Presurgical thalamocortical connectivity is associated with response to vagus nerve stimulation in children with intractable epilepsy

Abstract

Although chronic vagus nerve stimulation (VNS) is an established treatment for medically-intractable childhood epilepsy, there is considerable heterogeneity in seizure response and little data are available to pre-operatively identify patients who may benefit from treatment. Since the therapeutic effect of VNS may be mediated by afferent projections to the thalamus, we tested the hypothesis that intrinsic thalamocortical connectivity is associated with seizure response following chronic VNS in children with epilepsy. Twenty-one children (ages 5-21 years) with medically-intractable epilepsy underwent resting-state fMRI prior to implantation of VNS. Ten received sedation, while 11 did not. Whole brain connectivity to thalamic regions of interest was performed. Multivariate generalized linear models were used to correlate resting-state data with seizure outcomes, while adjusting for age and sedation status. A supervised support vector machine (SVM) algorithm was used to classify response to chronic VNS on the basis of intrinsic connectivity. Of the 21 subjects, 11 (52%) had 50% or greater improvement in seizure control after VNS. Enhanced connectivity of the thalami to the anterior cingulate cortex (ACC) and left insula was associated with greater VNS efficacy. Within our test cohort, SVM correctly classified response to chronic VNS with 86% accuracy. In an external cohort of 8 children, the predictive model correctly classified the seizure response with 88% accuracy. We find that enhanced intrinsic connectivity within thalamocortical circuitry is associated with seizure response following VNS. These results encourage the study of intrinsic connectivity to inform neural network-based, personalized treatment decisions for children with intractable epilepsy.

Keywords: Functional connectivity; Intrinsic connectivity networks; Low frequency neural oscillations; Resting-state fMRI; VNS.

Fig. 1

Whole-brain connectivity to right and left thalamic regions of interest. Seed-based analysis demonstrates widespread connectivity of the thalamus to cortical and subcortical regions in the entire cohort and the subset of patients who were and were not sedated. Mean statistical Z-maps (FWE-corrected) are shown with all analyses adjusted for patient age and main results (top panel) also adjusted for binarized sedation status.

Fig. 2

Generalized linear model of left thalamic whole-brain connectivity regressed against selected covariates. In children with good seizure response to VNS, the left thalamus is significantly more strongly connected to the anterior cingulate and bilateral operculo-insular cortices as well as the parietooccipital junction and peri-Rolandic cortex (top panel). This effect was dissociable from age-related differences in connectivity to the left thalamus (second panel). There was no significant sedation effect or interaction (lower panels). All clusters shown are significant at p < 0.05 following FWE-correction for multiple comparisons.

Fig. 3

Generalized linear model of right thalamic whole-brain connectivity regressed against selected covariates. In children with good seizure response to VNS, the right thalamus is significantly more strongly connected to the anterior cingulate and left insular cortices (top panel). Again this effect was dissociable from age-related differences in connectivity to the right thalamus (second panel). There was no significant sedation effect or interaction (lower panels). All clusters shown are significant at p < 0.05 following FWE-correction for multiple comparisons.

Fig. 4

Distribution of thalamo-cinguate and thalamo-insular connectivity correlation coefficients in responders and non-responders to VNS. For all brain regions expressing significant thalamocortical connectivity, the responders to VNS demonstrated higher positive correlation than non-responders. Blue denotes good, while pink denotes poor response to VNS.

Fig. 5

Support vector machines accurately classify response to VNS on the basis of intrinsic thalamocortical connectivity. (A) Classification of response to VNS on the basis of thalamocortical connectivity for the test and external cohorts. Blue denotes good, while red denotes poor response to VNS. Three of the four thalamocortical pair-wise connectivity correlation coefficients used as inputs in the SVM are shown. (B) On the basis of thalamocortical connectivity to the anterior cingulate and left insular cortices, linear support vector machines classified seizure response to VNS with an 86% accuracy in the test cohort (n = 21) and 88% in the external cohort (n = 8). (C) The ROC curve for the ability to identify VNS responders within the test cohort s shown in top right panel (AUC: 0.86).