Publication Date

8-2022

Date of Final Oral Examination (Defense)

4-18-2022

Type of Culminating Activity

Dissertation

Degree Title

Doctor of Philosophy in Materials Science and Engineering

Department

Materials Science and Engineering

Supervisory Committee Chair

Clare Fitzpatrick, Ph.D.

Supervisory Committee Member

Scott Phillips, Ph.D.

Supervisory Committee Member

Trevor Lujan, Ph.D.

Supervisory Committee Member

Paul Rullkoetter, Ph.D.

Abstract

Neurodegenerative and neurodevelopmental disorders are a group of conditions that stem from irregularities in the nervous system that lead to complications in function and movement. The goal of this work is to develop computational tools that: (1) measure the accuracy of surgical interventions in neurodegenerative and neurodevelopmental conditions, and (2) integrate neural and musculoskeletal frameworks to provide a platform to better investigate neurodegenerative and neurodevelopmental disorders. Parkinson’s disease (PD) is a neurodegenerative condition projected to affect over 1.2 million people by 2030 in the US. It is caused by atypical firing patterns in the basal ganglia region of the brain that leads to primary motor symptoms of tremor, slowness of movement, and rigidity. A potential treatment for PD is deep brain stimulation (DBS). DBS involves implanting electrodes into central brain structures to regulate the pathological signaling. Electrode placement accuracy is a key metric that helps to determine patient outcomes postoperatively. An automated measurement system was developed to quantify electrode placement accuracy in robot-assisted asleep DBS procedures (Chapter 2). This measurement system allows for precise metrics without human bias in large cohorts of patients. This measurement system was later modified to measure screw placement accuracy in spinal fusion procedures for the treatment of degenerative musculoskeletal conditions (Chapter 3).

DBS is an effective treatment for PD, but it is not a cure for the cause of the disease itself. To cure neurodegenerative and neurodevelopmental diseases, the underlying disease mechanisms must be better understood. A major limitation in studying neural conditions is the infeasibility of performing in vivo experiments, particularly in humans due to ethical considerations. Computational modeling, specifically fully predictive neuromusculoskeletal (NMS) models, can help to accumulate additional knowledge about neural pathways that cannot be determined experimentally. NMS models typically include complexity in either the neuromuscular or musculoskeletal system, but not both, making it difficult or infeasible to investigate the relationship between neural signaling and musculoskeletal function. To overcome this, a fully predictive NMS model was developed by integrating NEURON software within Abaqus, a finite element (FE) environment (Chapter 4). The neural model consisted of a pool of motor neurons innervating the soleus muscle in a FE human ankle model. Software integration was verified against previously published data, and the neuronal network was verified for motor unit recruitment and rate coding, which are the two principles required for in vivo muscle generation. To demonstrate the applicability of the model to study neurodegenerative and neurodevelopmental diseases, a fully predictive mouse hindlimb NMS model was developed using the integrated framework to investigate Rett syndrome (RS) (Chapter 5). RS is a neurodevelopmental disorder caused by a mutation of the Mecp2 gene with hallmark motor symptoms of a loss of purposeful hand movement, changes in muscle tone, and a loss of speech. Recent experimental analysis has found that the axon initial segment (AIS) in mice that model RS has torsional morphology compared to wildtype littermate controls. The effects these neural morphological changes have on joint motion will be studied using the mouse NMS model. This work encompasses a range of research that uses computational models to study the underlying mechanisms and design targeted treatment options for neurodegenerative and neurodevelopmental disorders. The outcomes of this work have quantified the accuracy at which surgical interventions for these conditions can be performed and have resulted in a neuromusculoskeletal model that can be applied to understand how neural morphology, and associated changes due to these disorders, affects musculoskeletal function.

DOI

https://doi.org/10.18122/td.2000.boisestate

Download

Share

COinS