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Related Experiment Video

Updated: Dec 25, 2025

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

Modeling brain dynamics after tumor resection using The Virtual Brain.

Hannelore Aerts¹, Michael Schirner², Thijs Dhollander³

¹Department of Data-Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium.

|March 21, 2020

Summary

This summary is machine-generated.

Personalized brain network models show stable parameters after tumor resection, aiding neurosurgery outcome prediction. Virtual neurosurgery improved functional connectivity predictions in some brain tumor patients.

Keywords:

Brain tumor Computational neuroscience Connectome Functional connectivity Graph theory Neuroinformatics

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Area of Science:

Computational neuroscience
Neuro-oncology
Medical simulation

Background:

Neurosurgery outcomes for brain tumors are unpredictable.
Personalized brain network models offer a promising predictive tool.
The Virtual Brain framework facilitates these complex models.

Purpose of the Study:

Validate personalized brain network models for neurosurgery.
Examine parameter stability before and after tumor resection.
Conduct the first virtual neurosurgery simulation.

Main Methods:

Optimized brain network model parameters for empirical brain activity.
Compared parameter stability in tumor patients versus healthy controls.
Simulated neural activity on a post-surgery connectome (virtual neurosurgery).

Main Results:

Brain network model parameters showed relative stability post-resection.
Parameter stability was higher in tumor patients than healthy controls.
Virtual neurosurgery improved post-surgical functional connectivity similarity in some patients.

Conclusions:

Brain network models demonstrate parameter stability relevant to neurosurgery.
Virtual neurosurgery shows potential but requires further investigation.
Findings highlight interdisciplinary opportunities for computational neuroscience and neuro-oncology.