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Jiang Y, Qi Z, Zhu H, Shen K, Liu R, Fang C, Lou W, Jiang Y, Yuan W, Cao X, Chen L, Zhuang Q. Role of the globus pallidus in motor and non-motor symptoms of Parkinson's disease. Neural Regen Res 2025; 20:1628-1643. [PMID: 38845220 DOI: 10.4103/nrr.nrr-d-23-01660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 04/21/2024] [Indexed: 08/07/2024] Open
Abstract
The globus pallidus plays a pivotal role in the basal ganglia circuit. Parkinson's disease is characterized by degeneration of dopamine-producing cells in the substantia nigra, which leads to dopamine deficiency in the brain that subsequently manifests as various motor and non-motor symptoms. This review aims to summarize the involvement of the globus pallidus in both motor and non-motor manifestations of Parkinson's disease. The firing activities of parvalbumin neurons in the medial globus pallidus, including both the firing rate and pattern, exhibit strong correlations with the bradykinesia and rigidity associated with Parkinson's disease. Increased beta oscillations, which are highly correlated with bradykinesia and rigidity, are regulated by the lateral globus pallidus. Furthermore, bradykinesia and rigidity are strongly linked to the loss of dopaminergic projections within the cortical-basal ganglia-thalamocortical loop. Resting tremors are attributed to the transmission of pathological signals from the basal ganglia through the motor cortex to the cerebellum-ventral intermediate nucleus circuit. The cortico-striato-pallidal loop is responsible for mediating pallidi-associated sleep disorders. Medication and deep brain stimulation are the primary therapeutic strategies addressing the globus pallidus in Parkinson's disease. Medication is the primary treatment for motor symptoms in the early stages of Parkinson's disease, while deep brain stimulation has been clinically proven to be effective in alleviating symptoms in patients with advanced Parkinson's disease, particularly for the movement disorders caused by levodopa. Deep brain stimulation targeting the globus pallidus internus can improve motor function in patients with tremor-dominant and non-tremor-dominant Parkinson's disease, while deep brain stimulation targeting the globus pallidus externus can alter the temporal pattern of neural activity throughout the basal ganglia-thalamus network. Therefore, the composition of the globus pallidus neurons, the neurotransmitters that act on them, their electrical activity, and the neural circuits they form can guide the search for new multi-target drugs to treat Parkinson's disease in clinical practice. Examining the potential intra-nuclear and neural circuit mechanisms of deep brain stimulation associated with the globus pallidus can facilitate the management of both motor and non-motor symptoms while minimizing the side effects caused by deep brain stimulation.
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Affiliation(s)
- Yimiao Jiang
- Department of Physiology, School of Medicine, Nantong University, Nantong, Jiangsu Province, China
| | - Zengxin Qi
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institute of Brain Science, Fudan University, Shanghai, China
| | - Huixian Zhu
- Department of Physiology, School of Medicine, Nantong University, Nantong, Jiangsu Province, China
| | - Kangli Shen
- Department of Physiology, School of Medicine, Nantong University, Nantong, Jiangsu Province, China
| | - Ruiqi Liu
- Department of Physiology, School of Medicine, Nantong University, Nantong, Jiangsu Province, China
| | - Chenxin Fang
- Department of Physiology, School of Medicine, Nantong University, Nantong, Jiangsu Province, China
| | - Weiwei Lou
- Department of Physiology, School of Medicine, Nantong University, Nantong, Jiangsu Province, China
| | - Yifan Jiang
- Department of Physiology, School of Medicine, Nantong University, Nantong, Jiangsu Province, China
| | - Wangrui Yuan
- Department of Physiology, School of Medicine, Nantong University, Nantong, Jiangsu Province, China
| | - Xin Cao
- Department of Physiology, School of Medicine, Nantong University, Nantong, Jiangsu Province, China
| | - Liang Chen
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institute of Brain Science, Fudan University, Shanghai, China
| | - Qianxing Zhuang
- Department of Physiology, School of Medicine, Nantong University, Nantong, Jiangsu Province, China
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2
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Wang R, Fu K, Zhao R, Wang D, Yang Z, Bin W, Gao Y, Ning X. Expanding the clinical application of OPM-MEG using an effective automatic suppression method for the dental brace metal artifact. Neuroimage 2024; 296:120661. [PMID: 38838840 DOI: 10.1016/j.neuroimage.2024.120661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 05/19/2024] [Accepted: 05/30/2024] [Indexed: 06/07/2024] Open
Abstract
