Journal article
Prediction of Neural Diameter From Morphology to Enable Accurate Simulation
Frontiers in neuroinformatics, Vol.15, pp.666695-666695
06/03/2021
DOI: 10.3389/fninf.2021.666695
PMCID: 8209307
PMID: 34149388
Abstract
Accurate neuron morphologies are paramount for computational model simulations of realistic neural responses. Over the last decade, the online repository 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 that are essential for computational simulations of electrical activity. To best utilize available neuron morphologies, we present a set of equations that predict dendritic diameter from other morphological features. To derive the equations, we used a set of archives with realistic neuron diameters, representing hippocampal pyramidal, cerebellar Purkinje, and striatal spiny projection neurons. Each morphology is separated into initial, branching children, and continuing nodes. Our analysis reveals that the diameter of preceding nodes, Parent Diameter, is correlated to diameter of subsequent nodes for all cell types. Branching children and initial nodes each required additional morphological features to predict diameter, such as path length to soma, total dendritic length, and longest path to terminal end. Model simulations reveal that membrane potential response with predicted diameters is similar to the original response for several tested morphologies. We provide our open source software to extend the utility of available morphologies, and suggest predictive equations may supplement morphologies that lack dendritic diameter and improve model simulations with realistic dendritic diameter.
Details
- Title: Subtitle
- Prediction of Neural Diameter From Morphology to Enable Accurate Simulation
- Creators
- Jonathan D. Reed - George Mason UniversityKim T. Blackwell - George Mason University
- Resource Type
- Journal article
- Publication Details
- Frontiers in neuroinformatics, Vol.15, pp.666695-666695
- DOI
- 10.3389/fninf.2021.666695
- PMID
- 34149388
- PMCID
- 8209307
- NLM abbreviation
- Front Neuroinform
- ISSN
- 1662-5196
- eISSN
- 1662-5196
- Publisher
- Frontiers Media Sa
- Number of pages
- 17
- Grant note
- R01DA038890 / NIH-NSF CRCNS program through NIDA R01AA16022 / NIAAA; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Alcohol Abuse & Alcoholism (NIAAA)
- Language
- English
- Date published
- 06/03/2021
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Iowa Neuroscience Institute
- Record Identifier
- 9984446417302771
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