Consensus between pipelines in structural brain networks.

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Network Science

Background:

• Structural brain networks derived from diffusion MRI tractography offer insights into brain organization.
• Network reconstruction involves complex, multi-stage processing pipelines with variable methodological choices.
• The impact of different pipeline configurations on network accuracy and the identification of key connections remains unclear.

Purpose of the Study:

• To assess the similarity between structural brain networks reconstructed using two independent, state-of-the-art pipelines.
• To quantify network similarity and identify core brain connections robustly identified across pipelines.
• To evaluate the influence of different atlases and node scales on network properties.

Main Methods:

• Reconstruction of structural brain networks using two distinct diffusion MRI tractography pipelines.
• Comparison of network connections across pipelines using merged parcels to a common node scale.
• Analysis of network similarity across various fiber density thresholds and node scales.

Main Results:

• High agreement was observed between the structural networks generated by the two pipelines across multiple fiber density thresholds.
• A robust core of highly interconnected brain regions was identified, showing peak similarity across network densities.
• Replication of results with different node scales confirmed the robustness of the core connections, although binary network properties showed some atlas-dependent differences.

Conclusions:

• Multiple diffusion MRI tractography pipelines can yield highly similar structural brain networks.
• Identifying a core set of robust connections is essential for reliable network analysis in neuroscience.
• This study highlights the utility of employing multiple reconstruction pipelines to validate and identify critical structural brain connections.