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      NSF DARE—transforming modeling in neurorehabilitation: a patient-in-the-loop framework

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          Abstract

          In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

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          Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results.

          We present a clinimetric assessment of the Movement Disorder Society (MDS)-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The MDS-UDPRS Task Force revised and expanded the UPDRS using recommendations from a published critique. The MDS-UPDRS has four parts, namely, I: Non-motor Experiences of Daily Living; II: Motor Experiences of Daily Living; III: Motor Examination; IV: Motor Complications. Twenty questions are completed by the patient/caregiver. Item-specific instructions and an appendix of complementary additional scales are provided. Movement disorder specialists and study coordinators administered the UPDRS (55 items) and MDS-UPDRS (65 items) to 877 English speaking (78% non-Latino Caucasian) patients with Parkinson's disease from 39 sites. We compared the two scales using correlative techniques and factor analysis. The MDS-UPDRS showed high internal consistency (Cronbach's alpha = 0.79-0.93 across parts) and correlated with the original UPDRS (rho = 0.96). MDS-UPDRS across-part correlations ranged from 0.22 to 0.66. Reliable factor structures for each part were obtained (comparative fit index > 0.90 for each part), which support the use of sum scores for each part in preference to a total score of all parts. The combined clinimetric results of this study support the validity of the MDS-UPDRS for rating PD.
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            DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

            Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
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              Humans integrate visual and haptic information in a statistically optimal fashion.

              When a person looks at an object while exploring it with their hand, vision and touch both provide information for estimating the properties of the object. Vision frequently dominates the integrated visual-haptic percept, for example when judging size, shape or position, but in some circumstances the percept is clearly affected by haptics. Here we propose that a general principle, which minimizes variance in the final estimate, determines the degree to which vision or haptics dominates. This principle is realized by using maximum-likelihood estimation to combine the inputs. To investigate cue combination quantitatively, we first measured the variances associated with visual and haptic estimation of height. We then used these measurements to construct a maximum-likelihood integrator. This model behaved very similarly to humans in a visual-haptic task. Thus, the nervous system seems to combine visual and haptic information in a fashion that is similar to a maximum-likelihood integrator. Visual dominance occurs when the variance associated with visual estimation is lower than that associated with haptic estimation.
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                Author and article information

                Contributors
                cashabackjga@gmail.com
                millerhl@umich.edu
                Journal
                J Neuroeng Rehabil
                J Neuroeng Rehabil
                Journal of NeuroEngineering and Rehabilitation
                BioMed Central (London )
                1743-0003
                13 February 2024
                13 February 2024
                2024
                : 21
                : 23
                Affiliations
                [1 ]Biomedical Engineering, Mechanical Engineering, Kinesiology and Applied Physiology, Biome chanics and Movement Science Program, Interdisciplinary Neuroscience Graduate Program, University of Delaware, ( https://ror.org/01sbq1a82) 540 S College Ave, Newark, DE 19711 USA
                [2 ]Department of Mechanical Engineering, University of Florida, ( https://ror.org/02y3ad647) Gainesville, USA
                [3 ]Electrical and Computer Engineering, University of Washington, ( https://ror.org/00cvxb145) Seattle, USA
                [4 ]GRID grid.38142.3c, ISNI 000000041936754X, Division of Neurocritical Care and Stroke Service, Department of Neurology, , Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, ; Boston, USA
                [5 ]Department of Veterans Affairs, Center for Neurorestoration and Neurotechnology, Rehabilitation Research and Development Service, ( https://ror.org/008qp6e21) Providence, USA
                [6 ]Department of Mechanical and Industrial Engineering, Department of Kinesiology, University of Massachusetts Amherst, ( https://ror.org/0072zz521) Amherst, USA
                [7 ]GRID grid.7149.b, ISNI 0000 0001 2166 9385, School of Electrical Engineering, The Mihajlo Pupin Institute, , University of Belgrade, ; Belgrade, Serbia
                [8 ]GRID grid.5801.c, ISNI 0000 0001 2156 2780, Laboratory for Neuroengineering, , Institute for Robotics and Intelligent Systems ETH Zürich, ; Zurich, Switzerland
                [9 ]Mechanical and Industrial Engineering, Northeastern University, ( https://ror.org/04t5xt781) Boston, USA
                [10 ]Department of Mechanical Engineering, University of Utah, ( https://ror.org/03r0ha626) Salt Lake City, USA
                [11 ]School of Kinesiology, University of Michigan, ( https://ror.org/00jmfr291) 830 N University Ave, Ann Arbor, MI 48109 USA
                Article
                1318
                10.1186/s12984-024-01318-9
                10863253
                38347597
                8a34c6a6-3532-45e9-b204-871e3845e88b
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 10 July 2023
                : 25 January 2024
                Funding
                Funded by: National Science Foundation
                Award ID: NSF CAREER 2146888
                Award ID: NSF 2245260
                Award Recipient :
                Funded by: Department of Veterans Affairs
                Award ID: 1IK2RX004237
                Award Recipient :
                Funded by: Project IDEJE by Science Fund of the Republic of Serbia
                Award ID: 7753949
                Award Recipient :
                Funded by: National Institutes of Health
                Award ID: NIH R00AG065524
                Award ID: K01-MH107774
                Award Recipient :
                Categories
                Review
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Neurosciences
                computational modeling,patient-in-the-loop,sensorimotor adaptation,digital twin,neuroplasticity,musculoskeletal,sensory,pain,neurological condition,neurodevelopment

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