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Sun J, Xing F, Feng J, Chen X, Lv L, Yao X, Wang M, Zhao Z, Zhou Q, Liu T, Zhan Y, Gong-Jun J, Wang K, Hu P. Differential symptom cluster responses and predictors to repetitive transcranial magnetic stimulation treatment in Parkinson's disease: A retrospective study. Heliyon 2024; 10:e32799. [PMID: 38975093 PMCID: PMC11226850 DOI: 10.1016/j.heliyon.2024.e32799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 06/08/2024] [Accepted: 06/10/2024] [Indexed: 07/09/2024] Open
Abstract
Background Repetitive transcranial magnetic stimulation (rTMS) is an effective noninvasive neuromodulation technique for Parkinson's disease (PD). However, the efficacy of rTMS varies widely between individuals. This study aimed to investigate the factors related to the response to rTMS in PD patients. Methods We retrospectively analyzed the response of 70 idiopathic PD patients who underwent rTMS for 14 consecutive days targeting the supplementary motor area (SMA) in either an open-label trail (n = 31) or a randomized, double-blind, placebo-controlled trial (RCT) (n = 39). The motor symptoms of PD patients were assessed by the United Parkinson's Disease Rating Scale Part III (UPDRSIII). Based on previous studies, the UPDRSIII were divided into six symptom clusters: axial dysfunction, resting tremor, rigidity, bradykinesia affecting right and left extremities, and postural tremor. Subsequently, the efficacy of rTMS to different motor symptom clusters and clinical predictors were analyzed in these two trails. Results After 14 days of treatment, only the total UPDRSIII scores and rigidity scores improved in both the open-label trial and the RCT. The results of multiple linear regression analysis indicated that baseline rigidity scores (β = 0.37, p = 0.047) and RMT (β = 0.30, P = 0.02) positively predicted the improvement of UPDRSIII. The baseline rigidity score (β = 0.55, P < 0.0001) was identified as an independent factor to predict the improvement of rigidity. Conclusion This study demonstrated significant improvements in total UPDRSIII scores and rigidity after 14-day treatment, with baseline rigidity scores and RMT identified as predictors of treatment response, underscoring the need for individualized therapy.
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Affiliation(s)
- Jinmei Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Fengbo Xing
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Jingjing Feng
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Xin Chen
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Lingling Lv
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Xiaoqing Yao
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Mengqi Wang
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230000, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Ziye Zhao
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Qian Zhou
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Tingting Liu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Yuqian Zhan
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - J.I. Gong-Jun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230000, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Institute of Translational Medicine, Hefei, 230000, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230000, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Institute of Translational Medicine, Hefei, 230000, China
| | - Panpan Hu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230000, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Institute of Translational Medicine, Hefei, 230000, China
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Lu J, Zhang X, Shu Z, Han J, Yu N. A dynamic brain network decomposition method discovers effective brain hemodynamic sub-networks for Parkinson's disease. J Neural Eng 2024; 21:026047. [PMID: 38621377 DOI: 10.1088/1741-2552/ad3eb6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 04/15/2024] [Indexed: 04/17/2024]
Abstract
Objective.Dopaminergic treatment is effective for Parkinson's disease (PD). Nevertheless, the conventional treatment assessment mainly focuses on human-administered behavior examination while the underlying functional improvements have not been well explored. This paper aims to investigate brain functional variations of PD patients after dopaminergic therapy.Approach.This paper proposed a dynamic brain network decomposition method and discovered brain hemodynamic sub-networks that well characterized the efficacy of dopaminergic treatment in PD. Firstly, a clinical walking procedure with functional near-infrared spectroscopy was developed, and brain activations during the procedure from fifty PD patients under the OFF and ON states (without and with dopaminergic medication) were captured. Then, dynamic brain networks were constructed with sliding-window analysis of phase lag index and integrated time-varying functional networks across all patients. Afterwards, an aggregated network decomposition algorithm was formulated based on aggregated effectiveness optimization of functional networks in spanning network topology and cross-validation network variations, and utilized to unveil effective brain hemodynamic sub-networks for PD patients. Further, dynamic sub-network features were constructed to characterize the brain flexibility and dynamics according to the temporal switching and activation variations of discovered sub-networks, and their correlations with differential treatment-induced gait alterations were analyzed.Results.The results demonstrated that PD patients exhibited significantly enhanced flexibility after dopaminergic therapy within a sub-network related to the improvement of motor functions. Other sub-networks were significantly correlated with trunk-related axial symptoms and exhibited no significant treatment-induced dynamic interactions.Significance.The proposed method promises a quantified and objective approach for dopaminergic treatment evaluation. Moreover, the findings suggest that the gait of PD patients comprises distinct motor domains, and the corresponding neural controls are selectively responsive to dopaminergic treatment.
