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Tran S, Heida TC, Heijs JJA, Al-Ozzi T, Sumarac S, Alanazi FI, Kalia SK, Hodaie M, Lozano AM, Milosevic L, Chen R, Hutchison WD. Subthalamic and pallidal neurons are modulated during externally cued movements in Parkinson's disease. Neurobiol Dis 2024; 190:106384. [PMID: 38135193 DOI: 10.1016/j.nbd.2023.106384] [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: 07/03/2023] [Revised: 12/13/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
External sensory cues can reduce freezing of gait in people with Parkinson's disease (PD), yet the role of the basal ganglia in these movements is unclear. We used microelectrode recordings to examine modulations in single unit (SU) and oscillatory local field potentials (LFP) during auditory-cued rhythmic pedaling movements of the feet. We tested five blocks of increasing cue frequencies (1 Hz, 1.5 Hz, 2 Hz, 2.5 Hz, and 3 Hz) in 24 people with PD undergoing deep brain stimulation surgery of the subthalamic nucleus (STN) or globus pallidus internus (GPi). Single unit firing and beta band LFPs (13-30 Hz) in response to movement onsets or cue onsets were examined. We found that the timing accuracy of foot pedaling decreased with faster cue frequencies. Increasing cue frequencies also attenuated firing rates in both STN and GPi neurons. Peak beta power in the GPi and STN showed different responses to the task. GPi beta power showed persistent suppression with fast cues and phasic modulation with slow cues. STN beta power showed enhanced beta synchronization following movement. STN beta power also correlated with rate of pedaling. Overall, we showed task-related responses in the GPi and STN during auditory-cued movements with differential roles in sensory and motor control. The results suggest a role for both input and output basal ganglia nuclei in auditory rhythmic pacing of gait-like movements in PD.
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Affiliation(s)
- Stephanie Tran
- Institute of Medical Science, University of Toronto, Ontario, Canada
| | - Tjitske C Heida
- Department of Biomedical Signals and Systems, University of Twente, Enschede, the Netherlands
| | - Janne J A Heijs
- Department of Biomedical Signals and Systems, University of Twente, Enschede, the Netherlands
| | - Tameem Al-Ozzi
- Department of Physiology, Temerty Faculty of Medicine, University of Toronto, Ontario, Canada
| | - Srdjan Sumarac
- Krembil Brain Institute, Leonard Ave, Toronto, Ontario, Canada; Department of Biomedical Engineering, University of Toronto, Canada
| | - Frhan I Alanazi
- Department of Physiology, Temerty Faculty of Medicine, University of Toronto, Ontario, Canada
| | - Suneil K Kalia
- Division of Neurosurgery, Toronto Western Hospital, 399 Bathurst St, Toronto, Canada; Department of Surgery, Temerty Faculty of Medicine, University of Toronto, Canada; Krembil Brain Institute, Leonard Ave, Toronto, Ontario, Canada
| | - Mojgan Hodaie
- Division of Neurosurgery, Toronto Western Hospital, 399 Bathurst St, Toronto, Canada; Department of Surgery, Temerty Faculty of Medicine, University of Toronto, Canada; Krembil Brain Institute, Leonard Ave, Toronto, Ontario, Canada
| | - Andres M Lozano
- Division of Neurosurgery, Toronto Western Hospital, 399 Bathurst St, Toronto, Canada; Department of Surgery, Temerty Faculty of Medicine, University of Toronto, Canada; Krembil Brain Institute, Leonard Ave, Toronto, Ontario, Canada
| | - Luka Milosevic
- Krembil Brain Institute, Leonard Ave, Toronto, Ontario, Canada; Department of Biomedical Engineering, University of Toronto, Canada
| | - Robert Chen
- Krembil Brain Institute, Leonard Ave, Toronto, Ontario, Canada; Dept of Neurology, Temerty Faculty of Medicine, University of Toronto, Canada
| | - William D Hutchison
- Departments of Surgery and Physiology, Temerty Faculty of Medicine, University of Toronto, Canada, and Krembil Brain Institute, Leonard Ave, Toronto, Ontario, Canada.
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Ma C, Zhang P, Wang J, Zhang J, Pan L, Li X, Yin C, Li A, Zong R, Zhang Z. Objective quantification of the severity of postural tremor based on kinematic parameters: A multi-sensory fusion study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106741. [PMID: 35338882 DOI: 10.1016/j.cmpb.2022.106741] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 02/27/2022] [Accepted: 03/08/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Current clinical assessments of essential tremor (ET) are primarily based on expert consultation combined with reviewing patient complaints, physician expertise, and diagnostic experience. Thus, traditional evaluation methods often lead to biased diagnostic results. There is a clinical demand for a method that can objectively quantify the severity of the patient's disease. METHODS This study aims to develop an artificial intelligence-aided diagnosis method based on multi-sensory fusion wearables. The experiment relies on a rigorous clinical trial paradigm to collect multi-modal fusion of signals from 98 ET patients. At the same time, three clinicians scored independently, and the consensus score obtained was used as the ground truth for the machine learning models. RESULTS Sixty kinematic parameters were extracted from the signals recorded by the nine-axis inertial measurement unit (IMU). The results showed that most of the features obtained by IMU could effectively characterize the severity of the tremors. The accuracy of the optimal model for three tasks classifying five severity levels was 97.71%, 97.54%, and 97.72%, respectively. CONCLUSIONS This paper reports the first attempt to combine multiple feature selection and machine learning algorithms for fine-grained automatic quantification of postural tremor in ET patients. The promising results showed the potential of the proposed approach to quantify the severity of ET objectively.
