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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] [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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2
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Abdulbaki A, Doll T, Helgers S, Heissler HE, Voges J, Krauss JK, Schwabe K, Alam M. Subthalamic Nucleus Deep Brain Stimulation Restores Motor and Sensorimotor Cortical Neuronal Oscillatory Activity in the Free-Moving 6-Hydroxydopamine Lesion Rat Parkinson Model. Neuromodulation 2024; 27:489-499. [PMID: 37002052 DOI: 10.1016/j.neurom.2023.01.014] [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: 08/10/2022] [Revised: 12/28/2022] [Accepted: 01/04/2023] [Indexed: 03/31/2023]
Abstract
OBJECTIVES Enhanced beta oscillations in cortical-basal ganglia (BG) thalamic circuitries have been linked to clinical symptoms of Parkinson's disease. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) reduces beta band activity in BG regions, whereas little is known about activity in cortical regions. In this study, we investigated the effect of STN DBS on the spectral power of oscillatory activity in the motor cortex (MCtx) and sensorimotor cortex (SMCtx) by recording via an electrocorticogram (ECoG) array in free-moving 6-hydroxydopamine (6-OHDA) lesioned rats and sham-lesioned controls. MATERIALS AND METHODS Male Sprague-Dawley rats (250-350 g) were injected either with 6-OHDA or with saline in the right medial forebrain bundle, under general anesthesia. A stimulation electrode was then implanted in the ipsilateral STN, and an ECoG array was placed subdurally above the MCtx and SMCtx areas. Six days after the second surgery, the free-moving rats were individually recorded in three conditions: 1) basal activity, 2) during STN DBS, and 3) directly after STN DBS. RESULTS In 6-OHDA-lesioned rats (N = 8), the relative power of theta band activity was reduced, whereas activity of broad-range beta band (12-30 Hz) along with two different subbeta bands, that is, low (12-30 Hz) and high (20-30 Hz) beta band and gamma band, was higher in MCtx and SMCtx than in sham-lesioned controls (N = 7). This was, to some extent, reverted toward control level by STN DBS during and after stimulation. No major differences were found between contacts of the electrode grid or between MCtx and SMCtx. CONCLUSION Loss of nigrostriatal dopamine leads to abnormal oscillatory activity in both MCtx and SMCtx, which is compensated by STN stimulation, suggesting that parkinsonism-related oscillations in the cortex and BG are linked through their anatomic connections.
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Affiliation(s)
- Arif Abdulbaki
- Hannover Medical School, Department of Neurosurgery, Hannover, Germany.
| | - Theodor Doll
- Fraunhofer Institute for Toxicology and Experimental Medicine, Hannover, Germany
| | - Simeon Helgers
- Hannover Medical School, Department of Neurosurgery, Hannover, Germany
| | - Hans E Heissler
- Hannover Medical School, Department of Neurosurgery, Hannover, Germany
| | - Jürgen Voges
- Department of Stereotactic Neurosurgery, University Hospital Magdeburg, Magdeburg, Germany
| | - Joachim K Krauss
- Hannover Medical School, Department of Neurosurgery, Hannover, Germany
| | - Kerstin Schwabe
- Hannover Medical School, Department of Neurosurgery, Hannover, Germany
| | - Mesbah Alam
- Hannover Medical School, Department of Neurosurgery, Hannover, Germany
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3
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Venkadesh S, Shaikh A, Shakeri H, Barreto E, Van Horn JD. Biophysical modulation and robustness of itinerant complexity in neuronal networks. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1302499. [PMID: 38516614 PMCID: PMC10954887 DOI: 10.3389/fnetp.2024.1302499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 02/26/2024] [Indexed: 03/23/2024]
Abstract
Transient synchronization of bursting activity in neuronal networks, which occurs in patterns of metastable itinerant phase relationships between neurons, is a notable feature of network dynamics observed in vivo. However, the mechanisms that contribute to this dynamical complexity in neuronal circuits are not well understood. Local circuits in cortical regions consist of populations of neurons with diverse intrinsic oscillatory features. In this study, we numerically show that the phenomenon of transient synchronization, also referred to as metastability, can emerge in an inhibitory neuronal population when the neurons' intrinsic fast-spiking dynamics are appropriately modulated by slower inputs from an excitatory neuronal population. Using a compact model of a mesoscopic-scale network consisting of excitatory pyramidal and inhibitory fast-spiking neurons, our work demonstrates a relationship between the frequency of pyramidal population oscillations and the features of emergent metastability in the inhibitory population. In addition, we introduce a method to characterize collective transitions in metastable networks. Finally, we discuss potential applications of this study in mechanistically understanding cortical network dynamics.
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Affiliation(s)
- Siva Venkadesh
- Department of Psychology, University of Virginia, Charlottesville, VA, United States
| | - Asmir Shaikh
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States
| | - Heman Shakeri
- School of Data Science, University of Virginia, Charlottesville, VA, United States
- Biomedical Engineering, University of Virginia, Charlottesville, VA, United States
| | - Ernest Barreto
- Department of Physics and Astronomy and the Interdisciplinary Program in Neuroscience, George Mason University, Fairfax, VA, United States
| | - John Darrell Van Horn
- Department of Psychology, University of Virginia, Charlottesville, VA, United States
- School of Data Science, University of Virginia, Charlottesville, VA, United States
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4
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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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5
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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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6
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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: 6] [Impact Index Per Article: 6.0] [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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7
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Su F, Wang H, Zu L, Chen Y. Closed-loop modulation of model parkinsonian beta oscillations based on CAR-fuzzy control algorithm. Cogn Neurodyn 2023; 17:1185-1199. [PMID: 37786652 PMCID: PMC10542090 DOI: 10.1007/s11571-022-09820-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 04/20/2022] [Accepted: 04/28/2022] [Indexed: 12/01/2022] Open
Abstract
Closed-loop deep brain stimulation (DBS) can apply on-demand stimulation based on the feedback signal (e.g. beta band oscillation), which is deemed to lower side effects of clinically used open-loop DBS. To facilitate the application of model-based closed-loop DBS in clinical, studies must consider state variations, e.g., variation of desired signal with different movement conditions and variation of model parameters with time. This paper proposes to use the controlled autoregressive (CAR)-fuzzy control algorithm to modulate the pathological beta band (13-35 Hz) oscillation of a basal ganglia-cortex-thalamus model. The CAR model is used to identify the relationship between DBS frequency parameter and beta oscillation power. Then the error between the one-step-ahead predicted beta power of CAR model and the desired value is innovatively used as the input of fuzzy controller to calculate the next step stimulation frequency. Compared with 130 Hz open-loop DBS, the proposed closed-loop DBS method could lower the mean stimulation frequency to 74.04 Hz with similar beta oscillation suppression performance. The Mamdani fuzzy controller is selected because which could establish fuzzy controller rules according to human operation experience. Adding prediction module to closed-loop control improves the accuracy of fuzzy control, compared with proportional-integral control and fuzzy control, the proposed CAR-fuzzy control algorithm has higher tracking reliability, response speed and robustness.
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Affiliation(s)
- Fei Su
- School of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, 271018 China
| | - Hong Wang
- School of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, 271018 China
| | - Linlu Zu
- School of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, 271018 China
| | - Yan Chen
- Department of Neurology, Shanghai Jiahui International Hospital, Shanghai, 200233 China
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8
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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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9
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Mirzac D, Kreis SL, Luhmann HJ, Gonzalez-Escamilla G, Groppa S. Translating Pathological Brain Activity Primers in Parkinson's Disease Research. RESEARCH (WASHINGTON, D.C.) 2023; 6:0183. [PMID: 37383218 PMCID: PMC10298229 DOI: 10.34133/research.0183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 06/02/2023] [Indexed: 06/30/2023]
Abstract
Translational experimental approaches that help us better trace Parkinson's disease (PD) pathophysiological mechanisms leading to new therapeutic targets are urgently needed. In this article, we review recent experimental and clinical studies addressing abnormal neuronal activity and pathological network oscillations, as well as their underlying mechanisms and modulation. Our aim is to enhance our knowledge about the progression of Parkinson's disease pathology and the timing of its symptom's manifestation. Here, we present mechanistic insights relevant for the generation of aberrant oscillatory activity within the cortico-basal ganglia circuits. We summarize recent achievements extrapolated from available PD animal models, discuss their advantages and limitations, debate on their differential applicability, and suggest approaches for transferring knowledge on disease pathology into future research and clinical applications.
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Affiliation(s)
- Daniela Mirzac
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience, Rhine Main Neuroscience Network, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Svenja L. Kreis
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Heiko J. Luhmann
- Institute of Physiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Gabriel Gonzalez-Escamilla
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience, Rhine Main Neuroscience Network, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Sergiu Groppa
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience, Rhine Main Neuroscience Network, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
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10
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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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11
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Darmani G, Drummond NM, Ramezanpour H, Saha U, Hoque T, Udupa K, Sarica C, Zeng K, Cortez Grippe T, Nankoo JF, Bergmann TO, Hodaie M, Kalia SK, Lozano AM, Hutchison WD, Fasano A, Chen R. Long-Term Recording of Subthalamic Aperiodic Activities and Beta Bursts in Parkinson's Disease. Mov Disord 2023; 38:232-243. [PMID: 36424835 DOI: 10.1002/mds.29276] [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: 07/02/2022] [Revised: 10/19/2022] [Accepted: 10/25/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Local field potentials (LFPs) represent the summation of periodic (oscillations) and aperiodic (fractal) signals. Although previous studies showed changes in beta band oscillations and burst characteristics of the subthalamic nucleus (STN) in Parkinson's disease (PD), how aperiodic activity in the STN is related to PD pathophysiology is unknown. OBJECTIVES The study aimed to characterize the long-term effects of STN-deep brain stimulation (DBS) and dopaminergic medications on aperiodic activities and beta bursts. METHODS A total of 10 patients with PD participated in this longitudinal study. Simultaneous bilateral STN-LFP recordings were conducted in six separate visits during a period of 18 months using the Activa PC + S device in the off and on dopaminergic medication states. We used irregular-resampling auto-spectral analysis to separate oscillations and aperiodic components (exponent and offset) in the power spectrum of STN-LFP signals in beta band. RESULTS Our results revealed a systematic increase in both the exponent and the offset of the aperiodic spectrum over 18 months following the DBS implantation, independent of the dopaminergic medication state of patients with PD. In contrast, beta burst durations and amplitudes were stable over time and were suppressed by dopaminergic medications. CONCLUSIONS These findings indicate that oscillations and aperiodic activities reflect at least partially distinct yet complementary neural mechanisms, which should be considered in the design of robust biomarkers to optimize adaptive DBS. Given the link between increased gamma-aminobutyric acidergic (GABAergic) transmission and higher aperiodic activity, our findings suggest that long-term STN-DBS may relate to increased inhibition in the basal ganglia. © 2022 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Ghazaleh Darmani
- Krembil Research Institute, University Health Network, Toronto, Canada
| | - Neil M Drummond
- Krembil Research Institute, University Health Network, Toronto, Canada
| | | | - Utpal Saha
- Krembil Research Institute, University Health Network, Toronto, Canada
| | - Tasnuva Hoque
- Krembil Research Institute, University Health Network, Toronto, Canada
| | - Kaviraja Udupa
- Department of Neurophysiology, National Institute of Mental Health & Neurosciences, Bengaluru, India
| | - Can Sarica
- Krembil Research Institute, University Health Network, Toronto, Canada
| | - Ke Zeng
- Krembil Research Institute, University Health Network, Toronto, Canada
| | | | | | - Til Ole Bergmann
- Neuroimaging Center, Johannes Gutenberg University Medical Center, Mainz, Germany
- Leibniz Institute for Resilience Research, Mainz, Germany
| | - Mojgan Hodaie
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
| | - Suneil K Kalia
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
| | - Andres M Lozano
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
| | - William D Hutchison
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Canada
| | - Alfonso Fasano
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, Canada
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Toronto, Canada
| | - Robert Chen
- Krembil Research Institute, University Health Network, Toronto, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, Canada
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Toronto, Canada
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12
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West TO, Duchet B, Farmer SF, Friston KJ, Cagnan H. When do bursts matter in the primary motor cortex? Investigating changes in the intermittencies of beta rhythms associated with movement states. Prog Neurobiol 2023; 221:102397. [PMID: 36565984 PMCID: PMC7614511 DOI: 10.1016/j.pneurobio.2022.102397] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/04/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Brain activity exhibits significant temporal structure that is not well captured in the power spectrum. Recently, attention has shifted to characterising the properties of intermittencies in rhythmic neural activity (i.e. bursts), yet the mechanisms that regulate them are unknown. Here, we present evidence from electrocorticography recordings made over the motor cortex to show that the statistics of bursts, such as duration or amplitude, in the beta frequency (14-30 Hz) band, significantly aid the classification of motor states such as rest, movement preparation, execution, and imagery. These features reflect nonlinearities not detectable in the power spectrum, with states increasing in nonlinearity from movement execution to preparation to rest. Further, we show using a computational model of the cortical microcircuit, constrained to account for burst features, that modulations of laminar specific inhibitory interneurons are responsible for the temporal organisation of activity. Finally, we show that the temporal characteristics of spontaneous activity can be used to infer the balance of cortical integration between incoming sensory information and endogenous activity. Critically, we contribute to the understanding of how transient brain rhythms may underwrite cortical processing, which in turn, could inform novel approaches for brain state classification, and modulation with novel brain-computer interfaces.
