# Structural-functional fingerprinting for abnormalities investigation in glioma patients

## TL;DR

This study explores how brain networks are disrupted by gliomas using a new method that combines structural and functional data to better understand the effects of tumors on brain connectivity.

## Contribution

The novel integration of functional, structural, and microstructural connectivity data provides a more comprehensive view of glioma-induced brain network changes.

## Key findings

- Lateralized gliomas affect brain networks in both hemispheres, with left hemisphere lesions altering homotopic and contralateral networks.
- Functional connectivity is more effective than structural measures in detecting abnormalities distant from the tumor.
- Combining multiple connectivity modalities is essential for understanding the heterogeneity of glioma-induced brain disruptions.

## Abstract

Gliomas alter brain function and integrity, but these disruptions are often studied separately. This study utilised a novel approach that integrated functional, structural and microstructural connectivity information to investigate glioma-induced brain network changes and their clinical implications. It focused on the impact of gliomas on key brain networks, with a particular emphasis on the relationship between tumour topology and its effect on homotopic areal-level parcellation. The investigation was grounded in a unique clinical dataset comprising functional and diffusion images of forty-one newly diagnosed glioma patients. Connectivity matrices (functional, structural, and microstructural) were generated using homotopic parcellations and combined into an integration connectivity matrix. A linear regression model compared patient data to pseudo-healthy references. This identified affected regions as those falling in the left tail of the distribution across patients and parcellations. The study revealed that lateralized gliomas affect networks in both hemispheres, with left hemisphere lesions primarily altering homotopic homolateral and contralateral networks in healthy tissues. Abnormalities were more easily detected in regions distant from the lesion using functional connectivity rather than structural measures. The approach highlighted the heterogeneity of functional and structural alterations and emphasised that a comprehensive understanding of glioma abnormalities requires integrating multiple connectivity modalities.