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Scully AE, Tan DML, de Oliveira BI, Hill KD, Clark R, Pua YH. Validity and reliability of a new clinician-rated tool for freezing of gait severity. Disabil Rehabil 2024; 46:3133-3140. [PMID: 37551868 DOI: 10.1080/09638288.2023.2242257] [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: 01/12/2023] [Accepted: 07/23/2023] [Indexed: 08/09/2023]
Abstract
PURPOSE The Freezing of Gait Severity Tool (FOG Tool) was developed because of limitations in existing assessments. This cross-sectional study investigated its validity and reliability. METHODS People with Parkinson's disease (PD) were recruited consecutively from clinics. Those who could not walk eight-metres independently (with or without an assistive device), comprehend instructions, or with co-morbidities affecting walking were excluded. Participants completed a set of assessments including the FOG Tool, Timed Up and Go (TUG), and Freezing of Gait Questionnaire. The FOG Tool was repeated and those reporting no medication state change evaluated for test-retest reliability. Validity and reliability were investigated through Spearman's correlations and ICC (two-way, random). McNemar's test was applied to compare the FOG Tool and TUG on the proportion of people with freezing. RESULTS Thirty-nine participants were recruited [79.5%(n = 31) male; Median(IQR): age-73.0(9.0) years; disease duration-4.0(5.8) years]. Fifteen (38.5%) contributed to test-retest reliability analyses. The FOG Tool demonstrated strongest associations with the Freezing of Gait Questionnaire (ρ = 0.67, 95%CI 0.43-0.83). Test-retest reliability was excellent (ICC = 0.96, 95%CI 0.88-0.99). The FOG Tool had 6.2 times the odds (95%CI 2.4-20.4, p < 0.001) of triggering freezing compared to the TUG. CONCLUSIONS The FOG Tool appeared adequately valid and reliable in this small sample of people with PD. It was more successful in triggering freezing than the TUG.
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Affiliation(s)
- Aileen Eugenia Scully
- School of Allied Health, Curtin University, Bentley, Perth, Australia
- Health and Social Sciences, Singapore Institute of Technology, Singapore, Singapore
| | - Dawn May Leng Tan
- Health and Social Sciences, Singapore Institute of Technology, Singapore, Singapore
- Department of Physiotherapy, Singapore General Hospital, Singapore, Singapore
| | | | - Keith David Hill
- Rehabilitation Ageing and Independent Living Research Centre, Monash University, Frankston, Australia
| | - Ross Clark
- School of Health and Behavioural Sciences, University of the Sunshine Coast, Sippy Downs, Australia
| | - Yong Hao Pua
- Department of Physiotherapy, Singapore General Hospital, Singapore, Singapore
- Duke-NUS Graduate Medical School, Medicine Academic Programme, Singapore, Singapore
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Wilkins KB, Petrucci MN, Lambert EF, Melbourne JA, Gala AS, Akella P, Parisi L, Cui C, Kehnemouyi YM, Hoffman SL, Aditham S, Diep C, Dorris HJ, Parker JE, Herron JA, Bronte-Stewart HM. Beta Burst-Driven Adaptive Deep Brain Stimulation Improves Gait Impairment and Freezing of Gait in Parkinson's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.26.24309418. [PMID: 38978669 PMCID: PMC11230310 DOI: 10.1101/2024.06.26.24309418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD) that is often refractory to medication. Pathological prolonged beta bursts within the subthalamic nucleus (STN) are associated with both worse impairment and freezing behavior in PD, which are improved with deep brain stimulation (DBS). The goal of the current study was to investigate the feasibility, safety, and tolerability of beta burst-driven adaptive DBS (aDBS) for FOG in PD. Methods Seven individuals with PD were implanted with the investigational Summit™ RC+S DBS system (Medtronic, PLC) with leads placed bilaterally in the STN. A PC-in-the-loop architecture was used to adjust stimulation amplitude in real-time based on the observed beta burst durations in the STN. Participants performed either a harnessed stepping-in-place task or a free walking turning and barrier course, as well as clinical motor assessments and instrumented measures of bradykinesia, OFF stimulation, on aDBS, continuous DBS (cDBS), or random intermittent DBS (iDBS). Results Beta burst driven aDBS was successfully implemented and deemed safe and tolerable in all seven participants. Gait metrics such as overall percent time freezing and mean peak shank angular velocity improved from OFF to aDBS and showed similar efficacy as cDBS. Similar improvements were also seen for overall clinical motor impairment, including tremor, as well as quantitative metrics of bradykinesia. Conclusion Beta burst driven adaptive DBS was feasible, safe, and tolerable in individuals with PD with gait impairment and FOG.
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Affiliation(s)
- K B Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - M 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
| | - E F Lambert
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - J A Melbourne
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - A S Gala
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - P Akella
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - L Parisi
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - C Cui
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Y 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
| | - S L Hoffman
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - S Aditham
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - C Diep
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - H J Dorris
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - J E Parker
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - J A Herron
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - H M Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
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Watts J, Niethammer M, Khojandi A, Ramdhani R. Machine learning model comparison for freezing of gait prediction in advanced Parkinson's disease. Front Aging Neurosci 2024; 16:1431280. [PMID: 39006221 PMCID: PMC11240851 DOI: 10.3389/fnagi.2024.1431280] [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: 05/11/2024] [Accepted: 06/10/2024] [Indexed: 07/16/2024] Open
Abstract
Introduction Freezing of gait (FOG) is a paroxysmal motor phenomenon that increases in prevalence as Parkinson's disease (PD) progresses. It is associated with a reduced quality of life and an increased risk of falls in this population. Precision-based detection and classification of freezers are critical to developing tailored treatments rooted in kinematic assessments. Methods This study analyzed instrumented stand-and-walk (SAW) trials from advanced PD patients with STN-DBS. Each patient performed two SAW trials in their OFF Medication-OFF DBS state. For each trial, gait summary statistics from wearable sensors were analyzed by machine learning classification algorithms. These algorithms include k-nearest neighbors, logistic regression, naïve Bayes, random forest, and support vector machines (SVM). Each of these models were selected for their high interpretability. Each algorithm was tasked with classifying patients whose SAW trials MDS-UPDRS FOG subscore was non-zero as assessed by a trained movement disorder specialist. These algorithms' performance was evaluated using stratified five-fold cross-validation. Results A total of 21 PD subjects were evaluated (average age 64.24 years, 16 males, mean disease duration of 14 years). Fourteen subjects had freezing of gait in the OFF MED/OFF DBS. All machine learning models achieved statistically similar predictive performance (p < 0.05) with high accuracy. Analysis of random forests' feature estimation revealed the top-ten spatiotemporal predictive features utilized in the model: foot strike angle, coronal range of motion [trunk and lumbar], stride length, gait speed, lateral step variability, and toe-off angle. Conclusion These results indicate that machine learning effectively classifies advanced PD patients as freezers or nonfreezers based on SAW trials in their non-medicated/non-stimulated condition. The machine learning models, specifically random forests, not only rely on but utilize salient spatial and temporal gait features for FOG classification.
