Neighbourhood topology unveils pathological hubs in the brain networks of epilepsy-surgery patients

TL;DR

This study uses a neighborhood-based analysis of brain networks from epilepsy patients to identify pathological hubs, revealing variability in hub locations around the resection area that do not predict surgical outcomes.

Contribution

It introduces a novel neighborhood-focused approach with generalized centrality metrics to analyze brain network hubs in epilepsy-surgery patients.

Findings

Hubs are found both in the resection area and its neighborhood.

Hub locations vary significantly across patients.

Hub location variability does not correlate with surgical success.

Abstract

Pathological hubs in the brain networks of epilepsy patients are hypothesized to drive seizure generation and propagation. In epilepsy-surgery patients, these hubs have traditionally been associated with the resection area (RA): the region removed during the surgery with the goal of stopping the seizures, and which is typically used as a proxy for the epileptogenic zone. However, recent studies hypothesize that pathological hubs may extend to the vicinity of the RA, potentially complicating post-surgical seizure control. Here we propose a neighbourhood-based analysis of brain organization to investigate this hypothesis. We exploit a large dataset of pre-surgical magnetoencephalography-derived whole-brain networks from 91 epilepsy-surgery patients. Our neighbourhood focus is 2-fold. Firstly, we propose a partition of the brain regions into three sets, namely resected nodes, their neighbours and the remaining network nodes. Secondly, we introduce generalized centrality metrics that describe the neighbourhood of each node, providing a regional measure of hubness. Our analyses reveal that both the RA and its neighbourhood present large hub status, but with significant variability across patients. For some, hubs appear in the RA; for others, in its neighbourhood. Moreover, this variability does not correlate with surgical outcome. These results highlight the potential of neighbourhood-based analyses to uncover novel insights into brain connectivity in brain pathologies, and the need for individualized studies, with large enough cohorts, that account for patient-specific variability.