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Affiliation(s)
- Timothy O West
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK; Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.
| | - Benoit Duchet
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
| | - Simon F Farmer
- Department of Neurology, National Hospital for Neurology & Neurosurgery, Queen Square, London WC1N 3BG, UK; Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Hayriye Cagnan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK; Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
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13
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Wu D, Zhao B, Xie H, Xu Y, Yin Z, Bai Y, Fan H, Zhang Q, Liu D, Hu T, Jiang Y, An Q, Zhang X, Yang A, Zhang J. Profiling the low-beta characteristics of the subthalamic nucleus in early- and late-onset Parkinson's disease. Front Aging Neurosci 2023; 15:1114466. [PMID: 36875708 PMCID: PMC9978704 DOI: 10.3389/fnagi.2023.1114466] [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: 12/02/2022] [Accepted: 01/30/2023] [Indexed: 02/18/2023] Open
Abstract
Objectives Low-beta oscillation (13-20 Hz) has rarely been studied in patients with early-onset Parkinson's disease (EOPD, age of onset ≤50 years). We aimed to explore the characteristics of low-beta oscillation in the subthalamic nucleus (STN) of patients with EOPD and investigate the differences between EOPD and late-onset Parkinson's disease (LOPD). Methods We enrolled 31 EOPD and 31 LOPD patients, who were matched using propensity score matching. Patients underwent bilateral STN deep brain stimulation (DBS). Local field potentials were recorded using intraoperative microelectrode recording. We analyzed the low-beta band parameters, including aperiodic/periodic components, beta burst, and phase-amplitude coupling. We compared low-beta band activity between EOPD and LOPD. Correlation analyses were performed between the low-beta parameters and clinical assessment results for each group. Results We found that the EOPD group had lower aperiodic parameters, including offset (p = 0.010) and exponent (p = 0.047). Low-beta burst analysis showed that EOPD patients had significantly higher average burst amplitude (p = 0.016) and longer average burst duration (p = 0.011). Furthermore, EOPD had higher proportion of long burst (500-650 ms, p = 0.008), while LOPD had higher proportion of short burst (200-350 ms, p = 0.007). There was a significant difference in phase-amplitude coupling values between low-beta phase and fast high frequency oscillation (300-460 Hz) amplitude (p = 0.019). Conclusion We found that low-beta activity in the STN of patients with EOPD had characteristics that varied when compared with LOPD, and provided electrophysiological evidence for different pathological mechanisms between the two types of PD. These differences need to be considered when applying adaptive DBS on patients of different ages.
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Affiliation(s)
- Delong Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hutao Xie
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yichen Xu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zixiao Yin
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yutong Bai
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing, China.,Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Houyou Fan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Quan Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Defeng Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tianqi Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yin Jiang
- Beijing Key Laboratory of Neurostimulation, Beijing, China.,Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Qi An
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xin Zhang
- Beijing Key Laboratory of Neurostimulation, Beijing, China.,Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Anchao Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing, China.,Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing, China.,Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
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14
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Bove F, Genovese D, Moro E. Developments in the mechanistic understanding and clinical application of deep brain stimulation for Parkinson's disease. Expert Rev Neurother 2022; 22:789-803. [PMID: 36228575 DOI: 10.1080/14737175.2022.2136030] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION. Deep brain stimulation (DBS) is a life-changing treatment for patients with Parkinson's disease (PD) and gives the unique opportunity to directly explore how basal ganglia work. Despite the rapid technological innovation of the last years, the untapped potential of DBS is still high. AREAS COVERED. This review summarizes the developments in the mechanistic understanding of DBS and the potential clinical applications of cutting-edge technological advances. Rather than a univocal local mechanism, DBS exerts its therapeutic effects through several multimodal mechanisms and involving both local and network-wide structures, although crucial questions remain unexplained. Nonetheless, new insights in mechanistic understanding of DBS in PD have provided solid bases for advances in preoperative selection phase, prediction of motor and non-motor outcomes, leads placement and postoperative stimulation programming. EXPERT OPINION. DBS has not only strong evidence of clinical effectiveness in PD treatment, but technological advancements are revamping its role of neuromodulation of brain circuits and key to better understanding PD pathophysiology. In the next few years, the worldwide use of new technologies in clinical practice will provide large data to elucidate their role and to expand their applications for PD patients, providing useful insights to personalize DBS treatment and follow-up.
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Affiliation(s)
- Francesco Bove
- Neurology Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Danilo Genovese
- Fresco Institute for Parkinson's and Movement Disorders, Department of Neurology, New York University School of Medicine, New York, New York, USA
| | - Elena Moro
- Grenoble Alpes University, CHU of Grenoble, Division of Neurology, Grenoble, France.,Grenoble Institute of Neurosciences, INSERM, U1216, Grenoble, France
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15
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Mizrahi-Kliger AD, Kaplan A, Israel Z, Bergman H. Entrainment to sleep spindles reflects dissociable patterns of connectivity between cortex and basal ganglia. Cell Rep 2022; 40:111367. [PMID: 36130495 DOI: 10.1016/j.celrep.2022.111367] [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: 03/01/2022] [Revised: 07/20/2022] [Accepted: 08/25/2022] [Indexed: 11/18/2022] Open
Abstract
Sleep spindles are crucial for learning in the cortex and basal ganglia (BG) because they facilitate the reactivation of previously active neuronal ensembles. Studying field potentials (FPs) and spiking in the cortex and BG during sleep in non-human primates following pre-sleep learning, we show that FP sleep spindles are widespread in the BG and are similar to cortical spindles in morphology, spectral content, and response to the pre-sleep task. Further, BG spindles are concordant with electroencephalogram (EEG) spindles and associated with increased cortico-BG correlation. However, spindles across the BG differ markedly in their entrainment of local spiking. The spiking activity of striatal projection neurons exhibits consistent phase locking to striatal and EEG spindles, producing phase windows of peaked cross-region spindling. In contrast, firing in other BG nuclei is not entrained to either local or EEG sleep spindles. These results suggest corticostriatal synapses as the main hub for offline cortico-BG communication.
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Affiliation(s)
- Aviv D Mizrahi-Kliger
- Department of Neurobiology, Institute of Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University of Jerusalem, 9112001 Jerusalem, Israel.
| | - Alexander Kaplan
- Department of Neurobiology, Institute of Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University of Jerusalem, 9112001 Jerusalem, Israel; The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, 9190401 Jerusalem, Israel
| | - Zvi Israel
- Department of Neurosurgery, Hadassah University Hospital, 9112001 Jerusalem, Israel
| | - Hagai Bergman
- Department of Neurobiology, Institute of Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University of Jerusalem, 9112001 Jerusalem, Israel; The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, 9190401 Jerusalem, Israel; Department of Neurosurgery, Hadassah University Hospital, 9112001 Jerusalem, Israel
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16
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Darcy N, Lofredi R, Al-Fatly B, Neumann WJ, Hübl J, Brücke C, Krause P, Schneider GH, Kühn A. Spectral and spatial distribution of subthalamic beta peak activity in Parkinson's disease patients. Exp Neurol 2022; 356:114150. [PMID: 35732220 DOI: 10.1016/j.expneurol.2022.114150] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 05/27/2022] [Accepted: 06/15/2022] [Indexed: 11/17/2022]
Abstract
Current efforts to optimize subthalamic deep brain stimulation in Parkinson's disease patients aim to harness local oscillatory activity in the beta frequency range (13-35 Hz) as a feedback-signal for demand-based adaptive stimulation paradigms. A high prevalence of beta peak activity is prerequisite for this approach to become routine clinical practice. In a large dataset of postoperative rest recordings from 106 patients we quantified occurrence and identified determinants of spectral peaks in the alpha, low and high beta bands. At least one peak in beta band occurred in 92% of patients and 84% of hemispheres off medication, irrespective of demographic parameters, clinical subtype or motor symptom severity. Distance to previously described clinical sweet spot was significantly related both to beta peak occurrence and to spectral power (rho -0.21, p 0.006), particularly in the high beta band. Electrophysiological landscapes of our cohort's dataset in normalised space showed divergent heatmaps for alpha and beta but found similar regions for low and high beta frequency bands. We discuss potential ramifications for clinicians' programming decisions. In summary, this report provides robust evidence that spectral peaks in beta frequency range can be detected in the vast majority of Parkinsonian subthalamic nuclei, increasing confidence in the broad applicability of beta-guided deep brain stimulation.
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Affiliation(s)
- Natasha Darcy
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health (BIH), Berlin, Germany.
| | - Roxanne Lofredi
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health (BIH), Berlin, Germany
| | - Bassam Al-Fatly
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Wolf-Julian Neumann
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Humboldt-Universität, Berlin, Germany
| | - Julius Hübl
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christof Brücke
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Patricia Krause
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Gerd-Helge Schneider
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Andrea Kühn
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Humboldt-Universität, Berlin, Germany; NeuroCure, Exzellenzcluster, Charité - Universitätsmedizin Berlin, Berlin, Germany; DZNE, German center for neurodegenerative diseases, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Germany
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17
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Rauschenberger L, Güttler C, Volkmann J, Kühn AA, Ip CW, Lofredi R. A translational perspective on pathophysiological changes of oscillatory activity in dystonia and parkinsonism. Exp Neurol 2022; 355:114140. [PMID: 35690132 DOI: 10.1016/j.expneurol.2022.114140] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/14/2022] [Accepted: 06/03/2022] [Indexed: 11/19/2022]
Abstract
Intracerebral recordings from movement disorders patients undergoing deep brain stimulation have allowed the identification of pathophysiological patterns in oscillatory activity that correlate with symptom severity. Changes in oscillatory synchrony occur within and across brain areas, matching the classification of movement disorders as network disorders. However, the underlying mechanisms of oscillatory changes are difficult to assess in patients, as experimental interventions are technically limited and ethically problematic. This is why animal models play an important role in neurophysiological research of movement disorders. In this review, we highlight the contributions of translational research to the mechanistic understanding of pathological changes in oscillatory activity, with a focus on parkinsonism and dystonia, while addressing the limitations of current findings and proposing possible future directions.
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Affiliation(s)
- Lisa Rauschenberger
- Department of Neurology, University Hospital of Würzburg, Josef-Schneider-Straße 11, 97080 Würzburg, Germany
| | - Christopher Güttler
- Department of Neurology, Movement Disorders and Neuromodulation Unit, Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jens Volkmann
- Department of Neurology, University Hospital of Würzburg, Josef-Schneider-Straße 11, 97080 Würzburg, Germany
| | - Andrea A Kühn
- Department of Neurology, Movement Disorders and Neuromodulation Unit, Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Humboldt-Universität, Berlin, Germany; NeuroCure, Exzellenzcluster, Charité-Universitätsmedizin Berlin, Berlin, Germany; DZNE, German Center for Neurodegenerative Diseases, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Chi Wang Ip
- Department of Neurology, University Hospital of Würzburg, Josef-Schneider-Straße 11, 97080 Würzburg, Germany
| | - Roxanne Lofredi
- Department of Neurology, Movement Disorders and Neuromodulation Unit, Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health (BIH), Berlin, Germany.
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18
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Fim Neto A, de Luccas JB, Bianqueti BL, da Silva LR, Almeida TP, Takahata AK, Teixeira MJ, Figueiredo EG, Nasuto SJ, Rocha MSG, Soriano DC, Godinho F. Subthalamic low beta bursts differ in Parkinson's disease phenotypes. Clin Neurophysiol 2022; 140:45-58. [PMID: 35728405 DOI: 10.1016/j.clinph.2022.05.013] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 05/20/2022] [Accepted: 05/26/2022] [Indexed: 11/24/2022]
Abstract
OBJECTIVE Parkinson's disease (PD) patients may be categorized into tremor-dominant (TD) and postural-instability and gait disorder (PIGD) motor phenotypes, but the dynamical aspects of subthalamic nucleus local field potentials (STN-LFP) and the neural correlates of this phenotypical classification remain unclear. METHODS 35 STN-LFP (20 PIGD and 15 TD) were investigated through continuous wavelet transform and machine-learning-based methods. The beta oscillation - the main band associated with motor impairment in PD - dynamics was characterized through beta burst parameters across phenotypes and burst intervals under specific proposed criteria for optimal burst threshold definition. RESULTS Low-frequency (13-22 Hz) beta burst probability was the best predictor for PD phenotypes (75% accuracy). PIGD patients presented higher average burst duration (p = 0.018), while TD patients exhibited higher burst probability (p = 0.014). Categorization into shorter and longer than 400 ms bursts led to significant interaction between burst length categories and the phenotypes (p < 0.050) as revealed by mixed-effects models. Long burst durations and short bursts probability positively correlated, respectively, with rigidity-bradykinesia (p = 0.029) and tremor (p = 0.038) scores. CONCLUSIONS Subthalamic low-frequency beta bursts differed between TD and PIGD phenotypes and correlated with motor symptoms. SIGNIFICANCE These findings improve the PD phenotypes' electrophysiological characterization and may define new criteria for adaptive deep brain stimulation.
