1
|
Gala AS, Wilkins KB, Petrucci MN, Kehnemouyi YM, Velisar A, Trager MH, Bronte-Stewart HM. The digital signature of emergent tremor in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:147. [PMID: 39112485 PMCID: PMC11306561 DOI: 10.1038/s41531-024-00754-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 07/22/2024] [Indexed: 08/10/2024] Open
Abstract
Emergent tremor in Parkinson's disease (PD) can occur during sustained postures or movements that are different from action tremor. Tremor can contaminate the clinical rating of bradykinesia during finger tapping. Currently, there is no reliable way of isolating emergent tremor and measuring the cardinal motor symptoms based on voluntary movements only. In this study, we investigated whether emergent tremor during repetitive alternating finger tapping (RAFT) on a quantitative digitography (QDG) device could be reliably identified and distinguished from voluntary tapping. Ninety-six individuals with PD and forty-two healthy controls performed a thirty-second QDG-RAFT task and the Movement Disorders Society - Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III). Visual identification of tremor during QDG-RAFT was labeled by an experienced movement disorders specialist. Two methods of identifying tremor were investigated: 1) physiologically informed temporal thresholds 2) XGBoost model using temporal and amplitude features of tapping. The XGBoost model showed high accuracy for identifying tremor (area under the precision-recall curve of 0.981) and outperformed temporal-based thresholds. Percent time duration of classifier-identified tremor showed significant correlations with MDS-UPDRS III tremor subscores (r = 0.50, p < 0.0001). There was a significant change in QDG metrics for bradykinesia, rigidity, and arrhythmicity after tremor strikes were excluded (p < 0.01). The results demonstrate that emergent tremor during QDG-RAFT has a unique digital signature and the duration of tremor correlated with the MDS-UPDRS III tremor items. When involuntary tremor strikes were excluded, the QDG metrics of bradykinesia and rigidity were significantly worse, demonstrating the importance of distinguishing tremor from voluntary movement when rating bradykinesia.
Collapse
Affiliation(s)
- Aryaman S Gala
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Kevin B Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | | | | | - Anca Velisar
- The Smith-Kettlewell Eye Research Institute, San Francisco, CA, USA
| | - Megan H Trager
- Columbia University College of Physicians and Surgeons, New York City, NY, USA
| | - Helen M Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, US.
| |
Collapse
|
2
|
Hoffman SL, Schmiedmayer P, Gala AS, Wilkins KB, Parisi L, Karjagi S, Negi AS, Revlock S, Coriz C, Revlock J, Ravi V, Bronte-Stewart H. Comprehensive real time remote monitoring for Parkinson's disease using Quantitative DigitoGraphy. NPJ Parkinsons Dis 2024; 10:137. [PMID: 39068150 PMCID: PMC11283542 DOI: 10.1038/s41531-024-00751-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Accepted: 07/12/2024] [Indexed: 07/30/2024] Open
Abstract
People with Parkinson's disease (PWP) face critical challenges, including lack of access to neurological care, inadequate measurement and communication of motor symptoms, and suboptimal medication management and compliance. We have developed QDG-Care: a comprehensive connected care platform for Parkinson's disease (PD) that delivers validated, quantitative metrics of all motor signs in PD in real time, monitors the effects of adjusting therapy and medication adherence and is accessible in the electronic health record. In this article, we describe the design and engineering of all components of QDG-Care, including the development and utility of the QDG Mobility and Tremor Severity Scores. We present the preliminary results and insights from an at-home trial using QDG-Care. QDG technology has enormous potential to improve access to, equity of, and quality of care for PWP, and improve compliance with complex time-critical medication regimens. It will enable rapid "Go-NoGo" decisions for new therapeutics by providing high-resolution data that require fewer participants at lower cost and allow more diverse recruitment.
Collapse
Affiliation(s)
- Shannon L Hoffman
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA, USA
| | - Paul Schmiedmayer
- Stanford Byers Center for Biodesign, Stanford University, Stanford, CA, USA
| | - Aryaman S Gala
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA, USA
| | - Kevin B Wilkins
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA, USA
| | - Laura Parisi
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA, USA
| | - Shreesh Karjagi
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA, USA
| | - Aarushi S Negi
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA, USA
| | | | - Christopher Coriz
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA, USA
| | - Jeremy Revlock
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA, USA
| | - Vishnu Ravi
- Stanford Byers Center for Biodesign, Stanford University, Stanford, CA, USA
- Stanford Medicine Catalyst, Stanford School of Medicine, Stanford, CA, USA
| | - Helen Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA, USA.
