A new gradient-based method for analyzing brain white matter fiber geometry

Precise geometric and morphometric analyses of brain fiber pathways are crucial for unraveling brain organization and mechanisms underlying normal and pathological brain functions. However, existing methods for white matter (WM) fiber geometry analysis remain limited.

We propose a novel Large-scale Gradient Feature (LsGF) metric to quantify the tangent direction change rate along fiber streamlines. Using intra-class correlation coefficients (ICC), we systematically evaluated the stability of LsGF maps under two key factors: streamline count and tractography algorithm. LsGF was then applied to investigate gender disparities in WM morphology, with sensitivity assessed by comparing LsGF maps against fiber length maps.

Results showed that LsGF exhibited remarkable robustness to variations in streamline count (99 % of ICCs > 0.8), but demonstrated significant dependency on tractography algorithm (less than 60 % of ICCs > 0.6). Application of the LsGF method to gender dimorphism analysis uncovered distinct geometric patterns primarily in the thalamus, internal capsule, cerebellum, corpus callosum, lingual gyrus, fusiform gyrus, precuneus, gyrus rectus, orbitofrontal cortex, cingulate cortex, calcarine, and olfactory regions.

Comparative analysis indicated that LsGF outperformed fiber length metrics in detecting microstructural geometric complexity, whereas the latter more effectively characterized macroscale architecture features. These findings underscore the complementary value of LsGF and fiber length metric in WM analysis.

The LsGF map enables voxel-wise analysis of quantitative streamline metrics across the whole brain, highlighting the necessity of consistent tractography methods for reliable results.