange M, Gonzalez-Escamilla G, Herz DM, Tinkhauser G, Glaser M, Ciolac D, Pogosyan A, Kreis SL, Luhmann HJ, Tan H, Groppa S. Subthalamic stimulation modulates context-dependent effects of beta bursts during fine motor control. Nat Commun 2024; 15:3166. [PMID: 38605062 PMCID: PMC11009405 DOI: 10.1038/s41467-024-47555-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 04/02/2024] [Indexed: 04/13/2024] Open
Abstract
Increasing evidence suggests a considerable role of pre-movement beta bursts for motor control and its impairment in Parkinson's disease. However, whether beta bursts occur during precise and prolonged movements and if they affect fine motor control remains unclear. To investigate the role of within-movement beta bursts for fine motor control, we here combine invasive electrophysiological recordings and clinical deep brain stimulation in the subthalamic nucleus in 19 patients with Parkinson's disease performing a context-varying task that comprised template-guided and free spiral drawing. We determined beta bursts in narrow frequency bands around patient-specific peaks and assessed burst amplitude, duration, and their immediate impact on drawing speed. We reveal that beta bursts occur during the execution of drawing movements with reduced duration and amplitude in comparison to rest. Exclusively when drawing freely, they parallel reductions in acceleration. Deep brain stimulation increases the acceleration around beta bursts in addition to a general increase in drawing velocity and improvements of clinical function. These results provide evidence for a diverse and task-specific role of subthalamic beta bursts for fine motor control in Parkinson's disease; suggesting that pathological beta bursts act in a context dependent manner, which can be targeted by clinical deep brain stimulation.
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Affiliation(s)
- Manuel Bange
- Section of Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Gabriel Gonzalez-Escamilla
- Section of Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Damian M Herz
- Section of Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Gerd Tinkhauser
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Martin Glaser
- Department of Neurosurgery, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Dumitru Ciolac
- Section of Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Alek Pogosyan
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Svenja L Kreis
- Institute of Physiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Heiko J Luhmann
- Institute of Physiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Huiling Tan
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Sergiu Groppa
- Section of Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
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19
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Ubeda Matzilevich E, Daniel PL, Little S. Towards therapeutic electrophysiological neurofeedback in Parkinson's disease. Parkinsonism Relat Disord 2024; 121:106010. [PMID: 38245382 DOI: 10.1016/j.parkreldis.2024.106010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 01/10/2024] [Accepted: 01/12/2024] [Indexed: 01/22/2024]
Abstract
Neurofeedback (NF) techniques support individuals to self-regulate specific features of brain activity, which has been shown to impact behavior and potentially ameliorate clinical symptoms. Electrophysiological NF (epNF) may be particularly impactful for patients with Parkinson's disease (PD), as evidence mounts to suggest a central role of pathological neural oscillations underlying symptoms in PD. Exaggerated beta oscillations (12-30 Hz) in the basal ganglia-cortical network are linked to motor symptoms (e.g., bradykinesia, rigidity), and beta is reduced by successful therapy with dopaminergic medication and Deep Brain Stimulation (DBS). PD patients also experience non-motor symptoms related to sleep, mood, motivation, and cognitive control. Although less is known about the mechanisms of non-motor symptoms in PD and how to successfully treat them, low frequency neural oscillations (1-12 Hz) in the basal ganglia-cortical network are particularly implicated in non-motor symptoms. Here, we review how cortical and subcortical epNF could be used to target motor and non-motor specific oscillations, and potentially serve as an adjunct therapy that enables PD patients to endogenously control their own pathological neural activities. Recent studies have demonstrated that epNF protocols can successfully support volitional control of cortical and subcortical beta rhythms. Importantly, this endogenous control of beta has been linked to changes in motor behavior. epNF for PD, as a casual intervention on neural signals, has the potential to increase understanding of the neurophysiology of movement, mood, and cognition and to identify new therapeutic approaches for motor and non-motor symptoms.
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Affiliation(s)
- Elena Ubeda Matzilevich
- Movement Disorders and Neuromodulation Division, Department of Neurology, University of California San Francisco, CA, USA
| | - Pria Lauren Daniel
- Movement Disorders and Neuromodulation Division, Department of Neurology, University of California San Francisco, CA, USA; Department of Psychology, University of California San Diego, CA, USA.
| | - Simon Little
- Movement Disorders and Neuromodulation Division, Department of Neurology, University of California San Francisco, CA, USA
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20
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Lewis S, Radcliffe E, Ojemann S, Kramer DR, Hirt L, Case M, Holt-Becker AB, Raike R, Kern DS, Thompson JA. Pilot Study to Investigate the Use of In-Clinic Sensing to Identify Optimal Stimulation Parameters for Deep Brain Stimulation Therapy in Parkinson's Disease. Neuromodulation 2024; 27:509-519. [PMID: 36797194 DOI: 10.1016/j.neurom.2023.01.006] [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: 10/26/2022] [Revised: 12/19/2022] [Accepted: 01/09/2023] [Indexed: 02/16/2023]
Abstract
BACKGROUND Deep brain stimulation (DBS) programming is time intensive. Recent advances in sensing technology of local field potentials (LFPs) may enable improvements. Few studies have compared the use of this technology with standard of care. OBJECTIVE/HYPOTHESIS Sensing technology of subthalamic nucleus (STN) DBS leads in Parkinson's disease (PD) is reliable and predicts the optimal contacts and settings as predicted by clinical assessment. MATERIALS AND METHODS Five subjects with PD (n = 9 hemispheres) with bilateral STN DBS and sensing capable battery replacement were recruited. An LFP sensing review of all bipolar contact pairs was performed three times. Contact with the maximal beta peak power (MBP) was then clinically assessed in a double-blinded fashion, and five conditions were tested: 1) entry settings, 2) off stimulation, 3) MBP at 30 μs, 4) MBP at 60 μs, and 5) MBP at 90 μs. RESULTS Contact and frequency of the MBP power in all hemispheres did not differ across sessions. The entry settings matched with the contact with the MBP power in 5 of 9 hemispheres. No clinical difference was evident in the stimulation conditions. The clinician and subject preferred settings determined by MBP power in 7 of 9 and 5 of 7 hemispheres, respectively. CONCLUSIONS This study indicates that STN LFPs in PD recorded directly from contacts of the DBS lead provide consistent recordings across the frequency range and a reliably detected beta peak. Furthermore, programming based on the MBP power provides at least clinical equivalence to standard of care programming with STN DBS.
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Affiliation(s)
- Sydnei Lewis
- Biomedical Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Erin Radcliffe
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Steven Ojemann
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Daniel R Kramer
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Lisa Hirt
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Michelle Case
- Brain Modulation Business, Neuromodulation Operating Unit, Medtronic, Plc, Minneapolis, MN, USA
| | - Abbey B Holt-Becker
- Brain Modulation Business, Neuromodulation Operating Unit, Medtronic, Plc, Minneapolis, MN, USA
| | - Robert Raike
- Brain Modulation Business, Neuromodulation Operating Unit, Medtronic, Plc, Minneapolis, MN, USA
| | - Drew S Kern
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - John A Thompson
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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21
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Vinding MC, Waldthaler J, Eriksson A, Manting CL, Ferreira D, Ingvar M, Svenningsson P, Lundqvist D. Oscillatory and non-oscillatory features of the magnetoencephalic sensorimotor rhythm in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:51. [PMID: 38443402 PMCID: PMC10915140 DOI: 10.1038/s41531-024-00669-3] [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: 05/27/2022] [Accepted: 02/26/2024] [Indexed: 03/07/2024] Open
Abstract
Parkinson's disease (PD) is associated with changes in neural activity in the sensorimotor alpha and beta bands. Using magnetoencephalography (MEG), we investigated the role of spontaneous neuronal activity within the somatosensory cortex in a large cohort of early- to mid-stage PD patients (N = 78) on Parkinsonian medication and age- and sex-matched healthy controls (N = 60) using source reconstructed resting-state MEG. We quantified features of the time series data in terms of oscillatory alpha power and central alpha frequency, beta power and central beta frequency, and 1/f broadband characteristics using power spectral density. Furthermore, we characterised transient oscillatory burst events in the mu-beta band time-domain signals. We examined the relationship between these signal features and the patients' disease state, symptom severity, age, sex, and cortical thickness. PD patients and healthy controls differed on PSD broadband characteristics, with PD patients showing a steeper 1/f exponential slope and higher 1/f offset. PD patients further showed a steeper age-related decrease in the burst rate. Out of all the signal features of the sensorimotor activity, the burst rate was associated with increased severity of bradykinesia, whereas the burst duration was associated with axial symptoms. Our study shows that general non-oscillatory features (broadband 1/f exponent and offset) of the sensorimotor signals are related to disease state and oscillatory burst rate scales with symptom severity in PD.
