Multimodal connectivity based eloquence score computation and visualisation for computer-aided neurosurgical path...

Area of Science:

• Neurosurgery
• Neuroimaging
• Computational Neuroscience

Background:

• Assessing the cognitive importance of brain regions non-invasively is crucial for neurosurgical planning.
• Traditional methods often rely on heuristics like minimal distance, which may not fully capture functional impact.

Purpose of the Study:

• To introduce and validate multi-modal metrics for estimating the impact of surgical access paths on brain networks.
• To refine neurosurgical planning by incorporating connectional importance beyond simple proximity.

Main Methods:

• Developing multi-modal metrics combining grey matter volume and axonal fibre numbers, weighted by network importance.
• Utilizing resting-state functional magnetic resonance imaging (fMRI) and deterministic tractography to build brain networks.
• Implementing an exhaustive search algorithm to characterize solution spaces and visualize connectional costs.

Main Results:

• The proposed approach refines traditional heuristics by supplementing connectional importance of resected tissue.
• Demonstrated capability to provide complementary information for surgeons to avoid critical functional hubs and their anatomical linkages.
• Successfully integrated neuroimaging-derived data with anatomical landmarks for enhanced surgical guidance.

Conclusions:

• The developed multi-modal metrics offer a more comprehensive approach to assessing surgical risk in neurosurgery.
• This method aids surgeons in preserving brain function by providing detailed insights into network connectivity.
• Non-invasive neuroimaging techniques are vital for advancing personalized neurosurgical strategies.