The impact of epilepsy surgery on the structural connectome and its relation to outcome

TL;DR

This study investigates how temporal lobe epilepsy surgery alters the brain's white matter network and demonstrates that changes in network properties can predict seizure outcomes with high accuracy.

Contribution

It introduces a novel method combining diffusion MRI, graph theory, and machine learning to predict surgical outcomes based on network changes.

Findings

Surgery modestly impacts global network efficiency.

Network change measurements predict seizure outcome with 79% accuracy.

Identified key connections linked to outcome prediction, especially involving the temporal pole.

Abstract

Temporal lobe surgical resection brings seizure remission in up to 80% of patients, with long-term complete seizure freedom in 41%. However, it is unclear how surgery impacts on the structural white matter network, and how the network changes relate to seizure outcome. We used white matter fibre tractography on preoperative diffusion MRI to generate a structural white matter network, and postoperative T1-weighted MRI to retrospectively infer the impact of surgical resection on this network. We then applied graph theory and machine learning to investigate the properties of change between the preoperative and predicted postoperative networks. Temporal lobe surgery had a modest impact on global network efficiency, despite the disruption caused. This was due to alternative shortest paths in the network leading to widespread increases in betweenness centrality post-surgery. Measurements of network change could retrospectively predict seizure outcomes with 79% accuracy and 65% specificity, which is twice as high as the empirical distribution. Fifteen connections which changed due to surgery were identified as useful for prediction of outcome, eight of which connected to the ipsilateral temporal pole. Our results suggest that the use of network change metrics may have clinical value for predicting seizure outcome. This approach could be used to prospectively predict outcomes given a suggested resection mask using preoperative data only.