Optically pumped magnetometer magnetoencephalography (OPM-MEG) holds significant promise for clinical functional brain imaging due to its superior spatiotemporal resolution. However, effectively suppressing metallic artifacts, particularly from devices such as orthodontic braces and vagal nerve stimulators remains a major challenge, hindering the wider clinical application of wearable OPM-MEG devices. A comprehensive analysis of metal artifact characteristics from time, frequency, and time-frequency perspectives was conducted for the first time using an OPM-MEG device in clinical medicine. This study focused on patients with metal orthodontics, examining the modulation of metal artifacts by breath and head movement, the incomplete regular sub-Gaussian distribution, and the high absolute power ratio in the 0.5-8 Hz band. The existing metal artifact suppression algorithms applied to SQUID-MEG, such as fast independent component analysis (FastICA), information maximization (Infomax), and algorithms for multiple unknown signal extraction (AMUSE), exhibit limited efficacy. Consequently, this study introduced the second-order blind identification (SOBI) algorithm, which utilized multiple time delays for the component separation of OPM-MEG measurement signals. We modified the time delays of the SOBI method to improve its efficacy in separating artifact components, particularly those in the ultralow frequency range. This approach employs the frequency-domain absolute power ratio, root mean square (RMS) value, and mutual information methods to automate the artifact component screening process. The effectiveness of this method was validated through simulation experiments involving four subjects in both resting and evoked experiments. In addition, the proposed method was also validated by the actual OPM-MEG evoked experiments of three subjects. Comparative analyses were conducted against the FastICA, Infomax, and AMUSE algorithms. Evaluation metrics included normalized mean square error, normalized delta band power error, RMS error, and signal-to-noise ratio, demonstrating that the proposed method provides optimal suppression of metal artifacts. This advancement holds promise for enhancing data quality and expanding the clinical applications of OPM-MEG.
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Affiliation(s)
- Ruonan Wang
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou 310051, China.
| | - Kaiwen Fu
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou 310051, China.
| | - Ruochen Zhao
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou 310051, China.
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan 250012, China; National Innovation Platform for industry-Education Integration in Medicine-Engineering Interdisciplinary, Shandong Key Laboratory for Magnetic Field-free Medicine and Functional Imaging, Shandong University, Research Institute of Shandong University, Jinan, 250014, China.
| | - Zhimin Yang
- State Key Laboratory of Traditional Chinese Medicine Syndrome/Health Construction Center, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China.
| | - Wei Bin
- State Key Laboratory of Traditional Chinese Medicine Syndrome/Health Construction Center, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China.
| | - Yang Gao
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou 310051, China; National Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou 310051, China.
| | - Xiaolin Ning
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; National Innovation Platform for industry-Education Integration in Medicine-Engineering Interdisciplinary, Shandong Key Laboratory for Magnetic Field-free Medicine and Functional Imaging, Shandong University, Research Institute of Shandong University, Jinan, 250014, China; National Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou 310051, China.
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3
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Bahador N, Saha J, Rezaei MR, Utpal S, Ghahremani A, Chen R, Lankarany M. Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering. Bioengineering (Basel) 2023; 10:719. [PMID: 37370650 DOI: 10.3390/bioengineering10060719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 06/07/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience, wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulation in decision making rely on the ways in which a burst of DBS pulses, usually delivered at a clinical frequency, i.e., 130 Hz, perturb participants' choices. It was observed that neural activities recorded during DBS were contaminated with large artifacts, which lasts for a few milliseconds, as well as a low-frequency (slow) signal (~1-2 Hz) that can persist for hundreds of milliseconds. While the focus of most of methods for removing DBS artifacts was on the former, the artifact removal capabilities of the slow signal have not been addressed. In this work, we propose a new method based on combining singular value decomposition (SVD) and normalized adaptive filtering to remove both large (fast) and slow artifacts in local field potentials, recorded during a cognitive task in which bursts of DBS were utilized. Using synthetic data, we show that our proposed algorithm outperforms four commonly used techniques in the literature, namely, (1) normalized least mean square adaptive filtering, (2) optimal FIR Wiener filtering, (3) Gaussian model matching, and (4) moving average. The algorithm's capabilities are further demonstrated by its ability to effectively remove DBS artifacts in local field potentials recorded from the subthalamic nucleus during a verbal Stroop task, highlighting its utility in real-world applications.
