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Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference.

Carlos Enrique Gutierrez1, Henrik Skibbe2, Ken Nakae3

  • 1Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan. carlos.gutierrez@oist.jp.

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This study introduces a data-driven framework to optimize diffusion-weighted magnetic resonance imaging (dMRI) fiber tracking parameters using neural tracer data. Optimized parameters significantly improve brain connectivity mapping accuracy and reliability.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biophysics

Background:

  • Diffusion-weighted magnetic resonance imaging (dMRI) enables non-invasive whole-brain connectivity studies, crucial for understanding neurological disorders.
  • Current dMRI tractography faces challenges with reliability, including low sensitivity, false positives, and inaccurate long-range connection reconstruction.
  • Heuristic parameter tuning in dMRI tracking algorithms can lead to manipulated results, necessitating a more objective approach.

Purpose of the Study:

  • To develop and validate a general data-driven framework for optimizing dMRI fiber tracking parameters.
  • To utilize neural tracer data as a reference for validating dMRI-based connectivity inferences.
  • To enhance the accuracy and reliability of brain connectivity mapping using dMRI.

Main Methods:

  • A novel framework employing multi-objective optimization with the non-dominated sorting genetic algorithm II (NSGA-II) was developed.
  • The framework was tested using primate dMRI and neural tracer data from the Japan's Brain/MINDS Project.
  • Two experiments evaluated the framework with seed-based (iFOD2) and global tracking algorithms, using distinct objective functions.

Main Results:

  • Optimized dMRI tracking parameters significantly improved fiber tracking performance, including coverage and fiber length, compared to default parameters.
  • Global tracking with refined objectives demonstrated substantial improvements: average fiber length (10–17 mm), coverage (0.9–15%), and target area correlation (40–68%).
  • Optimized parameters showed strong generalization capabilities across different brain samples, highlighting the framework's flexibility and applicability.

Conclusions:

  • The proposed data-driven framework effectively optimizes and validates dMRI fiber tracking parameters.
  • This approach enhances the accuracy and reliability of brain connectivity mapping, supporting the validity of dMRI tractography.
  • The findings underscore the importance of data-driven parameter adjustment for robust neuroimaging analyses.