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Saade J, Mestre TA. Huntington's Disease: Latest Frontiers in Therapeutics. Curr Neurol Neurosci Rep 2024:10.1007/s11910-024-01345-y. [PMID: 38861215 DOI: 10.1007/s11910-024-01345-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2024] [Indexed: 06/12/2024]
Abstract
PURPOSE OF REVIEW Huntington's disease (HD) is an autosomal-dominant disorder caused by a pathological expansion of a trinucleotide repeat (CAG) on exon 1 of the huntingtin (HTT) gene. HD is characterized by the presence of chorea, alongside other hyperkinesia, parkinsonism and a combination of cognitive and behavioural features. Currently, there are no disease-modifying therapies (DMTs) for HD, and the only intervention(s) with approved indication target the treatment of chorea. This article reviews recent research on the clinical development of DMTs and newly developed tools that enhance clinical trial design towards a successful DMT in the future. RECENT FINDINGS HD is living in an era of target-specific drug development with emphasis on the mechanisms related to mutant Huntingtin (HTT) protein. Examples include antisense oligonucleotides (ASO), splicing modifiers and microRNA molecules that aim to reduce the levels of mutant HTT protein. After initial negative results with ASO molecules Tominersen and WVE-120101/ WVE-120102, the therapeutic landscape continues to expand, with various trials currently under development to document proof-of-concept and safety/tolerability. Immune-targeted therapies have also been evaluated in early-phase clinical trials, with promising preliminary findings. The possibility of quantifying mHTT in CSF, along with the development of an integrated biological staging system in HD are important innovations applicable to clinical trial design that enhance the drug development process. Although a future in HD with DMTs remains a hope for those living with HD, care partners and care providers, the therapeutic landscape is promising, with various drug development programs underway following a targeted approach supported by disease-specific biomarkers and staging frameworks.
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Affiliation(s)
- Joseph Saade
- The Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Tiago A Mestre
- The Ottawa Hospital Research Institute, Ottawa, ON, Canada.
- The University of Ottawa Brain and Research Institute, Ottawa, ON, Canada.
- Parkinson's Disease and Movement Disorder Clinic, Division of Neurology, Department of Medicine, The Ottawa Hospital, Ottawa, ON, Canada.
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Siafarikas N. Personalized medicine in old age psychiatry and Alzheimer's disease. Front Psychiatry 2024; 15:1297798. [PMID: 38751423 PMCID: PMC11094449 DOI: 10.3389/fpsyt.2024.1297798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 04/15/2024] [Indexed: 05/18/2024] Open
Abstract
Elderly patients show us unfolded lives with unique individual characteristics. An increasing life span is associated with increasing physical and mental disease burden. Alzheimer's disease (AD) is an increasing challenge in old age. AD cannot be cured but it can be treated. The complexity of old age and AD offer targets for personalized medicine (PM). Targets for stratification of patients, detection of patients at risk for AD or for future targeted therapy are plentiful and can be found in several omic-levels.
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Affiliation(s)
- Nikias Siafarikas
- Department of Geriatric Psychiatry, Akershus University Hospital, Lørenskog, Norway
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3
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Lisi E, Abellan JJ. Statistical analysis of actigraphy data with generalised additive models. Pharm Stat 2024; 23:308-324. [PMID: 37973064 DOI: 10.1002/pst.2350] [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: 05/21/2022] [Revised: 09/23/2023] [Accepted: 10/29/2023] [Indexed: 11/19/2023]
Abstract
There is a growing interest in the use of physical activity data in clinical studies, particularly in diseases that limit mobility in patients. High-frequency data collected with digital sensors are typically summarised into actigraphy features aggregated at epoch level (e.g., by minute). The statistical analysis of such volume of data is not straightforward. The general trend is to derive metrics, capturing specific aspects of physical activity, that condense (say) a week worth of data into a single numerical value. Here we propose to analyse the entire time-series data using Generalised Additive Models (GAMs). GAMs are semi-parametric models that allow inclusion of both parametric and non-parametric terms in the linear predictor. The latter are smooth terms (e.g., splines) and, in the context of actigraphy minute-by-minute data analysis, they can be used to assess daily patterns of physical activity. This in turn can be used to better understand changes over time in longitudinal studies as well as to compare treatment groups. We illustrate the application of GAMs in two clinical studies where actigraphy data was collected: a non-drug, single-arm study in patients with amyotrophic lateral sclerosis, and a physical-activity sub-study included in a phase 2b clinical trial in patients with chronic obstructive pulmonary disease.
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Willemse IHJ, Schootemeijer S, van den Bergh R, Dawes H, Nonnekes JH, van de Warrenburg BPC. Smartphone applications for Movement Disorders: Towards collaboration and re-use. Parkinsonism Relat Disord 2024; 120:105988. [PMID: 38184466 DOI: 10.1016/j.parkreldis.2023.105988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/20/2023] [Accepted: 12/31/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND Numerous smartphone and tablet applications (apps) are available to monitor movement disorders, but an overview of their purpose and stage of development is missing. OBJECTIVES To systematically review published literature and classify smartphone and tablet apps with objective measurement capabilities for the diagnosis, monitoring, assessment, or treatment of movement disorders. METHODS We systematically searched for publications covering smartphone or tablet apps to monitor movement disorders until November 22nd, 2023. We reviewed the target population, measured domains, purpose, and technology readiness level (TRL) of the proposed app and checked their availability in common app stores. RESULTS We identified 113 apps. Most apps were developed for Parkinson's disease specifically (n = 82; 73%) or for movement disorders in general (n = 17; 15%). Apps were either designed to momentarily assess symptoms (n = 65; 58%), support treatment (n = 22; 19%), aid in diagnosis (n = 16; 14%), or passively track symptoms (n = 11; 10%). Commonly assessed domains across movement disorders included fine motor skills (n = 34; 30%), gait (n = 36; 32%), and tremor (n = 32; 28%) for the motor domain and cognition (n = 16; 14%) for the non-motor domain. Twenty-six (23%) apps were proof-of-concepts (TRL 1-3), while most apps were tested in a controlled setting (TRL 4-6; n = 63; 56%). Twenty-four apps were tested in their target setting (TRL 7-9) of which 10 were accessible in common app stores or as Android Package. CONCLUSIONS The development of apps strongly gravitates towards Parkinson's disease and a selection of motor symptoms. Collaboration, re-use and further development of existing apps is encouraged to avoid reinventions of the wheel.
