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Establishment of Orthotopic Patient-derived Xenograft Models for Brain Tumors using a Stereotaxic Device
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Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain.

Hannelore Aerts1, Michael Schirner2,3,4, Ben Jeurissen5

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Area of Science:

  • Computational neuroscience
  • Neuroimaging
  • Brain tumor research

Background:

  • Presurgical planning for brain tumor resection requires delineating eloquent brain tissue.
  • Current noninvasive techniques like functional MRI and diffusion-weighted imaging fiber tracking have limitations in predicting functional outcomes due to complex brain dynamics.
  • Large-scale brain network modeling offers a potential solution by integrating neuroimaging data with biophysical models.

Purpose of the Study:

  • To investigate the utility of large-scale brain network modeling for presurgical planning in brain tumor patients.
  • To select and optimize computational models for simulating brain dynamics.
  • To assess the relationship between model parameters, structural network topology, and cognitive performance.

Main Methods:

  • Simulated large-scale brain dynamics in 25 brain tumor patients and 11 controls using The Virtual Brain platform.
  • Individually optimized local and global parameters of the Reduced Wong-Wang model.
  • Compared model parameters between patient and control groups and assessed their relationship with structural network topology and cognitive performance.

Main Results:

  • Individually optimized model parameters significantly improved the prediction accuracy of individual functional connectivity.
  • Local model parameters could differentiate between tumor-affected regions, distant regions, and healthy brain regions.
  • Identified associations between optimized model parameters, structural network topology, and cognitive performance.

Conclusions:

  • Individualized brain network modeling enhances the prediction of functional connectivity, aiding presurgical planning.
  • The Reduced Wong-Wang model parameters can serve as biomarkers for tumor-related brain alterations.
  • This approach provides valuable insights into the relationship between brain network dynamics, structure, and cognition in patients with brain tumors.