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Affiliation(s)
- Jeremy Watts
- Department of Mathematics, University of Tennessee, Knoxville, TN, United States
| | - Martin Niethammer
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, United States
| | - Anahita Khojandi
- Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States
| | - Ritesh Ramdhani
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
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Kim J, Porciuncula F, Yang HD, Wendel N, Baker T, Chin A, Ellis TD, Walsh CJ. Soft robotic apparel to avert freezing of gait in Parkinson's disease. Nat Med 2024; 30:177-185. [PMID: 38182783 DOI: 10.1038/s41591-023-02731-8] [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: 04/07/2023] [Accepted: 11/21/2023] [Indexed: 01/07/2024]
Abstract
Freezing of gait (FoG) is a profoundly disruptive gait disturbance in Parkinson's disease, causing unintended stops while walking. Therapies for FoG reveal modest and transient effects, resulting in a lack of effective treatments. Here we show proof of concept that FoG can be averted using soft robotic apparel that augments hip flexion. The wearable garment uses cable-driven actuators and sensors, generating assistive moments in concert with biological muscles. In this n-of-1 trial with five repeated measurements spanning 6 months, a 73-year-old male with Parkinson's disease and substantial FoG demonstrated a robust response to robotic apparel. With assistance, FoG was instantaneously eliminated during indoor walking (0% versus 39 ± 16% time spent freezing when unassisted), accompanied by 49 ± 11 m (+55%) farther walking compared to unassisted walking, faster speeds (+0.18 m s-1) and improved gait quality (-25% in gait variability). FoG-targeting effects were repeatable across multiple days, provoking conditions and environment contexts, demonstrating potential for community use. This study demonstrated that FoG was averted using soft robotic apparel in an individual with Parkinson's disease, serving as an impetus for technological advancements in response to this serious yet unmet need.
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Affiliation(s)
- Jinsoo Kim
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Franchino Porciuncula
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
- Department of Physical Therapy, Boston University, Boston, MA, USA
| | - Hee Doo Yang
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Nicholas Wendel
- Department of Physical Therapy, Boston University, Boston, MA, USA
| | - Teresa Baker
- Department of Physical Therapy, Boston University, Boston, MA, USA
| | - Andrew Chin
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Terry D Ellis
- Department of Physical Therapy, Boston University, Boston, MA, USA.
| | - Conor J Walsh
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
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Conde CI, Lang C, Baumann CR, Easthope CA, Taylor WR, Ravi DK. Triggers for freezing of gait in individuals with Parkinson's disease: a systematic review. Front Neurol 2023; 14:1326300. [PMID: 38187152 PMCID: PMC10771308 DOI: 10.3389/fneur.2023.1326300] [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: 10/23/2023] [Accepted: 12/07/2023] [Indexed: 01/09/2024] Open
Abstract
Background Freezing of Gait (FOG) is a motor symptom frequently observed in advanced Parkinson's disease. However, due to its paroxysmal nature and diverse presentation, assessing FOG in a clinical setting can be challenging. Before FOG can be fully investigated, it is critical that a reliable experimental setting is established in which FOG can be evoked in a standardized manner, but the efficacy of various gait tasks and triggers for eliciting FOG remains unclear. Objectives This study aimed to conduct a systematic review of the existing literature and evaluate the available evidence for the relationship between specific motor tasks, triggers, and FOG episodes in individuals with Parkinson's disease (PwPD). Methods We conducted a literature search on four online databases (PubMed, Web of Science, EMBASE, and Cochrane Library) using the keywords "Parkinson's disease," "Freezing of Gait", "triggers" and "tasks". A total of 128 articles met the inclusion criteria and were included in our analysis. Results The review found that a wide range of gait tasks were employed in studies assessing FOG among PD patients. However, three tasks (turning, dual tasking, and straight walking) emerged as the most frequently used. Turning (28%) appears to be the most effective trigger for eliciting FOG in PwPD, followed by walking through a doorway (14%) and dual tasking (10%). Conclusion This review thereby supports the utilisation of turning, especially a 360-degree turn, as a reliable trigger for FOG in PwPD. This finding could be beneficial to clinicians conducting clinical evaluations and researchers aiming to assess FOG in a laboratory environment.
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Affiliation(s)
| | - Charlotte Lang
- Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
| | - Christian R. Baumann
- Department of Neurology, University Hospital Zurich, Zürich, Switzerland
- The LOOP Zurich – Medical Research Center, Zürich, Switzerland
| | - Chris A. Easthope
- The LOOP Zurich – Medical Research Center, Zürich, Switzerland
- Lake Lucerne Institute, Vitznau, Switzerland
- creneo Foundation – Center for Interdisciplinary Research, Vitznau, Switzerland
| | - William R. Taylor
- Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
- The LOOP Zurich – Medical Research Center, Zürich, Switzerland
| | - Deepak K. Ravi
- Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
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Wilkins KB, Melbourne JA, Akella P, Bronte-Stewart HM. Unraveling the complexities of programming neural adaptive deep brain stimulation in Parkinson's disease. Front Hum Neurosci 2023; 17:1310393. [PMID: 38094147 PMCID: PMC10716917 DOI: 10.3389/fnhum.2023.1310393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 11/09/2023] [Indexed: 02/01/2024] Open
Abstract
Over the past three decades, deep brain stimulation (DBS) for Parkinson's disease (PD) has been applied in a continuous open loop fashion, unresponsive to changes in a given patient's state or symptoms over the course of a day. Advances in recent neurostimulator technology enable the possibility for closed loop adaptive DBS (aDBS) for PD as a treatment option in the near future in which stimulation adjusts in a demand-based manner. Although aDBS offers great clinical potential for treatment of motor symptoms, it also brings with it the need for better understanding how to implement it in order to maximize its benefits. In this perspective, we outline considerations for programing several key parameters for aDBS based on our experience across several aDBS-capable research neurostimulators. At its core, aDBS hinges on successful identification of relevant biomarkers that can be measured reliably in real-time working in cohesion with a control policy that governs stimulation adaption. However, auxiliary parameters such as the window in which stimulation is allowed to adapt, as well as the rate it changes, can be just as impactful on performance and vary depending on the control policy and patient. A standardize protocol for programming aDBS will be crucial to ensuring its effective application in clinical practice.
