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Harmonizing network-based statistics across different atlases in brain connectome analysis.

Qingyuan Liu1, Yongbin Wei2, Dongxu Liu1

  • 1Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.

Communications Biology
|June 19, 2025
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Summary
This summary is machine-generated.

TACOS is a new tool that translates neuroimaging network statistics across different brain atlases without raw data. This harmonizes connectomic results, improving data sharing and analysis for brain research.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Data Science

Background:

  • Neuroimaging studies face challenges in translatability due to diverse analytical pipelines and brain atlases.
  • Combining summary statistics across studies is crucial for enhancing the generalizability of connectomic findings.
  • Existing methods often require raw data, limiting the integration of published summary statistics.

Purpose of the Study:

  • To introduce TACOS (Transform brAin COnnectomes across atlaSes), a novel computational tool.
  • To enable the translation of network-based statistics across different brain atlases without needing individual raw neuroimaging data.
  • To facilitate the harmonization of connectomic results from diverse studies and cohorts.

Main Methods:

  • TACOS utilizes linear models based on anatomical information from brain parcellations and white matter fiber data.
  • The tool performs cross-atlas transformations of network-based statistics, specifically t-statistics.
  • Validation involved testing across 17 different brain atlases using Human Connectome Project (HCP) surrogate statistics and independent datasets.

Main Results:

  • TACOS-transformed t-statistics showed strong correlations with ground truth for both structural (r=0.32-0.95) and functional networks (r=0.57-0.95).
  • These correlations remained consistent across different ancestries, demonstrating robustness.
  • The tool effectively harmonized connectomic results from multi-site schizophrenia cohorts (structural: r=0.57-0.94; functional: r=0.75-0.95).

Conclusions:

  • TACOS provides a robust method for cross-atlas transformation of connectomic summary statistics.
  • This tool significantly enhances the ability to share and combine multi-site and multi-atlas neuroimaging data.
  • TACOS holds great potential for advancing downstream connectomic analyses and meta-analyses in neuroscience.