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Affiliation(s)
- Jiewei Lu
- College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China
| | - Xinyuan Zhang
- College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China
| | - Zhilin Shu
- College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China
| | - Jianda Han
- College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China
- Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin, People's Republic of China
- Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen, People's Republic of China
| | - Ningbo Yu
- College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China
- Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin, People's Republic of China
- Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen, People's Republic of China
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Mertiens S, Sure M, Schnitzler A, Florin E. Alterations of PAC-based resting state networks in Parkinson's disease are partially alleviated by levodopa medication. Front Syst Neurosci 2023; 17:1219334. [PMID: 37588811 PMCID: PMC10427244 DOI: 10.3389/fnsys.2023.1219334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 07/10/2023] [Indexed: 08/18/2023] Open
Abstract
Introduction Parkinson's disease (PD) is a neurodegenerative disorder affecting the whole brain, leading to several motor and non-motor symptoms. In the past, it has been shown that PD alters resting state networks (RSN) in the brain. These networks are usually derived from fMRI BOLD signals. This study investigated RSN changes in PD patients based on maximum phase-amplitude coupling (PAC) throughout the cortex. We also tested the hypothesis that levodopa medication shifts network activity back toward a healthy state. Methods We recorded 23 PD patients and 24 healthy age-matched participants for 30 min at rest with magnetoencephalography (MEG). PD patients were measured once in the dopaminergic medication ON and once in the medication OFF state. A T1-MRI brain scan was acquired from each participant for source reconstruction. After correcting the data for artifacts and performing source reconstruction using a linearly constrained minimum variance beamformer, we extracted visual, sensorimotor (SMN), and frontal RSNs based on PAC. Results We found significant changes in all networks between healthy participants and PD patients in the medication OFF state. Levodopa had a significant effect on the SMN but not on the other networks. There was no significant change in the optimal PAC coupling frequencies between healthy participants and PD patients. Discussion Our results suggest that RSNs, based on PAC in different parts of the cortex, are altered in PD patients. Furthermore, levodopa significantly affects the SMN, reflecting the clinical alleviation of motor symptoms and leading to a network normalization compared to healthy controls.
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Affiliation(s)
- Sean Mertiens
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Matthias Sure
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Sure M, Mertiens S, Vesper J, Schnitzler A, Florin E. Alterations of resting-state networks of Parkinson's disease patients after subthalamic DBS surgery. Neuroimage Clin 2023; 37:103317. [PMID: 36610312 PMCID: PMC9850202 DOI: 10.1016/j.nicl.2023.103317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 12/27/2022] [Accepted: 01/02/2023] [Indexed: 01/05/2023]
Abstract
The implantation of deep brain stimulation (DBS) electrodes in Parkinson's disease (PD) patients can lead to a temporary improvement in motor symptoms, known as the stun effect. However, the network alterations induced by the stun effect are not well characterized. As therapeutic DBS is known to alter resting-state networks (RSN) and subsequent motor symptoms in patients with PD, we aimed to investigate whether the DBS-related stun effect also modulated RSNs. Therefore, we analyzed RSNs of 27 PD patients (8 females, 59.0 +- 8.7 years) using magnetoencephalography and compared them to RSNs of 24 age-matched healthy controls (8 females, 62.8 +- 5.1 years). We recorded 30 min of resting-state activity two days before and one day after implantation of the electrodes with and without dopaminergic medication. RSNs were determined by use of phase-amplitude coupling between a low frequency phase and a high gamma amplitude and examined for differences between conditions (i.e., pre vs post surgery). We identified four RSNs across all conditions: sensory-motor, visual, fronto-occipital, and frontal. Each RSN was altered due to electrode implantation. Importantly, these changes were not restricted to spatially close areas to the electrode trajectory. Interestingly, pre-operative RSNs corresponded better with healthy control RSNs regarding the spatial overlap, although the stun effect is associated with motor improvement. Our findings reveal that the stun effect induced by implantation of electrodes exerts brain wide changes in different functional RSNs.
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Affiliation(s)
- Matthias Sure
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany.
| | - Sean Mertiens
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany.
| | - Jan Vesper
- Department of Functional Neurosurgery and Stereotaxy, Medical Faculty, University Hospital, Düsseldorf, Germany.
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany; Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty, University Hospital, Düsseldorf, Germany.
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany.