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Affiliation(s)
- Nooshin Bahador
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON M5S 2E8, Canada
| | - Josh Saha
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Department of Electrical and Computer Engineering, University of Waterloo, Toronto, ON N2L 3G1, Canada
| | - Mohammad R Rezaei
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON M5S 2E8, Canada
| | - Saha Utpal
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
| | - Ayda Ghahremani
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Robert Chen
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Department of Medicine, Division of Neurology, University of Toronto, Toronto, ON M5S 2E8, Canada
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network (UHN), Toronto, ON M5G 2A2, Canada
| | - Milad Lankarany
- Krembil Research Institute, University Health Network (UHN), 60 Leonard Ave, Toronto, ON M5T 0S8, Canada
- Institute of Biomedical Engineering (BME), University of Toronto, Toronto, ON M5S 2E8, Canada
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network (UHN), Toronto, ON M5G 2A2, Canada
- Department of Physiology, University of Toronto, Toronto, ON M5S 2E8, Canada
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4
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Intermuscular coherence as a biomarker of subthalamic nucleus deep brain stimulation efficacy in Parkinson’s disease. Clin Neurophysiol 2022; 142:36-43. [DOI: 10.1016/j.clinph.2022.07.489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 06/08/2022] [Accepted: 07/04/2022] [Indexed: 11/18/2022]
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5
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Korsun O, Renvall H, Nurminen J, Mäkelä JP, Pekkonen E. Modulation of sensory cortical activity by deep brain stimulation in advanced Parkinson's Disease. Eur J Neurosci 2022; 56:3979-3990. [PMID: 35560964 PMCID: PMC9544049 DOI: 10.1111/ejn.15692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 04/28/2022] [Accepted: 05/10/2022] [Indexed: 11/28/2022]
Abstract
Despite optimal oral drug treatment, about 90% of patients with Parkinson's disease develop motor fluctuation and dyskinesia within 5-10 years from the diagnosis. Moreover, the patients show non-motor symptoms in different sensory domains. Bilateral deep brain stimulation applied to the subthalamic nucleus is considered the most effective treatment in advanced Parkinson's disease and it has been suggested to affect sensorimotor modulation and relate to motor improvement in patients. However, observations on the relationship between sensorimotor activity and clinical improvement have remained sparse. Here we studied the somatosensory evoked magnetic fields in thirteen right-handed patients with advanced Parkinson's disease before and 7 months after stimulator implantation. Somatosensory processing was addressed with magnetoencephalography during alternated median nerve stimulation at both wrists. The strengths and the latencies of the ~60-ms responses at the contralateral primary somatosensory cortices were highly variable but detectable and reliably localized in all patients. The response strengths did not differ between preoperative and postoperative DBSON measurements. The change in the response strength between pre- and postoperative condition in the dominant left hemisphere of our right-handed patients correlated with the alleviation of their motor symptoms (p = 0.04). However, the result did not survive correction for multiple comparisons. Magnetoencephalography appears an effective tool to explore non-motor effects in patients with Parkinson's disease, and it may help in understanding the neurophysiological basis of deep brain stimulation. However, the high interindividual variability in the somatosensory responses and poor tolerability of DBSOFF condition warrants larger patient groups and measurements also in non-medicated patients.
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Affiliation(s)
- Olesia Korsun
- Biomag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University, and Aalto University School of Science, Helsinki, Finland.,Department of Neuroscience and Biomedical Engineering, Aalto University, School of Science, Espoo, Finland
| | - Hanna Renvall
- Biomag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University, and Aalto University School of Science, Helsinki, Finland.,Department of Neuroscience and Biomedical Engineering, Aalto University, School of Science, Espoo, Finland
| | - Jussi Nurminen
- Biomag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University, and Aalto University School of Science, Helsinki, Finland.,Motion Analysis Laboratory, Children's Hospital, Helsinki University Hospital and Helsinki University, Helsinki, Finland
| | - Jyrki P Mäkelä
- Biomag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University, and Aalto University School of Science, Helsinki, Finland
| | - Eero Pekkonen
- Department of Neurology, Helsinki University Hospital and Department of Clinical Neurosciences (Neurology), University of Helsinki, Helsinki, Finland
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6
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Iorio-Morin C, Sarica C, Elias GJB, Harmsen I, Hodaie M. Neuroimaging of psychiatric disorders. PROGRESS IN BRAIN RESEARCH 2022; 270:149-169. [PMID: 35396025 DOI: 10.1016/bs.pbr.2021.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Psychiatry remains the only medical specialty where diagnoses are still based on clinical syndromes rather than measurable biological abnormalities. As imaging technology and analytical methods evolve, it is becoming clear that subtle but measurable radiological characteristics exist and can be used to experimentally classify psychiatric disorders, predict response to treatment and, hopefully, develop new, more effective therapies. This review highlights advances in neuroimaging modalities that are now allowing assessment of brain structure, connectivity and neural network function, describes technical aspects of the most promising methods, and summarizes observations made in some frequent psychiatric disorders.
