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Related Experiment Video

Updated: Jun 23, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Functional and structural reorganization in brain tumors: a machine learning approach using desynchronized functional

Joan Falcó-Roget¹, Alberto Cacciola², Fabio Sambataro³

¹Brain and More Lab, Computer Vision, Sano Centre for Computational Medicine, Kraków, Poland. j.roget@sanoscience.org.

Communications Biology

|April 6, 2024

Summary

This study reveals complex functional and structural brain network changes within brain tumors using advanced MRI. Understanding these alterations is crucial for predicting surgical outcomes and improving patient care.

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Last Updated: Jun 23, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures

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Published on: November 14, 2019

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Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Area of Science:

Neuroimaging
Brain Network Analysis
Medical Physics

Background:

Standard MRI identifies tumor core and edema but often overlooks signals within these regions.
The functional and diffusion signals within tumors and their impact on brain connectivity are poorly understood.

Purpose of the Study:

To explore functional activity and white matter structure considering the whole tumor.
To investigate the relationship between tumor signals and global connectivity reorganization.
To predict postsurgical brain network changes using preoperative data.

Main Methods:

Analysis of resting-state functional signals in the frequency domain.
Development of a fiber tracking pipeline for tumoral and peritumoral white matter.
Application of machine learning to predict structural rearrangement based on preoperative brain networks.

Main Results:

Intertwined alterations found in local and distributed functional signals within the tumor.
Successful reconstruction of white matter bundles in tumoral and peritumoral tissues.
Machine learning model accurately predicted postsurgical structural rearrangement and disentangled tumor-specific connectivity patterns.

Conclusions:

MR signals within damaged brain tissues are critical for understanding brain tumors.
Tumors exhibit and relate to complex patterns of structural and functional (dis-)connections.
This approach highlights the importance of comprehensive neuroimaging for surgical planning and outcome prediction.