Modeling the Connectome of a Simple Spinal Cord

Abstract

In this paper we develop a computational model of the anatomy of a spinal cord. We address a long-standing ambition of neuroscience to understand the structure–function problem by modeling the complete spinal cord connectome map in the 2-day old hatchling Xenopus tadpole. Our approach to modeling neuronal connectivity is based on developmental processes of axon growth. A simple mathematical model of axon growth allows us to reconstruct a biologically realistic connectome of the tadpole spinal cord based on neurobiological data. In our model we distribute neuron cell bodies and dendrites on both sides of the body based on experimental measurements. If growing axons cross the dendrite of another neuron, they make a synaptic contact with a defined probability. The total neuronal network contains ∼1,500 neurons of six cell-types with a total of ∼120,000 connections. The anatomical model contains random components so each repetition of the connectome reconstruction procedure generates a different neuronal network, though all share consistent features such as distributions of cell bodies, dendrites, and axon lengths. Our study reveals a complex structure for the connectome with many interesting specific features including contrasting distributions of connection length distributions. The connectome also shows some similarities to connectivity graphs for other animals such as the global neuronal network of C. elegans. In addition to the interesting intrinsic properties of the connectome, we expect the ability to grow and analyze a biologically realistic spinal cord connectome will provide valuable insights into the properties of the real neuronal networks underlying simple behavior.

Structural and functional properties of a probabilistic model of neuronal connectivity in a simple locomotor network

“…The probabilistic connectivity model is derived from multiple connectomes generated by our existing anatomical model: a developmentally inspired model which is biologically realistic and incorporates a large number of biological measurements ( Borisyuk et al, 2014 ; Li et al, 2007a ; Borisyuk et al, 2011 ). The anatomical model simulates axon growth guided by chemical gradients, with model parameters that are chosen by fitting the generated axons to experimental measurements.…”

Mapping the Connectome: Multi-Level Analysis of Brain Connectivity

“…Using a model of a simple spinal cord system, the developing spinal cord of the Xenopus , Borisyuk et al (2011 ) introduce a new approach toward characterizing connectomes by constructing the network on the basis of known developmental processes of neuronal and axonal growth. The resulting network (Figure 1Q) is then studied with a number of visualization and topological analysis tools, revealing relationships between sets of simple developmental rules and topological regularities.…”

A Developmental Approach to Predicting Neuronal Connectivity from Small Biological Datasets: A Gradient-Based Neuron Growth Model

“…When dendrites were allocated to neurons, this developmental approach [1], [35] could be used to assemble complete networks of neurons in the tadpole spinal cord based on limited biological datasets. Fundamental to this approach is generalization from measured data where the number of recorded cases is rather limited.…”