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Affiliation(s)
- Kevin B. Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Jillian A. Melbourne
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Pranav Akella
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Helen M. Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
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Exploring a New Cueing Device in People Who Experience Freezing of Gait: Acceptance of a Study Design. PARKINSON'S DISEASE 2022; 2022:1631169. [DOI: 10.1155/2022/1631169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/21/2022] [Accepted: 11/24/2022] [Indexed: 12/12/2022]
Abstract
Background. Freezing of Gait (FoG) is a disabling symptom of Parkinson’s Disease (PD) and is defined as a “brief episodic absence or marked reduction of forward progression of the feet despite the intention to walk.” Compensatory strategies such as cueing and high frequency vibrotactile stimulation can reduce FoG severity and improve gait parameters. A new Sternal high frequency Vibrotactile Stimulation Device (SVSD) with cueing function has been developed, however the clinical effects of this device are yet to be fully investigated. Objective. The aim of this study was to investigate, if the proposed study design using a SVSD and gait analysis sensor insoles was acceptable for people with PD. Methods. This feasibility study was designed as a randomized cross-over study. Thirteen participants took part in a one off 60-minute data collection session. The acceptability of the study design was assessed with a mixed methods questionnaire considering each step of the study process. Secondary outcome measures were the feasibility of using the 10 Metre Walk Test (10MWT), the Freezing of Gait Score (FoG-Score), and Patient Global Impression of Change (PGI-C) with and without the SVSD. Results. The participants scored all aspects of the study design as very satisfactory. In addition, all participants could perform the secondary outcome measures and were deemed feasible. Feedback from open ended questions provided ideas and considerations for adaptations of future clinical studies. Conclusion. The proposed study design was acceptable for people with PD. Implications. This study design, with small adaptations, can be used for larger studies to evaluate the effect of an SVSD on FoG in people with PD.
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Boolani A, Martin J, Huang H, Yu LF, Stark M, Grin Z, Roy M, Yager C, Teymouri S, Bradley D, Martin R, Fulk G, Kakar RS. Association between Self-Reported Prior Night's Sleep and Single-Task Gait in Healthy, Young Adults: A Study Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2022; 22:7406. [PMID: 36236511 PMCID: PMC9572361 DOI: 10.3390/s22197406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/18/2022] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
Failure to obtain the recommended 7−9 h of sleep has been associated with injuries in youth and adults. However, most research on the influence of prior night’s sleep and gait has been conducted on older adults and clinical populations. Therefore, the objective of this study was to identify individuals who experience partial sleep deprivation and/or sleep extension the prior night using single task gait. Participants (n = 123, age 24.3 ± 4.0 years; 65% female) agreed to participate in this study. Self-reported sleep duration of the night prior to testing was collected. Gait data was collected with inertial sensors during a 2 min walk test. Group differences (<7 h and >9 h, poor sleepers; 7−9 h, good sleepers) in gait characteristics were assessed using machine learning and a post-hoc ANCOVA. Results indicated a correlation (r = 0.79) between gait parameters and prior night’s sleep. The most accurate machine learning model was a Random Forest Classifier using the top 9 features, which had a mean accuracy of 65.03%. Our findings suggest that good sleepers had more asymmetrical gait patterns and were better at maintaining gait speed than poor sleepers. Further research with larger subject sizes is needed to develop more accurate machine learning models to identify prior night’s sleep using single-task gait.
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Affiliation(s)
- Ali Boolani
- Honors Program, Clarkson University, Potsdam, NY 13699, USA
| | - Joel Martin
- Sports Medicine Assessment Research & Testing (SMART) Laboratory, George Mason University, Manassas, VA 20110, USA
| | - Haikun Huang
- Department of Computer Science, George Mason University, Manassas, VA 20110, USA
| | - Lap-Fai Yu
- Department of Computer Science, George Mason University, Manassas, VA 20110, USA
| | - Maggie Stark
- Department of Medicine, Lake Erie College of Osteopathic Medicine, Elmira, NY 14901, USA
| | - Zachary Grin
- Honors Program, Clarkson University, Potsdam, NY 13699, USA
| | - Marissa Roy
- Sports Medicine Assessment Research & Testing (SMART) Laboratory, George Mason University, Manassas, VA 20110, USA
| | | | - Seema Teymouri
- Department of Engineering and Technology, State University of New York Canton, Canton, NY 13617, USA
| | - Dylan Bradley
- Department of Physical Therapy, Hanover College, Hanover, IN 47243, USA
| | - Rebecca Martin
- Department of Neurology, St. Joseph’s Hospital Health Center, Syracuse, NY 13203, USA
| | - George Fulk
- Department of Physical Therapy, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Rumit Singh Kakar
- Human Movement Science Department, Oakland University, Rochester, MI 48309, USA
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Scully AE, de Oliveira BIR, Hill KD, Tan D, Pua YH, Clark R, Burton E. Developing the Freezing of Gait Severity Tool: A Delphi consensus study to determine the content of a clinician-rated assessment for freezing of gait severity. Clin Rehabil 2022; 36:1679-1693. [DOI: 10.1177/02692155221121180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Objectives There is no standardisation of tasks or measures for evaluation of freezing of gait severity in people with Parkinson's disease. This study aimed to develop a clinician-rated tool for freezing of gait severity (i.e. Freezing of Gait Severity Tool), through determining clinicians’ ratings of the most important triggering circumstances to be examined and aspects of freezing of gait to be measured. Design A three-round, web-based Delphi study. Participants Healthcare professionals, with at least five years’ experience in managing freezing of gait in people with Parkinson. Main outcome measures Round 1 required participants ( n = 28) to rate items on a 5-point Likert scale, based on priority for inclusion in the Freezing of Gait Severity Tool. In Round 2, participants ( n = 18) ranked the items based on priority for inclusion. In Round 3, participants ( n = 18) confirmed or rejected the shortlisted items by judging their ability, on a binary scale, to screen for freezing of gait, detect changes in freezing severity, and discriminate between degrees of severity. Results Participants agreed with the triggering circumstances of turning hesitation, narrow space hesitation, start hesitation, cognitive dual-tasking, and open space hesitation should be assessed; and the aspects of gait freezing to be measured included freezing type, number of freezing episodes during a task, and average duration of freezing episodes. Conclusions This study attained a consensus for the items to be included in a clinician-rated tool for freezing of gait severity. Future studies should investigate psychometric properties and clinical feasibility of the Freezing of Gait Severity Tool.
