Effect of different spatial normalization approaches on tractography and structural brain networks.

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Brain Connectivity Analysis

Background:

• Comparing white matter morphologic connectivity across populations requires mapping data to standardized neuroanatomical space.
• Existing methods that normalize diffusion data before streamline reconstruction show poor reproducibility and alter native space connectivity.
• Reliable quantitative tractography measures depend on accurate normalization techniques.

Purpose of the Study:

• To evaluate direct streamline normalization (DSN) against two existing methods for spatial normalization of diffusion MRI data.
• To assess the impact of different normalization strategies on the reproducibility of quantitative tractography and network metrics.
• To determine if DSN can preserve native space connectivity information.

Main Methods:

• Direct streamline normalization (DSN) was applied, warping streamlines directly.
• Two publically available methods were used, involving spatial normalization of diffusion data followed by streamline reconstruction.
• Reproducibility of normalized connectivity matrices and network metrics was compared against native space results.

Main Results:

• Methods reconstructing streamlines after normalization significantly altered network metrics (large to huge effect sizes).
• DSN showed no significant difference in network metrics compared to native space (very small to small effect sizes).
• DSN demonstrated superior performance in preserving native space connectivity.

Conclusions:

• Direct streamline normalization (DSN) is a superior method for maintaining white matter connectivity during spatial normalization.
• DSN outperforms methods that normalize diffusion data first, offering greater accuracy in preserving network metrics.
• DSN enables the definition of connectome networks without reliance on gray matter parcellations, opening new research avenues.