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Affiliation(s)
- Christian Iorio-Morin
- Division of Neurosurgery, Department of Surgery, Université de Sherbrooke, Sherbrooke, QC, Canada; Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Can Sarica
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Gavin J B Elias
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Irene Harmsen
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Mojgan Hodaie
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada.
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7
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Wang MB, Boring MJ, Ward MJ, Richardson RM, Ghuman AS. Deep brain stimulation for parkinson's disease induces spontaneous cortical hypersynchrony in extended motor and cognitive networks. Cereb Cortex 2022; 32:4480-4491. [PMID: 35136991 PMCID: PMC9574237 DOI: 10.1093/cercor/bhab496] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 12/04/2021] [Accepted: 12/05/2021] [Indexed: 11/14/2022] Open
Abstract
The mechanism of action of deep brain stimulation (DBS) to the basal ganglia for Parkinson's disease remains unclear. Studies have shown that DBS decreases pathological beta hypersynchrony between the basal ganglia and motor cortex. However, little is known about DBS's effects on long range corticocortical synchronization. Here, we use machine learning combined with graph theory to compare resting-state cortical connectivity between the off and on-stimulation states and to healthy controls. We found that turning DBS on increased high beta and gamma band synchrony (26 to 50 Hz) in a cortical circuit spanning the motor, occipitoparietal, middle temporal, and prefrontal cortices. The synchrony in this network was greater in DBS on relative to both DBS off and controls, with no significant difference between DBS off and controls. Turning DBS on also increased network efficiency and strength and subnetwork modularity relative to both DBS off and controls in the beta and gamma band. Thus, unlike DBS's subcortical normalization of pathological basal ganglia activity, it introduces greater synchrony relative to healthy controls in cortical circuitry that includes both motor and non-motor systems. This increased high beta/gamma synchronization may reflect compensatory mechanisms related to DBS's clinical benefits, as well as undesirable non-motor side effects.
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Affiliation(s)
- Maxwell B Wang
- Address correspondence to Maxwell B Wang, BS, Medical Scientist Training Program, University of Pittsburgh School of Medicine, Program of Neural Computation, Carnegie Mellon University, Pittsburgh, PA 15213. Tel: 815-200-9533;
| | - Matthew J Boring
- Center for Neuroscience at the University of Pittsburgh, Pittsburgh, PA 15213, USA,Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA 15213, USA,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Michael J Ward
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - R Mark Richardson
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA,Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA,Harvard Medical School, Boston, MA 02115, USA
| | - Avniel Singh Ghuman
- Program of Neural Computation, Carnegie Mellon University, Pittsburgh, PA 15213, USA,Center for Neuroscience at the University of Pittsburgh, Pittsburgh, PA 15213, USA,Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA 15213, USA,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
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Watts J, Khojandi A, Shylo O, Ramdhani RA. Machine Learning's Application in Deep Brain Stimulation for Parkinson's Disease: A Review. Brain Sci 2020; 10:E809. [PMID: 33139614 PMCID: PMC7694006 DOI: 10.3390/brainsci10110809] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 10/16/2020] [Accepted: 10/29/2020] [Indexed: 01/07/2023] Open
Abstract
Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson's disease (PD) that has undergone technological evolution that parallels an expansion in clinical phenotyping, neurophysiology, and neuroimaging of the disease state. Machine learning (ML) has been successfully used in a wide range of healthcare problems, including DBS. As computational power increases and more data become available, the application of ML in DBS is expected to grow. We review the literature of ML in DBS and discuss future opportunities for such applications. Specifically, we perform a comprehensive review of the literature from PubMed, the Institute for Scientific Information's Web of Science, Cochrane Database of Systematic Reviews, and Institute of Electrical and Electronics Engineers' (IEEE) Xplore Digital Library for ML applications in DBS. These studies are broadly placed in the following categories: (1) DBS candidate selection; (2) programming optimization; (3) surgical targeting; and (4) insights into DBS mechanisms. For each category, we provide and contextualize the current body of research and discuss potential future directions for the application of ML in DBS.
