Network analysis of neuroimaging in mice

Area of Science:

• Neuroscience
• Network Science
• Computational Biology

Background:

• Graph theory is crucial for understanding brain connectivity and regional interactions, especially after lesions or in disease states.
• Network analysis using MRI connectivity matrices is common in human clinical studies but underutilized in mouse neuroimaging.
• Current mouse neuroimaging often focuses on basic connectivity metrics, neglecting advanced network analysis techniques.

Purpose of the Study:

• To review graph theoretical measures for describing brain networks derived from in vivo mouse studies.
• To facilitate the application of advanced network analysis in mouse neuroimaging research.
• To promote standardized measures for cross-species comparisons in translational brain disease research.

Main Methods:

• Summarizing graph theoretical measures and their interpretation for mouse brain networks.
• Explaining mathematical definitions relevant to network analysis.
• Providing a software toolkit and discussing practical considerations for applying these methods to resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI).

Main Results:

• The review details various graph theoretical measures applicable to mouse brain networks.
• It offers guidance on interpreting these measures in the context of neuroimaging data.
• A software toolkit is presented to aid researchers in applying these advanced analyses.

Conclusions:

• Advanced network analysis using graph theory can significantly enhance the understanding of mouse brain connectivity.
• Standardized application of these methods is essential for robust cross-species comparisons in translational neuroscience.
• This work aims to foster wider adoption of graph theory in mouse brain research for improved disease modeling and study.