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Affiliation(s)
- Aileen E Scully
- Curtin School of Allied Health, Curtin University, Bentley, Western Australia, Australia
| | - Beatriz IR de Oliveira
- Curtin School of Allied Health, Curtin University, Bentley, Western Australia, Australia
| | - Keith D Hill
- Rehabilitation Ageing and Independent Living (RAIL) Research Centre, Monash University, Frankston, Victoria, Australia
| | - Dawn Tan
- Health and Social Sciences, Singapore Institute of Technology, Singapore
- Department of Physiotherapy, Singapore General Hospital, Singapore
| | - Yong Hao Pua
- Department of Physiotherapy, Singapore General Hospital, Singapore
| | - Ross Clark
- School of Health and Behavioural Sciences, University of the Sunshine Coast, Sunshine Coast, Queensland, Australia
| | - Elissa Burton
- Curtin School of Allied Health, Curtin University, Bentley, Western Australia, Australia
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Li W, Chen X, Zhang J, Lu J, Zhang C, Bai H, Liang J, Wang J, Du H, Xue G, Ling Y, Ren K, Zou W, Chen C, Li M, Chen Z, Zou H. Recognition of Freezing of Gait in Parkinson’s Disease Based on Machine Vision. Front Aging Neurosci 2022; 14:921081. [PMID: 35912091 PMCID: PMC9329960 DOI: 10.3389/fnagi.2022.921081] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 06/21/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundFreezing of gait (FOG) is a common clinical manifestation of Parkinson’s disease (PD), mostly occurring in the intermediate and advanced stages. FOG is likely to cause patients to fall, resulting in fractures, disabilities and even death. Currently, the pathogenesis of FOG is unclear, and FOG detection and screening methods have various defects, including subjectivity, inconvenience, and high cost. Due to limited public healthcare and transportation resources during the COVID-19 pandemic, there are greater inconveniences for PD patients who need diagnosis and treatment.ObjectiveA method was established to automatically recognize FOG in PD patients through videos taken by mobile phone, which is time-saving, labor-saving, and low-cost for daily use, which may overcome the above defects. In the future, PD patients can undergo FOG assessment at any time in the home rather than in the hospital.MethodsIn this study, motion features were extracted from timed up and go (TUG) test and the narrow TUG (Narrow) test videos of 50 FOG-PD subjects through a machine learning method; then a motion recognition model to distinguish between walking and turning stages and a model to recognize FOG in these stages were constructed using the XGBoost algorithm. Finally, we combined these three models to form a multi-stage FOG recognition model.ResultsWe adopted the leave-one-subject-out (LOSO) method to evaluate model performance, and the multi-stage FOG recognition model achieved a sensitivity of 87.5% sensitivity and a specificity of 79.82%.ConclusionA method to realize remote PD patient FOG recognition based on mobile phone video is presented in this paper. This method is convenient with high recognition accuracy and can be used to rapidly evaluate FOG in the home environment and remotely manage FOG-PD, or screen patients in large-scale communities.
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Affiliation(s)
- Wendan Li
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
- Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China
| | | | - Jintao Zhang
- Department of Neurology, The 960th Hospital of PLA, Taian, China
| | - Jianjun Lu
- Department of Neurosurgery, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Chencheng Zhang
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongmin Bai
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
| | - Junchao Liang
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
| | - Jiajia Wang
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
| | - Hanqiang Du
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
| | - Gaici Xue
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
| | - Yun Ling
- GYENNO SCIENCE Co., LTD., Shenzhen, China
| | - Kang Ren
- GYENNO SCIENCE Co., LTD., Shenzhen, China
| | | | - Cheng Chen
- GYENNO SCIENCE Co., LTD., Shenzhen, China
| | - Mengyan Li
- Department of Neurology, Guangzhou First People’s Hospital, Guangzhou, China
- *Correspondence: Mengyan Li,
| | - Zhonglue Chen
- GYENNO SCIENCE Co., LTD., Shenzhen, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
- Zhonglue Chen,
| | - Haiqiang Zou
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
- Branch of National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Guangzhou, China
- Haiqiang Zou,
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Kim J, Quinlivan BT, Deprey LA, Arumukhom Revi D, Eckert-Erdheim A, Murphy P, Orzel D, Walsh CJ. Reducing the energy cost of walking with low assistance levels through optimized hip flexion assistance from a soft exosuit. Sci Rep 2022; 12:11004. [PMID: 35768486 PMCID: PMC9243082 DOI: 10.1038/s41598-022-14784-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 06/13/2022] [Indexed: 11/09/2022] Open
Abstract
As we age, humans see natural decreases in muscle force and power which leads to a slower, less efficient gait. Improving mobility for both healthy individuals and those with muscle impairments/weakness has been a goal for exoskeleton designers for decades. In this work, we discover that significant reductions in the energy cost required for walking can be achieved with almost 50% less mechanical power compared to the state of the art. This was achieved by leveraging human-in-the-loop optimization to understand the importance of individualized assistance for hip flexion, a relatively unexplored joint motion. Specifically, we show that a tethered hip flexion exosuit can reduce the metabolic rate of walking by up to 15.2 ± 2.6%, compared to locomotion with assistance turned off (equivalent to 14.8% reduction compared to not wearing the exosuit). This large metabolic reduction was achieved with surprisingly low assistance magnitudes (average of 89 N, ~ 24% of normal hip flexion torque). Furthermore, the ratio of metabolic reduction to the positive exosuit power delivered was 1.8 times higher than ratios previously found for hip extension and ankle plantarflexion. These findings motivated the design of a lightweight (2.31 kg) and portable hip flexion assisting exosuit, that demonstrated a 7.2 ± 2.9% metabolic reduction compared to walking without the exosuit. The high ratio of metabolic reduction to exosuit power measured in this study supports previous simulation findings and provides compelling evidence that hip flexion may be an efficient joint motion to target when considering how to create practical and lightweight wearable robots to support improved mobility.
