Multidimensional encoding of brain connectomes.

Area of Science:

• Neuroscience
• Medical Imaging
• Computational Biology

Background:

• Mapping brain networks is crucial for understanding human behavior, health, and disease.
• Current methods for mapping brain connectomes can be computationally intensive and require significant storage.
• Advances in network neuroscience necessitate novel frameworks for efficient connectome analysis.

Purpose of the Study:

• To present a novel framework for encoding structural brain connectomes and diffusion-weighted magnetic resonance (dMRI) data.
• To integrate relationships between connectome nodes, edges, white matter fascicles, and diffusion data.
• To demonstrate the framework's utility for in vivo white matter mapping and anatomical computing.

Main Methods:

• Utilizing multidimensional arrays to encode structural brain connectomes and dMRI data.
• Integrating connectome topology with diffusion data for comprehensive analysis.
• Evaluating the framework with 1,490 connectomes, 13 tractography methods, and 3 datasets.

Main Results:

• Achieved significant storage reduction (up to 40x compression) for connectome evaluation methods.
• Demonstrated that dMRI spatial resolution critically impacts connectome resolution, with increases up to 52%.
• Enabled anatomical manipulations on white matter tracts for statistical inference and geometrical organization studies.

Conclusions:

• The proposed framework offers a powerful and efficient approach for analyzing brain connectomes.
• The method significantly reduces data storage requirements and enhances the resolution of white matter mapping.
• Open-source software and data are provided, facilitating reproducibility and further research in network neuroscience.