The effects of interictal epileptiform discharges on neurocognitive development in children with medically-intractable epilepsy are poorly understood. Such discharges may have a deleterious effect on the brain's intrinsic connectivity networks, which reflect the organization of functional networks at rest, and in turn on neurocognitive development. Using a combined functional magnetic resonance imaging-magnetoencephalography approach, we examine the effects of interictal epileptiform discharges on intrinsic connectivity networks and neurocognitive outcome. Functional magnetic resonance imaging was used to determine the location of regions comprising various intrinsic connectivity networks in 26 children (7-17 years), and magnetoencephalography data were reconstructed from these locations. Inter-regional phase synchronization was then calculated across interictal epileptiform discharges and graph theoretical analysis was applied to measure event-related changes in network topology in the peri-discharge period. The magnitude of change in network topology (network resilience/vulnerability) to interictal epileptiform discharges was associated with neurocognitive outcomes and functional magnetic resonance imaging networks using dual regression. Three main findings are reported: (i) large-scale network changes precede and follow interictal epileptiform discharges; (ii) the resilience of network topologies to interictal discharges is associated with stronger resting-state network connectivity; and (iii) vulnerability to interictal discharges is associated with worse neurocognitive outcomes. By combining the spatial resolution of functional magnetic resonance imaging with the temporal resolution of magnetoencephalography, we describe the effects of interictal epileptiform discharges on neurophysiological synchrony in intrinsic connectivity networks and establish the impact of interictal disruption of functional networks on cognitive outcome in children with epilepsy. The association between interictal discharges, network changes and neurocognitive outcomes suggests that it is of clinical importance to suppress discharges to foster more typical brain network development in children with focal 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.

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 N early one-third of children with epilepsy are refractory to medications. 1,2 Persistent seizures are associated with mortality, disability, psychosocial isolation, and diminished quality of life. 3-6 Vagus nerve stimulation (VNS) is an effective, safe, and well-tolerated intervention for a subset of patients with treatment-resistant epilepsy. 7-10 Although the goal of VNS is not complete resolution of seizures, many children will show a significant reduction in seizure frequency, as well as a reduction in hospitalizations and psychosocial comorbidities. 11,12 View this article online at wileyonlinelibrary.com.

Magnetoencephalographic (MEG) investigations of inter-regional amplitude correlations have yielded new insights into the organization and neurophysiology of resting-state networks (RSNs) first identified using fMRI. Inter-regional MEG amplitude correlations in adult RSNs have been shown to be most prominent in alpha and beta frequency ranges and to express strong congruence with RSN topologies found using fMRI. Despite such advances, little is known about how oscillatory connectivity in RSNs develops throughout childhood and adolescence. This study used a novel fMRI-guided MEG approach to investigate the maturation of resting-state amplitude correlations in physiologically relevant frequency ranges within and among six RSNs in 59 participants, aged 6-34 years. We report age-related increases in inter-regional amplitude correlations that were largest in alpha and beta frequency bands. In contrast to fMRI reports, these changes were observed both within and between the various RSNs analyzed. Our results provide the first evidence of developmental changes in spontaneous neurophysiological connectivity in source-resolved RSNs, which indicate increasing integration within and among intrinsic functional brain networks throughout childhood, adolescence, and early adulthood.

Working memory (WM) - temporary storage and manipulation of information in the mind - is a key component of cognitive maturation, and structural brain changes throughout development are associated with refinements in WM. Recent functional neuroimaging studies have shown that there is greater activation in prefrontal and parietal brain regions with increasing age, with adults showing more refined, localized patterns of activations. However, few studies have investigated the neural basis of verbal WM development, as the majority of reports examine visuo-spatial WM. We used fMRI and a 1-back verbal WM task with six levels of difficulty to examine the neurodevelopmental changes in WM function in 40 participants, twenty-four children (ages 9-15 yr) and sixteen young adults (ages 20-25 yr). Children and adults both demonstrated an opposing system of cognitive processes with increasing cognitive demand, where areas related to WM (frontal and parietal regions) increased in activity, and areas associated with the default mode network decreased in activity. Although there were many similarities in the neural activation patterns associated with increasing verbal WM capacity in children and adults, significant changes in the fMRI responses were seen with age. Adults showed greater load-dependent changes than children in WM in the bilateral superior parietal gyri, inferior frontal and left middle frontal gyri and right cerebellum. Compared to children, adults also showed greater decreasing activation across WM load in the bilateral anterior cingulate, anterior medial prefrontal gyrus, right superior lateral temporal gyrus and left posterior cingulate. These results demonstrate that while children and adults activate similar neural networks in response to verbal WM tasks, the extent to which they rely on these areas in response to increasing cognitive load evolves between childhood and adulthood.

