Longitudinal quantitative streamline tractography: Robust estimation of white matter connectivity differences.
5 affiliations
- 1Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Melbourne, Australia.
- 2Department of Psychiatry, The University of Melbourne, Melbourne, Australia.
- 3The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia.
- 4Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States.
- 5Vanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, TN, United States.
Abstract
Longitudinal probing of structural connectivity via diffusion magnetic resonance imaging (dMRI) is experiencing uptake. However, the detection of biological effects is significantly hampered by the limitations of cross-sectional streamline tractography, where even small changes in the dMRI signal can produce drastically different trajectories and, therefore, quantitative parameterisation; if not properly dealt with, such effects will manifest as spurious longitudinal change, which can obscure subtle biological differences. To address this challenge, we here introduce a novel quantitative streamline tractography framework tailored for longitudinal analysis, wherein an individual's streamline trajectories remain fixed throughout the analysis, allowing only their ascribed density weights to vary between sessions. We present two strategies by which these quantitative streamline weights can be determined, both extensions of the widely adopted SIFT2 method. The performance of this framework is benchmarked against cross-sectional reconstruction with and without SIFT2 optimisation, in both in silico dMRI phantoms with known ground truths and three distinct human in vivo cohorts with clear a priori expectations of biological effects. We demonstrate that the proposed framework drastically reduces methodological imprecisions in synthetic dMRI phantoms and enhances statistical sensitivity and specificity to biological effects in human cohorts, enabling more robust quantification of longitudinal structural connectivity differences.
Related
- Migraine-related differences in connectivity patterns during emotional audiovisual processing revealed by functional connectivity multivariate pattern analysis.
- Gender Differences between Cyberbullying Victimization and Problematic Smartphone Use: The Longitudinal Mediating Effects of Psychological Distress and Fear of Missing Out
- Longitudinal quantitative MRI of blood-brain barrier leakage in chronic cerebral hypoperfusion rat model
- Ultra-rare coding variants in schizophrenia risk genes are associated with longitudinal phenotypic differences across psychotic disorders
- Sex differences in the genetic architecture of clinical quantitative traits in the electronic health record
BibTeX
@article{pruckner2026longitudinal,
title = {{Longitudinal quantitative streamline tractography: Robust estimation of white matter connectivity differences.}},
author = {Philip Pruckner and Remika Mito and David N Vaughan and Kurt G Schilling and Victoria L Morgan and Dario J Englot and Robert E Smith},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = oct,
doi = {10.1162/imag.a.1414},
url = {https://europepmc.org/article/MED/42953573},
note = {CC BY},
}
RIS
TY - JOUR
TI - Longitudinal quantitative streamline tractography: Robust estimation of white matter connectivity differences.
AU - Philip Pruckner
AU - Remika Mito
AU - David N Vaughan
AU - Kurt G Schilling
AU - Victoria L Morgan
AU - Dario J Englot
AU - Robert E Smith
PY - 2026
DA - 2026/10/27
JO - Imaging neuroscience (Cambridge, Mass.)
DO - 10.1162/imag.a.1414
UR - https://europepmc.org/article/MED/42953573
AB - Longitudinal probing of structural connectivity via diffusion magnetic resonance imaging (dMRI) is experiencing uptake. However, the detection of biological effects is significantly hampered by the limitations of cross-sectional streamline tractography, where even small changes in the dMRI signal can produce drastically different trajectories and, therefore, quantitative parameterisation; if not properly dealt with, such effects will manifest as spurious longitudinal change, which can obscure subtle biological differences. To address this challenge, we here introduce a novel quantitative streamline tractography framework tailored for longitudinal analysis, wherein an individual's streamline trajectories remain fixed throughout the analysis, allowing only their ascribed density weights to vary between sessions. We present two strategies by which these quantitative streamline weights can be determined, both extensions of the widely adopted SIFT2 method. The performance of this framework is benchmarked against cross-sectional reconstruction with and without SIFT2 optimisation, in both in silico dMRI phantoms with known ground truths and three distinct human in vivo cohorts with clear a priori expectations of biological effects. We demonstrate that the proposed framework drastically reduces methodological imprecisions in synthetic dMRI phantoms and enhances statistical sensitivity and specificity to biological effects in human cohorts, enabling more robust quantification of longitudinal structural connectivity differences.
SN - 2837-6056
ER -