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Affiliation(s)
- Jinsoo Kim
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, 02134, USA
| | - Brendan T Quinlivan
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, 02134, USA
| | - Lou-Ana Deprey
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, 02134, USA
- School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Dheepak Arumukhom Revi
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, 02134, USA
- Department of Mechanical Engineering, Boston University, Boston, 02215, USA
- College of Health and Rehabilitation Sciences: Sargent College, Boston University, Boston, 02215, USA
| | - Asa Eckert-Erdheim
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, 02134, USA
| | - Patrick Murphy
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, 02134, USA
| | - Dorothy Orzel
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, 02134, USA
| | - Conor J Walsh
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, 02134, USA.
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12
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Lewis S, Factor S, Giladi N, Nieuwboer A, Nutt J, Hallett M. Stepping up to meet the challenge of freezing of gait in Parkinson's disease. Transl Neurodegener 2022; 11:23. [PMID: 35490252 PMCID: PMC9057060 DOI: 10.1186/s40035-022-00298-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 03/31/2022] [Indexed: 11/20/2022] Open
Abstract
There has been a growing appreciation for freezing of gait as a disabling symptom that causes a significant burden in Parkinson’s disease. Previous research has highlighted some of the key components that underlie the phenomenon, but these reductionist approaches have yet to lead to a paradigm shift resulting in the development of novel treatment strategies. Addressing this issue will require greater integration of multi-modal data with complex computational modeling, but there are a number of critical aspects that need to be considered before embarking on such an approach. This paper highlights where the field needs to address current gaps and shortcomings including the standardization of definitions and measurement, phenomenology and pathophysiology, as well as considering what available data exist and how future studies should be constructed to achieve the greatest potential to better understand and treat this devastating symptom.
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Affiliation(s)
- Simon Lewis
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, School of Medical Sciences, University of Sydney, Sydney, NSW, Australia.
| | - Stewart Factor
- Jean and Paul Amos Parkinson's Disease and Movement Disorders Program, Emory University School of Medicine, Atlanta, GA, USA
| | - Nir Giladi
- Movement Disorders Unit, Department of Neurology, Tel-Aviv Sourasky Medical Center, Sackler School of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Alice Nieuwboer
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - John Nutt
- Movement Disorder Section, Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
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13
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O’Day J, Lee M, Seagers K, Hoffman S, Jih-Schiff A, Kidziński Ł, Delp S, Bronte-Stewart H. Assessing inertial measurement unit locations for freezing of gait detection and patient preference. J Neuroeng Rehabil 2022; 19:20. [PMID: 35152881 PMCID: PMC8842967 DOI: 10.1186/s12984-022-00992-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 01/13/2022] [Indexed: 12/28/2022] Open
Abstract
Background Freezing of gait, a common symptom of Parkinson’s disease, presents as sporadic episodes in which an individual’s feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference. Methods Sixteen people with Parkinson’s disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson’s disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events. Results The best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86) for the best technical set and minimal IMU set, respectively. Conclusions Several IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the development and adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait. Supplementary Information The online version contains supplementary material available at 10.1186/s12984-022-00992-x.
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14
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Horn MA, Gulberti A, Hidding U, Gerloff C, Hamel W, Moll CKE, Pötter-Nerger M. Comparison of Shod and Unshod Gait in Patients With Parkinson's Disease With Subthalamic and Nigral Stimulation. Front Hum Neurosci 2022; 15:751242. [PMID: 35095446 PMCID: PMC8790533 DOI: 10.3389/fnhum.2021.751242] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 12/06/2021] [Indexed: 12/19/2022] Open
Abstract
Background: The Parkinsonian [i.e., Parkinson's disease (PD)] gait disorder represents a therapeutical challenge with residual symptoms despite the use of deep brain stimulation of the subthalamic nucleus (STN DBS) and medical and rehabilitative strategies. The aim of this study was to assess the effect of different DBS modes as combined stimulation of the STN and substantia nigra (STN+SN DBS) and environmental rehabilitative factors as footwear on gait kinematics.Methods: This single-center, randomized, double-blind, crossover clinical trial assessed shod and unshod gait in patients with PD with medication in different DBS conditions (i.e., STIM OFF, STN DBS, and STN+SN DBS) during different gait tasks (i.e., normal gait, fast gait, and gait during dual task) and compared gait characteristics to healthy controls. Notably, 15 patients participated in the study, and 11 patients were analyzed after a dropout of four patients due to DBS-induced side effects.Results: Gait was modulated by both factors, namely, footwear and DBS mode, in patients with PD. Footwear impacted gait characteristics in patients with PD similarly to controls with longer step length, lower cadence, and shorter single-support time. Interestingly, DBS exerted specific effects depending on gait tasks with increased cognitive load. STN+SN DBS was the most efficient DBS mode compared to STIM OFF and STN DBS with intense effects as step length increment during dual task.Conclusion: The PD gait disorder is a multifactorial symptom, impacted by environmental factors as footwear and modulated by DBS. DBS effects on gait were specific depending on the gait task, with the most obvious effects with STN+SN DBS during gait with increased cognitive load.
