Publication Date
12-2025
Date of Final Oral Examination (Defense)
8-29-2025
Type of Culminating Activity
Dissertation
Degree Title
Doctor of Philosophy in Biomedical Engineering
Department
Biological Sciences
Supervisory Committee Chair
Clare Fitzpatrick, Ph.D.
Supervisory Committee Member
Benjamin Johnson, Ph.D.
Supervisory Committee Member
Shuqi Zhang, Ph.D.
Supervisory Committee Member
Landon Hamilton, Ph.D.
Abstract
Neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) disrupt the interplay between neural control and musculoskeletal function, leading to progressive motor decline and loss of force control and production. Traditional experimental and clinical approaches are limited in their ability to capture the multiscale, nonlinear mechanisms driving these impairments. This dissertation presents a novel predictive neuromusculoskeletal (NMS) modeling framework that integrates physiologically accurate motor neuron dynamics with three-dimensional finite element (FE) muscle models. Using subject-specific muscle geometry and fiber architecture of the tibialis anterior, the model simulates realistic force generation under both healthy and ALS-affected conditions. The neural component incorporates a motor neuron pool governed by Henneman's size principle and biophysical parameters calibrated to reproduce recruitment, rate coding, and degeneration patterns characteristic of specific phases of ALS. The musculoskeletal component uses advanced continuum mechanics to simulate muscle force production and joint biomechanics. Validation against experimental data demonstrates the model’s capacity to predict force loss and biomechanical alterations over time during specific phases of ALS. By capturing the interactions between descending neural drive, motor unit behavior, and muscle deformation, this integrated framework provides a powerful, non-invasive tool for investigating disease progression providing a foundation platform which has the potential to evaluate therapeutic interventions. The approach lays the groundwork for developing patient-specific simulations capable of informing clinical decision-making and advancing the study of neurodegenerative diseases.
DOI
https://doi.org/10.18122/td.2443.boisestate
Recommended Citation
Babcock, Colton, "Integrating Neural Control and Finite Element Muscle Modeling: A Predictive Neuromusculoskeletal Framework for Investigating Neurodegenerative Disease" (2025). Boise State University Theses and Dissertations. 2443.
https://doi.org/10.18122/td.2443.boisestate