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Affiliation(s)
- Arnaldo Fim Neto
- Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo, Brazil; Brazilian Institute of Neuroscience and Neurotechnology, Campinas, São Paulo, Brazil; Department of Cosmic Rays and Chronology, Institute of Physics, University of Campinas, Campinas, Brazil.
| | - Julia Baldi de Luccas
- Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo, Brazil; Brazilian Institute of Neuroscience and Neurotechnology, Campinas, São Paulo, Brazil
| | - Bruno Leonardo Bianqueti
- Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo, Brazil; Brazilian Institute of Neuroscience and Neurotechnology, Campinas, São Paulo, Brazil
| | - Luiz Ricardo da Silva
- Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Tiago Paggi Almeida
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - André Kazuo Takahata
- Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo, Brazil; Brazilian Institute of Neuroscience and Neurotechnology, Campinas, São Paulo, Brazil
| | | | | | | | | | - Diogo Coutinho Soriano
- Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo, Brazil; Brazilian Institute of Neuroscience and Neurotechnology, Campinas, São Paulo, Brazil
| | - Fabio Godinho
- Center for Engineering, Modeling and Applied Social Sciences, Federal University of ABC, São Bernardo do Campo, Brazil; Department of Functional Neurosurgery, Santa Marcelina Hospital, São Paulo, São Paulo, Brazil; Division of Functional Neurosurgery of Institute of Psychiatry, Department of Neurology, Medical School, University of São Paulo, São Paulo, São Paulo, Brazil
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19
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Wiest C, Torrecillos F, Tinkhauser G, Pogosyan A, Morgante F, Pereira EA, Tan H. Finely-tuned gamma oscillations: Spectral characteristics and links to dyskinesia. Exp Neurol 2022; 351:113999. [PMID: 35143832 PMCID: PMC7612436 DOI: 10.1016/j.expneurol.2022.113999] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 01/27/2022] [Accepted: 02/02/2022] [Indexed: 01/22/2023]
Abstract
Gamma oscillations comprise a loosely defined, heterogeneous group of functionally different activities between 30 and 100 Hz in the cortical and subcortical local field potential (LFP) of the motor network. Two distinct patterns seem to emerge which are easily conflated: Finely-tuned gamma (FTG) oscillations - a narrowband activity with peaks between 60 and 90 Hz - have been observed in multiple movement disorders and are induced by dopaminergic medication or deep brain stimulation (DBS). FTG has been linked with levodopa or DBS-induced dyskinesias, which makes it a putative biomarker for adaptive DBS. On the other hand, gamma activity can also present as a broad phenomenon (30-100 Hz) in the context of motor activation and dynamic processing. Here, we contrast FTG, either levodopa-induced or DBS-induced, from movement-related broadband gamma synchronisation and further elaborate on the functional role of FTG and its potential implications for adaptive DBS. Given the unclear distinction of FTG and broad gamma in literature, we appeal for more careful separation of the two. To better characterise cortical and subcortical FTG as biomarkers for dyskinesia, their sensitivity and specificity need to be investigated in a large clinical trial.
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Affiliation(s)
- C Wiest
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - F Torrecillos
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - G Tinkhauser
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - A Pogosyan
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - F Morgante
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London, UK; Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - E A Pereira
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London, UK
| | - H Tan
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
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20
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Khawaldeh S, Tinkhauser G, Torrecillos F, He S, Foltynie T, Limousin P, Zrinzo L, Oswal A, Quinn AJ, Vidaurre D, Tan H, Litvak V, Kühn A, Woolrich M, Brown P. Balance between competing spectral states in subthalamic nucleus is linked to motor impairment in Parkinson's disease. Brain 2022; 145:237-250. [PMID: 34264308 PMCID: PMC8967096 DOI: 10.1093/brain/awab264] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 06/11/2021] [Accepted: 07/04/2021] [Indexed: 11/14/2022] Open
Abstract
Exaggerated local field potential bursts of activity at frequencies in the low beta band are a well-established phenomenon in the subthalamic nucleus of patients with Parkinson's disease. However, such activity is only moderately correlated with motor impairment. Here we test the hypothesis that beta bursts are just one of several dynamic states in the subthalamic nucleus local field potential in Parkinson's disease, and that together these different states predict motor impairment with high fidelity. Local field potentials were recorded in 32 patients (64 hemispheres) undergoing deep brain stimulation surgery targeting the subthalamic nucleus. Recordings were performed following overnight withdrawal of anti-parkinsonian medication, and after administration of levodopa. Local field potentials were analysed using hidden Markov modelling to identify transient spectral states with frequencies under 40 Hz. Findings in the low beta frequency band were similar to those previously reported; levodopa reduced occurrence rate and duration of low beta states, and the greater the reductions, the greater the improvement in motor impairment. However, additional local field potential states were distinguished in the theta, alpha and high beta bands, and these behaved in an opposite manner. They were increased in occurrence rate and duration by levodopa, and the greater the increases, the greater the improvement in motor impairment. In addition, levodopa favoured the transition of low beta states to other spectral states. When all local field potential states and corresponding features were considered in a multivariate model it was possible to predict 50% of the variance in patients' hemibody impairment OFF medication, and in the change in hemibody impairment following levodopa. This only improved slightly if signal amplitude or gamma band features were also included in the multivariate model. In addition, it compares with a prediction of only 16% of the variance when using beta bursts alone. We conclude that multiple spectral states in the subthalamic nucleus local field potential have a bearing on motor impairment, and that levodopa-induced shifts in the balance between these states can predict clinical change with high fidelity. This is important in suggesting that some states might be upregulated to improve parkinsonism and in suggesting how local field potential feedback can be made more informative in closed-loop deep brain stimulation systems.
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Affiliation(s)
- Saed Khawaldeh
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 7JX, UK
| | - Gerd Tinkhauser
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
- Department of Neurology, Bern University Hospital and University of Bern, 3010 Bern, Switzerland
| | - Flavie Torrecillos
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Shenghong He
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Thomas Foltynie
- Unit of Functional Neurosurgery, Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, London WC1B 5EH, UK
| | - Patricia Limousin
- Unit of Functional Neurosurgery, Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, London WC1B 5EH, UK
| | - Ludvic Zrinzo
- Unit of Functional Neurosurgery, Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, London WC1B 5EH, UK
| | - Ashwini Oswal
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London WC1N 3AR, UK
| | - Andrew J Quinn
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 7JX, UK
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 7JX, UK
- Department of Clinical Health, Aarhus University, DK-8200 Aarhus, Denmark
| | - Huiling Tan
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Vladimir Litvak
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London WC1N 3AR, UK
| | - Andrea Kühn
- Department of Neurology, Charitè—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Mark Woolrich
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 7JX, UK
| | - Peter Brown
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
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21
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Stimulating at the right time to recover network states in a model of the cortico-basal ganglia-thalamic circuit. PLoS Comput Biol 2022; 18:e1009887. [PMID: 35245281 PMCID: PMC8939795 DOI: 10.1371/journal.pcbi.1009887] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 03/22/2022] [Accepted: 01/31/2022] [Indexed: 11/26/2022] Open
Abstract
Synchronization of neural oscillations is thought to facilitate communication in the brain. Neurodegenerative pathologies such as Parkinson’s disease (PD) can result in synaptic reorganization of the motor circuit, leading to altered neuronal dynamics and impaired neural communication. Treatments for PD aim to restore network function via pharmacological means such as dopamine replacement, or by suppressing pathological oscillations with deep brain stimulation. We tested the hypothesis that brain stimulation can operate beyond a simple “reversible lesion” effect to augment network communication. Specifically, we examined the modulation of beta band (14–30 Hz) activity, a known biomarker of motor deficits and potential control signal for stimulation in Parkinson’s. To do this we setup a neural mass model of population activity within the cortico-basal ganglia-thalamic (CBGT) circuit with parameters that were constrained to yield spectral features comparable to those in experimental Parkinsonism. We modulated the connectivity of two major pathways known to be disrupted in PD and constructed statistical summaries of the spectra and functional connectivity of the resulting spontaneous activity. These were then used to assess the network-wide outcomes of closed-loop stimulation delivered to motor cortex and phase locked to subthalamic beta activity. Our results demonstrate that the spatial pattern of beta synchrony is dependent upon the strength of inputs to the STN. Precisely timed stimulation has the capacity to recover network states, with stimulation phase inducing activity with distinct spectral and spatial properties. These results provide a theoretical basis for the design of the next-generation brain stimulators that aim to restore neural communication in disease. Diseases of the brain lead to a wide range of disabling symptoms for patients, by affecting their ability to move or think properly. These symptoms arise from disruption to both the organization of networks in the brain, but also the timing of neural activity that propagates around it. Treatments for disease with drugs can restore the organization of these networks to some extent, yet it is very difficult to deliver drugs with good spatial or temporal selectivity. Brain stimulation provides one way in which to improve the spatial specificity of treatment, yet understanding how to stimulate at the right time to achieve the best outcome for patients, remains an outstanding question. In this work we use simulations of an important circuit involved in Parkinson’s disease that has parameters chosen to reflect recordings made in animal models of the disease. Using this computer model, we show how brain rhythms can act as signatures of underlying changes in networks. Further, we simulate intervention with temporally precise stimulation to show how future approaches to brain stimulation can act to restore or even augment neural networks following their degeneration in disease.
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22
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Karekal A, Miocinovic S, Swann NC. Novel approaches for quantifying beta synchrony in Parkinson's disease. Exp Brain Res 2022; 240:991-1004. [PMID: 35099592 DOI: 10.1007/s00221-022-06308-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 01/12/2022] [Indexed: 11/25/2022]
Abstract
Despite the clinical and financial burden of Parkinson's disease (PD), there is no standardized, reliable biomarker to diagnose and track PD progression. Instead, PD is primarily assessed using subjective clinical rating scales and patient self-report. Such approaches can be imprecise, hindering diagnosis and disease monitoring. An objective biomarker would be beneficial for clinical care, refining diagnosis, and treatment. Due to widespread electrophysiological abnormalities both within and between brain structures in PD, development of electrophysiologic biomarkers may be feasible. Basal ganglia recordings acquired with neurosurgical approaches have revealed elevated power in the beta frequency range (13-30 Hz) in PD, suggesting that beta power could be a putative PD biomarker. However, there are limitations to the use of beta power as a biomarker. Recent advances in analytic approaches have led to novel methods to quantify oscillatory synchrony in the beta frequency range. Here we describe some of these novel approaches in the context of PD and explore how they may serve as electrophysiological biomarkers. These novel signatures include (1) interactions between beta phase and broadband (> 50 Hz, "gamma") amplitude (i.e., phase amplitude coupling, PAC), (2) asymmetries in waveform shape, (3) beta coherence, and (4) beta "bursts." Development of a robust, reliable, and readily accessible electrophysiologic biomarker would represent a major step towards more precise and personalized care in PD.
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Affiliation(s)
- Apoorva Karekal
- Department of Human Physiology, University of Oregon, Eugene, OR, USA
| | | | - Nicole C Swann
- Department of Human Physiology, University of Oregon, Eugene, OR, USA.
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23
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Foffani G, Alegre M. Brain oscillations and Parkinson disease. HANDBOOK OF CLINICAL NEUROLOGY 2022; 184:259-271. [PMID: 35034740 DOI: 10.1016/b978-0-12-819410-2.00014-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Brain oscillations have been associated with Parkinson's disease (PD) for a long time mainly due to the fundamental oscillatory nature of parkinsonian rest tremor. Over the years, this association has been extended to frequencies well above that of tremor, largely owing to the opportunities offered by deep brain stimulation (DBS) to record electrical activity directly from the patients' basal ganglia. This chapter reviews the results of research on brain oscillations in PD focusing on theta (4-7Hz), beta (13-35Hz), gamma (70-80Hz) and high-frequency oscillations (200-400Hz). For each of these oscillations, we describe localization and interaction with brain structures and between frequencies, changes due to dopamine intake, task-related modulation, and clinical relevance. The study of brain oscillations will also help to dissect the mechanisms of action of DBS. Overall, the chapter tentatively depicts PD in terms of "oscillopathy."
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Affiliation(s)
- Guglielmo Foffani
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain; Neural Bioengineering, Hospital Nacional de Parapléjicos, SESCAM, Toledo, Spain; CIBERNED, Instituto de Salud Carlos III, Madrid, Spain.
| | - Manuel Alegre
- Clinical Neurophysiology Section, Clínica Universidad de Navarra, Pamplona, Spain; Systems Neuroscience Lab, Program of Neuroscience, CIMA, Universidad de Navarra, Pamplona, Spain; IdisNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain.
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24
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Iskhakova L, Rappel P, Deffains M, Fonar G, Marmor O, Paz R, Israel Z, Eitan R, Bergman H. Modulation of dopamine tone induces frequency shifts in cortico-basal ganglia beta oscillations. Nat Commun 2021; 12:7026. [PMID: 34857767 PMCID: PMC8640051 DOI: 10.1038/s41467-021-27375-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 10/18/2021] [Indexed: 11/21/2022] Open
Abstract
Βeta oscillatory activity (human: 13-35 Hz; primate: 8-24 Hz) is pervasive within the cortex and basal ganglia. Studies in Parkinson's disease patients and animal models suggest that beta-power increases with dopamine depletion. However, the exact relationship between oscillatory power, frequency and dopamine tone remains unclear. We recorded neural activity in the cortex and basal ganglia of healthy non-human primates while acutely and chronically up- and down-modulating dopamine levels. We assessed changes in beta oscillations in patients with Parkinson's following acute and chronic changes in dopamine tone. Here we show beta oscillation frequency is strongly coupled with dopamine tone in both monkeys and humans. Power, coherence between single-units and local field potentials (LFP), spike-LFP phase-locking, and phase-amplitude coupling are not systematically regulated by dopamine levels. These results demonstrate that beta frequency is a key property of pathological oscillations in cortical and basal ganglia networks.
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Affiliation(s)
- L Iskhakova
- Department of Medical Neurobiology, Institute of Medical Research Israel-Canada (IMRIC), The Hebrew University-Hadassah Medical School, Jerusalem, Israel.
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel.
| | - P Rappel
- Department of Medical Neurobiology, Institute of Medical Research Israel-Canada (IMRIC), The Hebrew University-Hadassah Medical School, Jerusalem, Israel
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - M Deffains
- University of Bordeaux, UMR 5293, IMN, Bordeaux, France
- CNRS, UMR 5293, IMN, Bordeaux, France
| | - G Fonar
- Department of Medical Neurobiology, Institute of Medical Research Israel-Canada (IMRIC), The Hebrew University-Hadassah Medical School, Jerusalem, Israel
| | - O Marmor
- Department of Medical Neurobiology, Institute of Medical Research Israel-Canada (IMRIC), The Hebrew University-Hadassah Medical School, Jerusalem, Israel
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - R Paz
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | - Z Israel
- Department of Neurosurgery, Hadassah University Hospital, Jerusalem, Israel
| | - R Eitan
- Department of Medical Neurobiology, Institute of Medical Research Israel-Canada (IMRIC), The Hebrew University-Hadassah Medical School, Jerusalem, Israel
- Jerusalem Mental Health Center, Hebrew University Medical School, Jerusalem, Israel
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - H Bergman
- Department of Medical Neurobiology, Institute of Medical Research Israel-Canada (IMRIC), The Hebrew University-Hadassah Medical School, Jerusalem, Israel
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Neurosurgery, Hadassah University Hospital, Jerusalem, Israel
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25
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Anderson RW, Wilkins KB, Parker JE, Petrucci MN, Kehnemouyi Y, Neuville RS, Cassini D, Trager MH, Koop MM, Velisar A, Blumenfeld Z, Quinn EJ, Henderson J, Bronte-Stewart HM. Lack of progression of beta dynamics after long-term subthalamic neurostimulation. Ann Clin Transl Neurol 2021; 8:2110-2120. [PMID: 34636182 PMCID: PMC8607445 DOI: 10.1002/acn3.51463] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 09/15/2021] [Indexed: 11/07/2022] Open
Abstract
OBJECTIVE To investigate the progression of neural and motor features of Parkinson's disease in a longitudinal study, after washout of medication and bilateral subthalamic nucleus deep brain stimulation (STN DBS). METHODS Participants with clinically established Parkinson's disease underwent bilateral implantation of DBS leads (18 participants, 13 male) within the STN using standard functional frameless stereotactic technique and multi-pass microelectrode recording. Both DBS leads were connected to an implanted investigative sensing neurostimulator (Activa™ PC + S, Medtronic, PLC). Resting state STN local field potentials (LFPs) were recorded and motor disability, (the Movement Disorder Society-Unified Parkinson's Disease Rating Scale - motor subscale, MDS-UPDRS III) was assessed off therapy at initial programming, and after 6 months, 1 year, and yearly out to 5 years of treatment. The primary endpoint was measured at 3 years. At each visit, medication had been held for over 12/24 h and DBS was turned off for at least 60 min, by which time LFP spectra reached a steady state. RESULTS After 3 years of chronic DBS, there were no increases in STN beta band dynamics (p = 0.98) but there were increases in alpha band dynamics (p = 0.0027, 25 STNs). Similar results were observed in a smaller cohort out to 5 years. There was no increase in the MDS-UPDRS III score. INTERPRETATION These findings provide evidence that the beta oscillopathy does not substantially progress following combined STN DBS plus medication in moderate to advanced Parkinson's disease.
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Affiliation(s)
- Ross W Anderson
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA.,Department of Neurosurgery, Kaiser Permanente, Redwood City, California, USA
| | - Kevin B Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Jordan E Parker
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA.,Department of Psychology, The University of California, Los Angeles, California, USA
| | - Matthew N Petrucci
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Yasmine Kehnemouyi
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA.,Department of Bioengineering, Stanford Schools of Engineering & Medicine, Stanford, California, USA
| | - Raumin S Neuville
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA.,The University of California School of Medicine, Irvine, California, USA
| | - Declan Cassini
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Megan H Trager
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA.,Columbia University College of Physicians and Surgeons, New York City, New York, USA
| | - Mandy M Koop
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA.,Cleveland Clinic, Cleveland, Ohio, USA
| | - Anca Velisar
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA.,The Smith-Kettlewell Eye Research Institute, San Francisco, California, USA
| | - Zack Blumenfeld
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA.,California Institute of Technology, Pasadena, California, USA
| | - Emma J Quinn
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA.,Credit Karma, San Francisco, California, USA
| | - Jaimie Henderson
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA.,Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA
| | - Helen M Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA.,Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA
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26
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Magnusson JL, Leventhal DK. Revisiting the "Paradox of Stereotaxic Surgery": Insights Into Basal Ganglia-Thalamic Interactions. Front Syst Neurosci 2021; 15:725876. [PMID: 34512279 PMCID: PMC8429495 DOI: 10.3389/fnsys.2021.725876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 08/06/2021] [Indexed: 11/13/2022] Open
Abstract
Basal ganglia dysfunction is implicated in movement disorders including Parkinson Disease, dystonia, and choreiform disorders. Contradicting standard "rate models" of basal ganglia-thalamic interactions, internal pallidotomy improves both hypo- and hyper-kinetic movement disorders. This "paradox of stereotaxic surgery" was recognized shortly after rate models were developed, and is underscored by the outcomes of deep brain stimulation (DBS) for movement disorders. Despite strong evidence that DBS activates local axons, the clinical effects of lesions and DBS are nearly identical. These observations argue against standard models in which GABAergic basal ganglia output gates thalamic activity, and raise the question of how lesions and stimulation can have similar effects. These paradoxes may be resolved by considering thalamocortical loops as primary drivers of motor output. Rather than suppressing or releasing cortex via motor thalamus, the basal ganglia may modulate the timing of thalamic perturbations to cortical activity. Motor cortex exhibits rotational dynamics during movement, allowing the same thalamocortical perturbation to affect motor output differently depending on its timing with respect to the rotational cycle. We review classic and recent studies of basal ganglia, thalamic, and cortical physiology to propose a revised model of basal ganglia-thalamocortical function with implications for basic physiology and neuromodulation.
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Affiliation(s)
| | - Daniel K Leventhal
- Department of Neurology, University of Michigan, Ann Arbor, MI, United States.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States.,Parkinson Disease Foundation Research Center of Excellence, University of Michigan, Ann Arbor, MI, United States.,Department of Neurology, VA Ann Arbor Health System, Ann Arbor, MI, United States
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27
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Sirica D, Hewitt AL, Tarolli CG, Weber MT, Zimmerman C, Santiago A, Wensel A, Mink JW, Lizárraga KJ. Neurophysiological biomarkers to optimize deep brain stimulation in movement disorders. Neurodegener Dis Manag 2021; 11:315-328. [PMID: 34261338 PMCID: PMC8977945 DOI: 10.2217/nmt-2021-0002] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Intraoperative neurophysiological information could increase accuracy of surgical deep brain stimulation (DBS) lead placement. Subsequently, DBS therapy could be optimized by specifically targeting pathological activity. In Parkinson’s disease, local field potentials (LFPs) excessively synchronized in the beta band (13–35 Hz) correlate with akinetic-rigid symptoms and their response to DBS therapy, particularly low beta band suppression (13–20 Hz) and high frequency gamma facilitation (35–250 Hz). In dystonia, LFPs abnormally synchronize in the theta/alpha (4–13 Hz), beta and gamma (60–90 Hz) bands. Phasic dystonic symptoms and their response to DBS correlate with changes in theta/alpha synchronization. In essential tremor, LFPs excessively synchronize in the theta/alpha and beta bands. Adaptive DBS systems will individualize pathological characteristics of neurophysiological signals to automatically deliver therapeutic DBS pulses of specific spatial and temporal parameters.
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Affiliation(s)
- Daniel Sirica
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA
| | - Angela L Hewitt
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA.,Division of Child Neurology, Department of Neurology, University of Rochester, Rochester, NY 14623, USA
| | - Christopher G Tarolli
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA.,Center for Health & Technology (CHeT), University of Rochester, Rochester, NY 14642, USA
| | - Miriam T Weber
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA
| | - Carol Zimmerman
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA
| | - Aida Santiago
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA
| | - Andrew Wensel
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA.,Department of Neurosurgery, University of Rochester, Rochester, NY 14618, USA
| | - Jonathan W Mink
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA.,Division of Child Neurology, Department of Neurology, University of Rochester, Rochester, NY 14623, USA
| | - Karlo J Lizárraga
- Motor Physiology & Neuromodulation Program, Division of Movement Disorders, Department of Neurology, University of Rochester, Rochester, NY 14618, USA.,Center for Health & Technology (CHeT), University of Rochester, Rochester, NY 14642, USA
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28
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Bočková M, Rektor I. Electrophysiological biomarkers for deep brain stimulation outcomes in movement disorders: state of the art and future challenges. J Neural Transm (Vienna) 2021; 128:1169-1175. [PMID: 34245367 DOI: 10.1007/s00702-021-02381-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 07/02/2021] [Indexed: 11/25/2022]
Abstract
Several neurological diseases are accompanied by rhythmic oscillatory dysfunctions in various frequency ranges and disturbed cross-frequency relationships on regional, interregional, and whole brain levels. Knowledge of these disease-specific oscillopathies is important mainly in the context of deep brain stimulation (DBS) therapy. Electrophysiological biomarkers have been used as input signals for adaptive DBS (aDBS) as well as preoperative outcome predictors. As movement disorders, particularly Parkinson's disease (PD), are among the most frequent DBS indications, the current research of DBS is the most advanced in the movement disorders field. We reviewed the literature published mainly between 2010 and 2020 to identify the most important findings concerning the current evolution of electrophysiological biomarkers in DBS and to address future challenges for prospective research.
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Affiliation(s)
- Martina Bočková
- Central European Institute of Technology (CEITEC), Brain and Mind Research Program, Masaryk University, Brno, Czech Republic
- Movement Disorders Center, First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Pekařská 53, 656 91, Brno, Czech Republic
| | - Ivan Rektor
- Central European Institute of Technology (CEITEC), Brain and Mind Research Program, Masaryk University, Brno, Czech Republic.
- Movement Disorders Center, First Department of Neurology, Masaryk University School of Medicine, St. Anne's Hospital, Pekařská 53, 656 91, Brno, Czech Republic.
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Duchet B, Ghezzi F, Weerasinghe G, Tinkhauser G, Kühn AA, Brown P, Bick C, Bogacz R. Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson's disease. PLoS Comput Biol 2021; 17:e1009116. [PMID: 34233347 PMCID: PMC8263069 DOI: 10.1371/journal.pcbi.1009116] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 05/26/2021] [Indexed: 11/18/2022] Open
Abstract
Parkinson's disease motor symptoms are associated with an increase in subthalamic nucleus beta band oscillatory power. However, these oscillations are phasic, and there is a growing body of evidence suggesting that beta burst duration may be of critical importance to motor symptoms. This makes insights into the dynamics of beta bursting generation valuable, in particular to refine closed-loop deep brain stimulation in Parkinson's disease. In this study, we ask the question "Can average burst duration reveal how dynamics change between the ON and OFF medication states?". Our analysis of local field potentials from the subthalamic nucleus demonstrates using linear surrogates that the system generating beta oscillations is more likely to act in a non-linear regime OFF medication and that the change in a non-linearity measure is correlated with motor impairment. In addition, we pinpoint the simplest dynamical changes that could be responsible for changes in the temporal patterning of beta oscillations between medication states by fitting to data biologically inspired models, and simpler beta envelope models. Finally, we show that the non-linearity can be directly extracted from average burst duration profiles under the assumption of constant noise in envelope models. This reveals that average burst duration profiles provide a window into burst dynamics, which may underlie the success of burst duration as a biomarker. In summary, we demonstrate a relationship between average burst duration profiles, dynamics of the system generating beta oscillations, and motor impairment, which puts us in a better position to understand the pathology and improve therapies such as deep brain stimulation.
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Affiliation(s)
- Benoit Duchet
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
| | - Filippo Ghezzi
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, United Kingdom
| | - Gihan Weerasinghe
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
| | - Gerd Tinkhauser
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Andrea A. Kühn
- Charité - Universitätsmedizin Berlin, Department of Neurology, Movement Disorder and Neuromodulation Unit, Berlin, Germany
| | - Peter Brown
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
| | - Christian Bick
- Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience - Systems & Network Neuroscience, Amsterdam, the Netherlands
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
- Department of Mathematics, University of Exeter, Exeter, United Kingdom
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
| | - Rafal Bogacz
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom
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30
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Karvat G, Alyahyay M, Diester I. Spontaneous activity competes with externally evoked responses in sensory cortex. Proc Natl Acad Sci U S A 2021; 118:e2023286118. [PMID: 34155142 PMCID: PMC8237647 DOI: 10.1073/pnas.2023286118] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
The interaction between spontaneous and externally evoked neuronal activity is fundamental for a functional brain. Increasing evidence suggests that bursts of high-power oscillations in the 15- to 30-Hz beta-band represent activation of internally generated events and mask perception of external cues. Yet demonstration of the effect of beta-power modulation on perception in real time is missing, and little is known about the underlying mechanism. Here, we used a closed-loop stimulus-intensity adjustment system based on online burst-occupancy analyses in rats involved in a forepaw vibrotactile detection task. We found that the masking influence of burst occupancy on perception can be counterbalanced in real time by adjusting the vibration amplitude. Offline analysis of firing rates (FRs) and local field potentials across cortical layers and frequency bands confirmed that beta-power in the somatosensory cortex anticorrelated with sensory evoked responses. Mechanistically, bursts in all bands were accompanied by transient synchronization of cell assemblies, but only beta-bursts were followed by a reduction of FR. Our closed loop approach reveals that spontaneous beta-bursts reflect a dynamic state that competes with external stimuli.