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Affiliation(s)
- Ilse H J Willemse
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands.
| | - Sabine Schootemeijer
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Robin van den Bergh
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Helen Dawes
- NIHR Exeter BRC, Medical School, Faculty of Health and Life Sciences, University of Exeter, UK
| | - Jorik H Nonnekes
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Rehabilitation, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands; Department of Rehabilitation, Sint Maartenskliniek, Nijmegen, the Netherlands
| | - Bart P C van de Warrenburg
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
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Nunes AS, Pawlik M, Mishra RK, Waddell E, Coffey M, Tarolli CG, Schneider RB, Dorsey ER, Vaziri A, Adams JL. Digital assessment of speech in Huntington disease. Front Neurol 2024; 15:1310548. [PMID: 38322583 PMCID: PMC10844459 DOI: 10.3389/fneur.2024.1310548] [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: 10/09/2023] [Accepted: 01/08/2024] [Indexed: 02/08/2024] Open
Abstract
Background Speech changes are an early symptom of Huntington disease (HD) and may occur prior to other motor and cognitive symptoms. Assessment of HD commonly uses clinician-rated outcome measures, which can be limited by observer variability and episodic administration. Speech symptoms are well suited for evaluation by digital measures which can enable sensitive, frequent, passive, and remote administration. Methods We collected audio recordings using an external microphone of 36 (18 HD, 7 prodromal HD, and 11 control) participants completing passage reading, counting forward, and counting backwards speech tasks. Motor and cognitive assessments were also administered. Features including pausing, pitch, and accuracy were automatically extracted from recordings using the BioDigit Speech software and compared between the three groups. Speech features were also analyzed by the Unified Huntington Disease Rating Scale (UHDRS) dysarthria score. Random forest machine learning models were implemented to predict clinical status and clinical scores from speech features. Results Significant differences in pausing, intelligibility, and accuracy features were observed between HD, prodromal HD, and control groups for the passage reading task (e.g., p < 0.001 with Cohen'd = -2 between HD and control groups for pause ratio). A few parameters were significantly different between the HD and control groups for the counting forward and backwards speech tasks. A random forest classifier predicted clinical status from speech tasks with a balanced accuracy of 73% and an AUC of 0.92. Random forest regressors predicted clinical outcomes from speech features with mean absolute error ranging from 2.43-9.64 for UHDRS total functional capacity, motor and dysarthria scores, and explained variance ranging from 14 to 65%. Montreal Cognitive Assessment scores were predicted with mean absolute error of 2.3 and explained variance of 30%. Conclusion Speech data have the potential to be a valuable digital measure of HD progression, and can also enable remote, frequent disease assessment in prodromal HD and HD. Clinical status and disease severity were predicted from extracted speech features using random forest machine learning models. Speech measurements could be leveraged as sensitive marker of clinical onset and disease progression in future clinical trials.
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Affiliation(s)
| | - Meghan Pawlik
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States
| | | | - Emma Waddell
- Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Madeleine Coffey
- Donald and Barbara Zucker School of Medicine, Uniondale, NY, United States
| | - Christopher G. Tarolli
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States
| | - Ruth B. Schneider
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States
| | - E. Ray Dorsey
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States
| | | | - Jamie L. Adams
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States
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Metzler-Baddeley C, Busse M, Drew C, Pallmann P, Cantera J, Ioakeimidis V, Rosser A. HD-DRUM, a Tablet-Based Drumming Training App Intervention for People With Huntington Disease: App Development Study. JMIR Form Res 2023; 7:e48395. [PMID: 37801351 PMCID: PMC10589837 DOI: 10.2196/48395] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/31/2023] [Accepted: 08/04/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Huntington disease (HD) is a neurodegenerative condition that leads to progressive loss of cognitive-executive and motor functions, largely due to basal ganglia (BG) atrophy. Currently, there are no therapeutic interventions tailored to address executive and motor dysfunction in people with HD. Music-based interventions may aid executive abilities by compensating for impaired BG-reliant timing and rhythm generation using external rhythmic beats. Here, we applied an integrated knowledge translation (IKT) framework to co-design a tablet-based rhythmic drumming training app (HD-DRUM) to stimulate executive and motor abilities in people with HD. OBJECTIVE The primary aim was to develop the HD-DRUM app for at-home use that addressed the accessibility needs of people with HD and allowed for the quantification of performance improvements and adherence for controlled clinical evaluation. METHODS The IKT framework was applied to iteratively refine the design of HD-DRUM. This process involved 3 phases of knowledge user engagement and co-design: a web-based survey of people with HD (n=29) to inform about their accessibility needs, usability testing of tablet-based touch screens as hardware solutions, and usability testing of the design and build of HD-DRUM to meet the identified accessibility needs of people affected by HD and their clinicians (n=12). RESULTS The survey identified accessibility problems due to cognitive and motor control impairments such as difficulties in finding and navigating through information and using PC keyboards and mouses to interact with apps. Tablet-based touch screens were identified as feasible and accessible solutions for app delivery. Key elements to ensure that the app design and build met the needs of people with HD were identified and implemented. These included the facilitation of intuitive navigation through the app using large and visually distinctive buttons; the use of audio and visual cues as training guides; and gamification, positive feedback, and drumming to background music as a means to increase motivation and engagement. The co-design development process resulted in the proof-of-concept HD-DRUM app that is described here according to the Template for Intervention Description and Replication checklist. HD-DRUM can be used at home, allowing the quantification of performance improvements and adherence for clinical evaluation, matching of training difficulty to users' performance levels using gamification, and future scale-up to reach a wide range of interested users. CONCLUSIONS Applying an IKT-based co-design framework involving knowledge user engagement allowed for the iterative refinement of the design and build of the tablet-based HD-DRUM app intervention, with the aim of stimulating BG-reliant cognitive and motor functions. Mapping the intervention against the Template for Intervention Description and Replication framework to describe complex interventions allowed for the detailed description of the HD-DRUM intervention and identification of areas that required refinement before finalizing the intervention protocol.