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Si Q, Gan C, Zhang H, Cao X, Sun H, Wang M, Wang L, Yuan Y, Zhang K. Altered dynamic functional network connectivity in levodopa-induced dyskinesia of Parkinson's disease. CNS Neurosci Ther 2022; 29:192-201. [PMID: 36229900 PMCID: PMC9804048 DOI: 10.1111/cns.13994] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 09/15/2022] [Accepted: 09/21/2022] [Indexed: 02/06/2023] Open
Abstract
AIMS The aim of this study was to clarify the dynamic neural activity of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD). METHODS Using dynamic functional network connectivity (dFNC) analysis, we evaluated 41 PD patients with LID (LID group) and 34 PD patients without LID (No-LID group). Group spatial independent component analysis and sliding-window approach were employed. Moreover, we applied a k-means clustering algorithm on windowed functional connectivity (FC) matrices to identify reoccurring FC patterns (i.e., states). RESULTS The optimal number of states was determined to be five, the so-called State 1, 2, 3, 4, and 5. In ON phase, compared with No-LID group, LID group occurred more frequently and dwelled longer in strongly connected State 1, characterized by strong positive connections between visual network (VIS) and sensorimotor network (SMN). When switching from OFF to ON phase, LID group occurred less frequently in State 3 and State 4. Meanwhile, LID group dwelled longer in State 2 and shorter in State 3. No-LID group occurred more frequently in State 5 and less frequently in State 3. Additionally, correlation analysis demonstrated that dyskinesia's severity was associated with frequency of occurrence and dwell time in State 2, dominated by inferior frontal cortex in cognitive executive network (CEN). CONCLUSION Using dFNC analysis, we found that dyskinesia may be related to the dysfunctional inhibition of CEN on motor loops and excessive excitation of VIS and SMN, which provided evidence of the changes in brain dynamics associated with the occurrence of dyskinesia.
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Affiliation(s)
- Qianqian Si
- Department of NeurologyThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Caiting Gan
- Department of NeurologyThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Heng Zhang
- Department of NeurologyThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Xingyue Cao
- Department of NeurologyThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Huimin Sun
- Department of NeurologyThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Min Wang
- Department of RadiologyThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Lina Wang
- Department of NeurologyThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Yongsheng Yuan
- Department of NeurologyThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Kezhong Zhang
- Department of NeurologyThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
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Szturm T, Kolesar TA, Mahana B, Goertzen AL, Hobson DE, Marotta JJ, Strafella AP, Ko JH. Changes in Metabolic Activity and Gait Function by Dual-Task Cognitive Game-Based Treadmill System in Parkinson's Disease: Protocol of a Randomized Controlled Trial. Front Aging Neurosci 2021; 13:680270. [PMID: 34149399 PMCID: PMC8211751 DOI: 10.3389/fnagi.2021.680270] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 05/10/2021] [Indexed: 01/22/2023] Open
Abstract
Balance and gait impairments, and consequently, mobility restrictions and falls are common in Parkinson’s disease (PD). Various cognitive deficits are also common in PD and are associated with increased fall risk. These mobility and cognitive deficits are limiting factors in a person’s health, ability to perform activities of daily living, and overall quality of life. Community ambulation involves many dual-task (DT) conditions that require processing of several cognitive tasks while managing or reacting to sudden or unexpected balance challenges. DT training programs that can simultaneously target balance, gait, visuomotor, and cognitive functions are important to consider in rehabilitation and promotion of healthy active lives. In the proposed multi-center, randomized controlled trial (RCT), novel behavioral positron emission tomography (PET) brain imaging methods are used to evaluate the molecular basis and neural underpinnings of: (a) the decline of mobility function in PD, specifically, balance, gait, visuomotor, and cognitive function, and (b) the effects of an engaging, game-based DT treadmill walking program on mobility and cognitive functions. Both the interactive cognitive game tasks and treadmill walking require continuous visual attention, and share spatial processing functions, notably to minimize any balance disturbance or gait deviation/stumble. The ability to “walk and talk” normally includes activation of specific regions of the prefrontal cortex (PFC) and the basal ganglia (site of degeneration in PD). The PET imaging analysis and comparison with healthy age-matched controls will allow us to identify areas of abnormal, reduced activity levels, as well as areas of excessive activity (increased attentional resources) during DT-walking. We will then be able to identify areas of brain plasticity associated with improvements in mobility functions (balance, gait, and cognition) after intervention. We expect the gait-cognitive training effect to involve re-organization of PFC activity among other, yet to be identified brain regions. The DT mobility-training platform and behavioral PET brain imaging methods are directly applicable to other diseases that affect gait and cognition, e.g., cognitive vascular impairment, Alzheimer’s disease, as well as in aging.
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Affiliation(s)
- Tony Szturm
- College of Rehabilitation Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Tiffany A Kolesar
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada
| | - Bhuvan Mahana
- College of Rehabilitation Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Andrew L Goertzen
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
| | - Douglas E Hobson
- Department of Internal Medicine, University of Manitoba, Winnipeg, MB, Canada
| | | | - Antonio P Strafella
- Morton and Gloria Shulman Movement Disorder Unit, E. J. Safra Parkinson Disease Program, Neurology Division/Department of Medicine, Toronto Western Hospital, Krembil Brain Institute, University Health Network (UHN), Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, CAMH, University of Toronto, Toronto, ON, Canada
| | - Ji Hyun Ko
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB, Canada
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