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Affiliation(s)
- Golan Karvat
- Optophysiology Lab, Institute of Biology III, University of Freiburg, 79104 Freiburg, Germany
- Bernstein Center for Computational Neuroscience Freiburg, University of Freiburg, 79104 Freiburg, Germany
| | - Mansour Alyahyay
- Optophysiology Lab, Institute of Biology III, University of Freiburg, 79104 Freiburg, Germany
- BrainLinks-BrainTools, University of Freiburg, 79104 Freiburg, Germany
| | - Ilka Diester
- Optophysiology Lab, Institute of Biology III, University of Freiburg, 79104 Freiburg, Germany;
- Bernstein Center for Computational Neuroscience Freiburg, University of Freiburg, 79104 Freiburg, Germany
- BrainLinks-BrainTools, University of Freiburg, 79104 Freiburg, Germany
- Intelligent Machine Brain Interfacing Technology (IMBIT), 79110 Freiburg, Germany
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31
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Sand D, Rappel P, Marmor O, Bick AS, Arkadir D, Lu BL, Bergman H, Israel Z, Eitan R. Machine learning-based personalized subthalamic biomarkers predict ON-OFF levodopa states in Parkinson patients. J Neural Eng 2021; 18. [PMID: 33906182 DOI: 10.1088/1741-2552/abfc1d] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 04/27/2021] [Indexed: 01/20/2023]
Abstract
Objective.Adaptive deep brain stimulation (aDBS) based on subthalamic nucleus (STN) electrophysiology has recently been proposed to improve clinical outcomes of DBS for Parkinson's disease (PD) patients. Many current models for aDBS are based on one or two electrophysiological features of STN activity, such as beta or gamma activity. Although these models have shown interesting results, we hypothesized that an aDBS model that includes many STN activity parameters will yield better clinical results. The objective of this study was to investigate the most appropriate STN neurophysiological biomarkers, detectable over long periods of time, that can predict OFF and ON levodopa states in PD patients.Approach.Long-term local field potentials (LFPs) were recorded from eight STNs (four PD patients) during 92 recording sessions (44 OFF and 48 ON levodopa states), over a period of 3-12 months. Electrophysiological analysis included the power of frequency bands, band power ratio and burst features. A total of 140 engineered features was extracted for 20 040 epochs (each epoch lasting 5 s). Based on these engineered features, machine learning (ML) models classified LFPs as OFF vs ON levodopa states.Main results.Beta and gamma band activity alone poorly predicts OFF vs ON levodopa states, with an accuracy of 0.66 and 0.64, respectively. Group ML analysis slightly improved prediction rates, but personalized ML analysis, based on individualized engineered electrophysiological features, were markedly better, predicting OFF vs ON levodopa states with an accuracy of 0.8 for support vector machine learning models.Significance.We showed that individual patients have unique sets of STN neurophysiological biomarkers that can be detected over long periods of time. ML models revealed that personally classified engineered features most accurately predict OFF vs ON levodopa states. Future development of aDBS for PD patients might include personalized ML algorithms.
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Affiliation(s)
- Daniel Sand
- Department of Medical Neurobiology (Physiology), Institute of Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.,The Edmond and Lily Safra Center for Brain Research, The Hebrew University, Jerusalem, Israel
| | - Pnina Rappel
- Department of Medical Neurobiology (Physiology), Institute of Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.,The Edmond and Lily Safra Center for Brain Research, The Hebrew University, Jerusalem, Israel
| | - Odeya Marmor
- Department of Medical Neurobiology (Physiology), Institute of Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.,The Edmond and Lily Safra Center for Brain Research, The Hebrew University, Jerusalem, Israel
| | - Atira S Bick
- Department of Medical Neurobiology (Physiology), Institute of Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.,The Brain Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - David Arkadir
- The Brain Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Bao-Liang Lu
- Center for Brain-like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Hagai Bergman
- Department of Medical Neurobiology (Physiology), Institute of Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.,The Edmond and Lily Safra Center for Brain Research, The Hebrew University, Jerusalem, Israel.,Functional Neurosurgery Unit, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Zvi Israel
- The Brain Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.,Functional Neurosurgery Unit, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Renana Eitan
- Department of Medical Neurobiology (Physiology), Institute of Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel.,The Brain Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.,Jerusalem Mental Health Center, Hebrew University-Hadassah Medical School, Jerusalem, Israel.,Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States of America
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32
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Latorre A, Rocchi L, Sadnicka A. The Expanding Horizon of Neural Stimulation for Hyperkinetic Movement Disorders. Front Neurol 2021; 12:669690. [PMID: 34054710 PMCID: PMC8160223 DOI: 10.3389/fneur.2021.669690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 04/19/2021] [Indexed: 11/13/2022] Open
Abstract
Novel methods of neural stimulation are transforming the management of hyperkinetic movement disorders. In this review the diversity of approach available is showcased. We first describe the most commonly used features that can be extracted from oscillatory activity of the central nervous system, and how these can be combined with an expanding range of non-invasive and invasive brain stimulation techniques. We then shift our focus to the periphery using tremor and Tourette's syndrome to illustrate the utility of peripheral biomarkers and interventions. Finally, we discuss current innovations which are changing the landscape of stimulation strategy by integrating technological advances and the use of machine learning to drive optimization.
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Affiliation(s)
- Anna Latorre
- Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom
| | - Lorenzo Rocchi
- Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom.,Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
| | - Anna Sadnicka
- Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom.,Motor Control and Neuromodulation Group, St George's University of London, London, United Kingdom
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33
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Vedam-Mai V, Deisseroth K, Giordano J, Lazaro-Munoz G, Chiong W, Suthana N, Langevin JP, Gill J, Goodman W, Provenza NR, Halpern CH, Shivacharan RS, Cunningham TN, Sheth SA, Pouratian N, Scangos KW, Mayberg HS, Horn A, Johnson KA, Butson CR, Gilron R, de Hemptinne C, Wilt R, Yaroshinsky M, Little S, Starr P, Worrell G, Shirvalkar P, Chang E, Volkmann J, Muthuraman M, Groppa S, Kühn AA, Li L, Johnson M, Otto KJ, Raike R, Goetz S, Wu C, Silburn P, Cheeran B, Pathak YJ, Malekmohammadi M, Gunduz A, Wong JK, Cernera S, Wagle Shukla A, Ramirez-Zamora A, Deeb W, Patterson A, Foote KD, Okun MS. Proceedings of the Eighth Annual Deep Brain Stimulation Think Tank: Advances in Optogenetics, Ethical Issues Affecting DBS Research, Neuromodulatory Approaches for Depression, Adaptive Neurostimulation, and Emerging DBS Technologies. Front Hum Neurosci 2021; 15:644593. [PMID: 33953663 PMCID: PMC8092047 DOI: 10.3389/fnhum.2021.644593] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 03/10/2021] [Indexed: 12/20/2022] Open
Abstract
We estimate that 208,000 deep brain stimulation (DBS) devices have been implanted to address neurological and neuropsychiatric disorders worldwide. DBS Think Tank presenters pooled data and determined that DBS expanded in its scope and has been applied to multiple brain disorders in an effort to modulate neural circuitry. The DBS Think Tank was founded in 2012 providing a space where clinicians, engineers, researchers from industry and academia discuss current and emerging DBS technologies and logistical and ethical issues facing the field. The emphasis is on cutting edge research and collaboration aimed to advance the DBS field. The Eighth Annual DBS Think Tank was held virtually on September 1 and 2, 2020 (Zoom Video Communications) due to restrictions related to the COVID-19 pandemic. The meeting focused on advances in: (1) optogenetics as a tool for comprehending neurobiology of diseases and on optogenetically-inspired DBS, (2) cutting edge of emerging DBS technologies, (3) ethical issues affecting DBS research and access to care, (4) neuromodulatory approaches for depression, (5) advancing novel hardware, software and imaging methodologies, (6) use of neurophysiological signals in adaptive neurostimulation, and (7) use of more advanced technologies to improve DBS clinical outcomes. There were 178 attendees who participated in a DBS Think Tank survey, which revealed the expansion of DBS into several indications such as obesity, post-traumatic stress disorder, addiction and Alzheimer’s disease. This proceedings summarizes the advances discussed at the Eighth Annual DBS Think Tank.
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Affiliation(s)
- Vinata Vedam-Mai
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA, United States.,Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States
| | - James Giordano
- Department of Neurology and Neuroethics Studies Program, Georgetown University Medical Center, Washington, DC, United States
| | - Gabriel Lazaro-Munoz
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, United States
| | - Winston Chiong
- Weill Institute for Neurosciences, Memory and Aging Center, University of California, San Francisco, San Francisco, CA, United States
| | - Nanthia Suthana
- Department of Neurosurgery, David Geffen School of Medicine and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jean-Philippe Langevin
- Department of Neurosurgery, David Geffen School of Medicine and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Neurosurgery Service, Department of Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, United States
| | - Jay Gill
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Wayne Goodman
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Nicole R Provenza
- School of Engineering, Brown University, Providence, RI, United States
| | - Casey H Halpern
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, United States
| | - Rajat S Shivacharan
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, United States
| | - Tricia N Cunningham
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, United States
| | - Sameer A Sheth
- Department of Neurological Surgery, Baylor College of Medicine, Houston, TX, United States
| | - Nader Pouratian
- Department of Neurosurgery, David Geffen School of Medicine and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Katherine W Scangos
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Helen S Mayberg
- Department of Neurology and Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Andreas Horn
- Movement Disorders & Neuromodulation Unit, Department for Neurology, Charité - University Medicine Berlin, Berlin, Germany
| | - Kara A Johnson
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
| | - Christopher R Butson
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
| | - Ro'ee Gilron
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Coralie de Hemptinne
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States.,Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Robert Wilt
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Maria Yaroshinsky
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Simon Little
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Philip Starr
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Greg Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Prasad Shirvalkar
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States.,Department of Anesthesiology (Pain Management) and Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Edward Chang
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Jens Volkmann
- Neurologischen Klinik Universitätsklinikum Würzburg, Würzburg, Germany
| | - Muthuraman Muthuraman
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Sergiu Groppa
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Andrea A Kühn
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Luming Li
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Matthew Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Kevin J Otto
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Robert Raike
- Restorative Therapies Group Implantables, Research and Core Technology, Medtronic, Minneapolis, MN, United States
| | - Steve Goetz
- Restorative Therapies Group Implantables, Research and Core Technology, Medtronic, Minneapolis, MN, United States
| | - Chengyuan Wu
- Department of Neurological Surgery, Thomas Jefferson University Hospitals, Philadelphia, PA, United States
| | - Peter Silburn
- Asia Pacific Centre for Neuromodulation, Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Binith Cheeran
- Neuromodulation Division, Abbott, Plano, TX, United States
| | - Yagna J Pathak
- Neuromodulation Division, Abbott, Plano, TX, United States
| | | | - Aysegul Gunduz
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States.,J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Joshua K Wong
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Stephanie Cernera
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States.,J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Aparna Wagle Shukla
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Adolfo Ramirez-Zamora
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Wissam Deeb
- Department of Neurology, University of Massachusetts, Worchester, MA, United States
| | - Addie Patterson
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases and the Program for Movement Disorders and Neurorestoration, Department of Neurology, University of Florida, Gainesville, FL, United States
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34
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Noor MS, McIntyre CC. Biophysical characterization of local field potential recordings from directional deep brain stimulation electrodes. Clin Neurophysiol 2021; 132:1321-1329. [PMID: 33867263 DOI: 10.1016/j.clinph.2021.01.027] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 12/24/2020] [Accepted: 01/25/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE Two major advances in clinical deep brain stimulation (DBS) technology have been the introduction of local field potential (LFP) recording capabilities, and the deployment of directional DBS electrodes. However, these two technologies are not operationally integrated within current clinical DBS devices. Therefore, we evaluated the theoretical advantages of using directional DBS electrodes for LFP recordings, with a focus on measuring beta-band activity in the subthalamic nucleus (STN). METHODS We used a computational model of human STN neural activity to simulate LFP recordings. The model consisted of 235,280 anatomically and electrically detailed STN neurons surrounding the DBS electrode, which was previously optimized to mimic beta-band synchrony in the dorsolateral STN. We then used that model system to compare LFP recordings from cylindrical and directional DBS contacts, and evaluate how the selection of different contacts for bipolar recording affected the LFP measurements. RESULTS The model predicted two advantages of directional DBS electrodes over cylindrical DBS electrodes for STN LFP recording. First, recording from directional contacts could provide additional insight on the location of a synchronous volume of neurons within the STN. Second, directional contacts could detect a smaller volume of synchronous neurons than cylindrical contacts, which our simulations predicted to be a ~0.5 mm minimum radius. CONCLUSIONS STN LFP recordings from 8-contact directional DBS electrodes (28 possible bipolar pairs) can provide more information than 4-contact cylindrical DBS electrodes (6 possible bipolar pairs), but they also introduce additional complexity in analyzing the signals. SIGNIFICANCE Integration of directional electrodes with DBS systems that are capable of LFP recordings could improve localization of targeted volumes of synchronous neurons in PD patients.