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Affiliation(s)
- Chenbin Ma
- Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, 100853, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, 100191, Beijing, China; School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China; Shenyuan Honors College, Beihang University, 100191, Beijing, China
| | - Peng Zhang
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, 100191, Beijing, China; School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China
| | - Jiachen Wang
- Medical School of Chinese PLA, 100853, Beijing, China
| | - Jian Zhang
- Medical School of Chinese PLA, 100853, Beijing, China
| | - Longsheng Pan
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, 100853, Beijing, China
| | - Xuemei Li
- Clinics of Cadre, Department of Outpatient, First Medical Center of Chinese PLA General Hospital, 100853, Beijing, China
| | - Chunyu Yin
- Clinics of Cadre, Department of Outpatient, First Medical Center of Chinese PLA General Hospital, 100853, Beijing, China
| | - Ailing Li
- Pusheng Yixin (Beijing) Hospital Management Co., Ltd, 100020, Beijing, China
| | - Rui Zong
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, 100853, Beijing, China.
| | - Zhengbo Zhang
- Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, 100853, Beijing, China.
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Ma C, Li D, Pan L, Li X, Yin C, Li A, Zhang Z, Zong R. Quantitative assessment of essential tremor based on machine learning methods using wearable device. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103244] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Sato S, Lim J, Miehm JD, Buonaccorsi J, Rajala C, Khalighinejad F, Ionete C, Kent JA, van Emmerik RE. Rapid foot-tapping but not hand-tapping ability is different between relapsing-remitting and progressive multiple sclerosis. Mult Scler Relat Disord 2020; 41:102031. [DOI: 10.1016/j.msard.2020.102031] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 02/07/2020] [Accepted: 02/26/2020] [Indexed: 10/24/2022]
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Shawen N, O'Brien MK, Venkatesan S, Lonini L, Simuni T, Hamilton JL, Ghaffari R, Rogers JA, Jayaraman A. Role of data measurement characteristics in the accurate detection of Parkinson's disease symptoms using wearable sensors. J Neuroeng Rehabil 2020; 17:52. [PMID: 32312287 PMCID: PMC7168958 DOI: 10.1186/s12984-020-00684-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 04/03/2020] [Indexed: 01/07/2023] Open
Abstract
Background Parkinson’s disease (PD) is a progressive neurological disease, with characteristic motor symptoms such as tremor and bradykinesia. There is a growing interest to continuously monitor these and other symptoms through body-worn sensor technology. However, limited battery life and memory capacity hinder the potential for continuous, long-term monitoring with these devices. There is little information available on the relative value of adding sensors, increasing sampling rate, or computing complex signal features, all of which may improve accuracy of symptom detection at the expense of computational resources. Here we build on a previous study to investigate the relationship between data measurement characteristics and accuracy when using wearable sensor data to classify tremor and bradykinesia in patients with PD. Methods Thirteen individuals with PD wore a flexible, skin-mounted sensor (collecting tri-axial accelerometer and gyroscope data) and a commercial smart watch (collecting tri-axial accelerometer data) on their predominantly affected hand. The participants performed a series of standardized motor tasks, during which a clinician scored the severity of tremor and bradykinesia in that limb. Machine learning models were trained on scored data to classify tremor and bradykinesia. Model performance was compared when using different types of sensors (accelerometer and/or gyroscope), different data sampling rates (up to 62.5 Hz), and different categories of pre-engineered features (up to 148 features). Performance was also compared between the flexible sensor and smart watch for each analysis. Results First, there was no effect of device type for classifying tremor symptoms (p > 0.34), but bradykinesia models incorporating gyroscope data performed slightly better (up to 0.05 AUROC) than other models (p = 0.01). Second, model performance decreased with sampling frequency (p < 0.001) for tremor, but not bradykinesia (p > 0.47). Finally, model performance for both symptoms was maintained after substantially reducing the feature set. Conclusions Our findings demonstrate the ability to simplify measurement characteristics from body-worn sensors while maintaining performance in PD symptom detection. Understanding the trade-off between model performance and data resolution is crucial to design efficient, accurate wearable sensing systems. This approach may improve the feasibility of long-term, continuous, and real-time monitoring of PD symptoms by reducing computational burden on wearable devices.