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Affiliation(s)
- Mikkel C Vinding
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark.
| | - Josefine Waldthaler
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Section of Neurology, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, University Hospital Marburg, Marburg, Germany
| | - Allison Eriksson
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Cassia Low Manting
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Cognitive Neuroimaging Centre, Lee Kong Chien School of Medicine, Nanyang Technological University, Singapore, Singapore
- McGovern Institute of Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer's Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas de Gran, Canaria, España
| | - Martin Ingvar
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Per Svenningsson
- Section of Neurology, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Daniel Lundqvist
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
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22
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Guo Z, Lin JP, Simeone O, Mills KR, Cvetkovic Z, McClelland VM. Cross-frequency cortex-muscle interactions are abnormal in young people with dystonia. Brain Commun 2024; 6:fcae061. [PMID: 38487552 PMCID: PMC10939448 DOI: 10.1093/braincomms/fcae061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 01/10/2024] [Accepted: 02/23/2024] [Indexed: 03/17/2024] Open
Abstract
Sensory processing and sensorimotor integration are abnormal in dystonia, including impaired modulation of beta-corticomuscular coherence. However, cortex-muscle interactions in either direction are rarely described, with reports limited predominantly to investigation of linear coupling, using corticomuscular coherence or Granger causality. Information-theoretic tools such as transfer entropy detect both linear and non-linear interactions between processes. This observational case-control study applies transfer entropy to determine intra- and cross-frequency cortex-muscle coupling in young people with dystonia/dystonic cerebral palsy. Fifteen children with dystonia/dystonic cerebral palsy and 13 controls, aged 12-18 years, performed a grasp task with their dominant hand. Mechanical perturbations were provided by an electromechanical tapper. Bipolar scalp EEG over contralateral sensorimotor cortex and surface EMG over first dorsal interosseous were recorded. Multi-scale wavelet transfer entropy was applied to decompose signals into functional frequency bands of oscillatory activity and to quantify intra- and cross-frequency coupling between brain and muscle. Statistical significance against the null hypothesis of zero transfer entropy was established, setting individual 95% confidence thresholds. The proportion of individuals in each group showing significant transfer entropy for each frequency combination/direction was compared using Fisher's exact test, correcting for multiple comparisons. Intra-frequency transfer entropy was detected in all participants bidirectionally in the beta (16-32 Hz) range and in most participants from EEG to EMG in the alpha (8-16 Hz) range. Cross-frequency transfer entropy across multiple frequency bands was largely similar between groups, but a specific coupling from low-frequency EMG to beta EEG was significantly reduced in dystonia [P = 0.0061 (corrected)]. The demonstration of bidirectional cortex-muscle communication in dystonia emphasizes the value of transfer entropy for exploring neural communications in neurological disorders. The novel finding of diminished coupling from low-frequency EMG to beta EEG in dystonia suggests impaired cortical feedback of proprioceptive information with a specific frequency signature that could be relevant to the origin of the excessive low-frequency drive to muscle.
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Affiliation(s)
- Zhenghao Guo
- Department of Engineering, King's College London, London WC2R 2LS, UK
- School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Jean-Pierre Lin
- Children's Neuroscience, Evelina London Children's Hospital, Guy's & St Thomas' NHS Foundation Trust (GSTT), London SE1 7EH, UK
| | - Osvaldo Simeone
- Department of Engineering, King's College London, London WC2R 2LS, UK
| | - Kerry R Mills
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London SE5 9RX, UK
| | - Zoran Cvetkovic
- Department of Engineering, King's College London, London WC2R 2LS, UK
| | - Verity M McClelland
- Children's Neuroscience, Evelina London Children's Hospital, Guy's & St Thomas' NHS Foundation Trust (GSTT), London SE1 7EH, UK
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London SE5 9RX, UK
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23
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Bočková M, Lamoš M, Chrastina J, Daniel P, Kupcová S, Říha I, Šmahovská L, Baláž M, Rektor I. Coupling between beta band and high frequency oscillations as a clinically useful biomarker for DBS. NPJ Parkinsons Dis 2024; 10:40. [PMID: 38383550 PMCID: PMC10882016 DOI: 10.1038/s41531-024-00656-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
Beta hypersynchrony was recently introduced into clinical practice in Parkinson's disease (PD) to identify the best stimulation contacts and for adaptive deep brain stimulation (aDBS) sensing. However, many other oscillopathies accompany the disease, and beta power sensing may not be optimal for all patients. The aim of this work was to study the potential clinical usefulness of beta power phase-amplitude coupling (PAC) with high frequency oscillations (HFOs). Subthalamic nucleus (STN) local field potentials (LFPs) from externalized DBS electrodes were recorded and analyzed in PD patients (n = 19). Beta power and HFOs were evaluated in a resting-state condition; PAC was then studied and compared with the electrode contact positions, structural connectivity, and medication state. Beta-HFO PAC (mainly in the 200-500 Hz range) was observed in all subjects. PAC was detectable more specifically in the motor part of the STN compared to beta power and HFOs. Moreover, the presence of PAC better corresponds to the stimulation setup based on the clinical effect. PAC is also sensitive to the laterality of symptoms and dopaminergic therapy, where the greater PAC cluster reflects the more affected side and medication "off" state. Coupling between beta power and HFOs is known to be a correlate of the PD "off" state. Beta-HFO PAC seems to be more sensitive than beta power itself and could be more helpful in the selection of the best clinical stimulation contact and probably also as a potential future input signal for aDBS.
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Affiliation(s)
- Martina Bočková
- Brain and Mind Research Program, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
- First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Brno, Czech Republic
| | - Martin Lamoš
- Brain and Mind Research Program, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Jan Chrastina
- Department of Neurosurgery, Masaryk University School of Medicine, St. Anne's Hospital, Brno, Czech Republic
| | - Pavel Daniel
- Brain and Mind Research Program, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
- First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Brno, Czech Republic
| | - Silvia Kupcová
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Ivo Říha
- Department of Neurosurgery, Masaryk University School of Medicine, St. Anne's Hospital, Brno, Czech Republic
| | - Lucia Šmahovská
- First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Brno, Czech Republic
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Marek Baláž
- Brain and Mind Research Program, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
- First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Brno, Czech Republic
| | - Ivan Rektor
- Brain and Mind Research Program, Central European Institute of Technology, Masaryk University, Brno, Czech Republic.
- First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Brno, Czech Republic.
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24
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O'Keeffe AB, Merla A, Ashkan K. Deep brain stimulation of the subthalamic nucleus in Parkinson disease 2013-2023: where are we a further 10 years on? Br J Neurosurg 2024:1-9. [PMID: 38323603 DOI: 10.1080/02688697.2024.2311128] [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: 08/14/2023] [Accepted: 01/23/2024] [Indexed: 02/08/2024]
Abstract
Deep brain stimulation has been in clinical use for 30 years and during that time it has changed markedly from a small-scale treatment employed by only a few highly specialized centers into a widespread keystone approach to the management of disorders such as Parkinson's disease. In the intervening decades, many of the broad principles of deep brain stimulation have remained unchanged, that of electrode insertion into stereotactically targeted brain nuclei, however the underlying technology and understanding around the approach have progressed markedly. Some of the most significant advances have taken place over the last decade with the advent of artificial intelligence, directional electrodes, stimulation/recording implantable pulse generators and the potential for remote programming among many other innovations. New therapeutic targets are being assessed for their potential benefits and a surge in the number of deep brain stimulation implantations has given birth to a flourishing scientific literature surrounding the pathophysiology of brain disorders such as Parkinson's disease. Here we outline the developments of the last decade and look to the future of deep brain stimulation to attempt to discern some of the most promising lines of inquiry in this fast-paced and rapidly evolving field.
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Affiliation(s)
| | - Anca Merla
- King's College Hospital Department of Neurosurgery, Kings College Hospital, Denmark
| | - Keyoumars Ashkan
- King's College Hospital Department of Neurosurgery, Kings College Hospital, Denmark
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25
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Bandarabadi M, Li S, Aeschlimann L, Colombo G, Tzanoulinou S, Tafti M, Becchetti A, Boutrel B, Vassalli A. Inactivation of hypocretin receptor-2 signaling in dopaminergic neurons induces hyperarousal and enhanced cognition but impaired inhibitory control. Mol Psychiatry 2024; 29:327-341. [PMID: 38123729 PMCID: PMC11116111 DOI: 10.1038/s41380-023-02329-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 11/09/2023] [Accepted: 11/15/2023] [Indexed: 12/23/2023]
Abstract
Hypocretin/Orexin (HCRT/OX) and dopamine (DA) are both key effectors of salience processing, reward and stress-related behaviors and motivational states, yet their respective roles and interactions are poorly delineated. We inactivated HCRT-to-DA connectivity by genetic disruption of Hypocretin receptor-1 (Hcrtr1), Hypocretin receptor-2 (Hcrtr2), or both receptors (Hcrtr1&2) in DA neurons and analyzed the consequences on vigilance states, brain oscillations and cognitive performance in freely behaving mice. Unexpectedly, loss of Hcrtr2, but not Hcrtr1 or Hcrtr1&2, induced a dramatic increase in theta (7-11 Hz) electroencephalographic (EEG) activity in both wakefulness and rapid-eye-movement sleep (REMS). DAHcrtr2-deficient mice spent more time in an active (or theta activity-enriched) substate of wakefulness, and exhibited prolonged REMS. Additionally, both wake and REMS displayed enhanced theta-gamma phase-amplitude coupling. The baseline waking EEG of DAHcrtr2-deficient mice exhibited diminished infra-theta, but increased theta power, two hallmarks of EEG hyperarousal, that were however uncoupled from locomotor activity. Upon exposure to novel, either rewarding or stress-inducing environments, DAHcrtr2-deficient mice featured more pronounced waking theta and fast-gamma (52-80 Hz) EEG activity surges compared to littermate controls, further suggesting increased alertness. Cognitive performance was evaluated in an operant conditioning paradigm, which revealed that DAHcrtr2-ablated mice manifest faster task acquisition and higher choice accuracy under increasingly demanding task contingencies. However, the mice concurrently displayed maladaptive patterns of reward-seeking, with behavioral indices of enhanced impulsivity and compulsivity. None of the EEG changes observed in DAHcrtr2-deficient mice were seen in DAHcrtr1-ablated mice, which tended to show opposite EEG phenotypes. Our findings establish a clear genetically-defined link between monosynaptic HCRT-to-DA neurotransmission and theta oscillations, with a differential and novel role of HCRTR2 in theta-gamma cross-frequency coupling, attentional processes, and executive functions, relevant to disorders including narcolepsy, attention-deficit/hyperactivity disorder, and Parkinson's disease.