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Affiliation(s)
- Claudia Metzler-Baddeley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Monica Busse
- Centre for Trials Research, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Cheney Drew
- Centre for Trials Research, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Philip Pallmann
- Centre for Trials Research, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | | | - Vasileios Ioakeimidis
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Anne Rosser
- Department of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
- Cardiff Brain Repair Group, School of Biosciences, Cardiff University, Cardiff, United Kingdom
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Faisal MAA, Chowdhury MEH, Mahbub ZB, Pedersen S, Ahmed MU, Khandakar A, Alhatou M, Nabil M, Ara I, Bhuiyan EH, Mahmud S, AbdulMoniem M. NDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern. APPL INTELL 2023. [DOI: 10.1007/s10489-023-04557-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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8
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Hampel H, Gao P, Cummings J, Toschi N, Thompson PM, Hu Y, Cho M, Vergallo A. The foundation and architecture of precision medicine in neurology and psychiatry. Trends Neurosci 2023; 46:176-198. [PMID: 36642626 PMCID: PMC10720395 DOI: 10.1016/j.tins.2022.12.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/18/2022] [Accepted: 12/14/2022] [Indexed: 01/15/2023]
Abstract
Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.
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Affiliation(s)
- Harald Hampel
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA.
| | - Peng Gao
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), Las Vegas, NV, USA
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy; Athinoula A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yan Hu
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Min Cho
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Andrea Vergallo
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
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9
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Vanmechelen I, Haberfehlner H, De Vleeschhauwer J, Van Wonterghem E, Feys H, Desloovere K, Aerts JM, Monbaliu E. Assessment of movement disorders using wearable sensors during upper limb tasks: A scoping review. Front Robot AI 2023; 9:1068413. [PMID: 36714804 PMCID: PMC9879015 DOI: 10.3389/frobt.2022.1068413] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 11/30/2022] [Indexed: 01/10/2023] Open
Abstract
Background: Studies aiming to objectively quantify movement disorders during upper limb tasks using wearable sensors have recently increased, but there is a wide variety in described measurement and analyzing methods, hampering standardization of methods in research and clinics. Therefore, the primary objective of this review was to provide an overview of sensor set-up and type, included tasks, sensor features and methods used to quantify movement disorders during upper limb tasks in multiple pathological populations. The secondary objective was to identify the most sensitive sensor features for the detection and quantification of movement disorders on the one hand and to describe the clinical application of the proposed methods on the other hand. Methods: A literature search using Scopus, Web of Science, and PubMed was performed. Articles needed to meet following criteria: 1) participants were adults/children with a neurological disease, 2) (at least) one sensor was placed on the upper limb for evaluation of movement disorders during upper limb tasks, 3) comparisons between: groups with/without movement disorders, sensor features before/after intervention, or sensor features with a clinical scale for assessment of the movement disorder. 4) Outcome measures included sensor features from acceleration/angular velocity signals. Results: A total of 101 articles were included, of which 56 researched Parkinson's Disease. Wrist(s), hand(s) and index finger(s) were the most popular sensor locations. Most frequent tasks were: finger tapping, wrist pro/supination, keeping the arms extended in front of the body and finger-to-nose. Most frequently calculated sensor features were mean, standard deviation, root-mean-square, ranges, skewness, kurtosis/entropy of acceleration and/or angular velocity, in combination with dominant frequencies/power of acceleration signals. Examples of clinical applications were automatization of a clinical scale or discrimination between a patient/control group or different patient groups. Conclusion: Current overview can support clinicians and researchers in selecting the most sensitive pathology-dependent sensor features and methodologies for detection and quantification of upper limb movement disorders and objective evaluations of treatment effects. Insights from Parkinson's Disease studies can accelerate the development of wearable sensors protocols in the remaining pathologies, provided that there is sufficient attention for the standardisation of protocols, tasks, feasibility and data analysis methods.