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Affiliation(s)
- M Sohail Noor
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
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35
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Kehnemouyi YM, Wilkins KB, Anidi CM, Anderson RW, Afzal MF, Bronte-Stewart HM. Modulation of beta bursts in subthalamic sensorimotor circuits predicts improvement in bradykinesia. Brain 2021; 144:473-486. [PMID: 33301569 PMCID: PMC8240742 DOI: 10.1093/brain/awaa394] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 09/09/2020] [Indexed: 01/25/2023] Open
Abstract
No biomarker of Parkinson's disease exists that allows clinicians to adjust chronic therapy, either medication or deep brain stimulation, with real-time feedback. Consequently, clinicians rely on time-intensive, empirical, and subjective clinical assessments of motor behaviour and adverse events to adjust therapies. Accumulating evidence suggests that hypokinetic aspects of Parkinson's disease and their improvement with therapy are related to pathological neural activity in the beta band (beta oscillopathy) in the subthalamic nucleus. Additionally, effectiveness of deep brain stimulation may depend on modulation of the dorsolateral sensorimotor region of the subthalamic nucleus, which is the primary site of this beta oscillopathy. Despite the feasibility of utilizing this information to provide integrated, biomarker-driven precise deep brain stimulation, these measures have not been brought together in awake freely moving individuals. We sought to directly test whether stimulation-related improvements in bradykinesia were contingent on reduction of beta power and burst durations, and/or the volume of the sensorimotor subthalamic nucleus that was modulated. We recorded synchronized local field potentials and kinematic data in 16 subthalamic nuclei of individuals with Parkinson's disease chronically implanted with neurostimulators during a repetitive wrist-flexion extension task, while administering randomized different intensities of high frequency stimulation. Increased intensities of deep brain stimulation improved movement velocity and were associated with an intensity-dependent reduction in beta power and mean burst duration, measured during movement. The degree of reduction in this beta oscillopathy was associated with the improvement in movement velocity. Moreover, the reduction in beta power and beta burst durations was dependent on the theoretical degree of tissue modulated in the sensorimotor region of the subthalamic nucleus. Finally, the degree of attenuation of both beta power and beta burst durations, together with the degree of overlap of stimulation with the sensorimotor subthalamic nucleus significantly explained the stimulation-related improvement in movement velocity. The above results provide direct evidence that subthalamic nucleus deep brain stimulation-related improvements in bradykinesia are related to the reduction in beta oscillopathy within the sensorimotor region. With the advent of sensing neurostimulators, this beta oscillopathy combined with lead location could be used as a marker for real-time feedback to adjust clinical settings or to drive closed-loop deep brain stimulation in freely moving individuals with Parkinson's disease.
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Affiliation(s)
- Yasmine M Kehnemouyi
- Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA
| | - Kevin B Wilkins
- Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA
| | - Chioma M Anidi
- Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA
- The University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Ross W Anderson
- Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA
| | - Muhammad Furqan Afzal
- Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Helen M Bronte-Stewart
- Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA
- Stanford University School of Medicine, Department of Neurosurgery, Stanford, CA, USA
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36
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Parkinsonism Alters Beta Burst Dynamics across the Basal Ganglia-Motor Cortical Network. J Neurosci 2021; 41:2274-2286. [PMID: 33483430 DOI: 10.1523/jneurosci.1591-20.2021] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 01/13/2021] [Accepted: 01/15/2021] [Indexed: 01/30/2023] Open
Abstract
Elevated synchronized oscillatory activity in the beta band has been hypothesized to be a pathophysiological marker of Parkinson's disease (PD). Recent studies have suggested that parkinsonism is closely associated with increased amplitude and duration of beta burst activity in the subthalamic nucleus (STN). How beta burst dynamics are altered from the normal to parkinsonian state across the basal ganglia-thalamocortical (BGTC) motor network, however, remains unclear. In this study, we simultaneously recorded local field potential activity from the STN, internal segment of the globus pallidus (GPi), and primary motor cortex (M1) in three female rhesus macaques, and characterized how beta burst activity changed as the animals transitioned from normal to progressively more severe parkinsonian states. Parkinsonism was associated with an increased incidence of beta bursts with longer duration and higher amplitude in the low beta band (8-20 Hz) in both the STN and GPi, but not in M1. We observed greater concurrence of beta burst activity, however, across all recording sites (M1, STN, and GPi) in PD. The simultaneous presence of low beta burst activity across multiple nodes of the BGTC network that increased with severity of PD motor signs provides compelling evidence in support of the hypothesis that low beta synchronized oscillations play a significant role in the underlying pathophysiology of PD. Given its immersion throughout the motor circuit, we hypothesize that this elevated beta-band activity interferes with spatial-temporal processing of information flow in the BGTC network that contributes to the impairment of motor function in PD.SIGNIFICANCE STATEMENT This study fills a knowledge gap regarding the change in temporal dynamics and coupling of beta burst activity across the basal ganglia-thalamocortical (BGTC) network during the evolution from normal to progressively more severe parkinsonian states. We observed that changes in beta oscillatory activity occur throughout BGTC and that increasing severity of parkinsonism was associated with a higher incidence of longer duration, higher amplitude low beta bursts in the basal ganglia, and increased concurrence of beta bursts across the subthalamic nucleus, globus pallidus, and motor cortex. These data provide new insights into the potential role of changes in the temporal dynamics of low beta activity within the BGTC network in the pathogenesis of Parkinson's disease.
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Güttler C, Altschüler J, Tanev K, Böckmann S, Haumesser JK, Nikulin VV, Kühn AA, van Riesen C. Levodopa-Induced Dyskinesia Are Mediated by Cortical Gamma Oscillations in Experimental Parkinsonism. Mov Disord 2020; 36:927-937. [PMID: 33247603 DOI: 10.1002/mds.28403] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 10/08/2020] [Accepted: 10/30/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Levodopa is the most efficacious drug in the symptomatic therapy of motor symptoms in Parkinson's disease (PD); however, long-term treatment is often complicated by troublesome levodopa-induced dyskinesia (LID). Recent evidence suggests that LID might be related to increased cortical gamma oscillations. OBJECTIVE The objective of this study was to test the hypothesis that cortical high-gamma network activity relates to LID in the 6-hydroxydopamine model and to identify new biomarkers for adaptive deep brain stimulation (DBS) therapy in PD. METHODS We recorded and analyzed primary motor cortex (M1) electrocorticogram data and motor behavior in freely moving 6-OHDA lesioned rats before and during a daily treatment with levodopa for 3 weeks. The results were correlated with the abnormal involuntary movement score (AIMS) and used for generalized linear modeling (GLM). RESULTS Levodopa reverted motor impairment, suppressed beta activity, and, with repeated administration, led to a progressive enhancement of LID. Concurrently, we observed a highly significant stepwise amplitude increase in finely tuned gamma (FTG) activity and gamma centroid frequency. Whereas AIMS and FTG reached their maximum after the 4th injection and remained on a stable plateau thereafter, the centroid frequency of the FTG power continued to increase thereafter. Among the analyzed gamma activity parameters, the fraction of longest gamma bursts showed the strongest correlation with AIMS. Using a GLM, it was possible to accurately predict AIMS from cortical recordings. CONCLUSIONS FTG activity is tightly linked to LID and should be studied as a biomarker for adaptive DBS. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Christopher Güttler
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Berlin, Germany
| | - Jennifer Altschüler
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Berlin, Germany
| | - Kaloyan Tanev
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Berlin, Germany
| | - Saskia Böckmann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Berlin, Germany
| | - Jens Kersten Haumesser
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Berlin, Germany
| | - Vadim V Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Andrea A Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany.,German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Christoph van Riesen
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany.,German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurology, University Medical Center Göttingen, Göttingen, Germany
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38
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Krauss JK, Lipsman N, Aziz T, Boutet A, Brown P, Chang JW, Davidson B, Grill WM, Hariz MI, Horn A, Schulder M, Mammis A, Tass PA, Volkmann J, Lozano AM. Technology of deep brain stimulation: current status and future directions. Nat Rev Neurol 2020; 17:75-87. [PMID: 33244188 DOI: 10.1038/s41582-020-00426-z] [Citation(s) in RCA: 258] [Impact Index Per Article: 64.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/08/2020] [Indexed: 01/20/2023]
Abstract
Deep brain stimulation (DBS) is a neurosurgical procedure that allows targeted circuit-based neuromodulation. DBS is a standard of care in Parkinson disease, essential tremor and dystonia, and is also under active investigation for other conditions linked to pathological circuitry, including major depressive disorder and Alzheimer disease. Modern DBS systems, borrowed from the cardiac field, consist of an intracranial electrode, an extension wire and a pulse generator, and have evolved slowly over the past two decades. Advances in engineering and imaging along with an improved understanding of brain disorders are poised to reshape how DBS is viewed and delivered to patients. Breakthroughs in electrode and battery designs, stimulation paradigms, closed-loop and on-demand stimulation, and sensing technologies are expected to enhance the efficacy and tolerability of DBS. In this Review, we provide a comprehensive overview of the technical development of DBS, from its origins to its future. Understanding the evolution of DBS technology helps put the currently available systems in perspective and allows us to predict the next major technological advances and hurdles in the field.
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Affiliation(s)
- Joachim K Krauss
- Department of Neurosurgery, Hannover Medical School, Hannover, Germany
| | - Nir Lipsman
- Department of Neurosurgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Tipu Aziz
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Alexandre Boutet
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Peter Brown
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Jin Woo Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea
| | - Benjamin Davidson
- Department of Neurosurgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Marwan I Hariz
- Department of Clinical Neuroscience, University of Umea, Umea, Sweden
| | - Andreas Horn
- Department of Neurology, Movement Disorders and Neuromodulation Section, Charité Medicine University of Berlin, Berlin, Germany
| | - Michael Schulder
- Department of Neurosurgery, Zucker School of Medicine at Hofstra/Northwell, New York, NY, USA
| | - Antonios Mammis
- Department of Neurosurgery, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Peter A Tass
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Jens Volkmann
- Department of Neurosurgery, Hannover Medical School, Hannover, Germany.,Department of Neurology, University Hospital of Würzburg, Würzburg, Germany
| | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada.
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Vissani M, Isaias IU, Mazzoni A. Deep brain stimulation: a review of the open neural engineering challenges. J Neural Eng 2020; 17:051002. [PMID: 33052884 DOI: 10.1088/1741-2552/abb581] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Deep brain stimulation (DBS) is an established and valid therapy for a variety of pathological conditions ranging from motor to cognitive disorders. Still, much of the DBS-related mechanism of action is far from being understood, and there are several side effects of DBS whose origin is unclear. In the last years DBS limitations have been tackled by a variety of approaches, including adaptive deep brain stimulation (aDBS), a technique that relies on using chronically implanted electrodes on 'sensing mode' to detect the neural markers of specific motor symptoms and to deliver on-demand or modulate the stimulation parameters accordingly. Here we will review the state of the art of the several approaches to improve DBS and summarize the main challenges toward the development of an effective aDBS therapy. APPROACH We discuss models of basal ganglia disorders pathogenesis, hardware and software improvements for conventional DBS, and candidate neural and non-neural features and related control strategies for aDBS. MAIN RESULTS We identify then the main operative challenges toward optimal DBS such as (i) accurate target localization, (ii) increased spatial resolution of stimulation, (iii) development of in silico tests for DBS, (iv) identification of specific motor symptoms biomarkers, in particular (v) assessing how LFP oscillations relate to behavioral disfunctions, and (vi) clarify how stimulation affects the cortico-basal-ganglia-thalamic network to (vii) design optimal stimulation patterns. SIGNIFICANCE This roadmap will lead neural engineers novel to the field toward the most relevant open issues of DBS, while the in-depth readers might find a careful comparison of advantages and drawbacks of the most recent attempts to improve DBS-related neuromodulatory strategies.
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Affiliation(s)
- Matteo Vissani
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56025 Pisa, Italy. Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56025 Pisa, Italy
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Chen Y, Gong C, Tian Y, Orlov N, Zhang J, Guo Y, Xu S, Jiang C, Hao H, Neumann WJ, Kühn AA, Liu H, Li L. Neuromodulation effects of deep brain stimulation on beta rhythm: A longitudinal local field potential study. Brain Stimul 2020; 13:1784-1792. [PMID: 33038597 DOI: 10.1016/j.brs.2020.09.027] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 08/15/2020] [Accepted: 09/29/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Deep brain stimulation (DBS) holds great promise in treating various brain diseases but its chronic therapeutic mechanisms are unclear. OBJECTIVE To explore the immediate and chronic effects of DBS on brain oscillations, and understand how different sub-bands of oscillations may be related to symptom improvement in Parkinson's patients. METHODS We carried out a longitudinal study to examine the effects of DBS on local field potentials recorded by sensing-enabled neurostimulators in the subthalamic nuclei of Parkinson's patients, using a novel block-design stimulation paradigm. RESULTS DBS significantly suppressed beta activity (13-35Hz) but the suppression effect appeared to gradually attenuate during a 6-month follow-up period after surgery (p = 0.002). However, beta suppression did not attenuate after repeated stimulation over several minutes (p > 0.110), suggesting that the changes in beta suppression may reflect a slow reconfiguration of neural pathways instead of habituation. Suppression of beta was also associated with clinical symptom improvement across subjects. Importantly, symptom-relevant features fell within the high beta band at month 1 but shifted to the low beta band at month 6, indicating that the high beta and the low beta oscillations may play different functional roles and respond differently to stimulation over the long-term treatment. CONCLUSION These data may advance understanding of chronic DBS effects on beta oscillations and their association with clinical improvement, offering novel insights to the therapeutic mechanisms of DBS.
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Affiliation(s)
- Yue Chen
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, 10084, China
| | - Chen Gong
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, 10084, China
| | - Ye Tian
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, 10084, China
| | - Natasza Orlov
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Yi Guo
- Department of Neurosurgery, Peking Union Medical College Hospital, Beijing, 100032, China
| | - Shujun Xu
- Department of Neurosurgery, Qilu Hospital of Shandong University, Shandong, 250012, China
| | - Changqing Jiang
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, 10084, China
| | - Hongwei Hao
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, 10084, China
| | - Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, 10117, Germany
| | - Andrea A Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, 10117, Germany
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA; Department of Neuroscience, Medical University of South Carolina, Charleston, 29425, SC, USA.
| | - Luming Li
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, 10084, China; Precision Medicine & Healthcare Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, 518071, China; IDG/McGovern Institute for Brain Research at Tsinghua University, Beijing, 100084, China; Institute of Epilepsy, Beijing Institute for Brain Disorders, Beijing, 100093, China.