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Affiliation(s)
- Mojtaba Bandarabadi
- Department of Biomedical Sciences, University of Lausanne, Lausanne, Switzerland
| | - Sha Li
- Department of Biomedical Sciences, University of Lausanne, Lausanne, Switzerland
| | - Lea Aeschlimann
- Centre for Psychiatric Neuroscience, Department of Psychiatry, The Lausanne University Hospital, Lausanne, Switzerland
| | - Giulia Colombo
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, Italy
| | | | - Mehdi Tafti
- Department of Biomedical Sciences, University of Lausanne, Lausanne, Switzerland
| | - Andrea Becchetti
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, Italy
| | - Benjamin Boutrel
- Centre for Psychiatric Neuroscience, Department of Psychiatry, The Lausanne University Hospital, Lausanne, Switzerland
| | - Anne Vassalli
- Department of Biomedical Sciences, University of Lausanne, Lausanne, Switzerland.
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26
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Papadopoulos S, Szul MJ, Congedo M, Bonaiuto JJ, Mattout J. Beta bursts question the ruling power for brain-computer interfaces. J Neural Eng 2024; 21:016010. [PMID: 38167234 DOI: 10.1088/1741-2552/ad19ea] [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: 09/15/2023] [Accepted: 01/02/2024] [Indexed: 01/05/2024]
Abstract
Objective: Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience.Approach: In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in 'left vs. right hand' motor imagery (MI) tasks. Current decoding approaches for such tasks take advantage of the fact that MI generates time-locked changes in induced power in the sensorimotor cortex and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during MI, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements.Main results and Significance: We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.
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Affiliation(s)
- Sotirios Papadopoulos
- University Lyon 1, Lyon, France
- Lyon Neuroscience Research Center, CRNL, INSERM U1028, CNRS, UMR5292, Lyon, France
- Institut de Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
| | - Maciej J Szul
- University Lyon 1, Lyon, France
- Institut de Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
| | - Marco Congedo
- GIPSA-lab, University Grenoble Alpes, CNRS, Grenoble-INP, Grenoble, France
| | - James J Bonaiuto
- University Lyon 1, Lyon, France
- Institut de Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
| | - Jérémie Mattout
- University Lyon 1, Lyon, France
- Lyon Neuroscience Research Center, CRNL, INSERM U1028, CNRS, UMR5292, Lyon, France
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27
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Spiliotis K, Butenko K, Starke J, van Rienen U, Köhling R. Towards an optimised deep brain stimulation using a large-scale computational network and realistic volume conductor model. J Neural Eng 2024; 20:066045. [PMID: 37988747 DOI: 10.1088/1741-2552/ad0e7c] [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: 02/01/2023] [Accepted: 11/21/2023] [Indexed: 11/23/2023]
Abstract
Objective. Constructing a theoretical framework to improve deep brain stimulation (DBS) based on the neuronal spatiotemporal patterns of the stimulation-affected areas constitutes a primary target.Approach. We develop a large-scale biophysical network, paired with a realistic volume conductor model, to estimate theoretically efficacious stimulation protocols. Based on previously published anatomically defined structural connectivity, a biophysical basal ganglia-thalamo-cortical neuronal network is constructed using Hodgkin-Huxley dynamics. We define a new biomarker describing the thalamic spatiotemporal activity as a ratio of spiking vs. burst firing. The per cent activation of the different pathways is adapted in the simulation to minimise the differences of the biomarker with respect to its value under healthy conditions.Main results.This neuronal network reproduces spatiotemporal patterns that emerge in Parkinson's disease. Simulations of the fibre per cent activation for the defined biomarker propose desensitisation of pallido-thalamic synaptic efficacy, induced by high-frequency signals, as one possible crucial mechanism for DBS action. Based on this activation, we define both an optimal electrode position and stimulation protocol using pathway activation modelling.Significance. A key advantage of this research is that it combines different approaches, i.e. the spatiotemporal pattern with the electric field and axonal response modelling, to compute the optimal DBS protocol. By correlating the inherent network dynamics with the activation of white matter fibres, we obtain new insights into the DBS therapeutic action.
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Affiliation(s)
| | - Konstantin Butenko
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany
- Movement Disorders and Neuromodulation Unit, Department for Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jens Starke
- Institute of Mathematics, University of Rostock, Rostock, Germany
| | - Ursula van Rienen
- Institute of General Electrical Engineering, University of Rostock, Rostock, Germany
- Department Life, Light and Matter, University of Rostock, Rostock, Germany
- Department of Ageing of Individuals and Society, University of Rostock, Rostock, Germany
| | - Rüdiger Köhling
- Department of Ageing of Individuals and Society, University of Rostock, Rostock, Germany
- Oscar-Langendorff-Institute of Physiology, Rostock University Medical Center, Rostock, Germany
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28
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Pardo-Valencia J, Fernández-García C, Alonso-Frech F, Foffani G. Oscillatory vs. non-oscillatory subthalamic beta activity in Parkinson's disease. J Physiol 2024; 602:373-395. [PMID: 38084073 DOI: 10.1113/jp284768] [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: 04/02/2023] [Accepted: 11/13/2023] [Indexed: 01/16/2024] Open
Abstract
Parkinson's disease is characterized by exaggerated beta activity (13-35 Hz) in cortico-basal ganglia motor loops. Beta activity includes both periodic fluctuations (i.e. oscillatory activity) and aperiodic fluctuations reflecting spiking activity and excitation/inhibition balance (i.e. non-oscillatory activity). However, the relative contribution, dopamine dependency and clinical correlations of oscillatory vs. non-oscillatory beta activity remain unclear. We recorded, modelled and analysed subthalamic local field potentials in parkinsonian patients at rest while off or on medication. Autoregressive modelling with additive 1/f noise clarified the relationships between measures of beta activity in the time domain (i.e. amplitude and duration of beta bursts) or in the frequency domain (i.e. power and sharpness of the spectral peak) and oscillatory vs. non-oscillatory activity: burst duration and spectral sharpness are specifically sensitive to oscillatory activity, whereas burst amplitude and spectral power are ambiguously sensitive to both oscillatory and non-oscillatory activity. Our experimental data confirmed the model predictions and assumptions. We subsequently analysed the effect of levodopa, obtaining strong-to-extreme Bayesian evidence that oscillatory beta activity is reduced in patients on vs. off medication, with moderate evidence for absence of modulation of the non-oscillatory component. Finally, specifically the oscillatory component of beta activity correlated with the rate of motor progression of the disease. Methodologically, these results provide an integrative understanding of beta-based biomarkers relevant for adaptive deep brain stimulation. Biologically, they suggest that primarily the oscillatory component of subthalamic beta activity is dopamine dependent and may play a role not only in the pathophysiology but also in the progression of Parkinson's disease. KEY POINTS: Beta activity in Parkinson's disease includes both true periodic fluctuations (i.e. oscillatory activity) and aperiodic fluctuations reflecting spiking activity and synaptic balance (i.e. non-oscillatory activity). The relative contribution, dopamine dependency and clinical correlations of oscillatory vs. non-oscillatory beta activity remain unclear. Burst duration and spectral sharpness are specifically sensitive to oscillatory activity, while burst amplitude and spectral power are ambiguously sensitive to both oscillatory and non-oscillatory activity. Only the oscillatory component of subthalamic beta activity is dopamine-dependent. Stronger beta oscillatory activity correlates with faster motor progression of the disease.
