Connectomic Profiling Identifies Responders to Vagus Nerve Stimulation.

OBJECTIVE: Vagus nerve stimulation (VNS) is a common treatment for medically intractable epilepsy, but response rates are highly variable, with no preoperative means of identifying good candidates. This study aimed to predict VNS response using structural and functional connectomic profiling. METHODS: Fifty-six children, comprising discovery (n = 38) and validation (n = 18) cohorts, were recruited from 3 separate institutions. Diffusion tensor imaging was used to identify group differences in white matter microstructure, which in turn informed beamforming of resting-state magnetoencephalography recordings. The results were used to generate a support vector machine learning classifier, which was independently validated. This algorithm was compared to a second classifier generated using 31 clinical covariates. RESULTS: Treatment responders demonstrated greater fractional anisotropy in left thalamocortical, limbic, and association fibers, as well as greater connectivity in a functional network encompassing left thalamic, insular, and temporal nodes (p < 0.05). The resulting classifier demonstrated 89.5% accuracy and area under the receiver operating characteristic (ROC) curve of 0.93 on 10-fold cross-validation. In the external validation cohort, this model demonstrated an accuracy of 83.3%, with a sensitivity of 85.7% and specificity of 75.0%. This was significantly superior to predictions using clinical covariates alone, which exhibited an area under the ROC curve of 0.57 (p < 0.008). INTERPRETATION: This study provides the first multi-institutional, multimodal connectomic prediction algorithm for VNS, and provides new insights into its mechanism of action. Reliable identification of VNS responders is critical to mitigate surgical risks for children who may not benefit, and to ensure cost-effective allocation of health care resources. ANN NEUROL 2019;86:743-753.

Presurgical hyperconnectivity of the ablation volume is associated with seizure-freedom after magnetic resonance-guided laser interstitial thermal therapy.

PURPOSE: Magnetic Resonance-guided Laser Interstitial Thermal Therapy (MRgLITT) is an emerging minimally-invasive alternative to resective surgery for medically-intractable epilepsy. The precise lesioning effect produced by MRgLITT supplies opportunities to glean insights into epileptogenic regions and their interactions with functional brain networks. In this exploratory analysis, we sought to characterize associations between MRgLITT ablation zones and large-scale brain networks that portended seizure outcome using resting-state fMRI. METHODS: Presurgical fMRI and intraoperatively volumetric structural imaging were obtained, from which the ablation volume was segmented. The network properties of the ablation volume within the brain's large-scale brain networks were characterized using graph theory and compared between children who were and were not rendered seizure-free. RESULTS: Of the seventeen included children, five achieved seizure freedom following MRgLITT. Greater functional connectivity of the ablation volume to canonical resting-state networks was associated with seizure-freedom (p < 0.05, FDR-corrected). The ablated volume in children who subsequently became seizure-free following MRgLITT had significantly greater strength, and eigenvector centrality within the large-scale brain network. CONCLUSIONS: These findings provide novel insights into the interaction between epileptogenic cortex and large-scale brain networks. The association between ablation volume and resting-state networks may supply novel avenues for presurgical planning and patient stratification.

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

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.

Classification of fMRI patterns--a study of the language network segregation in pediatric localization related epilepsy.

This article describes a pattern classification algorithm for pediatric epilepsy using fMRI language-related activation maps. 122 fMRI datasets from a control group (64) and localization related epilepsy patients (58) provided by five children's hospitals were used. Each subject performed an auditory description decision task. Using the artificial data as training data, incremental Principal Component Analysis was used in order to generate the feature space while overcoming memory requirements of large datasets. The nearest-neighbor classifier (NNC) and the distance-based fuzzy classifier (DFC) were used to perform group separation into left dominant, right dominant, bilateral, and others. The results show no effect of age, age at seizure onset, seizure duration, or seizure etiology on group separation. Two sets of parameters were significant for group separation, the patient vs. control populations and handedness. Of the 122 real datasets, 90 subjects gave the same classification results across all the methods (three raters, LI, bootstrap LI, NNC, and DFC). For the remaining datasets, 18 cases for the IPCA-NNC and 21 cases for the IPCA-DFC agreed with the majority of the five classification results (three visual ratings and two LI results). Kappa values vary from 0.59 to 0.73 for NNC and 0.61 to 0.75 for DFC, which indicate good agreement between NNC or DFC with traditional methods. The proposed method as designed can serve as an alternative method to corroborate existing LI and visual rating classification methods and to resolve some of the cases near the boundaries in between categories.

