A tutorial in connectome analysis: topological and spatial features of brain networks

Area of Science:

• Neuroscience
• Computational Biology
• Network Science

Background:

• High-throughput methods are emerging for mapping neural connections (the connectome).
• Understanding the connectome's organization is crucial for neuroscience research and clinical applications.

Purpose of the Study:

• To provide an introductory tutorial on analyzing the topological and spatial organization of neural connectomes.
• To guide new researchers in understanding various analytical approaches for connectome data.

Main Methods:

• Analysis of topological features at local, regional, and global network scales.
• Characterization of network archetypes including random, scale-free, small-world, modular, and hierarchical networks.
• Methods for analyzing the spatial organization, specifically neural wiring lengths.

Main Results:

• Topological analysis can characterize network components and differentiate between networks (e.g., patient vs. control connectomes).
• Global network analysis allows for the distinction of various network archetypes.
• Spatial analysis methods, such as wiring length analysis, are essential for understanding neural system organization.

Conclusions:

• Connectome analysis requires understanding both topological and spatial properties across multiple scales.
• This tutorial equips researchers with foundational knowledge to analyze complex neural network data.
• The discussed methods offer insights into brain organization and potential clinical applications.