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Affiliation(s)
- Inti Vanmechelen
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium,*Correspondence: Inti Vanmechelen,
| | - Helga Haberfehlner
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium,Amsterdam Movement Sciences, Amsterdam UMC, Department of Rehabilitation Medicine, Amsterdam, Netherlands
| | - Joni De Vleeschhauwer
- Research Group for Neurorehabilitation (eNRGy), KU Leuven, Department of Rehabilitation Sciences, Leuven, Belgium
| | - Ellen Van Wonterghem
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium
| | - Hilde Feys
- Research Group for Neurorehabilitation (eNRGy), KU Leuven, Department of Rehabilitation Sciences, Leuven, Belgium
| | - Kaat Desloovere
- Research Group for Neurorehabilitation (eNRGy), KU Leuven, Department of Rehabilitation Sciences, Pellenberg, Belgium
| | - Jean-Marie Aerts
- Division of Animal and Human Health Engineering, KU Leuven, Department of Biosystems, Measure, Model and Manage Bioresponses (M3-BIORES), Leuven, Belgium
| | - Elegast Monbaliu
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium
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Mahmudiono T, Olegovich Bokov D, Abdalkareem Jasim S, Kamal Abdelbasset W, Dinora M. Khashirbaeva. State-of-the-art of convenient and low-cost electrochemical sensor for food contamination detection: Technical and analytical overview. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Tabrizi SJ, Estevez-Fraga C, van Roon-Mom WMC, Flower MD, Scahill RI, Wild EJ, Muñoz-Sanjuan I, Sampaio C, Rosser AE, Leavitt BR. Potential disease-modifying therapies for Huntington's disease: lessons learned and future opportunities. Lancet Neurol 2022; 21:645-658. [PMID: 35716694 PMCID: PMC7613206 DOI: 10.1016/s1474-4422(22)00121-1] [Citation(s) in RCA: 88] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 02/18/2022] [Accepted: 03/04/2022] [Indexed: 01/03/2023]
Abstract
Huntington's disease is the most frequent autosomal dominant neurodegenerative disorder; however, no disease-modifying interventions are available for patients with this disease. The molecular pathogenesis of Huntington's disease is complex, with toxicity that arises from full-length expanded huntingtin and N-terminal fragments of huntingtin, which are both prone to misfolding due to proteolysis; aberrant intron-1 splicing of the HTT gene; and somatic expansion of the CAG repeat in the HTT gene. Potential interventions for Huntington's disease include therapies targeting huntingtin DNA and RNA, clearance of huntingtin protein, DNA repair pathways, and other treatment strategies targeting inflammation and cell replacement. The early termination of trials of the antisense oligonucleotide tominersen suggest that it is time to reflect on lessons learned, where the field stands now, and the challenges and opportunities for the future.
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Affiliation(s)
- Sarah J Tabrizi
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Carlos Estevez-Fraga
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | | | - Michael D Flower
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Rachael I Scahill
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Edward J Wild
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | | | - Cristina Sampaio
- CHDI Management, CHDI Foundation Los Angeles, CA, USA; Laboratory of Clinical Pharmacology, Faculdade de Medicina de Lisboa, Lisbon, Portugal
| | - Anne E Rosser
- BRAIN unit, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Blair R Leavitt
- Centre for Huntington's disease, University of British Columbia, Vancouver, BC, Canada
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12
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Lipsmeier F, Simillion C, Bamdadian A, Tortelli R, Byrne LM, Zhang YP, Wolf D, Smith AV, Czech C, Gossens C, Weydt P, Schobel SA, Rodrigues FB, Wild EJ, Lindemann M. A Remote Digital Monitoring Platform to Assess Cognitive and Motor Symptoms in Huntington Disease: Cross-sectional Validation Study. J Med Internet Res 2022; 24:e32997. [PMID: 35763342 PMCID: PMC9277525 DOI: 10.2196/32997] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 02/17/2022] [Accepted: 05/04/2022] [Indexed: 11/13/2022] Open
Abstract
Background Remote monitoring of Huntington disease (HD) signs and symptoms using digital technologies may enhance early clinical diagnosis and tracking of disease progression, guide treatment decisions, and monitor response to disease-modifying agents. Several recent studies in neurodegenerative diseases have demonstrated the feasibility of digital symptom monitoring. Objective The aim of this study was to evaluate a novel smartwatch- and smartphone-based digital monitoring platform to remotely monitor signs and symptoms of HD. Methods This analysis aimed to determine the feasibility and reliability of the Roche HD Digital Monitoring Platform over a 4-week period and cross-sectional validity over a 2-week interval. Key criteria assessed were feasibility, evaluated by adherence and quality control failure rates; test-retest reliability; known-groups validity; and convergent validity of sensor-based measures with existing clinical measures. Data from 3 studies were used: the predrug screening phase of an open-label extension study evaluating tominersen (NCT03342053) and 2 untreated cohorts—the HD Natural History Study (NCT03664804) and the Digital-HD study. Across these studies, controls (n=20) and individuals with premanifest (n=20) or manifest (n=179) HD completed 6 motor and 2 cognitive tests at home and in the clinic. Results Participants in the open-label extension study, the HD Natural History Study, and the Digital-HD study completed 89.95% (1164/1294), 72.01% (2025/2812), and 68.98% (1454/2108) of the active tests, respectively. All sensor-based features showed good to excellent test-retest reliability (intraclass correlation coefficient 0.89-0.98) and generally low quality control failure rates. Good overall convergent validity of sensor-derived features to Unified HD Rating Scale outcomes and good overall known-groups validity among controls, premanifest, and manifest participants were observed. Among participants with manifest HD, the digital cognitive tests demonstrated the strongest correlations with analogous in-clinic tests (Pearson correlation coefficient 0.79-0.90). Conclusions These results show the potential of the HD Digital Monitoring Platform to provide reliable, valid, continuous remote monitoring of HD symptoms, facilitating the evaluation of novel treatments and enhanced clinical monitoring and care for individuals with HD.
