Generative modeling of the enteric nervous system employing point pattern analysis and graph construction
This work addresses the need for better understanding of the ENS connectome to improve neuromodulation treatments and diagnostic criteria for people with bowel motility disorders, representing an incremental advancement in domain-specific modeling.
The authors tackled the problem of modeling the architecture of the enteric nervous system (ENS) in the colon by developing a generative network model using spatial point pattern analysis and graph generation from confocal microscopy images of human and mouse tissue. They showed that the model is expressive enough to capture variations in age and health status, aiding in basic and translational studies.
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.