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Moënne-Loccoz C, Astudillo-Valenzuela C, Skovgård K, Salazar-Reyes CA, Barrientos SA, García-Núñez XP, Cenci MA, Petersson P, Fuentes-Flores RA. Cortico-Striatal Oscillations Are Correlated to Motor Activity Levels in Both Physiological and Parkinsonian Conditions. Front Syst Neurosci 2020; 14:56. [PMID: 32903888 PMCID: PMC7439091 DOI: 10.3389/fnsys.2020.00056] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 07/17/2020] [Indexed: 12/04/2022] Open
Abstract
Oscillatory neural activity in the cortico-basal ganglia-thalamocortical (CBGTC) loop is associated with the motor state of a subject, but also with the availability of modulatory neurotransmitters. For example, increased low-frequency oscillations in Parkinson’s disease (PD) are related to decreased levels of dopamine and have been proposed as biomarkers to adapt and optimize therapeutic interventions, such as deep brain stimulation. Using neural oscillations as biomarkers require differentiating between changes in oscillatory patterns associated with parkinsonism vs. those related to a subject’s motor state. To address this point, we studied the correlation between neural oscillatory activity in the motor cortex and striatum and varying degrees of motor activity under normal and parkinsonian conditions. Using rats with bilateral or unilateral 6-hydroxydopamine lesions as PD models, we correlated the motion index (MI)—a measure based on the physical acceleration of the head of rats—to the local field potential (LFP) oscillatory power in the 1–80 Hz range. In motor cortices and striata, we observed a robust correlation between the motion index and the oscillatory power in two main broad frequency ranges: a low-frequency range [5.0–26.5 Hz] was negatively correlated to motor activity, whereas a high-frequency range [35.0–79.9 Hz] was positively correlated. We observed these correlations in both normal and parkinsonian conditions. In addition to these general changes in broad-band power, we observed a more restricted narrow-band oscillation [25–40 Hz] in dopamine-denervated hemispheres. This oscillation, which seems to be selective to the parkinsonian state, showed a linear frequency dependence on the concurrent motor activity level. We conclude that, independently of the parkinsonian condition, changes in broad-band oscillatory activities of cortico-basal ganglia networks (including changes in the relative power of low- and high-frequency bands) are closely correlated to ongoing motions, most likely reflecting he operations of these neural circuits to control motor activity. Hence, biomarkers based on neural oscillations should focus on specific features, such as narrow frequency bands, to allow differentiation between parkinsonian states and physiological movement-dependent circuit modulation.
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Affiliation(s)
- Cristóbal Moënne-Loccoz
- Biomedical Neuroscience Institute, University of Chile, Santiago, Chile.,Laboratorio de Control Motor y Neuromodulación, Facultad de Medicina, Universidad de Chile, Santiago, Chile.,Department of Health Sciences, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Carolina Astudillo-Valenzuela
- Laboratorio de Control Motor y Neuromodulación, Facultad de Medicina, Universidad de Chile, Santiago, Chile.,Department of Health Sciences, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.,Programa de Doctorado en Ciencias Biomédicas, Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - Katrine Skovgård
- Department of Experimental Medical Science, The Group for Integrative Neurophysiology and Neurotechnology, Lund University, Lund, Sweden.,Basal Ganglia Pathophysiology Unit, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Carolina A Salazar-Reyes
- Laboratorio de Control Motor y Neuromodulación, Facultad de Medicina, Universidad de Chile, Santiago, Chile.,Programa de Magíster en Neurociencias, Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - Sebastian A Barrientos
- Department of Experimental Medical Science, The Group for Integrative Neurophysiology and Neurotechnology, Lund University, Lund, Sweden
| | - Ximena P García-Núñez
- Biomedical Neuroscience Institute, University of Chile, Santiago, Chile.,Laboratorio de Control Motor y Neuromodulación, Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - M Angela Cenci
- Basal Ganglia Pathophysiology Unit, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Per Petersson
- Department of Experimental Medical Science, The Group for Integrative Neurophysiology and Neurotechnology, Lund University, Lund, Sweden.,Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Rómulo A Fuentes-Flores
- Biomedical Neuroscience Institute, University of Chile, Santiago, Chile.,Laboratorio de Control Motor y Neuromodulación, Facultad de Medicina, Universidad de Chile, Santiago, Chile.,Departamento de Neurociencia, Facultad de Medicina, Universidad de Chile, Santiago, Chile
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42
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Singh A, Papa SM. Striatal Oscillations in Parkinsonian Non-Human Primates. Neuroscience 2020; 449:116-122. [PMID: 32905842 DOI: 10.1016/j.neuroscience.2020.09.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 09/01/2020] [Indexed: 02/06/2023]
Abstract
Dopamine loss in Parkinson's disease (PD) is associated with abnormal oscillatory activity in the cortico-basal ganglia network. However, the oscillatory pattern of striatal neurons in PD remains poorly defined. Here, we analyzed the local field potentials in one untreated and five MPTP-treated non-human primates (NHP) with chronic, advanced parkinsonism. Oscillatory activities in the alpha (8-13 Hz) and low-beta (13-20 Hz) frequency bands were found in the striatum similarly to the motor cortex and globus pallidus of the NHP model of PD. Both alpha and low-beta frequency band oscillations of the striatum were highly coherent with the cortical and pallidal oscillations, confirming the presence of abnormal 8-20 Hz oscillatory activity in the cortico-basal ganglia network in parkinsonian NHPs. The reversal of parkinsonism induced by acute levodopa administration was associated with reduced 8-20 Hz oscillations in the striatum. These findings indicate that pathological oscillations at alpha and low-beta bands are also present in the striatum concordant with basal ganglia network changes in the primate model of PD.
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Affiliation(s)
- Arun Singh
- Yerkes National Primate Research Center, Emory University Atlanta, GA, United States; Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, United States.
| | - Stella M Papa
- Yerkes National Primate Research Center, Emory University Atlanta, GA, United States; Department of Neurology, Emory University School of Medicine, Atlanta, GA, United States
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43
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Ahn M, Lee S, Lauro PM, Schaeffer EL, Akbar U, Asaad WF. Rapid motor fluctuations reveal short-timescale neurophysiological biomarkers of Parkinson's disease. J Neural Eng 2020; 17:046042. [PMID: 32756018 PMCID: PMC8140652 DOI: 10.1088/1741-2552/abaca3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Objective. Identifying neural activity biomarkers of brain disease is essential to provide objective estimates of disease burden, obtain reliable feedback regarding therapeutic efficacy, and potentially to serve as a source of control for closed-loop neuromodulation. In Parkinson’s disease (PD), microelectrode recordings (MER) are routinely performed in the basal ganglia to guide electrode implantation for deep brain stimulation (DBS). While pathologically-excessive oscillatory activity has been observed and linked to PD motor dysfunction broadly, the extent to which these signals provide quantitative information about disease expression and fluctuations, particularly at short timescales, is unknown. Furthermore, the degree to which informative signal features are similar or different across patients has not been rigorously investigated. We sought to determine the extent to which motor error in PD across patients can be decoded on a rapid timescale using spectral features of neural activity. Approach. Here, we recorded neural activity from the subthalamic nucleus (STN) of subjects with PD undergoing awake DBS surgery while they performed an objective, continuous behavioral assessment that synthesized heterogenous PD motor manifestations to generate a scalar measure of motor dysfunction at short timescales. We then leveraged natural motor performance variations as a ‘ground truth’ to identify corresponding neurophysiological biomarkers. Main results. Support vector machines using multi-spectral decoding of neural signals from the STN succeeded in tracking the degree of motor impairment at short timescales (as short as one second). Spectral power across a wide range of frequencies, beyond the classic ‘β’ oscillations, contributed to this decoding, and multi-spectral models consistently outperformed those generated using more isolated frequency bands. While generalized decoding models derived across subjects were able to estimate motor impairment, patient-specific models typically performed better. Significance. These results demonstrate that quantitative information about short-timescale PD motor dysfunction is available in STN neural activity, distributed across various patient-specific spectral components, such that an individualized approach will be critical to fully harness this information for optimal disease tracking and closed-loop neuromodulation.
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Affiliation(s)
- Minkyu Ahn
- Department of Neuroscience, Brown University, Providence, RI 02912, United States of America. Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI 02912, United States of America
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44
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Valsky D, Heiman Grosberg S, Israel Z, Boraud T, Bergman H, Deffains M. What is the true discharge rate and pattern of the striatal projection neurons in Parkinson's disease and Dystonia? eLife 2020; 9:e57445. [PMID: 32812870 PMCID: PMC7462612 DOI: 10.7554/elife.57445] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 08/14/2020] [Indexed: 02/06/2023] Open
Abstract
Dopamine and striatal dysfunctions play a key role in the pathophysiology of Parkinson's disease (PD) and Dystonia, but our understanding of the changes in the discharge rate and pattern of striatal projection neurons (SPNs) remains limited. Here, we recorded and examined multi-unit signals from the striatum of PD and dystonic patients undergoing deep brain stimulation surgeries. Contrary to earlier human findings, we found no drastic changes in the spontaneous discharge of the well-isolated and stationary SPNs of the PD patients compared to the dystonic patients or to the normal levels of striatal activity reported in healthy animals. Moreover, cluster analysis using SPN discharge properties did not characterize two well-separated SPN subpopulations, indicating no SPN subpopulation-specific (D1 or D2 SPNs) discharge alterations in the pathological state. Our results imply that small to moderate changes in spontaneous SPN discharge related to PD and Dystonia are likely amplified by basal ganglia downstream structures.
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Affiliation(s)
- Dan Valsky
- Department of Medical Neurobiology, Institute of Medical Research Israel - Canada (IMRIC), The Hebrew University - Hadassah Medical SchoolJerusalemIsrael
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew UniversityJerusalemIsrael
| | - Shai Heiman Grosberg
- Department of Medical Neurobiology, Institute of Medical Research Israel - Canada (IMRIC), The Hebrew University - Hadassah Medical SchoolJerusalemIsrael
| | - Zvi Israel
- Department of Neurosurgery, Hadassah University HospitalJerusalemIsrael
| | - Thomas Boraud
- University of Bordeaux, UMR 5293, IMNBordeauxFrance
- CNRS, UMR 5293, IMNBordeauxFrance
- CHU de Bordeaux, IMN CliniqueBordeauxFrance
| | - Hagai Bergman
- Department of Medical Neurobiology, Institute of Medical Research Israel - Canada (IMRIC), The Hebrew University - Hadassah Medical SchoolJerusalemIsrael
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew UniversityJerusalemIsrael
- Department of Neurosurgery, Hadassah University HospitalJerusalemIsrael
| | - Marc Deffains
- University of Bordeaux, UMR 5293, IMNBordeauxFrance
- CNRS, UMR 5293, IMNBordeauxFrance
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Wiest C, Tinkhauser G, Pogosyan A, Bange M, Muthuraman M, Groppa S, Baig F, Mostofi A, Pereira EA, Tan H, Brown P, Torrecillos F. Local field potential activity dynamics in response to deep brain stimulation of the subthalamic nucleus in Parkinson's disease. Neurobiol Dis 2020; 143:105019. [PMID: 32681881 PMCID: PMC7115855 DOI: 10.1016/j.nbd.2020.105019] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/17/2020] [Accepted: 07/11/2020] [Indexed: 02/06/2023] Open
Abstract
Local field potentials (LFPs) may afford insight into the mechanisms of action of deep brain stimulation (DBS) and potential feedback signals for adaptive DBS. In Parkinson's disease (PD) DBS of the subthalamic nucleus (STN) suppresses spontaneous activity in the beta band and drives evoked resonant neural activity (ERNA). Here, we investigate how STN LFP activities change over time following the onset and offset of DBS. To this end we recorded LFPs from the STN in 14 PD patients during long (mean: 181.2 s) and short (14.2 s) blocks of continuous stimulation at 130 Hz. LFP activities were evaluated in the temporal and spectral domains. During long stimulation blocks, the frequency and amplitude of the ERNA decreased before reaching a steady state after ~70 s. Maximal ERNA amplitudes diminished over repeated stimulation blocks. Upon DBS cessation, the ERNA was revealed as an under-damped oscillation, and was more marked and lasted longer after short duration stimulation blocks. In contrast, activity in the beta band suppressed within 0.5 s of continuous DBS onset and drifted less over time. Spontaneous activity was also suppressed in the low gamma band, suggesting that the effects of high frequency stimulation on spontaneous oscillations may not be selective for pathological beta activity. High frequency oscillations were present in only six STN recordings before stimulation onset and their frequency was depressed by stimulation. The different dynamics of the ERNA and beta activity with stimulation imply different DBS mechanisms and may impact how these activities may be used in adaptive feedback.
