OBJECTIVE

Currently, approximately 60-70% of patients with unilateral temporal lobe epilepsy (TLE) remain seizure-free 3 years after surgery. The goal of this work was to develop a presurgical connectivity-based biomarker to identify those patients who will have an unfavorable seizure outcome 1-year postsurgery.

METHODS

Resting-state functional and diffusion-weighted 3T magnetic resonance imaging (MRI) was acquired from 22 unilateral (15 right, 7 left) patients with TLE and 35 healthy controls. A seizure propagation network was identified including ipsilateral (to seizure focus) and contralateral hippocampus, thalamus, and insula, with bilateral midcingulate and precuneus. Between each pair of regions, functional connectivity based on correlations of low frequency functional MRI signals, and structural connectivity based on streamline density of diffusion MRI data were computed and transformed to metrics related to healthy controls of the same age.

RESULTS

A consistent connectivity pattern representing the network expected in patients with seizure-free outcome was identified using eight patients who were seizure-free at 1-year postsurgery. The hypothesis that increased similarity to the model would be associated with better seizure outcome was tested in 14 other patients (Engel class IA, seizure-free: n = 5; Engel class IB-II, favorable: n = 4; Engel class III-IV, unfavorable: n = 5) using two similarity metrics: Pearson correlation and Euclidean distance. The seizure-free connectivity model successfully separated all the patients with unfavorable outcome from the seizure-free and favorable outcome patients (p = 0.0005, two-tailed Fisher's exact test) through the combination of the two similarity metrics with 100% accuracy. No other clinical and demographic predictors were successful in this regard.

SIGNIFICANCE

This work introduces a methodologic framework to assess individual patients, and demonstrates the ability to use network connectivity as a potential clinical tool for epilepsy surgery outcome prediction after more comprehensive validation.