Dynamic hub load predicts cognitive decline after resective neurosurgery.

Area of Science:

• Neuroscience
• Cognitive Science
• Medical Imaging

Background:

• Resective neurosurgery can lead to postoperative cognitive deterioration.
• The theoretical concept of 'hub overload' in brain networks lacks experimental validation.
• Understanding brain network dynamics is crucial for predicting cognitive outcomes.

Purpose of the Study:

• To experimentally investigate the relationship between functional hub load and cognitive deterioration after lesion resection.
• To determine if hub load metrics can predict postsurgical cognitive decline.
• To explore the potential of hub load as a prognostic marker in neurosurgery.

Main Methods:

• Resting-state magnetoencephalography (MEG) and neuropsychological assessments were performed preoperatively and 1 year post-surgery in 28 patients.
• Calculated stationary hub load (SHub) to quantify processing pressure on hub regions.
• Calculated dynamic hub load (DHub) to assess the variability of hub region usage over time.

Main Results:

• Deteriorating verbal memory performance correlated with decreasing upper alpha dynamic hub load (DHub).
• Preoperative low DHub values accurately predicted subsequent decline in verbal memory.
• Findings suggest dynamic hub load is linked to cognitive function post-lesion resection.

Conclusions:

• Dynamic hub load is a significant factor in cognitive functioning after resective neurosurgery.
• Dynamic hub load can be used to track and predict postoperative cognitive decline.
• This metric shows promise as a prognostic marker for personalized neurosurgical treatment planning.