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Purpose <p>Connectomics-based neurosurgery enables patient-specific characterization of white matter architecture and may provide novel insights into network adaptation following brain tumor treatment. Quantitative tractometry allows longitudinal measurement of white matter microstructural integrity, yet its application in neurosurgical oncology remains limited. We evaluated the utility of combining tract-level asymmetry metrics with exploratory network-level analyses to characterize white matter reorganization following tumor resection.</p> Methods <p>We retrospectively analyzed serial connectome imaging obtained at several clinical timepoints, including before and after surgery or radiation therapy in three patients undergoing brain tumor resection. Fractional anisotropy (FA) values were extracted from six bilateral white matter tracts. Hemispheric asymmetry was quantified using asymmetry index (AI) and percentage asymmetry (%Asym) metrics. A novel global asymmetry burden (GAB) metric was introduced to summarize cumulative hemispheric lateralization across tracts. To explore coordinated longitudinal tract remodeling, patient-specific structural covariance networks were constructed from pairwise correlations of tract FA values across imaging timepoints. Network strength was summarized as the mean absolute correlation between tract pairs and evaluated using permutation testing.</p> Results <p>Distinct longitudinal asymmetry patterns were observed across patients. GAB demonstrated heterogeneous trajectories over time, while tract-level ranking identified the arcuate fasciculus and superior longitudinal fasciculus as the most variable pathways. Exploratory structural covariance analysis showed one patient exhibited greater temporal coordination of tract remodeling than expected under the applied permutation framework (network strength = 0.648; <i>p</i> = 0.045), whereas the others did not.</p> Conclusion <p>Longitudinal connectomics-based tractometry is a useful framework for characterizing patient-specific white matter reorganization following brain tumor surgery.</p>

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Longitudinal connectomics-based tractometry for characterizing white matter reorganization following brain tumor surgery: a proof-of-concept case series

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • Shoaib A. Syed,
  • Fatimah M. Coppin,
  • Samuel Latzman,
  • Ching-Ling Teng,
  • Siyar Bahadir,
  • Iñigo L. Sistiaga,
  • Michael Schulder,
  • Randy S. D’Amico

摘要

Purpose

Connectomics-based neurosurgery enables patient-specific characterization of white matter architecture and may provide novel insights into network adaptation following brain tumor treatment. Quantitative tractometry allows longitudinal measurement of white matter microstructural integrity, yet its application in neurosurgical oncology remains limited. We evaluated the utility of combining tract-level asymmetry metrics with exploratory network-level analyses to characterize white matter reorganization following tumor resection.

Methods

We retrospectively analyzed serial connectome imaging obtained at several clinical timepoints, including before and after surgery or radiation therapy in three patients undergoing brain tumor resection. Fractional anisotropy (FA) values were extracted from six bilateral white matter tracts. Hemispheric asymmetry was quantified using asymmetry index (AI) and percentage asymmetry (%Asym) metrics. A novel global asymmetry burden (GAB) metric was introduced to summarize cumulative hemispheric lateralization across tracts. To explore coordinated longitudinal tract remodeling, patient-specific structural covariance networks were constructed from pairwise correlations of tract FA values across imaging timepoints. Network strength was summarized as the mean absolute correlation between tract pairs and evaluated using permutation testing.

Results

Distinct longitudinal asymmetry patterns were observed across patients. GAB demonstrated heterogeneous trajectories over time, while tract-level ranking identified the arcuate fasciculus and superior longitudinal fasciculus as the most variable pathways. Exploratory structural covariance analysis showed one patient exhibited greater temporal coordination of tract remodeling than expected under the applied permutation framework (network strength = 0.648; p = 0.045), whereas the others did not.

Conclusion

Longitudinal connectomics-based tractometry is a useful framework for characterizing patient-specific white matter reorganization following brain tumor surgery.