Discernible interindividual patterns of global efficiency decline during theoretical brain surgery

Area of Science:

• Neurosurgery
• Graph Theory
• Neuroscience

Background:

• Functional localization and resection risks are key in neurosurgery for infiltrating tumors like diffuse gliomas.
• Global efficiency (GE) models connectome disruption after tumor removal.
• Understanding brain network vulnerability is crucial for surgical planning.

Purpose of the Study:

• To simulate the impact of brain parcellation resection on global efficiency (GE).
• To identify critical brain regions and patterns ('connectotypes') whose removal significantly impacts GE.
• To explore the potential of connectotype identification for mitigating cognitive deficits post-neurosurgery.

Main Methods:

• Created structural connectivity graphs from diffusion tractography in 80 healthy adults.
• Simulated parcellation resection by systematically deleting adjacent nodes in each cerebral region.
• Measured the resulting drop in global efficiency (GE) following nodal deletion.

Main Results:

• Progressive parcellation removal showed predictable GE decline patterns with inter-subject variability.
• Specific node deletions caused disproportionately large GE drops.
• Identified patient-specific 'connectotypes' – common nodes critical for GE.
• Some connectotype deletions were more detrimental to GE than removing larger node sets elsewhere.

Conclusions:

• Brain network vulnerability to resection is complex and patient-specific.
• Connectotypes represent critical nodes whose preservation may be vital.
• Investigating connectotypes could lead to strategies for preserving neuro-cognitive function during brain tumor surgery.