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Affiliation(s)
- C Wiest
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - G Tinkhauser
- Department of Neurology, Bern University Hospital, Bern, Switzerland
| | - A Pogosyan
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - M Bange
- Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Mainz University Hospital, Mainz, Germany
| | - M Muthuraman
- Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Mainz University Hospital, Mainz, Germany
| | - S Groppa
- Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Mainz University Hospital, Mainz, Germany
| | - F Baig
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK; Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London, UK
| | - A Mostofi
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London, UK
| | - E A Pereira
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London, UK
| | - H Tan
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - P Brown
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - F Torrecillos
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
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46
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Khawaldeh S, Tinkhauser G, Shah SA, Peterman K, Debove I, Nguyen TAK, Nowacki A, Lachenmayer ML, Schuepbach M, Pollo C, Krack P, Woolrich M, Brown P. Subthalamic nucleus activity dynamics and limb movement prediction in Parkinson's disease. Brain 2020; 143:582-596. [PMID: 32040563 PMCID: PMC7009471 DOI: 10.1093/brain/awz417] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 10/21/2019] [Accepted: 11/19/2019] [Indexed: 02/01/2023] Open
Abstract
Whilst exaggerated bursts of beta frequency band oscillatory synchronization in the subthalamic nucleus have been associated with motor impairment in Parkinson's disease, a plausible mechanism linking the two phenomena has been lacking. Here we test the hypothesis that increased synchronization denoted by beta bursting might compromise information coding capacity in basal ganglia networks. To this end we recorded local field potential activity in the subthalamic nucleus of 18 patients with Parkinson's disease as they executed cued upper and lower limb movements. We used the accuracy of local field potential-based classification of the limb to be moved on each trial as an index of the information held by the system with respect to intended action. Machine learning using the naïve Bayes conditional probability model was used for classification. Local field potential dynamics allowed accurate prediction of intended movements well ahead of their execution, with an area under the receiver operator characteristic curve of 0.80 ± 0.04 before imperative cues when the demanded action was known ahead of time. The presence of bursts of local field potential activity in the alpha, and even more so, in the beta frequency band significantly compromised the prediction of the limb to be moved. We conclude that low frequency bursts, particularly those in the beta band, restrict the capacity of the basal ganglia system to encode physiologically relevant information about intended actions. The current findings are also important as they suggest that local subthalamic activity may potentially be decoded to enable effector selection, in addition to force control in restorative brain-machine interface applications.
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Affiliation(s)
- Saed Khawaldeh
- MRC Brain Network Dynamics Unit, University of Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, UK.,Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | - Gerd Tinkhauser
- MRC Brain Network Dynamics Unit, University of Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, UK.,Department of Neurology, Bern University Hospital and University of Bern, Switzerland
| | - Syed Ahmar Shah
- MRC Brain Network Dynamics Unit, University of Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, UK.,Usher Institute of Population Health Sciences and Informatics, Edinburgh Medical School, The University of Edinburgh, Edinburgh, UK
| | - Katrin Peterman
- Department of Neurology, Bern University Hospital and University of Bern, Switzerland
| | - Ines Debove
- Department of Neurology, Bern University Hospital and University of Bern, Switzerland
| | - T A Khoa Nguyen
- Department of Neurosurgery, Bern University Hospital and University of Bern, Switzerland
| | - Andreas Nowacki
- Department of Neurosurgery, Bern University Hospital and University of Bern, Switzerland
| | - M Lenard Lachenmayer
- Department of Neurology, Bern University Hospital and University of Bern, Switzerland
| | - Michael Schuepbach
- Department of Neurology, Bern University Hospital and University of Bern, Switzerland
| | - Claudio Pollo
- Department of Neurosurgery, Bern University Hospital and University of Bern, Switzerland
| | - Paul Krack
- Department of Neurology, Bern University Hospital and University of Bern, Switzerland
| | - Mark Woolrich
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK.,Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | - Peter Brown
- MRC Brain Network Dynamics Unit, University of Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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47
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Yeh CH, Al-Fatly B, Kühn AA, Meidahl AC, Tinkhauser G, Tan H, Brown P. Waveform changes with the evolution of beta bursts in the human subthalamic nucleus. Clin Neurophysiol 2020; 131:2086-2099. [PMID: 32682236 DOI: 10.1016/j.clinph.2020.05.035] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 05/19/2020] [Accepted: 05/26/2020] [Indexed: 01/11/2023]
Abstract
OBJECTIVE Phasic bursts of beta band synchronisation have been linked to motor impairment in Parkinson's disease (PD). However, little is known about what terminates bursts. METHODS We used the Hilbert-Huang transform to investigate beta bursts in the local field potential recorded from the subthalamic nucleus in nine patients with PD on and off levodopa. RESULTS The sharpness of the beta waveform extrema fell as burst amplitude dropped. Conversely, an index of phase slips between waveform extrema, and the power of concurrent theta activity increased as burst amplitude fell. Theta activity was also increased on levodopa when beta bursts were attenuated. These phenomena were associated with reduction in coupling between beta phase and high gamma activity amplitude. We discuss how these findings may suggest that beta burst termination is associated with relative desynchronization of the beta drive, increase in competing theta activity and increased phase slips in the beta activity. CONCLUSIONS We characterise the dynamical nature of beta bursts, thereby permitting inferences about underlying activities and, in particular, about why bursts terminate. SIGNIFICANCE Understanding the dynamical nature of beta bursts may help point to interventions that can cause their termination and potentially treat motor impairment in PD.
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Affiliation(s)
- Chien-Hung Yeh
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, United Kingdom; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom; School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
| | - Bassam Al-Fatly
- Department of Neurology, Charitè-Universitätsmedizin Berlin, 10177 Berlin, Germany
| | - Andrea A Kühn
- Department of Neurology, Charitè-Universitätsmedizin Berlin, 10177 Berlin, Germany
| | - Anders C Meidahl
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, United Kingdom; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Gerd Tinkhauser
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, United Kingdom; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom; Department of Neurology, Bern University Hospital and University of Bern, 3010 Bern, Switzerland
| | - Huiling Tan
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, United Kingdom; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Peter Brown
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, United Kingdom; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
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48
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Anderson RW, Kehnemouyi YM, Neuville RS, Wilkins KB, Anidi CM, Petrucci MN, Parker JE, Velisar A, Brontë-Stewart HM. A novel method for calculating beta band burst durations in Parkinson's disease using a physiological baseline. J Neurosci Methods 2020; 343:108811. [PMID: 32565222 DOI: 10.1016/j.jneumeth.2020.108811] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 05/26/2020] [Accepted: 06/14/2020] [Indexed: 01/23/2023]
Abstract
BACKGROUND Pathologically prolonged bursts of neural activity in the 8-30 Hz frequency range in Parkinson's disease have been measured using high power event detector thresholds. NEW METHOD This study introduces a novel method for determining beta bursts using a power baseline based on spectral activity that overlapped a simulated 1/f spectrum. We used resting state local field potentials from people with Parkinson's disease and a simulated 1/f signal to measure beta burst durations, to demonstrate how tuning parameters (i.e., bandwidth and center frequency) affect burst durations, to compare burst duration distributions with high power threshold methods, and to study the effect of increasing neurostimulation intensities on burst duration. RESULTS The baseline method captured a broad distribution of resting state beta band burst durations. Mean beta band burst durations were significantly shorter on compared to off neurostimulation (p = 0.0046), and their distribution shifted towards that of the 1/f spectrum during increasing intensities of stimulation. COMPARISON WITH EXISTING METHODS High power event detection methods, measure duration of higher power bursts and omit portions of the neural signal. The baseline method captured the broadest distribution of burst durations and was more sensitive than high power detection methods in demonstrating the effect of neurostimulation on beta burst duration. CONCLUSIONS The baseline method captured a broad range of fluctuations in beta band neural activity and demonstrated that subthalamic neurostimulation shortened burst durations in a dose (intensity) dependent manner, suggesting that beta burst duration is a useful control variable for closed loop algorithms.
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Affiliation(s)
- R W Anderson
- Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA
| | - Y M Kehnemouyi
- Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA
| | - R S Neuville
- Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA; The University of California School of Medicine, Irvine, CA, USA
| | - K B Wilkins
- Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA
| | - C M Anidi
- Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA; The University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - M N Petrucci
- Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA
| | - J E Parker
- Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA
| | - A Velisar
- Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA; The Smith-Kettlewell Eye Research Institute, San Francisco, CA, USA
| | - H M Brontë-Stewart
- Stanford University School of Medicine, Department of Neurology and Neurological Sciences, Stanford, CA, USA; Stanford University School of Medicine, Department of Neurosurgery, Stanford, CA, USA.
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49
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Asch N, Herschman Y, Maoz R, Auerbach-Asch CR, Valsky D, Abu-Snineh M, Arkadir D, Linetsky E, Eitan R, Marmor O, Bergman H, Israel Z. Independently together: subthalamic theta and beta opposite roles in predicting Parkinson's tremor. Brain Commun 2020; 2:fcaa074. [PMID: 33585815 PMCID: PMC7869429 DOI: 10.1093/braincomms/fcaa074] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 04/23/2020] [Accepted: 04/29/2020] [Indexed: 01/20/2023] Open
Abstract
Tremor is a core feature of Parkinson’s disease and the most easily recognized Parkinsonian sign. Nonetheless, its pathophysiology remains poorly understood. Here, we show that multispectral spiking activity in the posterior-dorso-lateral oscillatory (motor) region of the subthalamic nucleus distinguishes resting tremor from the other Parkinsonian motor signs and strongly correlates with its severity. We evaluated microelectrode-spiking activity from the subthalamic dorsolateral oscillatory region of 70 Parkinson’s disease patients who underwent deep brain stimulation surgery (114 subthalamic nuclei, 166 electrode trajectories). We then investigated the relationship between patients’ clinical Unified Parkinson’s Disease Rating Scale score and their peak theta (4–7 Hz) and beta (13–30 Hz) powers. We found a positive correlation between resting tremor and theta activity (r = 0.41, P < 0.01) and a non-significant negative correlation with beta activity (r = −0.2, P = 0.5). Hypothesizing that the two neuronal frequencies mask each other’s relationship with resting tremor, we created a non-linear model of their proportional spectral powers and investigated its relationship with resting tremor. As hypothesized, patients’ proportional scores correlated better than either theta or beta alone (r = 0.54, P < 0.001). However, theta and beta oscillations were frequently temporally correlated (38/70 patients manifested significant positive temporal correlations and 1/70 exhibited significant negative correlation between the two frequency bands). When comparing theta and beta temporal relationship (r θ β) to patients’ resting tremor scores, we found a significant negative correlation between the two (r = −0.38, P < 0.01). Patients manifesting a positive correlation between the two bands (i.e. theta and beta were likely to appear simultaneously) were found to have lower resting tremor scores than those with near-zero correlation values (i.e. theta and beta were likely to appear separately). We therefore created a new model incorporating patients’ proportional theta–beta power and r θ βscores to obtain an improved neural correlate of resting tremor (r = 0.62, P < 0.001). We then used the Akaike and Bayesian information criteria for model selection and found the multispectral model, incorporating theta–beta proportional power and their correlation, to be the best fitting model, with 0.96 and 0.89 probabilities, respectively. Here we found that as theta increases, beta decreases and the two appear separately—resting tremor is worsened. Our results therefore show that theta and beta convey information about resting tremor in opposite ways. Furthermore, the finding that theta and beta coactivity is negatively correlated with resting tremor suggests that theta–beta non-linear scale may be a valuable biomarker for Parkinson’s resting tremor in future adaptive deep brain stimulation techniques.
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Affiliation(s)
- Nir Asch
- Department of Medical Neurobiology, The Hebrew University of Jerusalem, Israel
| | - Yehuda Herschman
- Functional Neurosurgery Unit, Department of Neurosurgery, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Rotem Maoz
- Department of Medical Neurobiology, The Hebrew University of Jerusalem, Israel
| | - Carmel R Auerbach-Asch
- Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem, Israel
| | - Dan Valsky
- Department of Medical Neurobiology, The Hebrew University of Jerusalem, Israel
| | | | - David Arkadir
- Department of Neurology, Hadassah Medical Center, Jerusalem, Israel
| | - Eduard Linetsky
- Department of Neurology, Hadassah Medical Center, Jerusalem, Israel
| | - Renana Eitan
- Research and Training Unit, Jerusalem Mental Health Center, Kfar Shaul Eitanim Hospital, Jerusalem, Israel
| | - Odeya Marmor
- Department of Medical Neurobiology, The Hebrew University of Jerusalem, Israel
| | - Hagai Bergman
- Department of Medical Neurobiology, The Hebrew University of Jerusalem, Israel
| | - Zvi Israel
- Functional Neurosurgery Unit, Department of Neurosurgery, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
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50
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Petersson P, Halje P, Cenci MA. Significance and Translational Value of High-Frequency Cortico-Basal Ganglia Oscillations in Parkinson's Disease. JOURNAL OF PARKINSONS DISEASE 2020; 9:183-196. [PMID: 30594935 PMCID: PMC6484276 DOI: 10.3233/jpd-181480] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The mechanisms and significance of basal ganglia oscillations is a fundamental research question engaging both clinical and basic investigators. In Parkinson’s disease (PD), neural activity in basal ganglia nuclei is characterized by oscillatory patterns that are believed to disrupt the dynamic processing of movement-related information and thus generate motor symptoms. Beta-band oscillations associated with hypokinetic states have been reviewed in several excellent previous articles. Here we focus on faster oscillatory phenomena that have been reported in association with a diverse range of motor states. We review the occurrence of different types of fast oscillations and the evidence supporting their pathophysiological role. We also provide a general discussion on the definition, possible mechanisms, and translational value of synchronized oscillations of different frequencies in cortico-basal ganglia structures. Revealing how oscillatory phenomena are caused and spread in cortico-basal ganglia-thalamocortical networks will offer a key to unlock the neural codes of both motor and non-motor symptoms in PD. In preclinical therapeutic research, recording of oscillatory neural activities holds the promise to unravel mechanisms of action of current and future treatments.
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Affiliation(s)
- Per Petersson
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden.,Department of Experimental Medical Science, The Group for Integrative Neurophysiology and Neurotechnology, Lund University, Lund, Sweden
| | - Pär Halje
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden.,Department of Experimental Medical Science, The Group for Integrative Neurophysiology and Neurotechnology, Lund University, Lund, Sweden
| | - M Angela Cenci
- Department of Experimental Medical Science, Basal Ganglia Pathophysiology Unit, Lund University, Lund, Sweden
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