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PloS one · 2025

Neuromusculoskeletal modeling of spasticity: A scoping review.

СтатьяScoping ReviewНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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da Silva Verônica Andrade, da Silva Rafael Lucio, Withers Joseana Wendling, Massenz Kátia Janine Veiga, Orselli Maria Isabel Veras, Menegaldo Luciano Luporini, Manffra Elisangela Ferretti

Аннотация

INTRODUCTION: This scoping review aimed to provide an overview of neuromusculoskeletal models used to investigate the mechanisms underlying spasticity and identify issues to be addressed in future models.

MATERIALS AND METHODS: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines and searched four bibliographic databases (PubMed, Compendex Engineering Village, IEEE Xplore, and Science Direct). Inclusion criteria were original studies written in English that investigated the underlying mechanisms of spasticity in humans with no age restrictions. Two independent reviewers selected studies.

RESULTS: Eighteen studies met the inclusion criteria. Stroke was the neurological condition addressed by most studies, followed by cerebral palsy. The studies focused mainly on passive tasks with the knee joint as the primary target. All studies considered that spasticity was associated with alterations in the stretch reflex loop. Among the parameters tested by the studies, the reflex gains and thresholds were the parameters that could better represent levels of severity or effects of botulinum toxin type-A treatment. Recent studies proposed that stretching acceleration, muscle force, and force rate could be fed back into the feedback loop besides the muscle length and stretching velocity. However, no consensus was found among them. Finally, it has been that stiffness and viscosity of muscle-tendon-unit are also relevant for describing resistance to passive movement.

CONCLUSION: In order to provide relevant clinical and physiological information, future modeling should include supraspinal mechanisms in-depth, use image-based data to personalize non-neural parameters, specify models according to etiology and tasks, especially the active tasks of daily life activities.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты