Prediction of Neural Diameter From Morphology to Enable Accurate Simulation
| dc.contributor.advisor | Blackwell, Kim "Avrama" | |
| dc.contributor.author | Reed, Jonathan | |
| dc.creator | Reed, Jonathan | |
| dc.date | 2020-12-04 | |
| dc.date.accessioned | 2021-09-28T00:47:52Z | |
| dc.date.available | 2022-12-04T08:07:53Z | |
| dc.description | This thesis has been embargoed for 2 years. It will not be available until December 2022 at the earliest. | |
| dc.description.abstract | Accurate neuron morphologies are paramount for computational model simulations with realistic neural response. Over the last decade, the online repository NeuroMorpho.Org has collected over 140,000 available neuron morphologies to understand brain function and promote interaction between experimental and computational research. Neuron morphologies describe spatial aspects of neural structure; however, many of the available morphologies do not contain accurate diameters essential for computational simulations of electrical activity. To best utilize available neuron morphologies, we present a set of equations which predict dendritic diameter from other morphology features. To derive the equations, we used a set of NeuroMorpho.Org archives with realistic neuron diameters, representing hippocampal pyramidal, cerebellar Purkinje, and striatal spiny projections neurons (SPNs). Our analysis revealed several neuron morphology features that may predict dendritic diameter. We demonstrate that the diameter of preceding dendritic nodes, Parent Diameter, is strongly correlated to diameter of subsequent nodes for all cell types. Each morphology can be further separated into initial, branching children, and continuing nodes, each requiring different combinations of morphology features to predict diameter. Model simulations reveal that membrane potential response with predicted diameters matches within 8.3% of original response for several tested morphologies. Predictions that use the original diameter of initial nodes generally improve membrane potential response as compared to predicted initial node diameters. We provide our open source software to extend the utility of available NeuroMorpho.Org morphologies, and suggest predictive equations may supplement morphologies without dendritic diameter and improve model simulations with realistic dendritic diameter. | |
| dc.identifier.uri | https://hdl.handle.net/1920/12074 | |
| dc.language.iso | en | |
| dc.subject | Dendritic diameter | |
| dc.subject | NeuroMorpho.org | |
| dc.subject | Neuron simulation | |
| dc.subject | Neuron morphology | |
| dc.title | Prediction of Neural Diameter From Morphology to Enable Accurate Simulation | |
| dc.type | Thesis | |
| thesis.degree.discipline | Biology | |
| thesis.degree.grantor | George Mason University | |
| thesis.degree.level | Master's | |
| thesis.degree.name | Master of Science in Biology |