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Affiliation(s)
- Florian Lipsmeier
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Cedric Simillion
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Atieh Bamdadian
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Rosanna Tortelli
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Lauren M Byrne
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yan-Ping Zhang
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Detlef Wolf
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Anne V Smith
- Ionis Pharmaceuticals Inc, Carlsbad, CA, United States
| | - Christian Czech
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland.,Rare Disease Research Unit, Pfizer, Nice, France
| | - Christian Gossens
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Patrick Weydt
- Department of Neurology, University of Ulm Medical Center, Ulm, Germany.,Department of Neurodegenerative Disease and Gerontopsychiatry/Neurology, University of Bonn Medical Center, Bonn, Germany
| | | | - Filipe B Rodrigues
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Edward J Wild
- Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Michael Lindemann
- Roche Pharma Research and Early Development, pRED Informatics, Pharmaceutical Sciences, Clinical Pharmacology, and Neuroscience, Ophthalmology, and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
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13
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Rosser AE, Busse ME, Gray WP, Badin RA, Perrier AL, Wheelock V, Cozzi E, Martin UP, Salado-Manzano C, Mills LJ, Drew C, Goldman SA, Canals JM, Thompson LM. Translating cell therapies for neurodegenerative diseases: Huntington's disease as a model disorder. Brain 2022; 145:1584-1597. [PMID: 35262656 PMCID: PMC9166564 DOI: 10.1093/brain/awac086] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 01/29/2022] [Accepted: 02/06/2022] [Indexed: 11/17/2022] Open
Abstract
There has been substantial progress in the development of regenerative medicine strategies for CNS disorders over the last decade, with progression to early clinical studies for some conditions. However, there are multiple challenges along the translational pipeline, many of which are common across diseases and pertinent to multiple donor cell types. These include defining the point at which the preclinical data are sufficiently compelling to permit progression to the first clinical studies; scaling-up, characterization, quality control and validation of the cell product; design, validation and approval of the surgical device; and operative procedures for safe and effective delivery of cell product to the brain. Furthermore, clinical trials that incorporate principles of efficient design and disease-specific outcomes are urgently needed (particularly for those undertaken in rare diseases, where relatively small cohorts are an additional limiting factor), and all processes must be adaptable in a dynamic regulatory environment. Here we set out the challenges associated with the clinical translation of cell therapy, using Huntington's disease as a specific example, and suggest potential strategies to address these challenges. Huntington's disease presents a clear unmet need, but, importantly, it is an autosomal dominant condition with a readily available gene test, full genetic penetrance and a wide range of associated animal models, which together mean that it is a powerful condition in which to develop principles and test experimental therapeutics. We propose that solving these challenges in Huntington's disease would provide a road map for many other neurological conditions. This white paper represents a consensus opinion emerging from a series of meetings of the international translational platforms Stem Cells for Huntington's Disease and the European Huntington's Disease Network Advanced Therapies Working Group, established to identify the challenges of cell therapy, share experience, develop guidance and highlight future directions, with the aim to expedite progress towards therapies for clinical benefit in Huntington's disease.
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Affiliation(s)
- Anne E Rosser
- Cardiff University Neuroscience and Mental Health Research Institute, Hadyn Ellis Building, Cardiff CF24 4HQ, UK.,Cardiff University Brain Repair Group, School of Biosciences, Life Sciences Building, Cardiff CF10 3AX, UK.,Brain Repair and Intracranial Neurotherapeutics (B.R.A.I.N.) Biomedical Research Unit, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF14 4EP, UK
| | - Monica E Busse
- Cardiff University Centre for Trials Research, College of Biomedical and Life Sciences Cardiff University, 4th Floor Neuadd Meirionnydd, Heath Park, Cardiff CF14 4YS, UK
| | - William P Gray
- Cardiff University Neuroscience and Mental Health Research Institute, Hadyn Ellis Building, Cardiff CF24 4HQ, UK.,Brain Repair and Intracranial Neurotherapeutics (B.R.A.I.N.) Biomedical Research Unit, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF14 4EP, UK.,University Hospital of Wales Healthcare NHS Trust, Department of Neurosurgery, Cardiff CF14 4XW, UK
| | - Romina Aron Badin
- Université Paris-Saclay, CEA, CNRS, Laboratoire des Maladies Neurodégénératives: mécanismes, thérapies, imagerie, 92265 Fontenay-aux-Roses, France.,Université Paris-Saclay, CEA, Molecular Imaging Research Center, 92265 Fontenay-aux-Roses, France
| | - Anselme L Perrier
- Université Paris-Saclay, CEA, CNRS, Laboratoire des Maladies Neurodégénératives: mécanismes, thérapies, imagerie, 92265 Fontenay-aux-Roses, France.,Université Paris-Saclay, CEA, Molecular Imaging Research Center, 92265 Fontenay-aux-Roses, France
| | - Vicki Wheelock
- University of California Davis, Department of Neurology, 95817 Sacramento, CA, USA
| | - Emanuele Cozzi
- Transplant Immunology Unit, Department of Cardiac, Thoracic and Vascular Sciences, Padua University Hospital-Ospedale Giustinianeo, Padova, Italy
| | - Unai Perpiña Martin
- Laboratory of Stem Cells and Regenerative Medicine, Department of Biomedical Sciences, and Creatio-Production and Validation Center of Advanced Therapies, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain.,August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain.,Networked Biomedical Research Centre for Neurodegenerative Disorders (CIBERNED), Barcelona, Spain
| | - Cristina Salado-Manzano
- Laboratory of Stem Cells and Regenerative Medicine, Department of Biomedical Sciences, and Creatio-Production and Validation Center of Advanced Therapies, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain.,August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain.,Networked Biomedical Research Centre for Neurodegenerative Disorders (CIBERNED), Barcelona, Spain
