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.

Connectomic Profiling Identifies Responders to Vagus Nerve Stimulation

“…Thalamic nuclei form an integral component of the VagAN, receiving input directly from the NTS and PBN. 18 Our finding of more robust white matter microarchitecture in thalamocortical circuitry in VNS responders, particularly within the anterior and retrolenticular limbs of the internal capsule, anterior, superior, and posterior corona radiata, and posterior thalamic radiation, are therefore consistent with the known mechanism of action of VNS. 51 In a hypothesis-driven manner, we have previously shown that presurgical thalamocortical connectivity in resting-state fMRI is strongly associated with treatment response.…”

“…14 Although the exact mechanism of seizure reduction remains unclear, this vagus afferent network (VagAN) is thought to be the neural substrate of VNS efficacy. 18 In other conditions, brain connectomic profiling has been successfully used to predict poststroke motor recovery and response to psychiatric pharmacotherapy. 16,17 Recently, we described patterns of intrinsic connectivity within the thalamocortical components of the VagAN associated with treatment responsiveness.…”

“…16,17 Recently, we described patterns of intrinsic connectivity within the thalamocortical components of the VagAN associated with treatment responsiveness. 18 In other conditions, brain connectomic profiling has been successfully used to predict poststroke motor recovery and response to psychiatric pharmacotherapy. [19][20][21] Given converging evidence of a neuronal basis for VNS responsiveness and increasing application of machine learning to predict outcomes on an individual patient level, we perform the first multimodal connectomic profiling of individuals undergoing VNS.…”

Machine learning applications in epilepsy

“…More recently, a neural network classifier examining DTI‐based structural connectomes from 50 TLE patients yielded a positive predictive value of 88% and negative predictive value of 79% in predicting Engel I outcomes, comparing favorably to a discriminant function classifier using only clinical variables . Measures of thalamocortical connectivity on resting‐state fMRI have similarly been used to predict favorable response (>50% seizure reduction) to vagus nerve stimulation, achieving an AUC of 86% using an SVM classifier in 21 pediatric patients . Antony et al, in contrast, applied an SVM classifier to measures of functional connectivity from stereo‐EEG in 23 patients undergoing anterior temporal lobectomy, achieving a sensitivity of 90% and specificity of 85% in predicting seizure freedom at 1 year of follow‐up .…”

“…79 Measures of thalamocortical connectivity on resting-state fMRI have similarly been used to predict favorable response (>50% seizure reduction) to vagus nerve stimulation, achieving an AUC of 86% using an SVM classifier in 21 pediatric patients. 80 Antony et al, in contrast, applied an SVM classifier to measures of functional connectivity from stereo-EEG in 23 patients undergoing anterior temporal lobectomy, achieving a sensitivity of 90% and specificity of 85% in predicting seizure freedom at 1 year of follow-up. 81 Tomlinson et al also examined functional connectivity using interictal intracranial EEG recordings from 17 pediatric patients, achieving 100% sensitivity and 87.5% specificity in predicting Engel I outcomes with an SVM classifier.…”