Generative modeling of the enteric nervous system employing point pattern analysis and graph construction

TL;DR

This paper presents a generative model of the enteric nervous system's architecture using spatial point pattern analysis and graph construction, aiding understanding of its structure across different health conditions.

Contribution

It introduces a novel hybrid generative model combining spatial point processes and graph generation to simulate ENS architecture in health and disease.

Findings

Model accurately captures ENS spatial and topological features

Applicable to individuals of varying age and health status

Potential to inform neuromodulation therapies and diagnostics

Abstract

We describe a generative network model of the architecture of the enteric nervous system (ENS) in the colon employing data from images of human and mouse tissue samples obtained through confocal microscopy. Our models combine spatial point pattern analysis with graph generation to characterize the spatial and topological properties of the ganglia (clusters of neurons and glial cells), the inter-ganglionic connections, and the neuronal organization within the ganglia. We employ a hybrid hardcore-Strauss process for spatial patterns and a planar random graph generation for constructing the spatially embedded network. We show that our generative model may be helpful in both basic and translational studies, and it is sufficiently expressive to model the ENS architecture of individuals who vary in age and health status. Increased understanding of the ENS connectome will enable the use of neuromodulation strategies in treatment and clarify anatomic diagnostic criteria for people with bowel motility disorders.