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Affiliation(s)
- Jesús Pardo-Valencia
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
- Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | - Carla Fernández-García
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
| | - Fernando Alonso-Frech
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
- Department of Neurology, San Carlos Research Health Intitute (IdISSC), Hospital Clínico San Carlos, Madrid, Spain
| | - Guglielmo Foffani
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
- Hospital Nacional de Parapléjicos, SESCAM, Toledo, Spain
- Instituto de Salud Carlos III, CIBERNED, Madrid, Spain
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Busch JL, Kaplan J, Habets JGV, Feldmann LK, Roediger J, Köhler RM, Merk T, Faust K, Schneider GH, Bergman H, Neumann WJ, Kühn AA. Single threshold adaptive deep brain stimulation in Parkinson's disease depends on parameter selection, movement state and controllability of subthalamic beta activity. Brain Stimul 2024; 17:125-133. [PMID: 38266773 DOI: 10.1016/j.brs.2024.01.007] [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: 06/05/2023] [Revised: 12/22/2023] [Accepted: 01/16/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Deep brain stimulation (DBS) is an invasive treatment option for patients with Parkinson's disease. Recently, adaptive DBS (aDBS) systems have been developed, which adjust stimulation timing and amplitude in real-time. However, it is unknown how changes in parameters, movement states and the controllability of subthalamic beta activity affect aDBS performance. OBJECTIVE To characterize how parameter choice, movement state and controllability interactively affect the electrophysiological and behavioral response to single threshold aDBS. METHODS We recorded subthalamic local field potentials in 12 patients with Parkinson's disease receiving single threshold aDBS in the acute post-operative state. We investigated changes in two aDBS parameters: the onset time and the smoothing of real-time beta power. Electrophysiological patterns and motor performance were assessed while patients were at rest and during a simple motor task. We further studied the impact of controllability on aDBS performance by comparing patients with and without beta power modulation during continuous stimulation. RESULTS Our findings reveal that changes in the onset time control the extent of beta power suppression achievable with single threshold adaptive stimulation during rest. Behavioral data indicate that only specific parameter combinations yield a beneficial effect of single threshold aDBS. During movement, action induced beta power suppression reduces the responsivity of the closed loop algorithm. We further demonstrate that controllability of beta power is a prerequisite for effective parameter dependent modulation of subthalamic beta activity. CONCLUSION Our results highlight the interaction between single threshold aDBS parameter selection, movement state and controllability in driving subthalamic beta activity and motor performance. By this means, we identify directions for the further development of closed-loop DBS algorithms.
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Affiliation(s)
- Johannes L Busch
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jonathan Kaplan
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jeroen G V Habets
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Lucia K Feldmann
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jan Roediger
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Richard M Köhler
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Timon Merk
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Katharina Faust
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Gerd-Helge Schneider
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Hagai Bergman
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, Jerusalem, Israel; Department of Medical Neurobiology, Institute of Medical Research Israel-Canada, The Hebrew University, Hassadah Medical School, Jerusalem, Israel; Department of Neurosurgery, Hadassah Medical Center, Jerusalem, Israel
| | - Wolf-Julian Neumann
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Andrea A Kühn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; NeuroCure, Charité - Universitätsmedizin Berlin, Berlin, Germany; Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Berlin, Germany.
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30
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Zhang B, Tian H, Yu Y, Zhen X, Zhang L, Yuan Y, Wang L. A localized pallidal physiomarker in Meige syndrome. Front Neurol 2023; 14:1286634. [PMID: 38178893 PMCID: PMC10764606 DOI: 10.3389/fneur.2023.1286634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/06/2023] [Indexed: 01/06/2024] Open
Abstract
Objectives Oscillatory patterns in local field potentials (LFPs) have been recognized as disease-specific physiomarkers, particularly in the context of Parkinson's disease and cervical dystonia. This characteristic oscillatory feature is currently employed in adaptive deep brain stimulation (aDBS). However, for other types of dystonia, especially Meige syndrome, a distinct physiomarker of this nature is yet to be identified. Methods Local field potentials were recorded during microelectrode-guided deep brain stimulation surgery from 28 patients with primary Meige syndrome. Before surgery, the severity of patients' motor syndrome were assessed using the Burke-Fahn-Marsden Dystonia Rating Scale-Motor (BFMDRS-M). An instantaneous oscillation detection method was employed to identify true narrowband oscillations. Subsequently, a linear mixed effects model was utilized to examine the relationship between oscillatory activities (including power amplitude and burst duration) and symptom severity. Results The focal peaks of "oscillatory activities" detected were predominantly concentrated in the narrow theta band (4-8 Hz), constituting 81.5% of the total detected oscillations in all recording sites near active DBS contacts in the globus pallidus internus (GPi). The linear mixed effects model revealed a positive correlation between the theta burst duration and the severity of preoperative motor impairment, but no correlation with postoperative motor scores. Additionally, there was no significant lateralization effect observed between the left and right GPi. Conclusion Our findings suggest that the exaggerated narrowband theta activity (mainly the burst duration) in the GPi is predictive of dystonia symptom severity and may be used as a physiomarker for optimized DBS target during surgery and adaptive DBS for the treatment of Meige syndrome.
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Affiliation(s)
- Bo Zhang
- Key Laboratory of Brain Science, Zunyi Medical University, Zunyi, China
- Guizhou Key Laboratory of Anesthesia and Organ Protection, Zunyi Medical University, Zunyi, China
| | - Hong Tian
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Yanbing Yu
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Xueke Zhen
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Li Zhang
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Yue Yuan
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Liang Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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31
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Hoy CW, de Hemptinne C, Wang SS, Harmer CJ, Apps MAJ, Husain M, Starr PA, Little S. Beta and theta oscillations track effort and previous reward in human basal ganglia and prefrontal cortex during decision making. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.05.570285. [PMID: 38106063 PMCID: PMC10723308 DOI: 10.1101/2023.12.05.570285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Choosing whether to exert effort to obtain rewards is fundamental to human motivated behavior. However, the neural dynamics underlying the evaluation of reward and effort in humans is poorly understood. Here, we investigate this with chronic intracranial recordings from prefrontal cortex (PFC) and basal ganglia (BG; subthalamic nuclei and globus pallidus) in people with Parkinson's disease performing a decision-making task with offers that varied in levels of reward and physical effort required. This revealed dissociable neural signatures of reward and effort, with BG beta (12-20 Hz) oscillations tracking subjective effort on a single trial basis and PFC theta (4-7 Hz) signaling previous trial reward. Stimulation of PFC increased overall acceptance of offers in addition to increasing the impact of reward on choices. This work uncovers oscillatory mechanisms that guide fundamental decisions to exert effort for reward across BG and PFC, as well as supporting a causal role of PFC for such choices.
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Affiliation(s)
- Colin W. Hoy
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Coralie de Hemptinne
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
- Department of Neurology, University of Florida, Gainesville, FL, USA
| | - Sarah S. Wang
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | | | - Mathew A. J. Apps
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- Institute for Mental Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Masud Husain
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Philip A. Starr
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Simon Little
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
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32
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da Silva Castanheira J, Wiesman AI, Hansen JY, Misic B, Baillet S. The neurophysiological brain-fingerprint of Parkinson's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.03.23285441. [PMID: 36798232 PMCID: PMC9934726 DOI: 10.1101/2023.02.03.23285441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
In this study, we investigate the clinical potential of brain-fingerprints derived from electrophysiological brain activity for diagnostics and progression monitoring of Parkinson's disease (PD). We obtained brain-fingerprints from PD patients and age-matched healthy controls using short, task-free magnetoencephalographic recordings. The rhythmic components of the individual brain-fingerprint distinguished between patients and healthy participants with approximately 90% accuracy. The most prominent cortical features of the Parkinson's brain-fingerprint mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also show that Parkinson's disease stages can be decoded directly from cortical neurophysiological activity. Additionally, our study reveals that the cortical topography of the Parkinson's brain-fingerprint aligns with that of neurotransmitter systems affected by the disease's pathophysiology. We further demonstrate that the arrhythmic components of cortical activity are more variable over short periods of time in patients with Parkinson's disease than in healthy controls, making individual differentiation between patients based on these features more challenging and explaining previous negative published results. Overall, we outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and clinical staging of Parkinson's disease. For this reason, the proposed definition of a rhythmic brain-fingerprint of Parkinson's disease may contribute to novel, refined approaches to patient stratification and to the improved identification and testing of therapeutic neurostimulation targets.
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Affiliation(s)
| | - Alex I. Wiesman
- Montreal Neurological Institute, McGill University, Montreal QC, Canada
| | - Justine Y. Hansen
- Montreal Neurological Institute, McGill University, Montreal QC, Canada
| | - Bratislav Misic
- Montreal Neurological Institute, McGill University, Montreal QC, Canada
| | - Sylvain Baillet
- Montreal Neurological Institute, McGill University, Montreal QC, Canada
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33
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He S, Baig F, Merla A, Torrecillos F, Perera A, Wiest C, Debarros J, Benjaber M, Hart MG, Ricciardi L, Morgante F, Hasegawa H, Samuel M, Edwards M, Denison T, Pogosyan A, Ashkan K, Pereira E, Tan H. Beta-triggered adaptive deep brain stimulation during reaching movement in Parkinson's disease. Brain 2023; 146:5015-5030. [PMID: 37433037 PMCID: PMC10690014 DOI: 10.1093/brain/awad233] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 05/30/2023] [Accepted: 06/28/2023] [Indexed: 07/13/2023] Open
Abstract
Subthalamic nucleus (STN) beta-triggered adaptive deep brain stimulation (ADBS) has been shown to provide clinical improvement comparable to conventional continuous DBS (CDBS) with less energy delivered to the brain and less stimulation induced side effects. However, several questions remain unanswered. First, there is a normal physiological reduction of STN beta band power just prior to and during voluntary movement. ADBS systems will therefore reduce or cease stimulation during movement in people with Parkinson's disease and could therefore compromise motor performance compared to CDBS. Second, beta power was smoothed and estimated over a time period of 400 ms in most previous ADBS studies, but a shorter smoothing period could have the advantage of being more sensitive to changes in beta power, which could enhance motor performance. In this study, we addressed these two questions by evaluating the effectiveness of STN beta-triggered ADBS using a standard 400 ms and a shorter 200 ms smoothing window during reaching movements. Results from 13 people with Parkinson's disease showed that reducing the smoothing window for quantifying beta did lead to shortened beta burst durations by increasing the number of beta bursts shorter than 200 ms and more frequent switching on/off of the stimulator but had no behavioural effects. Both ADBS and CDBS improved motor performance to an equivalent extent compared to no DBS. Secondary analysis revealed that there were independent effects of a decrease in beta power and an increase in gamma power in predicting faster movement speed, while a decrease in beta event related desynchronization (ERD) predicted quicker movement initiation. CDBS suppressed both beta and gamma more than ADBS, whereas beta ERD was reduced to a similar level during CDBS and ADBS compared with no DBS, which together explained the achieved similar performance improvement in reaching movements during CDBS and ADBS. In addition, ADBS significantly improved tremor compared with no DBS but was not as effective as CDBS. These results suggest that STN beta-triggered ADBS is effective in improving motor performance during reaching movements in people with Parkinson's disease, and that shortening of the smoothing window does not result in any additional behavioural benefit. When developing ADBS systems for Parkinson's disease, it might not be necessary to track very fast beta dynamics; combining beta, gamma, and information from motor decoding might be more beneficial with additional biomarkers needed for optimal treatment of tremor.
