Neuronal graphs: a graph theory primer for microscopic, functional networks of neurons recorded by Calcium imaging

TL;DR

This paper introduces graph theory concepts tailored for analyzing microscopic neuronal networks from calcium imaging, providing a foundational guide for neuroscientists to understand and apply graph algorithms to neural data.

Contribution

It offers a primer on graph theory specifically adapted for microscopic neuronal networks, bridging the gap between mathematical tools and neurobiological data analysis.

Findings

Graph theory can effectively characterize neuronal network structures.

Recent applications of graph algorithms to calcium imaging data reveal new insights.

Guidelines are provided to avoid common pitfalls in applying graph theory to neural data.

Abstract

Connected networks are a fundamental structure of neurobiology. Understanding these networks will help us elucidate the neural mechanisms of computation. Mathematically speaking these networks are `graphs' - structures containing objects that are connected. In neuroscience, the objects could be regions of the brain, e.g. fMRI data, or be individual neurons, e.g. calcium imaging with fluorescence microscopy. The formal study of graphs, graph theory, can provide neuroscientists with a large bank of algorithms for exploring networks. Graph theory has already been applied in a variety of ways to fMRI data but, more recently, has begun to be applied at the scales of neurons, e.g. from functional calcium imaging. In this primer we explain the basics of graph theory and relate them to features of microscopic functional networks of neurons from calcium imaging - neuronal graphs. We explore recent examples of graph theory applied to calcium imaging and we highlight some areas where researchers new to the field could go awry.