- Department of Neurosurgery, Stanford School of Medicine, Stanford, CA, USA.
| |
Collapse
|
3
|
Hoffman SL, Schmiedmayer P, Gala AS, Wilkins KB, Parisi L, Karjagi S, Negi AS, Revlock S, Coriz C, Revlock J, Ravi V, Bronte-Stewart H. Quantitative DigitoGraphy: a Comprehensive Real-Time Remote Monitoring System for Parkinson's Disease. RESEARCH SQUARE 2024:rs.3.rs-3783294. [PMID: 38343821 PMCID: PMC10854288 DOI: 10.21203/rs.3.rs-3783294/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
People with Parkinson's disease (PWP) face critical challenges, including lack of access to neurological care, inadequate measurement and communication of motor symptoms, and suboptimal medication management and compliance. We have developed QDG-Care: a comprehensive connected care platform for Parkinson's disease (PD) that delivers validated, quantitative metrics of all motor signs in PD in real time, monitors the effects of adjusting therapy and medication adherence and is accessible in the electronic health record. In this article, we describe the design and engineering of all components of QDG-Care, including the development and utility of the QDG Mobility and Tremor Severity Scores. We present the preliminary results and insights from the first at-home trial using QDG-Care. QDG technology has enormous potential to improve access to, equity of, and quality of care for PWP, and improve compliance with complex time-critical medication regimens. It will enable rapid "Go-NoGo" decisions for new therapeutics by providing high-resolution data that require fewer participants at lower cost and allow more diverse recruitment.
Collapse
Affiliation(s)
- Shannon L Hoffman
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
| | - Paul Schmiedmayer
- Stanford Byers Center for Biodesign, Stanford University, Stanford, CA
| | - Aryaman S Gala
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
| | - Kevin B Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
| | - Laura Parisi
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
| | - Shreesh Karjagi
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
| | - Aarushi S Negi
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
| | | | - Christopher Coriz
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
| | - Jeremy Revlock
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
| | - Vishnu Ravi
- Stanford Byers Center for Biodesign, Stanford University, Stanford, CA
- Stanford Medicine Catalyst, Stanford School of Medicine, Stanford, CA
| | - Helen Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
- Department of Neurosurgery, Stanford School of Medicine, Stanford, CA
| |
Collapse
|
4
|
Paredes-Acuna N, Utpadel-Fischler D, Ding K, Thakor NV, Cheng G. Upper limb intention tremor assessment: opportunities and challenges in wearable technology. J Neuroeng Rehabil 2024; 21:8. [PMID: 38218890 PMCID: PMC10787996 DOI: 10.1186/s12984-023-01302-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 12/26/2023] [Indexed: 01/15/2024] Open
Abstract
BACKGROUND Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature. METHODOLOGY & RESULTS This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table. CONCLUSIONS Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.
Collapse
Affiliation(s)
- Natalia Paredes-Acuna
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany.