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Affiliation(s)
- Shenghong He
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Fahd Baig
- Neurosciences Research Centre, St George’s, University of London & St George’s University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Cranmer Terrace, London SW17 0QT, UK
| | - Anca Merla
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Flavie Torrecillos
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Andrea Perera
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Christoph Wiest
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Jean Debarros
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Moaad Benjaber
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Michael G Hart
- Neurosciences Research Centre, St George’s, University of London & St George’s University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Cranmer Terrace, London SW17 0QT, UK
| | - Lucia Ricciardi
- Neurosciences Research Centre, St George’s, University of London & St George’s University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Cranmer Terrace, London SW17 0QT, UK
| | - Francesca Morgante
- Neurosciences Research Centre, St George’s, University of London & St George’s University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Cranmer Terrace, London SW17 0QT, UK
| | - Harutomo Hasegawa
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Michael Samuel
- Department of Neurology, King’s College Hospital NHS Foundation Trust, London, SE5 9RS, UK
| | - Mark Edwards
- Department of Clinical and Basic Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London WC2R 2LS, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Alek Pogosyan
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Keyoumars Ashkan
- Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Erlick Pereira
- Neurosciences Research Centre, St George’s, University of London & St George’s University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Cranmer Terrace, London SW17 0QT, UK
| | - Huiling Tan
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
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34
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Neumann WJ, Steiner LA, Milosevic L. Neurophysiological mechanisms of deep brain stimulation across spatiotemporal resolutions. Brain 2023; 146:4456-4468. [PMID: 37450573 PMCID: PMC10629774 DOI: 10.1093/brain/awad239] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/04/2023] [Accepted: 06/28/2023] [Indexed: 07/18/2023] Open
Abstract
Deep brain stimulation is a neuromodulatory treatment for managing the symptoms of Parkinson's disease and other neurological and psychiatric disorders. Electrodes are chronically implanted in disease-relevant brain regions and pulsatile electrical stimulation delivery is intended to restore neurocircuit function. However, the widespread interest in the application and expansion of this clinical therapy has preceded an overarching understanding of the neurocircuit alterations invoked by deep brain stimulation. Over the years, various forms of neurophysiological evidence have emerged which demonstrate changes to brain activity across spatiotemporal resolutions; from single neuron, to local field potential, to brain-wide cortical network effects. Though fruitful, such studies have often led to debate about a singular putative mechanism. In this Update we aim to produce an integrative account of complementary instead of mutually exclusive neurophysiological effects to derive a generalizable concept of the mechanisms of deep brain stimulation. In particular, we offer a critical review of the most common historical competing theories, an updated discussion on recent literature from animal and human neurophysiological studies, and a synthesis of synaptic and network effects of deep brain stimulation across scales of observation, including micro-, meso- and macroscale circuit alterations.
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Affiliation(s)
- Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
| | - Leon A Steiner
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin 10117, Germany
- Department of Clinical and Computational Neuroscience, Krembil Brain Institute, University Health Network, Toronto M5T 1M8, Canada
| | - Luka Milosevic
- Department of Clinical and Computational Neuroscience, Krembil Brain Institute, University Health Network, Toronto M5T 1M8, Canada
- Institute of Biomedical Engineering, Institute of Medical Sciences, and CRANIA Neuromodulation Institute, University of Toronto, Toronto M5S 3G9, Canada
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35
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Sil T, Hanafi I, Eldebakey H, Palmisano C, Volkmann J, Muthuraman M, Reich MM, Peach R. Wavelet-Based Bracketing, Time-Frequency Beta Burst Detection: New Insights in Parkinson's Disease. Neurotherapeutics 2023; 20:1767-1778. [PMID: 37819489 PMCID: PMC10684463 DOI: 10.1007/s13311-023-01447-4] [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] [Accepted: 09/25/2023] [Indexed: 10/13/2023] Open
Abstract
Studies have shown that beta band activity is not tonically elevated but comprises exaggerated phasic bursts of varying durations and magnitudes, for Parkinson's disease (PD) patients. Current methods for detecting beta bursts target a single frequency peak in beta band, potentially ignoring bursts in the wider beta band. In this study, we propose a new robust framework for beta burst identification across wide frequency ranges. Chronic local field potential at-rest recordings were obtained from seven PD patients implanted with Medtronic SenSight™ deep brain stimulation (DBS) electrodes. The proposed method uses wavelet decomposition to compute the time-frequency spectrum and identifies bursts spanning multiple frequency bins by thresholding, offering an additional burst measure, ∆f, that captures the width of a burst in the frequency domain. Analysis included calculating burst duration, magnitude, and ∆f and evaluating the distribution and likelihood of bursts between the low beta (13-20 Hz) and high beta (21-35 Hz). Finally, the results of the analysis were correlated to motor impairment (MDS-UPDRS III) med off scores. We found that low beta bursts with longer durations and larger width in the frequency domain (∆f) were positively correlated, while high beta bursts with longer durations and larger ∆f were negatively correlated with motor impairment. The proposed method, finding clear differences between bursting behavior in high and low beta bands, has clearly demonstrated the importance of considering wide frequency bands for beta burst behavior with implications for closed-loop DBS paradigms.
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Affiliation(s)
- Tanmoy Sil
- Department of Neurology, University Hospital Würzburg (UKW), Josef-Schneider-Str. 11, 97080, Würzburg, Germany
| | - Ibrahem Hanafi
- Department of Neurology, University Hospital Würzburg (UKW), Josef-Schneider-Str. 11, 97080, Würzburg, Germany
| | - Hazem Eldebakey
- Department of Neurology, University Hospital Würzburg (UKW), Josef-Schneider-Str. 11, 97080, Würzburg, Germany
| | - Chiara Palmisano
- Department of Neurology, University Hospital Würzburg (UKW), Josef-Schneider-Str. 11, 97080, Würzburg, Germany
| | - Jens Volkmann
- Department of Neurology, University Hospital Würzburg (UKW), Josef-Schneider-Str. 11, 97080, Würzburg, Germany
| | - Muthuraman Muthuraman
- Department of Neurology, University Hospital Würzburg (UKW), Josef-Schneider-Str. 11, 97080, Würzburg, Germany.
| | - Martin M Reich
- Department of Neurology, University Hospital Würzburg (UKW), Josef-Schneider-Str. 11, 97080, Würzburg, Germany
| | - Robert Peach
- Department of Neurology, University Hospital Würzburg (UKW), Josef-Schneider-Str. 11, 97080, Würzburg, Germany
- Department of Brain Sciences, Imperial College London, London, UK
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36
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Yin Z, Ma R, An Q, Xu Y, Gan Y, Zhu G, Jiang Y, Zhang N, Yang A, Meng F, Kühn AA, Bergman H, Neumann WJ, Zhang J. Pathological pallidal beta activity in Parkinson's disease is sustained during sleep and associated with sleep disturbance. Nat Commun 2023; 14:5434. [PMID: 37669927 PMCID: PMC10480217 DOI: 10.1038/s41467-023-41128-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/23/2023] [Indexed: 09/07/2023] Open
Abstract
Parkinson's disease (PD) is associated with excessive beta activity in the basal ganglia. Brain sensing implants aim to leverage this biomarker for demand-dependent adaptive stimulation. Sleep disturbance is among the most common non-motor symptoms in PD, but its relationship with beta activity is unknown. To investigate the clinical potential of beta activity as a biomarker for sleep quality in PD, we recorded pallidal local field potentials during polysomnography in PD patients off dopaminergic medication and compared the results to dystonia patients. PD patients exhibited sustained and elevated beta activity across wakefulness, rapid eye movement (REM), and non-REM sleep, which was correlated with sleep disturbance. Simulation of adaptive stimulation revealed that sleep-related beta activity changes remain unaccounted for by current algorithms, with potential negative outcomes in sleep quality and overall quality of life for patients.
