Computational Model for Electromagnetic Gradient Cues Promoting Induced Growth Cone Turning
Document Type
Oral Presentation
Campus where you would like to present
Ellensburg
Event Website
https://digitalcommons.cwu.edu/source
Start Date
18-5-2020
Abstract
The present study seeks to develop a computational model to investigate a method of neural regenerative treatment for neurodegenerative diseases such as Alzheimer’s, multiple sclerosis, and sleep disorders. During neural development, growth cones of neurons respond to physical and chemical cues to turn and move along the correct path. After reaching its destination, the neuron connects with a neighboring nerve cell to create an intricate circuitry of neurons. Dysfunctional neural activity occurs when a neuron becomes injured or connects to a cell that is unable to receive electrical impulses. Recent studies have explored the use of near infrared (NIR) lasers to rewire neural connections and promote regeneration in damaged neurons. The electromagnetic field of a NIR laser provides a gradient to induce a repulsive and/or attractive response in the growth cone. Studies have shown that this method is highly effective for encouraging permanent turning of the growth cone, without damaging the neuron and the substrate necessary for motility. A key hypothesis of our study is that the growth cone structure interprets optical “turn signals” by growing in the direction of increasing electromagnetic field intensity. As an initial first step in predicting the success of NIR-based treatment of neurodegenerative diseases, we have created a model that defines the relationships that govern the dynamics of electromagnetic guidance cues. An expected outcome of this project is to produce new testable predictions for neuronal response to tunable features of an electromagnetic gradient, yielding insight into the potential effectiveness of different neural stimulation strategies. College of the Sciences Presentation Award Winner.
Recommended Citation
Ford, Kahmina, "Computational Model for Electromagnetic Gradient Cues Promoting Induced Growth Cone Turning" (2020). Symposium Of University Research and Creative Expression (SOURCE). 81.
https://digitalcommons.cwu.edu/source/2020/COTS/81
Department/Program
Physics
Additional Mentoring Department
https://cwu.studentopportunitycenter.com/2020/04/computational-model-for-electromagnetic-gradient-cues-promoting-induced-growth-cone-turning/
Computational Model for Electromagnetic Gradient Cues Promoting Induced Growth Cone Turning
Ellensburg
The present study seeks to develop a computational model to investigate a method of neural regenerative treatment for neurodegenerative diseases such as Alzheimer’s, multiple sclerosis, and sleep disorders. During neural development, growth cones of neurons respond to physical and chemical cues to turn and move along the correct path. After reaching its destination, the neuron connects with a neighboring nerve cell to create an intricate circuitry of neurons. Dysfunctional neural activity occurs when a neuron becomes injured or connects to a cell that is unable to receive electrical impulses. Recent studies have explored the use of near infrared (NIR) lasers to rewire neural connections and promote regeneration in damaged neurons. The electromagnetic field of a NIR laser provides a gradient to induce a repulsive and/or attractive response in the growth cone. Studies have shown that this method is highly effective for encouraging permanent turning of the growth cone, without damaging the neuron and the substrate necessary for motility. A key hypothesis of our study is that the growth cone structure interprets optical “turn signals” by growing in the direction of increasing electromagnetic field intensity. As an initial first step in predicting the success of NIR-based treatment of neurodegenerative diseases, we have created a model that defines the relationships that govern the dynamics of electromagnetic guidance cues. An expected outcome of this project is to produce new testable predictions for neuronal response to tunable features of an electromagnetic gradient, yielding insight into the potential effectiveness of different neural stimulation strategies. College of the Sciences Presentation Award Winner.
https://digitalcommons.cwu.edu/source/2020/COTS/81
Faculty Mentor(s)
Erin Craig