Abstract
Accurate neuron morphologies are paramount for computational model simulations of realistic neural responses. 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 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 NeuroMorpho.org 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 NeuroMorpho.org morphologies, and suggest predictive equations may supplement morphologies that lack dendritic diameter and improve model simulations with realistic dendritic diameter.
Highlights
Neuronal morphology is the foundation for computational models which integrate molecular and cellular processes to understand brain function and behavior (Fan and Markram, 2019)
In order to derive equations to predict diameter, we identified three cell types consisting of six separate archives: hippocampal pyramidal (Chitwood et al, 1999; Groen et al, 2014), cerebellar Purkinje (Chen et al, 2013; Nedelescu et al, 2018), and striatal SPNs (Chen et al, 2014; Goodliffe et al, 2018; Table 1)
We used a combination of morphological features to create predictive diameter equations for multiple neuron cell types: hippocampal pyramidal, cerebellar Purkinje, and striatal SPNs
Summary
Neuronal morphology is the foundation for computational models which integrate molecular and cellular processes to understand brain function and behavior (Fan and Markram, 2019). Individual neuron morphologies are the basis for computational simulation of neural response, branching (Cuntz et al, 2007, 2010; Donohue and Ascoli, 2008), and growth (Koene et al, 2009). The diameter of neuronal branches is important for controlling the flow of ionic current and signaling molecules, and is critically important for simulating neuron electrical activity. Changes in dendritic branch diameter can maximize current transfer (Bird and Cuntz, 2016) and influence Ca2+ dynamics (Anwar et al, 2014) or other second messengers (Luczak et al, 2017). Understanding neuron function from morphology requires measures of diameter
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
