Neural-driven activation of 3D muscle within a finite element framework: exploring applications in healthy and neurodegenerative simulations.

This paper presents a novel computational framework for neural-driven finite element muscle models, with an application to amyotrophic lateral sclerosis (ALS). The multiscale neuromusculoskeletal (NMS) model incorporates physiologically accurate motor neurons, 3D muscle geometry, and muscle fiber recruitment. It successfully predicts healthy muscle force and tendon elongation and demonstrates a progressive decline in muscle force due to ALS, dropping from 203 N (healthy) to 155 N (120 days after ALS onset). This approach represents a preliminary step towards developing integrated neural and musculoskeletal simulations to enhance our understanding of neurodegenerative and neurodevelopmental conditions through predictive NMS models.

Pedicle Screw Placement Accuracy in Robot-Assisted Spinal Fusion in a Multicenter Study.

Pedicle screw fixation is a spinal fusion technique that involves the implantation of screws into vertebral pedicles to restrict movement between those vertebrae. The objective of this research is to measure pedicle screw placement accuracy using a novel automated measurement system that directly compares the implanted screw location to the planned target in all three anatomical views. Preoperative CT scans were used to plan the screw trajectories in 122 patients across four surgical centers. Postoperative scans were fused to the preoperative plan to quantify placement accuracy using an automated measurement algorithm. The mean medial-lateral and superior-inferior deviations in the pedicle region for 500 screws were 1.75 ± 1.36 mm and 1.52 ± 1.26 mm, respectively. These deviations were measured using an automated system and were statistically different from manually determined values. The uncertainty associated with the fusion of preoperative to postoperative images was also quantified to better understand the screw-to-plan accuracy results. This study uses a novel automated measurement system to quantify screw placement accuracy as it relates directly to the planned target location, instead of analyzing for breaches of the pedicle, to quantify the validity of using of a robotic-guidance system for accurate pedicle screw placement.

Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling.

Neuromusculoskeletal (NMS) models can aid in studying the impacts of the nervous and musculoskeletal systems on one another. These computational models facilitate studies investigating mechanisms and treatment of musculoskeletal and neurodegenerative conditions. In this study, we present a predictive NMS model that uses an embedded neural architecture within a finite element (FE) framework to simulate muscle activation. A previously developed neuromuscular model of a motor neuron was embedded into a simple FE musculoskeletal model. Input stimulation profiles from literature were simulated in the FE NMS model to verify effective integration of the software platforms. Motor unit recruitment and rate coding capabilities of the model were evaluated. The integrated model reproduced previously published output muscle forces with an average error of 0.0435 N. The integrated model effectively demonstrated motor unit recruitment and rate coding in the physiological range based upon motor unit discharge rates and muscle force output. The combined capability of a predictive NMS model within a FE framework can aid in improving our understanding of how the nervous and musculoskeletal systems work together. While this study focused on a simple FE application, the framework presented here easily accommodates increased complexity in the neuromuscular model, the FE simulation, or both.