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Affiliation(s)
- Zixiao Yin
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ruoyu Ma
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Qi An
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yichen Xu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yifei Gan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Guanyu Zhu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yin Jiang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Ning Zhang
- Department of Neuropsychiatry, Behavioral Neurology and Sleep Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Anchao Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fangang Meng
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Andrea A Kühn
- Department of Neurology, Movement Disorders and Neuromodulation Unit, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117, Berlin, Germany
- Exzellenzcluster - NeuroCure, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Hagai Bergman
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, Jerusalem, Israel
- Department of Medical Neurobiology (Physiology), Institute of Medical Research - Israel Canada (IMRIC), Faculty of Medicine, The Hebrew University, Jerusalem, Israel
- Department of Neurosurgery, Hadassah Medical Center, Jerusalem, Israel
| | - Wolf-Julian Neumann
- Department of Neurology, Movement Disorders and Neuromodulation Unit, Charité - Universitätsmedizin Berlin, Chariteplatz 1, 10117, Berlin, Germany.
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Neurostimulation, Beijing, China.
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37
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Boon LI, Buijink AWG. Parkinsonian Tremor: Shaking in Synchrony. Mov Disord 2023; 38:1582-1584. [PMID: 37718270 DOI: 10.1002/mds.29568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 07/18/2023] [Indexed: 09/19/2023] Open
Affiliation(s)
- Lennard I Boon
- Department of Neurology and Clinical Neurophysiology, Amsterdam University Medical Centers, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, the Netherlands
| | - Arthur W G Buijink
- Department of Neurology and Clinical Neurophysiology, Amsterdam University Medical Centers, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, the Netherlands
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38
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Rouzitalab A, Boulay CB, Sachs AJ. Volitional control of beta activities in Parkinson's disease patients. Brain Res 2023; 1814:148394. [PMID: 37156320 DOI: 10.1016/j.brainres.2023.148394] [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: 02/27/2023] [Revised: 04/20/2023] [Accepted: 05/03/2023] [Indexed: 05/10/2023]
Abstract
Patients diagnosed with Parkinson's disease (PD) have difficulty initiating and executing movements due to an acquired imbalance of the basal ganglia thalamocortical circuit secondary to loss of dopaminergic input into the striatum. The unbalanced circuit is hyper-synchronized, presenting as larger and longer bursts of beta-band (13-30 Hz) oscillations in the subthalamic nucleus (STN). As a first step toward a novel PD therapy that aims to improve symptoms through beta desynchronization, we sought to determine if individuals with PD could acquire volitional control of STN beta power in a neurofeedback task. We found a significant difference in STN beta power between task conditions, and relevant brain signal features could be detected and decoded in real time. This demonstration of volitional control of STN beta motivates development of a neurofeedback therapy to modulate PD symptom severity.
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Affiliation(s)
- Alireza Rouzitalab
- School of Electrical Engineering and Computer Science, University of Ottawa, K1N 6N5 Ottawa, ON, Canada; The Ottawa Hospital Research Institute, Ottawa, ON, Canada.
| | | | - Adam J Sachs
- The Ottawa Hospital Research Institute, Ottawa, ON, Canada; The University of Ottawa Brain and Mind Research Institute, Ottawa, ON, Canada; Division of Neurosurgery, Department of Surgery, The Ottawa Hospital, Ottawa, ON, Canada
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39
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Wang K, Yang L, Zhou S, Lin W. Desynchronizing oscillators coupled in multi-cluster networks through adaptively controlling partial networks. CHAOS (WOODBURY, N.Y.) 2023; 33:091101. [PMID: 37676113 DOI: 10.1063/5.0167555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/17/2023] [Indexed: 09/08/2023]
Abstract
This article introduces an adaptive control scheme with a feedback delay, specifically designed for controlling partial networks, to achieve desynchronization in a coupled network with two or multiple clusters. The proposed scheme's effectiveness is validated through several representative examples of coupled neuronal networks with two interconnected clusters. The efficacy of this scheme is attributed to the rigorous and numerical analyses on the corresponding transcendental characteristic equation, which includes time delay and other network parameters. In addition to investigating the impact of time delay and inter-connectivity on the stability of an incoherent state, we also rigorously find that controlling only one cluster cannot realize the desynchronization in the coupled oscillators within three or more clusters. All these, we believe, can deepen the understanding of the deep brain stimulation techniques presently used in the clinical treatment of neurodegenerative diseases and suggest future avenues for enhancing these clinical techniques through adaptive feedback settings.
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Affiliation(s)
- Kaidian Wang
- School of Mathematical Sciences, Shandong University, Jinan, Shandong 250100, China
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
| | - Luan Yang
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
| | - Shijie Zhou
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai 200032, China
| | - Wei Lin
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai 200032, China
- School of Mathematical Sciences, LMNS, and SCMS, Fudan University, Shanghai 200433, China
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40
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Binder T, Lange F, Pozzi N, Musacchio T, Daniels C, Odorfer T, Fricke P, Matthies C, Volkmann J, Capetian P. Feasibility of local field potential-guided programming for deep brain stimulation in Parkinson's disease: A comparison with clinical and neuro-imaging guided approaches in a randomized, controlled pilot trial. Brain Stimul 2023; 16:1243-1251. [PMID: 37619891 DOI: 10.1016/j.brs.2023.08.017] [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: 02/04/2023] [Revised: 08/17/2023] [Accepted: 08/18/2023] [Indexed: 08/26/2023] Open
Abstract
BACKGROUND Subthalamic nucleus deep brain stimulation (STN-DBS) is an effective treatment for advanced Parkinson's disease (PD). Clinical outcomes after DBS can be limited by poor programming, which remains a clinically driven, lengthy and iterative process. Electrophysiological recordings in PD patients undergoing STN-DBS have shown an association between STN spectral power in the beta frequency band (beta power) and the severity of clinical symptoms. New commercially-available DBS devices now enable the recording of STN beta oscillations in chronically-implanted PD patients, thereby allowing investigation into the use of beta power as a biomarker for DBS programming. OBJECTIVE To determine the potential advantages of beta-guided DBS programming over clinically and image-guided programming in terms of clinical efficacy and programming time. METHODS We conducted a randomized, blinded, three-arm, crossover clinical trial in eight Parkinson's patients with STN-DBS who were evaluated three months after DBS surgery. We compared clinical efficacy and time required for each DBS programming paradigm, as well as DBS parameters and total energy delivered between the three strategies (beta-, clinically- and image-guided). RESULTS All three programming methods showed similar clinical efficacy, but the time needed for programming was significantly shorter for beta- and image-guided programming compared to clinically-guided programming (p < 0.001). CONCLUSION Beta-guided programming may be a useful and more efficient approach to DBS programming in Parkinson's patients with STN-DBS. It takes significantly less time to program than traditional clinically-based programming, while providing similar symptom control. In addition, it is readily available within the clinical DBS programmer, making it a valuable tool for improving current clinical practice.
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Affiliation(s)
- Tobias Binder
- Department of Neurology, University Hospital and Julius-Maximilians-University, Wuerzburg, Germany
| | - Florian Lange
- Department of Neurology, University Hospital and Julius-Maximilians-University, Wuerzburg, Germany.
| | - Nicolò Pozzi
- Department of Neurology, University Hospital and Julius-Maximilians-University, Wuerzburg, Germany
| | - Thomas Musacchio
- Department of Neurology, University Hospital and Julius-Maximilians-University, Wuerzburg, Germany
| | - Christine Daniels
- Department of Neurology, University Hospital and Julius-Maximilians-University, Wuerzburg, Germany
| | - Thorsten Odorfer
- Department of Neurology, University Hospital and Julius-Maximilians-University, Wuerzburg, Germany
| | - Patrick Fricke
- Department of Neurosurgery, University Hospital and Julius-Maximilians-University, Wuerzburg, Germany
| | - Cordula Matthies
- Department of Neurosurgery, University Hospital and Julius-Maximilians-University, Wuerzburg, Germany
| | - Jens Volkmann
- Department of Neurology, University Hospital and Julius-Maximilians-University, Wuerzburg, Germany
| | - Philipp Capetian
- Department of Neurology, University Hospital and Julius-Maximilians-University, Wuerzburg, Germany
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41
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Szul MJ, Papadopoulos S, Alavizadeh S, Daligaut S, Schwartz D, Mattout J, Bonaiuto JJ. Diverse beta burst waveform motifs characterize movement-related cortical dynamics. Prog Neurobiol 2023; 228:102490. [PMID: 37391061 DOI: 10.1016/j.pneurobio.2023.102490] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/03/2023] [Accepted: 06/21/2023] [Indexed: 07/02/2023]
Abstract
Classical analyses of induced, frequency-specific neural activity typically average band-limited power over trials. More recently, it has become widely appreciated that in individual trials, beta band activity occurs as transient bursts rather than amplitude-modulated oscillations. Most studies of beta bursts treat them as unitary, and having a stereotyped waveform. However, we show there is a wide diversity of burst shapes. Using a biophysical model of burst generation, we demonstrate that waveform variability is predicted by variability in the synaptic drives that generate beta bursts. We then use a novel, adaptive burst detection algorithm to identify bursts from human MEG sensor data recorded during a joystick-based reaching task, and apply principal component analysis to burst waveforms to define a set of dimensions, or motifs, that best explain waveform variance. Finally, we show that bursts with a particular range of waveform motifs, ones not fully accounted for by the biophysical model, differentially contribute to movement-related beta dynamics. Sensorimotor beta bursts are therefore not homogeneous events and likely reflect distinct computational processes.
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Affiliation(s)
- Maciej J Szul
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Lyon, France; Université Claude Bernard Lyon 1, Université de Lyon, France.
| | - Sotirios Papadopoulos
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Lyon, France; Université Claude Bernard Lyon 1, Université de Lyon, France; Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France
| | - Sanaz Alavizadeh
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Lyon, France; Université Claude Bernard Lyon 1, Université de Lyon, France
| | | | - Denis Schwartz
- CERMEP - Imagerie du Vivant, MEG Departement, Lyon, France
| | - Jérémie Mattout
- Université Claude Bernard Lyon 1, Université de Lyon, France; Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France
| | - James J Bonaiuto
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Lyon, France; Université Claude Bernard Lyon 1, Université de Lyon, France
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Ranieri CM, Moioli RC, Vargas PA, Romero RAF. A neurorobotics approach to behaviour selection based on human activity recognition. Cogn Neurodyn 2023; 17:1009-1028. [PMID: 37522044 PMCID: PMC10374508 DOI: 10.1007/s11571-022-09886-z] [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: 12/03/2021] [Revised: 08/04/2022] [Accepted: 09/14/2022] [Indexed: 11/03/2022] Open
Abstract
Behaviour selection has been an active research topic for robotics, in particular in the field of human-robot interaction. For a robot to interact autonomously and effectively with humans, the coupling between techniques for human activity recognition and robot behaviour selection is of paramount importance. However, most approaches to date consist of deterministic associations between the recognised activities and the robot behaviours, neglecting the uncertainty inherent to sequential predictions in real-time applications. In this paper, we address this gap by presenting an initial neurorobotics model that embeds, in a simulated robot, computational models of parts of the mammalian brain that resembles neurophysiological aspects of the basal ganglia-thalamus-cortex (BG-T-C) circuit, coupled with human activity recognition techniques. A robotics simulation environment was developed for assessing the model, where a mobile robot accomplished tasks by using behaviour selection in accordance with the activity being performed by the inhabitant of an intelligent home. Initial results revealed that the initial neurorobotics model is advantageous, especially considering the coupling between the most accurate activity recognition approaches and the computational models of more complex animals.
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Affiliation(s)
- Caetano M. Ranieri
- Institute of Mathematical and Computer Sciences, University of Sao Paulo, Avenida Trabalhador Sao Carlense, 400, Sao Carlos, SP 13566-590 Brazil
| | - Renan C. Moioli
- Bioinformatics Multidisciplinary Environment (BioME), Digital Metropolis Institute, Federal University of Rio Grande do Norte, Avenida Senador Salgado Filho, 3000, Natal, RN 59078-970 Brazil
| | - Patricia A. Vargas
- Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, EH14 4AS Scotland, UK
| | - Roseli A. F. Romero
- Institute of Mathematical and Computer Sciences, University of Sao Paulo, Avenida Trabalhador Sao Carlense, 400, Sao Carlos, SP 13566-590 Brazil
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43
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Alva L, Bernasconi E, Torrecillos F, Fischer P, Averna A, Bange M, Mostofi A, Pogosyan A, Ashkan K, Muthuraman M, Groppa S, Pereira EA, Tan H, Tinkhauser G. Clinical neurophysiological interrogation of motor slowing: A critical step towards tuning adaptive deep brain stimulation. Clin Neurophysiol 2023; 152:43-56. [PMID: 37285747 PMCID: PMC7615935 DOI: 10.1016/j.clinph.2023.04.013] [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: 08/23/2022] [Revised: 03/07/2023] [Accepted: 04/18/2023] [Indexed: 06/09/2023]
Abstract
OBJECTIVE Subthalamic nucleus (STN) beta activity (13-30 Hz) is the most accepted biomarker for adaptive deep brain stimulation (aDBS) for Parkinson's disease (PD). We hypothesize that different frequencies within the beta range may exhibit distinct temporal dynamics and, as a consequence, different relationships to motor slowing and adaptive stimulation patterns. We aim to highlight the need for an objective method to determine the aDBS feedback signal. METHODS STN LFPs were recorded in 15 PD patients at rest and while performing a cued motor task. The impact of beta bursts on motor performance was assessed for different beta candidate frequencies: the individual frequency strongest associated with motor slowing, the individual beta peak frequency, the frequency most modulated by movement execution, as well as the entire-, low- and high beta band. How these candidate frequencies differed in their bursting dynamics and theoretical aDBS stimulation patterns was further investigated. RESULTS The individual motor slowing frequency often differs from the individual beta peak or beta-related movement-modulation frequency. Minimal deviations from a selected target frequency as feedback signal for aDBS leads to a substantial drop in the burst overlapping and in the alignment of the theoretical onset of stimulation triggers (to ∼ 75% for 1 Hz, to ∼ 40% for 3 Hz deviation). CONCLUSIONS Clinical-temporal dynamics within the beta frequency range are highly diverse and deviating from a reference biomarker frequency can result in altered adaptive stimulation patterns. SIGNIFICANCE A clinical-neurophysiological interrogation could be helpful to determine the patient-specific feedback signal for aDBS.
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Affiliation(s)
- Laura Alva
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Elena Bernasconi
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Flavie Torrecillos
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Petra Fischer
- School of Physiology, Pharmacology & Neuroscience, University of Bristol, University Walk, BS8 1TD Bristol, United Kingdom
| | - Alberto Averna
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Manuel Bange
- Movement Disorders and Neurostimulation, Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Abteen Mostofi
- Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's, University of London, London SW17 0RE, United Kingdom
| | - Alek Pogosyan
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Keyoumars Ashkan
- Department of Neurosurgery, King's College Hospital, King's College London, SE59RS, United Kingdom
| | - Muthuraman Muthuraman
- Movement Disorders and Neurostimulation, Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Sergiu Groppa
- Movement Disorders and Neurostimulation, Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Erlick A Pereira
- Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's, University of London, London SW17 0RE, United Kingdom
| | - Huiling Tan
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Gerd Tinkhauser
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland.
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44
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Cho S, Choi JH. A guide towards optimal detection of transient oscillatory bursts with unknown parameters. J Neural Eng 2023; 20:046007. [PMID: 37339619 DOI: 10.1088/1741-2552/acdffd] [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: 09/26/2022] [Accepted: 06/20/2023] [Indexed: 06/22/2023]
Abstract
Objectives. Recent event-based analyses of transient neural activities have characterized the oscillatory bursts as a neural signature that bridges dynamic neural states to cognition and behaviors. Following this insight, our study aimed to (1) compare the efficacy of common burst detection algorithms under varying signal-to-noise ratios and event durations using synthetic signals and (2) establish a strategic guideline for selecting the optimal algorithm for real datasets with undefined properties.Approach.We tested the robustness of burst detection algorithms using a simulation dataset comprising bursts of multiple frequencies. To systematically assess their performance, we used a metric called 'detection confidence', quantifying classification accuracy and temporal precision in a balanced manner. Given that burst properties in empirical data are often unknown in advance, we then proposed a selection rule to identify an optimal algorithm for a given dataset and validated its application on local field potentials of basolateral amygdala recorded from male mice (n=8) exposed to a natural threat.Main Results.Our simulation-based evaluation demonstrated that burst detection is contingent upon event duration, whereas accurately pinpointing burst onsets is more susceptible to noise level. For real data, the algorithm chosen based on the selection rule exhibited superior detection and temporal accuracy, although its statistical significance differed across frequency bands. Notably, the algorithm chosen by human visual screening differed from the one recommended by the rule, implying a potential misalignment between human priors and mathematical assumptions of the algorithms.Significance.Therefore, our findings underscore that the precise detection of transient bursts is fundamentally influenced by the chosen algorithm. The proposed algorithm-selection rule suggests a potentially viable solution, while also emphasizing the inherent limitations originating from algorithmic design and volatile performances across datasets. Consequently, this study cautions against relying solely on heuristic-based approaches, advocating for a careful algorithm selection in burst detection studies.
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Affiliation(s)
- SungJun Cho
- Center for Neuroscience, Korea Institute of Science and Technology, Hwarang-ro 14-gil 5, Seongbuk-gu, Seoul 02792, Republic of Korea
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford OX3 7JX, United Kingdom
| | - Jee Hyun Choi
- Center for Neuroscience, Korea Institute of Science and Technology, Hwarang-ro 14-gil 5, Seongbuk-gu, Seoul 02792, Republic of Korea
- Department of Neural Sciences, University of Science and Technology, 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea
- Department of Physics and Center for Theoretical Physics, Seoul National University, Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
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45
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Bronte-Stewart H, Merola A. Hope vs. Hype: Closed loop technology will provide more meaningful improvement vs. directional leads in deep brain stimulation. Parkinsonism Relat Disord 2023:105452. [PMID: 37355400 DOI: 10.1016/j.parkreldis.2023.105452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 05/20/2023] [Indexed: 06/26/2023]
Affiliation(s)
- Helen Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford Comprehensive Movement Disorders Center, United States.
| | - Aristide Merola
- Center for Parkinson's Disease and Related Movement Disorders, Wexner Medical Center, The Ohio State University, Columbus, United States.