| | - Laura J Mills
- Cardiff University Centre for Trials Research, College of Biomedical and Life Sciences Cardiff University, 4th Floor Neuadd Meirionnydd, Heath Park, Cardiff CF14 4YS, UK
| | - Cheney Drew
- Cardiff University Centre for Trials Research, College of Biomedical and Life Sciences Cardiff University, 4th Floor Neuadd Meirionnydd, Heath Park, Cardiff CF14 4YS, UK
| | - Steven A Goldman
- Centre for Translational Neuromedicine, University of Rochester, 14642 Rochester, NY, USA.,University of Copenhagen Faculty of Health and Medical Sciences, DK-2200 Kobenhavn, Denmark
| | - Josep M Canals
- Laboratory of Stem Cells and Regenerative Medicine, Department of Biomedical Sciences, and Creatio-Production and Validation Center of Advanced Therapies, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain.,August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain.,Networked Biomedical Research Centre for Neurodegenerative Disorders (CIBERNED), Barcelona, Spain
| | - Leslie M Thompson
- University of California Irvine, Department of Psychiatry and Human Behaviour, Department of Neurobiology and Behavior and the Sue and Bill Gross Stem Cell Center, 92697 Irvine, CA, USA
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14
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Desai R, Blacutt M, Youdan G, Fritz NE, Muratori LM, Hausdorff JM, Busse M, Quinn L. Postural control and gait measures derived from wearable inertial measurement unit devices in Huntington's disease: Recommendations for clinical outcomes. Clin Biomech (Bristol, Avon) 2022; 96:105658. [PMID: 35588586 DOI: 10.1016/j.clinbiomech.2022.105658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 04/13/2022] [Accepted: 04/22/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND Postural control impairments begin early in Huntington's disease yet measures most sensitive to progression have not been identified. The aims of this study were to: 1) evaluate postural control and gait in people with and without Huntington's disease using wearable sensors; and 2) identify measures related to diagnosis and clinical severity. METHODS 43 individuals with Huntington's disease and 15 age-matched peers performed standing with feet together and feet apart, sitting, and walking with wearable inertial sensors. One-way analysis of variance determined differences in measures of postural control and gait between early and mid-disease stage, and non-Huntington's disease peers. A random forest analysis identified feature importance for Huntington's disease diagnosis. Stepwise and ordinal regressions were used to determine predictors of clinical chorea and tandem walking scores respectively. FINDINGS There was a significant main effect for all postural control and gait measures comparing early stage, mid stage and non-Huntington's disease peers, except for gait cycle duration and step duration. Total sway, root mean square and mean velocity during sitting, as well as gait speed had the greatest importance in classifying disease status. Stepwise regression showed that root mean square during standing with feet apart significantly predicted clinical measure of chorea, and ordinal regression model showed that root mean square and total sway standing feet together significantly predicted clinical measure of tandem walking. INTERPRETATIONS Root mean square measures obtained in sitting and standing using wearable sensors have the potential to serve as biomarkers of postural control impairments in Huntington's disease.
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Affiliation(s)
- Radhika Desai
- Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, USA.
| | - Miguel Blacutt
- Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, USA.
| | - Gregory Youdan
- Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, USA.
| | - Nora E Fritz
- Wayne State University, Departments of Health Care Sciences and Neurology, Detroit, MI, USA.
| | - Lisa M Muratori
- Department Physical Therapy, Stony Brook University, New York, USA.
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Physical Therapy, Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel; Rush Alzheimer's Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA.
| | - Monica Busse
- Centre for Trials Research, Cardiff University, Cardiff, UK.
| | - Lori Quinn
- Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, USA; Centre for Trials Research, Cardiff University, Cardiff, UK.
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15
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Information Retrieval from Photoplethysmographic Sensors: A Comprehensive Comparison of Practical Interpolation and Breath-Extraction Techniques at Different Sampling Rates. SENSORS 2022; 22:s22041428. [PMID: 35214329 PMCID: PMC8877143 DOI: 10.3390/s22041428] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/07/2022] [Accepted: 02/08/2022] [Indexed: 11/17/2022]
Abstract
The increasingly widespread diffusion of wearable devices makes possible the continuous monitoring of vital signs, such as heart rate (HR), heart rate variability (HRV), and breath signal. However, these devices usually do not record the “gold-standard” signals, namely the electrocardiography (ECG) and respiratory activity, but a single photoplethysmographic (PPG) signal, which can be exploited to estimate HR and respiratory activity. In addition, these devices employ low sampling rates to limit power consumption. Hence, proper methods should be adopted to compensate for the resulting increased discretization error, while diverse breath-extraction algorithms may be differently sensitive to PPG sampling rate. Here, we assessed the efficacy of parabola interpolation, cubic-spline, and linear regression methods to improve the accuracy of the inter-beat intervals (IBIs) extracted from PPG sampled at decreasing rates from 64 to 8 Hz. PPG-derived IBIs and HRV indices were compared with those extracted from a standard ECG. In addition, breath signals extracted from PPG using three different techniques were compared with the gold-standard signal from a thoracic belt. Signals were recorded from eight healthy volunteers during an experimental protocol comprising sitting and standing postures and a controlled respiration task. Parabola and cubic-spline interpolation significantly increased IBIs accuracy at 32, 16, and 8 Hz sampling rates. Concerning breath signal extraction, the method holding higher accuracy was based on PPG bandpass filtering. Our results support the efficacy of parabola and spline interpolations to improve the accuracy of the IBIs obtained from low-sampling rate PPG signals, and also indicate a robust method for breath signal extraction.