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Affiliation(s)
- Jeremy Watts
- Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA; (J.W.); (A.K.); (O.S.)
| | - Anahita Khojandi
- Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA; (J.W.); (A.K.); (O.S.)
| | - Oleg Shylo
- Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA; (J.W.); (A.K.); (O.S.)
| | - Ritesh A. Ramdhani
- Department of Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
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Litvak V, Florin E, Tamás G, Groppa S, Muthuraman M. EEG and MEG primers for tracking DBS network effects. Neuroimage 2020; 224:117447. [PMID: 33059051 DOI: 10.1016/j.neuroimage.2020.117447] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 10/08/2020] [Accepted: 10/08/2020] [Indexed: 10/23/2022] Open
Abstract
Deep brain stimulation (DBS) is an effective treatment method for a range of neurological and psychiatric disorders. It involves implantation of stimulating electrodes in a precisely guided fashion into subcortical structures and, at a later stage, chronic stimulation of these structures with an implantable pulse generator. While the DBS surgery makes it possible to both record brain activity and stimulate parts of the brain that are difficult to reach with non-invasive techniques, electroencephalography (EEG) and magnetoencephalography (MEG) provide complementary information from other brain areas, which can be used to characterize brain networks targeted through DBS. This requires, however, the careful consideration of different types of artifacts in the data acquisition and the subsequent analyses. Here, we review both the technical issues associated with EEG/MEG recordings in DBS patients and the experimental findings to date. One major line of research is simultaneous recording of local field potentials (LFPs) from DBS targets and EEG/MEG. These studies revealed a set of cortico-subcortical coherent networks functioning at distinguishable physiological frequencies. Specific network responses were linked to clinical state, task or stimulation parameters. Another experimental approach is mapping of DBS-targeted networks in chronically implanted patients by recording EEG/MEG responses during stimulation. One can track responses evoked by single stimulation pulses or bursts as well as brain state shifts caused by DBS. These studies have the potential to provide biomarkers for network responses that can be adapted to guide stereotactic implantation or optimization of stimulation parameters. This is especially important for diseases where the clinical effect of DBS is delayed or develops slowly over time. The same biomarkers could also potentially be utilized for the online control of DBS network effects in the new generation of closed-loop stimulators that are currently entering clinical use. Through future studies, the use of network biomarkers may facilitate the integration of circuit physiology into clinical decision making.
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Affiliation(s)
- Vladimir Litvak
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Gertrúd Tamás
- Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Sergiu Groppa
- Movement disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Muthuraman Muthuraman
- Movement disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, University Medical Center of the Johannes Gutenberg University, Langenbeckstrasse 1, 55131 Mainz, Germany.
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10
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Kandemir AL, Litvak V, Florin E. The comparative performance of DBS artefact rejection methods for MEG recordings. Neuroimage 2020; 219:117057. [PMID: 32540355 PMCID: PMC7443703 DOI: 10.1016/j.neuroimage.2020.117057] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 06/05/2020] [Accepted: 06/11/2020] [Indexed: 01/01/2023] Open
Abstract
Deep brain stimulation (DBS) can be a very efficient treatment option for movement disorders and psychiatric diseases. To better understand DBS mechanisms, brain activity can be recorded using magnetoencephalography (MEG) with the stimulator turned on. However, DBS produces large artefacts compromising MEG data quality due to both the applied current and the movement of wires connecting the stimulator with the electrode. To filter out these artefacts, several methods to suppress the DBS artefact have been proposed in the literature. A comparative study evaluating each method’s effectiveness, however, is missing so far. In this study, we evaluate the performance of four artefact rejection methods on MEG data from phantom recordings with DBS acquired with an Elekta Neuromag and a CTF system: (i) Hampel-filter, (ii) spectral signal space projection (S3P), (iii) independent component analysis with mutual information (ICA-MI), and (iv) temporal signal space separation (tSSS). In the sensor space, the largest increase in signal-to-noise (SNR) ratio was achieved by ICA-MI, while the best correspondence in terms of source activations was obtained by tSSS. LCMV beamforming alone was not sufficient to suppress the DBS-induced artefacts. Phantom MEG measurement with Elekta Neuromag and CTF MEG system with DBS. Systematic comparison of cleaning algorithms to remove DBS artefact from MEG data. Sensor level ICA-MI yielded the best results. Source level: tSSS provided the best correspondence to recording without DBS.