A decisional space for fMRI pattern separation using the principal component analysis--a comparative study of language networks in pediatric epilepsy.

Atypical functional magnetic resonance imaging (fMRI) language patterns may be identified by visual inspection or by region of interest (ROI)-based laterality indices (LI) but are constrained by a priori assumptions. We compared a data-driven novel application of principal component analysis (PCA) to conventional methods. We studied 122 fMRI data sets from control and localization-related epilepsy patients provided by five children's hospitals. Each subject performed an auditory description decision task. The data sets, acquired with different scanners but similar acquisition parameters, were processed through fMRIB software library to obtain 3D activation maps in standard space. A PCA analysis was applied to generate the decisional space and the data cluster into three distinct activation patterns. The classified activation maps were interpreted by (1) blinded reader rating based on predefined language patterns and (2) by language area ROI-based LI (i.e., fixed threshold vs. bootstrap approaches). The different classification results were compared through κ inter-rater agreement statistics. The unique decisional space classified activation maps into three clusters (a) lower intensity typical language representation, (b) higher intensity typical, as well as (c) higher intensity atypical representation. Inter-rater agreements among the three raters were excellent (Fleiss κ = 0.85, P = 0.05). There was substantial to excellent agreement between the conventional visual rating and LI methods (κ = 0.69-0.82, P = 0.05). The PCA-based method yielded excellent agreement with conventional methods (κ = 0.82, P = 0.05). The automated and data-driven PCA decisional space segregates language-related activation patterns in excellent agreement with current clinical rating and ROI-based methods.

A 3-D liver segmentation method with parallel computing for selective internal radiation therapy.

This study describes a new 3-D liver segmentation method in support of the selective internal radiation treatment as a treatment for liver tumors. This 3-D segmentation is based on coupling a modified k-means segmentation method with a special localized contouring algorithm. In the segmentation process, five separate regions are identified on the computerized tomography image frames. The merit of the proposed method lays in its potential to provide fast and accurate liver segmentation and 3-D rendering as well as in delineating tumor region(s), all with minimal user interaction. Leveraging of multicore platforms is shown to speed up the processing of medical images considerably, making this method more suitable in clinical settings. Experiments were performed to assess the effect of parallelization using up to 442 slices. Empirical results, using a single workstation, show a reduction in processing time from 4.5 h to almost 1 h for a 78% gain. Most important is the accuracy achieved in estimating the volumes of the liver and tumor region(s), yielding an average error of less than 2% in volume estimation over volumes generated on the basis of the current manually guided segmentation processes. Results were assessed using the analysis of variance statistical analysis.

Sub-patterns of language network reorganization in pediatric localization related epilepsy: a multisite study.

To study the neural networks reorganization in pediatric epilepsy, a consortium of imaging centers was established to collect functional imaging data. Common paradigms and similar acquisition parameters were used. We studied 122 children (64 control and 58 LRE patients) across five sites using EPI BOLD fMRI and an auditory description decision task. After normalization to the MNI atlas, activation maps generated by FSL were separated into three sub-groups using a distance method in the principal component analysis (PCA)-based decisional space. Three activation patterns were identified: (1) the typical distributed network expected for task in left inferior frontal gyrus (Broca's) and along left superior temporal gyrus (Wernicke's) (60 controls, 35 patients); (2) a variant left dominant pattern with greater activation in IFG, mesial left frontal lobe, and right cerebellum (three controls, 15 patients); and (3) activation in the right counterparts of the first pattern in Broca's area (one control, eight patients). Patients were over represented in Groups 2 and 3 (P < 0.0004). There were no scanner (P = 0.4) or site effects (P = 0.6). Our data-driven method for fMRI activation pattern separation is independent of a priori notions and bias inherent in region of interest and visual analyses. In addition to the anticipated atypical right dominant activation pattern, a sub-pattern was identified that involved intensity and extent differences of activation within the distributed left hemisphere language processing network. These findings suggest a different, perhaps less efficient, cognitive strategy for LRE group to perform the task.