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16
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Vignoud G, Desjardins C, Salardaine Q, Mongin M, Garcin B, Venance L, Degos B. Video-Based Automated Assessment of Movement Parameters Consistent with MDS-UPDRS III in Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2022; 12:2211-2222. [PMID: 35964204 PMCID: PMC9661322 DOI: 10.3233/jpd-223445] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/24/2022] [Indexed: 06/01/2023]
Abstract
BACKGROUND Among motor symptoms of Parkinson's disease (PD), including rigidity and resting tremor, bradykinesia is a mandatory feature to define the parkinsonian syndrome. MDS-UPDRS III is the worldwide reference scale to evaluate the parkinsonian motor impairment, especially bradykinesia. However, MDS-UPDRS III is an agent-based score making reproducible measurements and follow-up challenging. OBJECTIVE Using a deep learning approach, we developed a tool to compute an objective score of bradykinesia based on the guidelines of the gold-standard MDS-UPDRS III. METHODS We adapted and applied two deep learning algorithms to detect a two-dimensional (2D) skeleton of the hand composed of 21 predefined points, and transposed it into a three-dimensional (3D) skeleton for a large database of videos of parkinsonian patients performing MDS-UPDRS III protocols acquired in the Movement Disorder unit of Avicenne University Hospital. RESULTS We developed a 2D and 3D automated analysis tool to study the evolution of several key parameters during the protocol repetitions of the MDS-UPDRS III. Scores from 2D automated analysis showed a significant correlation with gold-standard ratings of MDS-UPDRS III, measured with coefficients of determination for the tapping (0.609) and hand movements (0.701) protocols using decision tree algorithms. The individual correlations of the different parameters measured with MDS-UPDRS III scores carry meaningful information and are consistent with MDS-UPDRS III guidelines. CONCLUSION We developed a deep learning-based tool to precisely analyze movement parameters allowing to reliably score bradykinesia for parkinsonian patients in a MDS-UPDRS manner.
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Affiliation(s)
- Gaëtan Vignoud
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université PSL, Paris, France
- INRIA Paris, MAMBA (Modelling and Analysis in Medical and Biological Applications), Paris, France
| | - Clément Desjardins
- APHP, Hôpital Avicenne, Hôpitaux Universitaires de Paris-Seine Saint Denis (HUPSSD), Department of Neurology, Sorbonne Paris Nord, NS-PARK/FCRIN network, Bobigny, France
| | - Quentin Salardaine
- APHP, Hôpital Avicenne, Hôpitaux Universitaires de Paris-Seine Saint Denis (HUPSSD), Department of Neurology, Sorbonne Paris Nord, NS-PARK/FCRIN network, Bobigny, France
| | - Marie Mongin
- APHP, Hôpital Avicenne, Hôpitaux Universitaires de Paris-Seine Saint Denis (HUPSSD), Department of Neurology, Sorbonne Paris Nord, NS-PARK/FCRIN network, Bobigny, France
| | - Béatrice Garcin
- APHP, Hôpital Avicenne, Hôpitaux Universitaires de Paris-Seine Saint Denis (HUPSSD), Department of Neurology, Sorbonne Paris Nord, NS-PARK/FCRIN network, Bobigny, France
| | - Laurent Venance
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université PSL, Paris, France
| | - Bertrand Degos
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université PSL, Paris, France
- APHP, Hôpital Avicenne, Hôpitaux Universitaires de Paris-Seine Saint Denis (HUPSSD), Department of Neurology, Sorbonne Paris Nord, NS-PARK/FCRIN network, Bobigny, France
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17
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Keren K, Busse M, Fritz NE, Muratori LM, Gazit E, Hillel I, Scheinowitz M, Gurevich T, Inbar N, Omer N, Hausdorff JM, Quinn L. Quantification of Daily-Living Gait Quantity and Quality Using a Wrist-Worn Accelerometer in Huntington's Disease. Front Neurol 2021; 12:719442. [PMID: 34777196 PMCID: PMC8579964 DOI: 10.3389/fneur.2021.719442] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/06/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Huntington's disease (HD) leads to altered gait patterns and reduced daily-living physical activity. Accurate measurement of daily-living walking that takes into account involuntary movements (e.g. chorea) is needed. Objective: To evaluate daily-living gait quantity and quality in HD, taking into account irregular movements. Methods: Forty-two individuals with HD and fourteen age-matched non-HD peers completed clinic-based assessments and a standardized laboratory-based circuit of functional activities, wearing inertial measurement units on the wrists, legs, and trunk. These activities were used to train and test an algorithm for the automated detection of walking. Subsequently, 29 HD participants and 22 age-matched non-HD peers wore a tri-axial accelerometer on their non-dominant wrist for 7 days. Measures included gait quantity (e.g., steps per day), gait quality (e.g., regularity) metrics, and percentage of walking bouts with irregular movements. Results: Measures of daily-living gait quantity including step counts, walking time and bouts per day were similar in HD participants and non-HD peers (p > 0.05). HD participants with higher clinician-rated upper body chorea had a greater percentage of walking bouts with irregular movements compared to those with lower chorea (p = 0.060) and non-HD peers (p < 0.001). Even after accounting for irregular movements, within-bout walking consistency was lower in HD participants compared to non-HD peers (p < 0.001), while across-bout variability of these measures was higher (p < 0.001). Many of the daily-living measures were associated with disease-specific measures of motor function. Conclusions: Results suggest that a wrist-worn accelerometer can be used to evaluate the quantity and quality of daily-living gait in people with HD, while accounting for the influence of irregular (choreic-like) movements, and that gait features related to within- and across-bout consistency markedly differ in individuals with HD and non-HD peers.