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Affiliation(s)
- Nicholas Shawen
- Max Nader Lab for Rehabilitation Technologies and Outcomes, Shirley Ryan AbilityLab, Chicago, IL, 60611, USA.,Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Megan K O'Brien
- Max Nader Lab for Rehabilitation Technologies and Outcomes, Shirley Ryan AbilityLab, Chicago, IL, 60611, USA.,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, 60611, USA
| | - Sanjeev Venkatesan
- Max Nader Lab for Rehabilitation Technologies and Outcomes, Shirley Ryan AbilityLab, Chicago, IL, 60611, USA.,Department of Computer Science, University of Illinois at Urbana-Champagne, Urbana, IL, 61801, USA
| | - Luca Lonini
- Max Nader Lab for Rehabilitation Technologies and Outcomes, Shirley Ryan AbilityLab, Chicago, IL, 60611, USA.,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, 60611, USA
| | - Tanya Simuni
- Department of Neurology, Northwestern University, Chicago, IL, 60611, USA
| | - Jamie L Hamilton
- The Michael J. Fox Foundation for Parkinson's Research, New York, NY, 10120, USA
| | - Roozbeh Ghaffari
- Center for Bio-Integrated Electronics, Departments of Materials Science and Engineering, Biomedical Engineering, Chemistry, Mechanical Engineering, Electrical Engineering and Computer Science, Neurological Surgery, Simpson Querrey Institute for Nano/Biotechnology, McCormick School of Engineering, Feinberg School of Medicine, Northwestern University, Evanston, IL, 60208, USA
| | - John A Rogers
- Center for Bio-Integrated Electronics, Departments of Materials Science and Engineering, Biomedical Engineering, Chemistry, Mechanical Engineering, Electrical Engineering and Computer Science, Neurological Surgery, Simpson Querrey Institute for Nano/Biotechnology, McCormick School of Engineering, Feinberg School of Medicine, Northwestern University, Evanston, IL, 60208, USA
| | - Arun Jayaraman
- Max Nader Lab for Rehabilitation Technologies and Outcomes, Shirley Ryan AbilityLab, Chicago, IL, 60611, USA. .,Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, 60611, USA. .,Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, 60611, USA.
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Luft F, Sharifi S, Mugge W, Schouten AC, Bour LJ, van Rootselaar AF, Veltink PH, Heida T. Distinct cortical activity patterns in Parkinson's disease and essential tremor during a bimanual tapping task. J Neuroeng Rehabil 2020; 17:45. [PMID: 32183867 PMCID: PMC7079392 DOI: 10.1186/s12984-020-00670-w] [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: 09/17/2019] [Accepted: 02/27/2020] [Indexed: 11/13/2022] Open
Abstract
Background Parkinson’s disease (PD) and essential tremor (ET) are neurodegenerative diseases characterized by movement deficits. Especially in PD, maintaining cyclic movement can be significantly disturbed due to pathological changes in the basal ganglia and the cerebellum. Providing external cues improves timing of these movements in PD and also affects ET. The aim of this study is to determine differences in cortical activation patterns in PD and ET patients during externally and internally cued movements. Methods Eleven PD patients, twelve ET patients, OFF tremor suppressing medication, and nineteen age-matched healthy controls (HC) were included and asked to perform a bimanual tapping task at two predefined cue frequencies. The auditory cue, a metronome sound presented at 2 or 4 Hz, was alternately switched on and off every 30 s. Tapping at two different frequencies were used since it is expected that different brain networks are involved at different frequencies as has been shown in previous studies. Cortical activity was recorded using a 64-channel EEG cap. To establish the cortical activation pattern in each group, the task related power (TRP) was calculated for each subject. For inter-groups analysis, EEG electrodes for divided into 5 different areas. Results Inter-group analysis revealed significant differences in areas responsible for motor planning, organization and regulation and involved in initiation, maintenance, coordination and planning of complex sequences of movements. Within the area of the primary motor cortex the ET group showed a significantly lower TRP than the HC group. In the area responsible for combining somatosensory, auditory and visual information both patient groups had a higher TRP than the HC group. Conclusions Different neurological networks are involved during cued and non-cued movements in ET, PD and HC. Distinct cortical activation patterns were revealed using task related power calculations. Different activation patterns were revealed during the 2 and 4 Hz tapping task indicating different strategies to execute movements at these rates. The results suggest that a including a cued/non-cued tapping task during clinical decision making could be a valuable tool in an objective diagnostic protocol.
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Affiliation(s)
- Frauke Luft
- Department of Biomedical Signals and Systems, Faculty EEMCS, University of Twente, Postbox 217, 7500 AE, Enschede, The Netherlands.
| | - Sarvi Sharifi
- Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Winfred Mugge
- Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, Netherlands
| | - Alfred C Schouten
- Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, Netherlands.,Department of Biomechanical Engineering, University of Twente, Enschede, The Netherlands
| | - Lo J Bour
- Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Anne Fleur van Rootselaar
- Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Peter H Veltink
- Department of Biomedical Signals and Systems, Faculty EEMCS, University of Twente, Postbox 217, 7500 AE, Enschede, The Netherlands
| | - Tijtske Heida
- Department of Biomedical Signals and Systems, Faculty EEMCS, University of Twente, Postbox 217, 7500 AE, Enschede, The Netherlands
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