| | - Daniel Utpadel-Fischler
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Keqin Ding
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Nitish V Thakor
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Gordon Cheng
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany
| |
Collapse
|
5
|
Xie L, Hong R, Wu Z, Yue L, Peng K, Li S, Zhang J, Wang X, Jin L, Guan Q. Kinect-based objective assessment for early frailty identification in patients with Parkinson's disease. Aging Clin Exp Res 2023; 35:2507-2516. [PMID: 37639172 DOI: 10.1007/s40520-023-02525-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 07/31/2023] [Indexed: 08/29/2023]
Abstract
BACKGROUND Frailty is common in Parkinson's disease (PD) and increases vulnerability to adverse outcomes. Early detection of this syndrome aids in early intervention. AIMS To objectively identify frailty at an early stage during routine motor tasks in PD patients using a Kinect-based system. METHODS PD patients were recruited and assessed with the Fried criteria to determine their frailty status. Each participant was recorded performing the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) extremity tasks with a Kinect-based system. Statistically significant kinematic parameters were selected to discriminate the pre-frail from the non-frail group. RESULTS Of the fifty-two participants, twenty were non-frail and thirty-two were pre-frail. Decreased frequency in finger tapping (P = 0.005), hand grasping (P = 0.002), toe tapping (P = 0.002), and leg agility (P = 0.019) alongside reduced hand grasping speed (P = 0.030), lifting (P < 0.001) and falling speed (P < 0.001) in leg agility were observed in the pre-frail group. Amplitude in leg agility (P = 0.048) and amplitude decrement rate (P = 0.046) in hand grasping showed marginally significant differences between two groups. Moderate discriminative values were found in frequency and speed of the extremity tasks to identify pre-frailty with sensitivity, specificity, and area under the curve (AUC) in the range of 45.00-85.00%, 68.75-100%, and 0.701-0.836, respectively. The combination of frequency and speed in extremity tasks showed moderate to high discriminatory ability, with AUC of 0.775 (95% CI 0.637-0.913, P < 0.001) for upper limb tasks and 0.909 (95% CI 0.832-0.987, P < 0.001) for lower limb tasks. When combining these features in both upper and lower limb tasks, the AUC increased to 0.942 (95% CI 0.886-0.999, P < 0.001). CONCLUSIONS Our findings demonstrated the promise of utilizing Kinect-based kinematic data from MDS-UPDRS III tasks as early indicators of frailty in PD patients.
Collapse
Affiliation(s)
- Ludi Xie
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ronghua Hong
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- Department of Neurology and Neurological Rehabilitation, Shanghai Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Zhuang Wu
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lei Yue
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Kangwen Peng
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Shuangfang Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jingxing Zhang
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xijin Wang
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lingjing Jin
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
- Department of Neurology and Neurological Rehabilitation, Shanghai Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University, Shanghai, China.
- Shanghai Clinical Research Center for Aging and Medicine, Shanghai, China.
| | - Qiang Guan
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
| |
Collapse
|
6
|
Bronte-Stewart H, Gala A, Wilkins K, Pettruci M, Kehnemouyi Y, Velisar A, Trager M. The digital signature of emergent tremor in Parkinson's disease. RESEARCH SQUARE 2023:rs.3.rs-3467667. [PMID: 37961117 PMCID: PMC10635351 DOI: 10.21203/rs.3.rs-3467667/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background Emergent tremor in Parkinson's disease (PD) can occur during sustained postures or movement that is different from action tremor. Tremor can contaminate the clinical rating of bradykinesia during finger tapping. Currently, there is no reliable way of isolating emergent tremor and measuring the cardinal motor symptoms based on voluntary movements only. Objective Investigate whether emergent tremor during repetitive alternating finger tapping (RAFT) on a quantitative digitography (QDG) device can be reliably identified and distinguished from voluntary tapping. Methods Ninety-six individuals with PD and forty-two healthy controls performed a thirty-second QDG-RAFT task and the Movement Disorders Society - Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III). Visual identification of tremor during QDG-RAFT was labelled by an experienced movement disorders specialist. Two methods of identifying tremor were investigated: 1) physiologically-informed temporal thresholds 2) XGBoost model using temporal and amplitude features of tapping. Results The XGBoost model showed high accuracy for identifying tremor (area under the precision-recall curve of 0.981) and outperformed temporal-based thresholds. Percent time duration of classifier-identified tremor showed significant correlations with MDS-UPDRS III tremor subscores (r = 0.50, P < 0.0001). There was a significant change in QDG metrics for bradykinesia, rigidity and arrhythmicity after tremor strikes were excluded (p < 0.01). Conclusions Emergent tremor during QDG-RAFT has a unique digital signature and the duration of tremor correlated with the MDS-UPDRS III tremor items. When involuntary tremor strikes were excluded, the QDG metrics of bradykinesia and rigidity were significantly worse, demonstrating the importance of distinguishing tremor from voluntary movement when rating bradykinesia.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Megan Trager
