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The Case for Optimized Edge-Centric Tractography at Scale.

Joseph Y Moon1, Pratik Mukherjee2, Ravi K Madduri3

  • 1Lawrence Livermore National Laboratory, Livermore, CA, United States.

Frontiers in Neuroinformatics
|June 2, 2022
PubMed
Summary
This summary is machine-generated.

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Reducing streamline count in edge-centric connectomes significantly speeds up neuroimaging analysis without compromising data quality. Subject demographics, not streamline numbers, primarily influence connectome identifiability.

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Brain Connectomics

Background:

  • Structural connectomes are crucial for understanding brain organization but their anatomic validity is uncertain.
  • Edge-centric tractography offers improved anatomic embedding but is computationally intensive.
  • Reducing computational cost is essential for widespread adoption of advanced connectomic methods.

Purpose of the Study:

  • To investigate if reducing streamline count in probabilistic tractography impacts the identifiability of edge-centric connectomes.
  • To determine the primary sources of variation in connectome identifiability.
  • To compare the effectiveness of different similarity metrics for assessing identifiability.

Main Methods:

  • Edge-centric connectomes were reconstructed using probabilistic tractography (PROBTRACKX2 and MRTrix).
Keywords:
EDIconnectomesdiffusion MRIedge-centricidentifiabilityoptimizationtractography

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  • Streamline counts were systematically reduced, from multiple streamlines per voxel to as few as one.
  • Connectome identifiability was assessed using test-retest sessions, with Jaccard similarity and Pearson correlation compared.

    • Main Results:

    • Running PROBTRACKX2 with just one streamline per voxel per region-pair showed no significant impact on identifiability.
    • Variations in identifiability due to streamline count were negligible compared to variations from subject demographics.
    • Jaccard similarity proved more effective than Pearson correlation for assessing identifiability in this population.

    Conclusions:

    • Streamline count reduction is a viable strategy for accelerating edge-centric connectome computation without sacrificing information content.
    • Subject demographics are a more significant factor influencing connectome identifiability than streamline count.
    • Jaccard similarity is recommended over Pearson correlation for identifiability calculations in similar neuroimaging studies.