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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Connectomic consistency: a systematic stability analysis of structural and functional connectivity.

Yusuf Osmanlıoğlu¹, Jacob A Alappatt¹, Drew Parker²

¹Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, United States of America.

Journal of Neural Engineering

|May 20, 2020

Summary

Structural connectomes are more consistent than functional connectomes, but consistency varies greatly based on generation methods. This study provides a reference for connectome consistency, crucial for understanding brain structure-function relationships.

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Area of Science:

Neuroscience
Computational Neuroscience
Brain Imaging

Background:

Connectomics, the study of brain connectivity, is vital for understanding brain organization.
Linking structural and functional brain organization is key to explaining behavior.
Understanding variability in connectome modalities across individuals and time is lacking.

Purpose of the Study:

To systematically analyze the consistency of structural and functional connectomes.
To compare connectome consistency across different generation methods and modalities.
To establish a reference point for connectome consistency in structure-function coupling studies.

Main Methods:

Analyzed connectome consistency using correlation and graph matching accuracy.
Evaluated structural connectomes generated with varying algorithms, parcellations, weighting, and pruning techniques.
Assessed functional connectomes considering full, positive, negative connectivity, and edge thresholding.

Main Results:

Structural connectomes exhibited higher consistency than functional connectomes.
Significant variation in consistency was observed based on connectome generation methods.
Consistency differed depending on the analysis approach (edge-level vs. network topology).

Conclusions:

Connectome consistency is modality-dependent and method-sensitive.
Provides a benchmark for evaluating structural and functional connectome variations.
Essential for accurately interpreting structure-function coupling and identifying mismatches.