Biology subjects

Salati, R. M.

Publications and source records attributed to Salati, R. M..

2 recordsLinked to original sources

bioRxiv2026

Reproducible Research: Computational Design of PersonalizedClinical Treatments for Walking Impairments Using the Neuromusculoskeletal Modeling Pipeline

Background: Personalized computational neuromusculoskeletal models have great potential for optimizing the design of clinical treatments for movement impairments. While many software tools address specific parts of the model personalization and treatment optimization processes, they typically require significant programming experience to use and do not cover the full breadth of these two processes. Furthermore, published neuromusculoskeletal modeling studies typically do not provide all the methodological details needed for others to reproduce the work. Consequently, researchers seeking to develop skills in the model personalization and treatment optimization processes face a steep learning curve due to the lack of detailed training materials that demonstrate both processes for real-life clinical problems using real-life subject movement data. Methods: This article presents detailed training tutorials for the model personalization and treatment optimization processes using two real-life clinical problems and the Neuromusculoskeletal Modeling (NMSM) Pipeline. The first clinical problem involves the design of personalized gait modifications and high tibial osteotomy surgery for an individual with bilateral medial knee osteoarthritis, where the goal is to reduce the peak adduction moment in both knees to a specified target level. The second clinical problem involves the design of a synergy-based functional electrical stimulation prescription for an individual post-stroke with impaired walking function, where the goal is to equalize the propulsive and braking impulses between the two legs. Both tutorials were implemented as course projects given to novice users in a combined undergraduate/graduate mechanical engineering course. Results: Both tutorials produced personalized neuromusculoskeletal models and associated dynamically consistent tracking optimizations that closely reproduced subject-specific experimental joint angles, joint moments, ground reaction forces and moments, and (if applicable) muscle activations measured during walking. Subsequent design optimizations predicted personalized treatments that achieved target values of peak knee adduction moments or propulsive and braking impulses. Conclusions: The detailed step-by-step tutorials presented with this article are the first to walk users through the entire process of creating personalized neuromusculoskeletal models and then using them to design personalized treatments for two subjects with two different clinical problems. These tutorials can be used to introduce new users to the NMSM Pipeline and as projects in neuromusculoskeletal modeling courses.

bioengineering↗

bioRxiv2024

The Neuromusculoskeletal Modeling Pipeline: MATLAB-based Model Personalization and Treatment Optimization Functionality for OpenSim

Neuromusculoskeletal injuries including osteoarthritis, stroke, spinal cord injury, and traumatic brain injury affect roughly 19% of the U.S. adult population. Standardized interventions have produced suboptimal functional outcomes due to the unique treatment needs of each patient. Strides have been made to utilize computational models to develop personalized treatments, but researchers and clinicians have yet to cross the "valley of death" between fundamental research and clinical usefulness. This article introduces the Neuromusculoskeletal Modeling (NMSM) Pipeline, two MATLAB-based toolsets that add Model Personalization and Treatment Optimization functionality to OpenSim. The two toolsets facilitate computational design of individualized treatments for neuromusculoskeletal impairments through the use of personalized neuromusculoskeletal models and predictive simulation. The Model Personalization toolset contains four tools for personalizing 1) joint structure models, 2) muscle-tendon models, 3) neural control models, and 4) foot-ground contact models. The Treatment Optimization toolset contains three tools for predicting and optimizing a patients functional outcome for different treatment options using a patients personalized neuromusculoskeletal model with direct collocation optimal control methods. Support for user-defined cost functions and model modification functions facilitate simulation of a vast number of possible treatments. An NMSM Pipeline use case is presented for an individual post-stroke with impaired walking function, where the goal was to predict how the subjects neural control could be changed to improve walking speed without increasing metabolic cost. First the Model Personalization toolset was used to develop a personalized neuromusculoskeletal model of the subject starting from a generic OpenSim full- body model and experimental walking data (video motion capture, ground reaction, and electromyography) collected from the subject at his self-selected speed. Next the Treatment Optimization toolset was used with the personalized model to predict how the subject could recruit existing muscle synergies more effectively to reduce muscle activation disparities between the paretic and non-paretic legs. The software predicted that the subject could increase his walking speed by 60% without increasing his metabolic cost per unit time by modifying existing muscle synergy recruitment. This hypothetical treatment demonstrates how NMSM Pipeline tools could allow researchers working collaboratively with clinicians to develop personalized neuromusculoskeletal models of individual patients and to perform predictive simulations for the purpose of designing personalized treatments that maximize a patients post-treatment functional outcome.

bioengineering↗