- Columbia University College of Physicians and Surgeons
| |
Collapse
|
7
|
Radcliffe EM, Baumgartner AJ, Kern DS, Al Borno M, Ojemann S, Kramer DR, Thompson JA. Oscillatory beta dynamics inform biomarker-driven treatment optimization for Parkinson's disease. J Neurophysiol 2023; 129:1492-1504. [PMID: 37198135 DOI: 10.1152/jn.00055.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/23/2023] [Accepted: 05/17/2023] [Indexed: 05/19/2023] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder characterized by loss of dopaminergic neurons and dysregulation of the basal ganglia. Cardinal motor symptoms include bradykinesia, rigidity, and tremor. Deep brain stimulation (DBS) of select subcortical nuclei is standard of care for medication-refractory PD. Conventional open-loop DBS delivers continuous stimulation with fixed parameters that do not account for a patient's dynamic activity state or medication cycle. In comparison, closed-loop DBS, or adaptive DBS (aDBS), adjusts stimulation based on biomarker feedback that correlates with clinical state. Recent work has identified several neurophysiological biomarkers in local field potential recordings from PD patients, the most promising of which are 1) elevated beta (∼13-30 Hz) power in the subthalamic nucleus (STN), 2) increased beta synchrony throughout basal ganglia-thalamocortical circuits, notably observed as coupling between the STN beta phase and cortical broadband gamma (∼50-200 Hz) amplitude, and 3) prolonged beta bursts in the STN and cortex. In this review, we highlight relevant frequency and time domain features of STN beta measured in PD patients and summarize how spectral beta power, oscillatory beta synchrony, phase-amplitude coupling, and temporal beta bursting inform PD pathology, neurosurgical targeting, and DBS therapy. We then review how STN beta dynamics inform predictive, biomarker-driven aDBS approaches for optimizing PD treatment. We therefore provide clinically useful and actionable insight that can be applied toward aDBS implementation for PD.
Collapse
Affiliation(s)
- Erin M Radcliffe
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - Alexander J Baumgartner
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - Drew S Kern
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - Mazen Al Borno
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Computer Science and Engineering, University of Colorado Denver, Denver, Colorado, United States
| | - Steven Ojemann
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - Daniel R Kramer
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - John A Thompson
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| |
Collapse
|
8
|
Wilkins KB, Kehnemouyi YM, Petrucci MN, Anderson RW, Parker JE, Trager MH, Neuville RS, Koop MM, Velisar A, Blumenfeld Z, Quinn EJ, Bronte-Stewart HM. Bradykinesia and Its Progression Are Related to Interhemispheric Beta Coherence. Ann Neurol 2023; 93:1029-1039. [PMID: 36641645 PMCID: PMC10191890 DOI: 10.1002/ana.26605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/05/2022] [Accepted: 01/09/2023] [Indexed: 01/16/2023]
Abstract
OBJECTIVE Bradykinesia is the major cardinal motor sign of Parkinson disease (PD), but its neural underpinnings are unclear. The goal of this study was to examine whether changes in bradykinesia following long-term subthalamic nucleus (STN) deep brain stimulation (DBS) are linked to local STN beta (13-30 Hz) dynamics or a wider bilateral network dysfunction. METHODS Twenty-one individuals with PD implanted with sensing neurostimulators (Activa® PC + S, Medtronic, PLC) in the STN participated in a longitudinal 'washout' therapy study every three to 6 months for an average of 3 years. At each visit, participants were withdrawn from medication (12/24/48 hours) and had DBS turned off (>60 minutes) before completing a repetitive wrist-flexion extension task, a validated quantitative assessment of bradykinesia, while local field potentials were recorded. Local STN beta dynamics were investigated via beta power and burst duration, while interhemispheric beta synchrony was assessed with STN-STN beta coherence. RESULTS Higher interhemispheric STN beta coherence, but not contralateral beta power or burst duration, was significantly associated with worse bradykinesia. Bradykinesia worsened off therapy over time. Interhemispheric STN-STN beta coherence also increased over time, whereas beta power and burst duration remained stable. The observed change in bradykinesia was related to the change in interhemispheric beta coherence, with greater increases in synchrony associated with further worsening of bradykinesia. INTERPRETATION Together, these findings implicate interhemispheric beta synchrony as a neural correlate of the progression of bradykinesia following chronic STN DBS. This could imply the existence of a pathological bilateral network contributing to bradykinesia in PD. ANN NEUROL 2023;93:1029-1039.
Collapse
Affiliation(s)
- Kevin B Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Yasmine M Kehnemouyi
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Department of Bioengineering, Stanford Schools of Engineering & Medicine, Stanford, CA, United States
| | - Matthew N Petrucci
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Department of Bioengineering, Stanford Schools of Engineering & Medicine, Stanford, CA, United States
| | - Ross W Anderson
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Department of Neurosurgery, Kaiser Permanente, Redwood City, CA, United States
| | - Jordan E Parker
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Department of Psychology, University of California, Los Angeles, CA, United States
| | - Megan H Trager
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Columbia University Irving Medical Center, New York, NY, United States
| | - Raumin S Neuville
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- The University of California School of Medicine, Irvine, CA, United States
| | - Mandy M Koop
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Cleveland Clinic, Cleveland, OH, United States
| | - Anca Velisar
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- The Smith-Kettlewell Eye Research Institute, San Francisco, CA, United States
| | - Zack Blumenfeld
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States
- University of Southern California, Keck School of Medicine, Los Angeles, CA, United States
| | - Emma J Quinn
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Credit Karma, San Francisco, CA, United States
| | - Helen M Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Department of Bioengineering, Stanford Schools of Engineering & Medicine, Stanford, CA, United States
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
| |
Collapse
|
9
|
Ma LY, Shi WK, Chen C, Wang Z, Wang XM, Jin JN, Chen L, Ren K, Chen ZL, Ling Y, Feng T. Remote scoring models of rigidity and postural stability of Parkinson's disease based on indirect motions and a low-cost RGB algorithm. Front Aging Neurosci 2023; 15:1034376. [PMID: 36875695 PMCID: PMC9983361 DOI: 10.3389/fnagi.2023.1034376] [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: 09/01/2022] [Accepted: 01/12/2023] [Indexed: 02/19/2023] Open
Abstract
Background and objectives The Movement Disorder Society's Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III) is mostly common used for assessing the motor symptoms of Parkinson's disease (PD). In remote circumstances, vision-based techniques have many strengths over wearable sensors. However, rigidity (item 3.3) and postural stability (item 3.12) in the MDS-UPDRS III cannot be assessed remotely since participants need to be touched by a trained examiner during testing. We developed the four scoring models of rigidity of the neck, rigidity of the lower extremities, rigidity of the upper extremities, and postural stability based on features extracted from other available and touchless motions. Methods The red, green, and blue (RGB) computer vision algorithm and machine learning were combined with other available motions from the MDS-UPDRS III evaluation. A total of 104 patients with PD were split into a train set (89 individuals) and a test set (15 individuals). The light gradient boosting machine (LightGBM) multiclassification model was trained. Weighted kappa (k), absolute accuracy (ACC ± 0), and Spearman's correlation coefficient (rho) were used to evaluate the performance of model. Results For model of rigidity of the upper extremities, k = 0.58 (moderate), ACC ± 0 = 0.73, and rho = 0.64 (moderate). For model of rigidity of the lower extremities, k = 0.66 (substantial), ACC ± 0 = 0.70, and rho = 0.76 (strong). For model of rigidity of the neck, k = 0.60 (moderate), ACC ± 0 = 0.73, and rho = 0.60 (moderate). For model of postural stability, k = 0.66 (substantial), ACC ± 0 = 0.73, and rho = 0.68 (moderate). Conclusion Our study can be meaningful for remote assessments, especially when people have to maintain social distance, e.g., in situations such as the coronavirus disease-2019 (COVID-19) pandemic.
Collapse
Affiliation(s)
- Ling-Yan Ma
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Wei-Kun Shi
- GYENNO SCIENCE CO., LTD., Shenzhen, China.,HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Cheng Chen
- GYENNO SCIENCE CO., LTD., Shenzhen, China.,HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Zhan Wang
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Xue-Mei Wang
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jia-Ning Jin
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Lu Chen
- Department of Encephalopathy I, Dong Fang Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China
| | - Kang Ren
- GYENNO SCIENCE CO., LTD., Shenzhen, China.,HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Zhong-Lue Chen
- GYENNO SCIENCE CO., LTD., Shenzhen, China.,HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Yun Ling
- GYENNO SCIENCE CO., LTD., Shenzhen, China.,HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Tao Feng
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Parkinson's Disease Center, Beijing Institute for Brain Disorders, Beijing, China
| |
Collapse
|