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Affiliation(s)
- Martin A. Horn
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alessandro Gulberti
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ute Hidding
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Gerloff
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Wolfgang Hamel
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian K. E. Moll
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Monika Pötter-Nerger
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- *Correspondence: Monika Pötter-Nerger
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15
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Wilkins KB, Petrucci MN, Kehnemouyi Y, Velisar A, Han K, Orthlieb G, Trager MH, O’Day JJ, Aditham S, Bronte-Stewart H. Quantitative Digitography Measures Motor Symptoms and Disease Progression in Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2022; 12:1979-1990. [PMID: 35694934 PMCID: PMC9535590 DOI: 10.3233/jpd-223264] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/22/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND Assessment of motor signs in Parkinson's disease (PD) requires an in-person examination. However, 50% of people with PD do not have access to a neurologist. Wearable sensors can provide remote measures of some motor signs but require continuous monitoring for several days. A major unmet need is reliable metrics of all cardinal motor signs, including rigidity, from a simple short active task that can be performed remotely or in the clinic. OBJECTIVE Investigate whether thirty seconds of repetitive alternating finger tapping (RAFT) on a portable quantitative digitography (QDG) device, which measures amplitude and timing, produces reliable metrics of all cardinal motor signs in PD. METHODS Ninety-six individuals with PD and forty-two healthy controls performed a thirty-second QDG-RAFT task and clinical motor assessment. Eighteen individuals were followed longitudinally with repeated assessments for an average of three years and up to six years. RESULTS QDG-RAFT metrics showed differences between PD and controls and provided correlated metrics for total motor disability (MDS-UPDRS III) and for rigidity, bradykinesia, tremor, gait impairment, and freezing of gait (FOG). Additionally, QDG-RAFT tracked disease progression over several years off therapy and showed differences between akinetic-rigid and tremor-dominant phenotypes, as well as people with and without FOG. CONCLUSIONS QDG is a reliable technology, which could be used in the clinic or remotely. This could improve access to care, allow complex remote disease management based on data received in real time, and accurate monitoring of disease progression over time in PD. QDG-RAFT also provides the comprehensive motor metrics needed for therapeutic trials.
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Affiliation(s)
- Kevin B. Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Matthew N. Petrucci
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Yasmine Kehnemouyi
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Anca Velisar
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- The Smith-Kettlewell Eye Research Institute, San Francisco, CA, USA
| | - Katie Han
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Gerrit Orthlieb
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Megan H. Trager
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Columbia University College of Physicians and Surgeons, New York City, NY, USA
| | - Johanna J. O’Day
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Sudeep Aditham
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Helen 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, USA
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16
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Bange M, Gonzalez-Escamilla G, Marquardt T, Radetz A, Dresel C, Herz D, Schöllhorn WI, Groppa S, Muthuraman M. Deficient Interhemispheric Connectivity Underlies Movement Irregularities in Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2022; 12:381-395. [PMID: 34719510 DOI: 10.3233/jpd-212840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
BACKGROUND Movement execution is impaired in patients with Parkinson's disease. Evolving neurodegeneration leads to altered connectivity between distinct regions of the brain and altered activity at interconnected areas. How connectivity alterations influence complex movements like drawing spirals in Parkinson's disease patients remains largely unexplored. OBJECTIVE We investigated whether deteriorations in interregional connectivity relate to impaired execution of drawing. METHODS Twenty-nine patients and 31 age-matched healthy control participants drew spirals with both hands on a digital graphics tablet, and the regularity of drawing execution was evaluated by sample entropy. We recorded resting-state fMRI and task-related EEG, and calculated the time-resolved partial directed coherence to estimate effective connectivity for both imaging modalities to determine the extent and directionality of interregional interactions. RESULTS Movement performance in Parkinson's disease patients was characterized by increased sample entropy, corresponding to enhanced irregularities in task execution. Effective connectivity between the motor cortices of both hemispheres, derived from resting-state fMRI, was significantly reduced in Parkinson's disease patients in comparison to controls. The connectivity strength in the nondominant to dominant hemisphere direction in both modalities was inversely correlated with irregularities during drawing, but not with the clinical state. CONCLUSION Our findings suggest that interhemispheric connections are affected both at rest and during drawing movements by Parkinson's disease. This provides novel evidence that disruptions of interhemispheric information exchange play a pivotal role for impairments of complex movement execution in Parkinson's disease patients.
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Affiliation(s)
- Manuel Bange
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Gabriel Gonzalez-Escamilla
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Tabea Marquardt
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Angela Radetz
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Christian Dresel
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Damian Herz
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, UK
| | | | - Sergiu Groppa
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Muthuraman Muthuraman
- Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing Unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
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17
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D'Cruz N, Seuthe J, De Somer C, Hulzinga F, Ginis P, Schlenstedt C, Nieuwboer A. Dual Task Turning in Place: A Reliable, Valid, and Responsive Outcome Measure of Freezing of Gait. Mov Disord 2021; 37:269-278. [PMID: 34939224 DOI: 10.1002/mds.28887] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/31/2021] [Accepted: 11/24/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Freezing of gait (FOG) is a complex symptom in Parkinson's disease (PD) that is both elusive to elicit and varied in its presentation. These complexities present a challenge to measuring FOG in a sensitive and reliable way, precluding therapeutic advancement. OBJECTIVE We investigated the reliability, validity, and responsiveness of manual video annotations of the turning-in-place task and compared it to the sensor-based FOG ratio. METHODS Forty-five optimally medicated people with PD and FOG performed rapid alternating 360° turns without and with an auditory stroop dual task, thrice over two consecutive days. The tasks were video recorded, and inertial sensors were placed on the lower back and shins. Interrater reliability between three raters, criterion validity with self-reported FOG, and responsiveness to single-session split-belt treadmill (SBT) training were investigated and contrasted with the sensor-based FOG ratio. RESULTS Visual ratings showed excellent agreement between raters for the percentage time frozen (%TF) (ICC [intra-class correlation coefficient] = 0.99), the median duration of a FOG episode (ICC = 0.90), and the number of FOG episodes (ICC = 0.86). Dual tasking improved the sensitivity and validity of visual FOG ratings resulting in increased FOG detection, criterion validity with self-reported FOG ratings, and responsiveness to a short SBT intervention. The sensor-based FOG ratio, on the contrary, showed complex FOG presentation-contingent relationships with visual and self-reported FOG ratings and limited responsiveness to SBT training. CONCLUSIONS Manual video annotations of FOG during dual task turning in place generate reliable, valid, and sensitive outcomes for investigating therapeutic effects on FOG. © 2021 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Nicholas D'Cruz