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Affiliation(s)
- Ahmet Levent Kandemir
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany
| | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, 12 Queen Square, London, UK
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany.
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11
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Boon LI, Hillebrand A, Potters WV, de Bie RMA, Prent N, Bot M, Schuurman PR, Stam CJ, van Rootselaar AF, Berendse HW. Motor effects of deep brain stimulation correlate with increased functional connectivity in Parkinson's disease: An MEG study. Neuroimage Clin 2020; 26:102225. [PMID: 32120294 PMCID: PMC7049661 DOI: 10.1016/j.nicl.2020.102225] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 01/27/2020] [Accepted: 02/20/2020] [Indexed: 11/06/2022]
Abstract
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an established symptomatic treatment in Parkinson's disease, yet its mechanism of action is not fully understood. Locally in the STN, stimulation lowers beta band power, in parallel with symptom relief. Therefore, beta band oscillations are sometimes referred to as "anti-kinetic". However, in recent studies functional interactions have been observed beyond the STN, which we hypothesized to reflect clinical effects of DBS. Resting-state, whole-brain magnetoencephalography (MEG) recordings and assessments on motor function were obtained in 18 Parkinson's disease patients with bilateral STN-DBS, on and off stimulation. For each brain region, we estimated source-space spectral power and functional connectivity with the rest of the brain. Stimulation led to an increase in average peak frequency and a suppression of absolute band power (delta to low-beta band) in the sensorimotor cortices. Significant changes (decreases and increases) in low-beta band functional connectivity were observed upon stimulation. Improvement in bradykinesia/rigidity was significantly related to increases in alpha2 and low-beta band functional connectivity (of sensorimotor regions, the cortex as a whole, and subcortical regions). By contrast, tremor improvement did not correlate with changes in functional connectivity. Our results highlight the distributed effects of DBS on the resting-state brain and suggest that DBS-related improvements in rigidity and bradykinesia, but not tremor, may be mediated by an increase in alpha2 and low-beta functional connectivity. Beyond the local effects of DBS in and around the STN, functional connectivity changes in these frequency bands might therefore be considered as "pro-kinetic".
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Affiliation(s)
- Lennard I Boon
- Amsterdam UMC, Vrije Universiteit Amsterdam, Neurology, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Clinical Neurophysiology and Magnetoencephalography Centre, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands.
| | - Arjan Hillebrand
- Amsterdam UMC, Vrije Universiteit Amsterdam, Clinical Neurophysiology and Magnetoencephalography Centre, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Wouter V Potters
- Amsterdam UMC, University of Amsterdam, Neurology and Clinical Neurophysiology, Amsterdam Neuroscience, Meibergdreef 9, Amsterdam, the Netherlands
| | - Rob M A de Bie
- Amsterdam UMC, University of Amsterdam, Neurology and Clinical Neurophysiology, Amsterdam Neuroscience, Meibergdreef 9, Amsterdam, the Netherlands
| | - Naomi Prent
- Amsterdam UMC, University of Amsterdam, Neurology and Clinical Neurophysiology, Amsterdam Neuroscience, Meibergdreef 9, Amsterdam, the Netherlands
| | - Maarten Bot
- Amsterdam UMC, University of Amsterdam, Neurosurgery, Amsterdam Neuroscience, Meibergdreef 9, Amsterdam, the Netherlands
| | - P Richard Schuurman
- Amsterdam UMC, University of Amsterdam, Neurosurgery, Amsterdam Neuroscience, Meibergdreef 9, Amsterdam, the Netherlands
| | - Cornelis J Stam
- Amsterdam UMC, Vrije Universiteit Amsterdam, Clinical Neurophysiology and Magnetoencephalography Centre, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Anne-Fleur van Rootselaar
- Amsterdam UMC, University of Amsterdam, Neurology and Clinical Neurophysiology, Amsterdam Neuroscience, Meibergdreef 9, Amsterdam, the Netherlands
| | - Henk W Berendse
- Amsterdam UMC, Vrije Universiteit Amsterdam, Neurology, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
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