BackgroundResearch on the neural bases of cognitive deficits in autism spectrum disorder (ASD) has shown that working memory (WM) difficulties are associated with abnormalities in the prefrontal cortex. However, cognitive load impacts these findings, and no studies have examined the relation between WM load and neural underpinnings in children with ASD. Thus, the current study determined the effects of cognitive load on WM, using a visuo-spatial WM capacity task in children with and without ASD with functional magnetic resonance imaging (fMRI).MethodsWe used fMRI and a 1-back colour matching task (CMT) task with four levels of difficulty to compare the cortical activation patterns associated with WM in children (7–13 years old) with high functioning autism (N = 19) and matched controls (N = 17) across cognitive load.ResultsPerformance on CMT was comparable between groups, with the exception of one difficulty level. Using linear trend analyses, the control group showed increasing activation as a function of difficulty level in frontal and parietal lobes, particularly between the highest difficulty levels, and decreasing activation as a function of difficulty level in the posterior cingulate and medial frontal gyri. In contrast, children with ASD showed increasing activation only in posterior brain regions and decreasing activation in the posterior cingulate and medial frontal gyri, as a function of difficulty level. Significant differences were found in the precuneus, dorsolateral prefrontal cortex and medial premotor cortex, where control children showed greater positive linear relations between cortical activity and task difficulty level, particularly at the highest difficulty levels, but children with ASD did not show these trends.ConclusionsChildren with ASD showed differences in activation in the frontal and parietal lobes—both critical substrates for visuo-spatial WM. Our data suggest that children with ASD rely mainly on posterior brain regions associated with visual and lower level processing, whereas controls showed activity in frontal lobes related to the classic WM network. Findings will help guide future work by localizing areas of vulnerability to developmental disturbances.

IntroductionThe underlying microstructural properties of white matter differences in children born very preterm (<32 weeks gestational age) can be investigated in depth using multi-shell diffusion imaging. The present study compared white matter across the whole brain using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) metrics in children born very preterm and full-term children at six years of age. We also investigated associations between white matter microstructure with early brain injury and developmental outcomes.MethodMulti-shell diffusion imaging, T1-weighted anatomical MR images and developmental assessments were acquired in 23 children born very preterm (16 males; mean scan age: 6.57 ± 0.34 years) and 24 full-term controls (10 males, mean scan age: 6.62 ± 0.37 years). DTI metrics were obtained and neurite orientation dispersion index (ODI) and density index (NDI) were estimated using the NODDI diffusion model. FSL's tract-based spatial statistics were performed on traditional DTI metrics and NODDI metrics. Voxel-wise comparisons were performed to test between-group differences and within-group associations with developmental outcomes (intelligence and visual motor abilities) as well as early white matter injury and germinal matrix/intraventricular haemorrhage (GMH/IVH).ResultsIn comparison to term-born children, the children born very preterm exhibited lower fractional anisotropy (FA) across many white matter regions as well as higher mean diffusivity (MD), radial diffusivity (RD), and ODI. Within-group analyses of the children born very preterm revealed associations between higher FA and NDI with higher IQ and VMI. Lower ODI was found within the corona radiata in those with a history of white matter injury. Within the full-term group, associations were found between higher NDI and ODI with lower IQ.ConclusionChildren born very preterm exhibit lower FA and higher ODI than full-term children. NODDI metrics provide more biologically specific information beyond DTI metrics as well as additional information of the impact of prematurity and white matter microstructure on cognitive outcomes at six years of age.

Typical childhood development is characterized by the emergence of intrinsic connectivity networks (ICNs) by way of internetwork segregation and intranetwork integration. The impact of childhood epilepsy on the maturation of ICNs is, however, poorly understood. The developmental trajectory of ICNs in 26 children (8-17 years) with localization-related epilepsy and 28 propensity-score matched controls was evaluated using graph theoretical analysis of whole brain connectomes from resting-state functional magnetic resonance imaging (fMRI) data. Children with epilepsy demonstrated impaired development of regional hubs in nodes of the salience and default mode networks (DMN). Seed-based connectivity and hierarchical clustering analysis revealed significantly decreased intranetwork connections, and greater internetwork connectivity in children with epilepsy compared to controls. Significant interactions were identified between epilepsy duration and the expected developmental trajectory of ICNs, indicating that prolonged epilepsy may cause progressive alternations in large-scale networks throughout childhood. DMN integration was also associated with better working memory, whereas internetwork segregation was associated with higher full-scale intelligence quotient scores. Furthermore, subgroup analyses revealed the thalamus, hippocampus, and caudate were weaker hubs in children with secondarily generalized seizures, relative to other patient subgroups. Our findings underscore that epilepsy interferes with the developmental trajectory of brain networks underlying cognition, providing evidence supporting the early treatment of affected children.