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group, Leuven, Belgium
| | - Jana Seuthe
- Department of Neurology, Christian-Albrechts-University (CAU) Kiel, University Hospital Schleswig-Holstein, Kiel, Germany.,Institute of Interdisciplinary Exercise Science and Sports Medicine, MSH Medical School Hamburg, Hamburg, Germany
| | - Clara De Somer
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group, Leuven, Belgium
| | - Femke Hulzinga
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group, Leuven, Belgium
| | - Pieter Ginis
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group, Leuven, Belgium
| | - Christian Schlenstedt
- Department of Neurology, Christian-Albrechts-University (CAU) Kiel, University Hospital Schleswig-Holstein, Kiel, Germany.,Institute of Interdisciplinary Exercise Science and Sports Medicine, MSH Medical School Hamburg, Hamburg, Germany
| | - Alice Nieuwboer
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group, Leuven, Belgium
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18
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Cui CK, Lewis SJG. Future Therapeutic Strategies for Freezing of Gait in Parkinson's Disease. Front Hum Neurosci 2021; 15:741918. [PMID: 34795568 PMCID: PMC8592896 DOI: 10.3389/fnhum.2021.741918] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 10/05/2021] [Indexed: 12/28/2022] Open
Abstract
Freezing of gait (FOG) is a common and challenging clinical symptom in Parkinson’s disease. In this review, we summarise the recent insights into freezing of gait and highlight the strategies that should be considered to improve future treatment. There is a need to develop individualised and on-demand therapies, through improved detection and wearable technologies. Whilst there already exist a number of pharmacological (e.g., dopaminergic and beyond dopamine), non-pharmacological (physiotherapy and cueing, cognitive training, and non-invasive brain stimulation) and surgical approaches to freezing (i.e., dual-site deep brain stimulation, closed-loop programming), an integrated collaborative approach to future research in this complex area will be necessary to systematically investigate new therapeutic avenues. A review of the literature suggests standardising how gait freezing is measured, enriching patient cohorts for preventative studies, and harnessing the power of existing data, could help lead to more effective treatments for freezing of gait and offer relief to many patients.
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Affiliation(s)
- Cathy K Cui
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, School of Medical Sciences, The University of Sydney, Camperdown, NSW, Australia
| | - Simon J G Lewis
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, School of Medical Sciences, The University of Sydney, Camperdown, NSW, Australia
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19
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Ravi DK, Baumann CR, Bernasconi E, Gwerder M, Ignasiak NK, Uhl M, Stieglitz L, Taylor WR, Singh NB. Does Subthalamic Deep Brain Stimulation Impact Asymmetry and Dyscoordination of Gait in Parkinson's Disease? Neurorehabil Neural Repair 2021; 35:1020-1029. [PMID: 34551639 PMCID: PMC8593318 DOI: 10.1177/15459683211041309] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Background. Subthalamic deep brain stimulation (STN-DBS) is an effective treatment for selected Parkinson's disease (PD) patients. Gait characteristics are often altered after surgery, but quantitative therapeutic effects are poorly described. Objective. The goal of this study was to systematically investigate modifications in asymmetry and dyscoordination of gait 6 months postoperatively in patients with PD and compare the outcomes with preoperative baseline and to asymptomatic controls without PD. Methods. A convenience sample of thirty-two patients with PD (19 with postural instability and gait disorder (PIGD) type and 13 with tremor dominant disease) and 51 asymptomatic controls participated. Parkinson patients were tested prior to the surgery in both OFF and ON medication states, and 6-months postoperatively in the ON stimulation condition. Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) I to IV and medication were compared to preoperative conditions. Asymmetry ratios, phase coordination index, and walking speed were assessed. Results. MDS-UPDRS I to IV at 6 months improved significantly, and levodopa equivalent daily dosages significantly decreased. STN-DBS increased step time asymmetry (hedges' g effect sizes [95% confidence interval] between pre- and post-surgery: .27 [-.13, .73]) and phase coordination index (.29 [-.08, .67]). These effects were higher in the PIGD subgroup than the tremor dominant (step time asymmetry: .38 [-.06, .90] vs .09 [-.83, 1.0] and phase coordination index: .39 [-.04, .84] vs .13 [-.76, .96]). Conclusions. This study provides objective evidence of how STN-DBS increases asymmetry and dyscoordination of gait in patients with PD and suggests motor subtypes-associated differences in the treatment response.
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Affiliation(s)
- Deepak K Ravi
- Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
| | | | | | | | - Niklas K Ignasiak
- Department of Physical Therapy, 6226Chapman University, Irvine, CA, USA
| | - Mechtild Uhl
- Department of Neurology, University Hospital Zürich, Zürich, Switzerland
| | - Lennart Stieglitz
- Department of Neurology, University Hospital Zürich, Zürich, Switzerland
| | | | - Navrag B Singh
- Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
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20
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Petrucci MN, Wilkins KB, Orthlieb GC, Kehnemouyi YM, O'Day JJ, Herron JA, Bronte-Stewart HM. Ramp Rate Evaluation and Configuration for Safe and Tolerable Closed-Loop Deep Brain Stimulation. INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING : [PROCEEDINGS]. INTERNATIONAL IEEE EMBS CONFERENCE ON NEURAL ENGINEERING 2021; 2021:959-962. [PMID: 35574294 PMCID: PMC9097241 DOI: 10.1109/ner49283.2021.9441336] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Closed-loop deep brain stimulation is a novel form of therapy that has shown benefit in preliminary studies and may be clinically available in the near future. Initial closed-loop studies have primarily focused on responding to sensed biomarkers with adjustments to stimulation amplitude, which is often perceptible to study participants depending on the slew or "ramp" rate of the amplitude changes. These subjective responses to stimulation ramping can result in transient side effects, illustrating that ramp rate is a unique safety parameter for closed-loop neural systems. This presents a challenge to the future of closed-loop neuromodulation systems: depending on the goal of the control policy, clinicians will need to balance ramp rates to avoid side effects and keep the stimulation therapeutic by responding in time to affect neural dynamics. In this paper, we demonstrate the results of an initial investigation into methodology for finding safe and tolerable ramp rates in four people with Parkinson's disease (PD). Results suggest that optimal ramp rates were found more accurately during varying stimulation when compared to simply toggling between maximal and minimal intensity levels. Additionally, switching frequency instantaneously was tolerable at therapeutic levels of stimulation. Future work should focus on including optimization techniques to find ramp rates.