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46
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Torrecillos F, He S, Kühn AA, Tan H. Average power and burst analysis revealed complementary information on drug-related changes of motor performance in Parkinson's disease. NPJ Parkinsons Dis 2023; 9:93. [PMID: 37328511 PMCID: PMC10275865 DOI: 10.1038/s41531-023-00540-x] [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: 09/16/2022] [Accepted: 06/05/2023] [Indexed: 06/18/2023] Open
Abstract
In patients with Parkinson's disease (PD), suppression of beta and increase in gamma oscillations in the subthalamic nucleus (STN) have been associated with both levodopa treatment and motor functions. Recent results suggest that modulation of the temporal dynamics of theses oscillations (bursting activity) might contain more information about pathological states and behaviour than their average power. Here we directly compared the information provided by power and burst analyses about the drug-related changes in STN activities and their impact on motor performance within PD patients. STN local field potential (LFP) signals were recorded from externalized patients performing self-paced movements ON and OFF levodopa. When normalised across medication states, both power and burst analyses showed an increase in low-beta oscillations in the dopamine-depleted state during rest. When normalised within-medication state, both analyses revealed that levodopa increased movement-related modulation in the alpha and low-gamma bands, with higher gamma activity around movement predicting faster reaches. Finally, burst analyses helped to reveal opposite drug-related changes in low- and high-beta frequency bands, and identified additional within-patient relationships between high-beta bursting and movement performance. Our findings suggest that although power and burst analyses share a lot in common they also provide complementary information on how STN-LFP activity is associated with motor performance, and how levodopa treatment may modify these relationships in a way that helps explain drug-related changes in motor performance. Different ways of normalisation in the power analysis can reveal different information. Similarly, the burst analysis is sensitive to how the threshold is defined - either for separate medication conditions separately, or across pooled conditions. In addition, the burst interpretation has far-reaching implications about the nature of neural oscillations - whether the oscillations happen as isolated burst-events or are they sustained phenomena with dynamic amplitude variations? This can be different for different frequency bands, and different for different medication states even for the same frequency band.
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Affiliation(s)
- Flavie Torrecillos
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Shenghong He
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Andrea A Kühn
- Department of Neurology, Charitè, Universitätsmedizin, Berlin, Germany
| | - Huiling Tan
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK.
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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47
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Radcliffe EM, Baumgartner AJ, Kern DS, Al Borno M, Ojemann S, Kramer DR, Thompson JA. Oscillatory beta dynamics inform biomarker-driven treatment optimization for Parkinson's disease. J Neurophysiol 2023; 129:1492-1504. [PMID: 37198135 DOI: 10.1152/jn.00055.2023] [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: 02/03/2023] [Revised: 04/23/2023] [Accepted: 05/17/2023] [Indexed: 05/19/2023] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder characterized by loss of dopaminergic neurons and dysregulation of the basal ganglia. Cardinal motor symptoms include bradykinesia, rigidity, and tremor. Deep brain stimulation (DBS) of select subcortical nuclei is standard of care for medication-refractory PD. Conventional open-loop DBS delivers continuous stimulation with fixed parameters that do not account for a patient's dynamic activity state or medication cycle. In comparison, closed-loop DBS, or adaptive DBS (aDBS), adjusts stimulation based on biomarker feedback that correlates with clinical state. Recent work has identified several neurophysiological biomarkers in local field potential recordings from PD patients, the most promising of which are 1) elevated beta (∼13-30 Hz) power in the subthalamic nucleus (STN), 2) increased beta synchrony throughout basal ganglia-thalamocortical circuits, notably observed as coupling between the STN beta phase and cortical broadband gamma (∼50-200 Hz) amplitude, and 3) prolonged beta bursts in the STN and cortex. In this review, we highlight relevant frequency and time domain features of STN beta measured in PD patients and summarize how spectral beta power, oscillatory beta synchrony, phase-amplitude coupling, and temporal beta bursting inform PD pathology, neurosurgical targeting, and DBS therapy. We then review how STN beta dynamics inform predictive, biomarker-driven aDBS approaches for optimizing PD treatment. We therefore provide clinically useful and actionable insight that can be applied toward aDBS implementation for PD.
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Affiliation(s)
- Erin M Radcliffe
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - Alexander J Baumgartner
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - Drew S Kern
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - Mazen Al Borno
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Computer Science and Engineering, University of Colorado Denver, Denver, Colorado, United States
| | - Steven Ojemann
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - Daniel R Kramer
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - John A Thompson
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
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48
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Karakaş MF, Latifoğlu F. Distinguishing Parkinson's Disease with GLCM Features from the Hankelization of EEG Signals. Diagnostics (Basel) 2023; 13:1769. [PMID: 37238253 PMCID: PMC10216898 DOI: 10.3390/diagnostics13101769] [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: 03/20/2023] [Revised: 04/30/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
This study proposes a novel method that uses electroencephalography (EEG) signals to classify Parkinson's Disease (PD) and demographically matched healthy control groups. The method utilizes the reduced beta activity and amplitude decrease in EEG signals that are associated with PD. The study involved 61 PD patients and 61 demographically matched controls groups, and EEG signals were recorded in various conditions (eyes closed, eyes open, eyes both open and closed, on-drug, off-drug) from three publicly available EEG data sources (New Mexico, Iowa, and Turku). The preprocessed EEG signals were classified using features obtained from gray-level co-occurrence matrix (GLCM) features through the Hankelization of EEG signals. The performance of classifiers with these novel features was evaluated using extensive cross-validations (CV) and leave-one-out cross-validation (LOOCV) schemes. This method under 10 × 10 fold CV, the method was able to differentiate PD groups from healthy control groups using a support vector machine (SVM) with an accuracy of 92.4 ± 0.01, 85.7 ± 0.02, and 77.1 ± 0.06 for New Mexico, Iowa, and Turku datasets, respectively. After a head-to-head comparison with state-of-the-art methods, this study showed an increase in the classification of PD and controls.
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Affiliation(s)
- Mehmet Fatih Karakaş
- Faculty of Engineering and Architecture, Department of Biomedical Engineering, Erzincan Binali Yildirim University, Erzincan 24002, Turkey
| | - Fatma Latifoğlu
- Faculty of Engineering, Department of Biomedical Engineering, Erciyes University, Kayseri 38280, Turkey
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49
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Neumann WJ, Gilron R, Little S, Tinkhauser G. Adaptive Deep Brain Stimulation: From Experimental Evidence Toward Practical Implementation. Mov Disord 2023. [PMID: 37148553 DOI: 10.1002/mds.29415] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 03/27/2023] [Accepted: 04/05/2023] [Indexed: 05/08/2023] Open
Abstract
Closed-loop adaptive deep brain stimulation (aDBS) can deliver individualized therapy at an unprecedented temporal precision for neurological disorders. This has the potential to lead to a breakthrough in neurotechnology, but the translation to clinical practice remains a significant challenge. Via bidirectional implantable brain-computer-interfaces that have become commercially available, aDBS can now sense and selectively modulate pathophysiological brain circuit activity. Pilot studies investigating different aDBS control strategies showed promising results, but the short experimental study designs have not yet supported individualized analyses of patient-specific factors in biomarker and therapeutic response dynamics. Notwithstanding the clear theoretical advantages of a patient-tailored approach, these new stimulation possibilities open a vast and mostly unexplored parameter space, leading to practical hurdles in the implementation and development of clinical trials. Therefore, a thorough understanding of the neurophysiological and neurotechnological aspects related to aDBS is crucial to develop evidence-based treatment regimens for clinical practice. Therapeutic success of aDBS will depend on the integrated development of strategies for feedback signal identification, artifact mitigation, signal processing, and control policy adjustment, for precise stimulation delivery tailored to individual patients. The present review introduces the reader to the neurophysiological foundation of aDBS for Parkinson's disease (PD) and other network disorders, explains currently available aDBS control policies, and highlights practical pitfalls and difficulties to be addressed in the upcoming years. Finally, it highlights the importance of interdisciplinary clinical neurotechnological research within and across DBS centers, toward an individualized patient-centered approach to invasive brain stimulation. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | | | - Simon Little
- Movement Disorders and Neuromodulation Centre, University of California San Francisco, San Francisco, California, USA
| | - Gerd Tinkhauser
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
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50
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Wang S, Zhu G, Shi L, Zhang C, Wu B, Yang A, Meng F, Jiang Y, Zhang J. Closed-Loop Adaptive Deep Brain Stimulation in Parkinson's Disease: Procedures to Achieve It and Future Perspectives. JOURNAL OF PARKINSON'S DISEASE 2023:JPD225053. [PMID: 37182899 DOI: 10.3233/jpd-225053] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Parkinson's disease (PD) is a neurodegenerative disease with a heavy burden on patients, families, and society. Deep brain stimulation (DBS) can improve the symptoms of PD patients for whom medication is insufficient. However, current open-loop uninterrupted conventional DBS (cDBS) has inherent limitations, such as adverse effects, rapid battery consumption, and a need for frequent parameter adjustment. To overcome these shortcomings, adaptive DBS (aDBS) was proposed to provide responsive optimized stimulation for PD. This topic has attracted scientific interest, and a growing body of preclinical and clinical evidence has shown its benefits. However, both achievements and challenges have emerged in this novel field. To date, only limited reviews comprehensively analyzed the full framework and procedures for aDBS implementation. Herein, we review current preclinical and clinical data on aDBS for PD to discuss the full procedures for its achievement and to provide future perspectives on this treatment.
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Affiliation(s)
- Shu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Guanyu Zhu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lin Shi
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunkui Zhang
- Center of Cognition and Brain Science, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Bing Wu
- Center of Cognition and Brain Science, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Anchao Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fangang Meng
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Yin Jiang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
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