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[Preprint]. 2025 Aug 19:2025.08.19.669949.
doi: 10.1101/2025.08.19.669949.

Deterministic versus Probabilistic Tractography: Impact on White Matter Bundle Shape

Yuhan Shuai  1 , Bramsh Qamar Chandio  1 , Yixue Feng  1 , Julio E Villalon-Reina  1 , Talia M Nir  1 , Christopher R K Ching  1 , Iyad Ba Gari  1 , Sophia I Thomopoulos  1 , Jonathan Davis Alibrando  1 , John P John  2 , Ganesan Venkatasubramanian  3 , Neda Jahanshad  1 , Paul M Thompson  1
Affiliations

Deterministic versus Probabilistic Tractography: Impact on White Matter Bundle Shape

Yuhan Shuai et al. bioRxiv. .

Abstract

In diffusion MRI-based tractography, deterministic and probabilistic algorithms reconstruct white matter using distinct strategies, yet their impact on bundle morphology remains uncertain. Using bundle shape similarity analysis, we compared both methods for the left arcuate fasciculus (AF_L) (The left arcuate fasciculus is a critical white matter tract that connects language comprehension and production areas in the human brain, enabling fluent language processing) across four datasets: Alzheimer's Disease Neuroimaging Initiative (ADNI), Human Connectome Project-Aging (HCP-A), National Institute of Mental Health and Neurosciences (NIMHANS), and Pediatric Imaging, Neurocognition, and Genetics (PING). Probabilistic tractography consistently produced higher inter-subject shape similarity, by capturing broader anatomical trajectories and enhancing reproducibility. However, this extensive coverage may obscure subtle pathological variations critical for clinical detection. Bundle shape similarity analysis with atlas corroborated these findings, showing stronger alignment for probabilistic tracking and highlighting its utility in quantitative quality control. These results emphasize the need to balance morphological consistency with sensitivity to neuroanatomical variation when selecting tractography methods for research and clinical applications.

Keywords: Bundle Adjacency; Deterministic Tracking; Diffusion MRI; Probabilistic Tracking; Tractography.

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Figures

Fig. 1:
Fig. 1:
Fully connected bundle shape similarty network (A), adjacency matrix (B) and comparison with HCP1065 atlas (C) of the AF_L bundle in PING dataset (probabilistic tracking).
Fig. 2:
Fig. 2:
Fully connected bundle shape similarty network (A), adjacency matrix (B) and comparison with HCP1065 atlas (C) of the AF_L bundle in PING dataset (deterministic tracking).
Fig. 3:
Fig. 3:
Shape similarity matrices for the AF_L bundle in the ADNI dataset using deterministic (left) and probabilistic (right). Each matrix shows pairwise shape similarity scores across subjects, with darker colors indicating higher similarity.
Fig. 4:
Fig. 4:
Example of tractometry visualization of the AF_L in a representative ADNI subject compared to the HCP-based atlas. Panels show (left) deterministic tracking, (middle) probabilistic tracking, and (right) reference image from HCP atlas.
Fig. 5:
Fig. 5:
Example visualization of the AF_L in a representative ADNI subject compared to the HCP-based atlas. Each panel show (left) deterministic tracking, (right) probabilistic tracking.
See this image and copyright information in PMC

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