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Affiliation(s)
- Karin Keren
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Monica Busse
- Centre for Trials Research, Cardiff University, Cardiff, United Kingdom
| | - Nora E. Fritz
- Departments of Health Care Sciences and Neurology, Wayne State University, Detroit, MI, United States
| | - Lisa M. Muratori
- Department of Physical Therapy, School of Health Technology and Management, Stony Brook University, Stony Brook, NY, United States
- George Huntington's Institute, Muenster, Germany
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Inbar Hillel
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Micky Scheinowitz
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- School of Public Health, Tel Aviv University, Tel Aviv, Israel
| | - Tanya Gurevich
- Movement Disorders Unit, Tel Aviv Medical Center, Tel Aviv, Israel
- Sackler School of Medicine and Sagol, School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Noit Inbar
- Movement Disorders Unit, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Nurit Omer
- Movement Disorders Unit, Tel Aviv Medical Center, Tel Aviv, Israel
- Sackler School of Medicine and Sagol, School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sackler School of Medicine and Sagol, School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Rush Alzheimer's Disease Center and Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, United States
| | - Lori Quinn
- Centre for Trials Research, Cardiff University, Cardiff, United Kingdom
- Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, United States
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18
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Neethirajan S, Kemp B. Digital Phenotyping in Livestock Farming. Animals (Basel) 2021; 11:2009. [PMID: 34359137 PMCID: PMC8300347 DOI: 10.3390/ani11072009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 06/22/2021] [Accepted: 06/29/2021] [Indexed: 12/18/2022] Open
Abstract
Currently, large volumes of data are being collected on farms using multimodal sensor technologies. These sensors measure the activity, housing conditions, feed intake, and health of farm animals. With traditional methods, the data from farm animals and their environment can be collected intermittently. However, with the advancement of wearable and non-invasive sensing tools, these measurements can be made in real-time for continuous quantitation relating to clinical biomarkers, resilience indicators, and behavioral predictors. The digital phenotyping of humans has drawn enormous attention recently due to its medical significance, but much research is still needed for the digital phenotyping of farm animals. Implications from human studies show great promise for the application of digital phenotyping technology in modern livestock farming, but these technologies must be directly applied to animals to understand their true capacities. Due to species-specific traits, certain technologies required to assess phenotypes need to be tailored efficiently and accurately. Such devices allow for the collection of information that can better inform farmers on aspects of animal welfare and production that need improvement. By explicitly addressing farm animals' individual physiological and mental (affective states) needs, sensor-based digital phenotyping has the potential to serve as an effective intervention platform. Future research is warranted for the design and development of digital phenotyping technology platforms that create shared data standards, metrics, and repositories.
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Affiliation(s)
- Suresh Neethirajan
- Adaptation Physiology Group, Department of Animal Sciences, Wageningen University & Research, 6700 AH Wageningen, The Netherlands;
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19
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Lee KFA, Gan WS, Christopoulos G. Biomarker-Informed Machine Learning Model of Cognitive Fatigue from a Heart Rate Response Perspective. SENSORS 2021; 21:s21113843. [PMID: 34199416 PMCID: PMC8199616 DOI: 10.3390/s21113843] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 05/18/2021] [Accepted: 05/21/2021] [Indexed: 01/14/2023]
Abstract
Cognitive fatigue is a psychological state characterised by feelings of tiredness and impaired cognitive functioning arising from high cognitive demands. This paper examines the recent research progress on the assessment of cognitive fatigue and provides informed recommendations for future research. Traditionally, cognitive fatigue is introspectively assessed through self-report or objectively inferred from a decline in behavioural performance. However, more recently, researchers have attempted to explore the biological underpinnings of cognitive fatigue to understand and measure this phenomenon. In particular, there is evidence indicating that the imbalance between sympathetic and parasympathetic nervous activity appears to be a physiological correlate of cognitive fatigue. This imbalance has been indexed through various heart rate variability indices that have also been proposed as putative biomarkers of cognitive fatigue. Moreover, in contrast to traditional inferential methods, there is also a growing research interest in using data-driven approaches to assessing cognitive fatigue. The ubiquity of wearables with the capability to collect large amounts of physiological data appears to be a major facilitator in the growth of data-driven research in this area. Preliminary findings indicate that such large datasets can be used to accurately predict cognitive fatigue through various machine learning approaches. Overall, the potential of combining domain-specific knowledge gained from biomarker research with machine learning approaches should be further explored to build more robust predictive models of cognitive fatigue.
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Affiliation(s)
- Kar Fye Alvin Lee
- Smart Nation Translational Laboratory, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;
- Correspondence:
| | - Woon-Seng Gan
- Smart Nation Translational Laboratory, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;
| | - Georgios Christopoulos
- Decision, Environmental and Organizational Neuroscience Lab (DeonLab), Nanyang Business School, Nanyang Technological University, Singapore 639798, Singapore;
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