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Affiliation(s)
- Matthew N Petrucci
- Department of Neurology and Neurological Sciences at Stanford University, Stanford, CA, 94305 USA
| | - Kevin B Wilkins
- Department of Neurology and Neurological Sciences at Stanford University, Stanford, CA, 94305 USA
| | - Gerrit C Orthlieb
- Department of Neurology and Neurological Sciences at Stanford University, Stanford, CA, 94305 USA
| | - Yasmine M Kehnemouyi
- Department of Neurology and Neurological Sciences at Stanford University, Stanford, CA, 94305 USA
| | - Johanna J O'Day
- Department of Bioengineering at Stanford University, Stanford, CA, 94305 USA
| | - Jeffrey A Herron
- Department of Neurological Surgery at the University of Washington, Seattle, WA, 98104 USA
| | - Helen M Bronte-Stewart
- Department of Neurology and Neurological Sciences at Stanford University, Stanford, CA, 94305 USA
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21
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Gait Parameters Measured from Wearable Sensors Reliably Detect Freezing of Gait in a Stepping in Place Task. SENSORS 2021; 21:s21082661. [PMID: 33920070 PMCID: PMC8069332 DOI: 10.3390/s21082661] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 03/31/2021] [Accepted: 04/08/2021] [Indexed: 11/17/2022]
Abstract
Freezing of gait (FOG), a debilitating symptom of Parkinson’s disease (PD), can be safely studied using the stepping in place (SIP) task. However, clinical, visual identification of FOG during SIP is subjective and time consuming, and automatic FOG detection during SIP currently requires measuring the center of pressure on dual force plates. This study examines whether FOG elicited during SIP in 10 individuals with PD could be reliably detected using kinematic data measured from wearable inertial measurement unit sensors (IMUs). A general, logistic regression model (area under the curve = 0.81) determined that three gait parameters together were overall the most robust predictors of FOG during SIP: arrhythmicity, swing time coefficient of variation, and swing angular range. Participant-specific models revealed varying sets of gait parameters that best predicted FOG for each participant, highlighting variable FOG behaviors, and demonstrated equal or better performance for 6 out of the 10 participants, suggesting the opportunity for model personalization. The results of this study demonstrated that gait parameters measured from wearable IMUs reliably detected FOG during SIP, and the general and participant-specific gait parameters allude to variable FOG behaviors that could inform more personalized approaches for treatment of FOG and gait impairment in PD.
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O'Day JJ, Kehnemouyi YM, Petrucci MN, Anderson RW, Herron JA, Bronte-Stewart HM. Demonstration of Kinematic-Based Closed-loop Deep Brain Stimulation for Mitigating Freezing of Gait in People with Parkinson's Disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3612-3616. [PMID: 33018784 DOI: 10.1109/embc44109.2020.9176638] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Impaired gait in Parkinson's disease is marked by slow, arrhythmic stepping, and often includes freezing of gait episodes where alternating stepping halts completely. Wearable inertial sensors offer a way to detect these gait changes and novel deep brain stimulation (DBS) systems can respond with clinical therapy in a real-time, closed-loop fashion. In this paper, we present two novel closed-loop DBS algorithms, one using gait arrhythmicity and one using a logistic-regression model of freezing of gait detection as control signals. Benchtop validation results demonstrate the feasibility of running these algorithms in conjunction with a closed-loop DBS system by responding to real-time human subject kinematic data and pre-recorded data from leg-worn inertial sensors from a participant with Parkinson's disease. We also present a novel control policy algorithm that changes neurostimulator frequency in response to the kinematic inputs. These results provide a foundation for further development, iteration, and testing in a clinical trial for the first closed-loop DBS algorithms using kinematic signals to therapeutically improve and understand the pathophysiological mechanisms of gait impairment in Parkinson's disease.
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Bronte-Stewart HM, Petrucci MN, O’Day JJ, Afzal MF, Parker JE, Kehnemouyi YM, Wilkins KB, Orthlieb GC, Hoffman SL. Perspective: Evolution of Control Variables and Policies for Closed-Loop Deep Brain Stimulation for Parkinson's Disease Using Bidirectional Deep-Brain-Computer Interfaces. Front Hum Neurosci 2020; 14:353. [PMID: 33061899 PMCID: PMC7489234 DOI: 10.3389/fnhum.2020.00353] [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: 06/30/2020] [Accepted: 08/05/2020] [Indexed: 11/21/2022] Open
Abstract
A deep brain stimulation system capable of closed-loop neuromodulation is a type of bidirectional deep brain-computer interface (dBCI), in which neural signals are recorded, decoded, and then used as the input commands for neuromodulation at the same site in the brain. The challenge in assuring successful implementation of bidirectional dBCIs in Parkinson's disease (PD) is to discover and decode stable, robust and reliable neural inputs that can be tracked during stimulation, and to optimize neurostimulation patterns and parameters (control policies) for motor behaviors at the brain interface, which are customized to the individual. In this perspective, we will outline the work done in our lab regarding the evolution of the discovery of neural and behavioral control variables relevant to PD, the development of a novel personalized dual-threshold control policy relevant to the individual's therapeutic window and the application of these to investigations of closed-loop STN DBS driven by neural or kinematic inputs, using the first generation of bidirectional dBCIs.
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Affiliation(s)
- Helen M. Bronte-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
| | - Matthew N. Petrucci
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Johanna J. O’Day
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Muhammad Furqan Afzal
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Jordan E. Parker
- 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
| | - Kevin B. Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Gerrit C. Orthlieb
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Shannon L. Hoffman
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
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