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Updated: Mar 11, 2026

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

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Anomaly Detection for Structural and Functional Connectivity in Glioma Patients.

Maria Colpo^1,2,3, Ryan Pollitt³, Alexander Leemans³

¹Padova Neuroscience Center, University of Padova, Padova, Italy.

NMR in Biomedicine

|March 10, 2026

Summary

This study introduces a novel method using variational autoencoders (VAEs) to integrate structural connectivity (SC) and functional connectivity (FC) data, identifying brain network abnormalities in glioma patients for improved diagnostics and treatment planning.

Keywords:

anomaly detection brain tumor functional connectivity glioma integration single subject structural connectivity variational autoencoder

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Area of Science:

Neuroimaging
Computational Neuroscience
Oncology

Background:

Glioma-brain network interactions are crucial but lack standardized methods for integrating structural connectivity (SC) and functional connectivity (FC).
Existing approaches struggle to identify single-patient, multimodal connectivity abnormalities caused by tumors.

Purpose of the Study:

To explore the potential of variational autoencoders (VAEs) for integrating SC and FC data.
To detect multimodal brain connectivity anomalies in glioma patients at the single-patient level.

Main Methods:

Trained VAEs on healthy brain FC-SC data to learn normative connectivity patterns.
Applied transfer learning to oncological data to reconstruct healthy connectivity matrices.
Developed a statistic to identify SC, FC, and integrated FC+SC alterations in glioma patients.

Main Results:

Functional connectivity (FC) showed more distant alterations, while structural connectivity (SC) was affected near the tumor.
FC-identified alterations aligned better with integrated FC+SC findings than SC-identified ones.
SC abnormalities did not overlap with FC+SC outside the tumor; FC and SC impairments showed partial overlap within the tumor core.

Conclusions:

The VAE-based multimodal approach effectively detects glioma-induced brain connectivity changes.
Differentiating SC and FC alterations provides insights into tumor impact on brain networks.
Findings support potential applications in patient stratification, prognostic modeling, and personalized treatment planning.