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.
