Hyperbolic embedding of brain networks can predict the surgery outcome in temporal lobe epilepsy
Journal:
arXiv
Published Date:
Dec 9, 2024
Abstract
Epilepsy surgery, particularly for temporal lobe epilepsy (TLE), remains a
vital treatment option for patients with drug-resistant seizures. However,
accurately predicting surgical outcomes remains a significant challenge. This
study introduces a novel biomarker derived from brain connectivity, analyzed
using non-Euclidean network geometry, to predict the surgery outcome in TLE.
Using structural and diffusion magnetic resonance imaging (MRI) data from 51
patients, we examined differences in structural connectivity networks
associated to surgical outcomes. Our approach uniquely utilized hyperbolic
embeddings of pre- and post-surgery brain networks, successfully distinguishing
patients with favorable outcomes from those with poor outcomes. Notably, the
method identified regions in the contralateral hemisphere relative to the
epileptogenic zone, whose connectivity patterns emerged as a potential
biomarker for favorable surgical outcomes. The prediction model achieves an
area under the curve (AUC) of 0.87 and a balanced accuracy of 0.81. These
results underscore the predictive capability of our model and its effectiveness
in individual outcome forecasting based on structural network changes. Our
findings highlight the value of non-Euclidean representation of brain networks
in gaining deeper insights into connectivity alterations in epilepsy, and
advancing personalized prediction of surgical outcomes in TLE.
