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ObjectiveBenign epilepsy with centrotemporal spikes (BECTS) is the most common childhood idiopathic localization-related epilepsy syndrome. BECTS presents normal routine magnetic resonance imaging (MRI); however, quantitative analytic techniques have captured subtle cortical and subcortical magnetic resonance anomalies. Network science, including graph theory (GT) analyses, facilitates understanding of brain covariance patterns, potentially informing in important ways how this common self-limiting epilepsy syndrome may impact normal patterns of brain and cognitive development.MethodsGT analyses examined the developmental covariance among cortical and subcortical regions in children with new/recent onset BECTS (n = 19) and typically developing healthy controls (n = 22) who underwent high-resolution MRI and cognitive assessment at baseline and 2 years later. Global (transitivity, global efficiency, and modularity index [Q]) and regional measures (local efficiency and hubs) were investigated to characterize network development in each group. Associations between baseline-based GT measures and cognition at both time points addressed the implications of GT analyses for cognition and prospective cognitive development. Furthermore, an individual contribution measure was investigated, reflecting how important for cognition it is for BECTS to resemble the correlation matrices of controls.ResultsGroups exhibited similar Q and overall network configuration, with BECTS presenting significantly higher transitivity and both global and local efficiency. Furthermore, both groups presented a similar number of hubs, with BECTS showing a higher number in temporal lobe regions compared to controls. The investigated measures were negatively associated with 2-year cognitive outcomes in BECTS.SignificanceChildren with BECTS present a higher-than-normal global developmental configuration compared to controls, along with divergence from normality in terms of regional configuration. Baseline GT measures demonstrate potential as a cognitive biomarker to predict cognitive outcome in BECTS 2 years after diagnosis. Similarities and differences in developmental network configurations and their implications for cognition and behavior across common epilepsy syndromes are of theoretical interest and clinical relevance.

ObjectiveStructural and functional magnetic resonance imaging (MRI) studies have consistently documented cortical and subcortical abnormalities in patients with juvenile myoclonic epilepsy (JME). However, little is known about how these structural abnormalities emerge from the time of epilepsy onset and how network interactions between and within cortical and subcortical regions may diverge in youth with JME compared to typically developing children.MethodsWe examined prospective covariations of volumetric differences derived from high-resolution structural MRI during the first 2 years of epilepsy diagnosis in a group of youth with JME (n = 21) compared to healthy controls (n = 22). We indexed developmental brain changes using graph theory by computing network metrics based on the correlation of the cortical and subcortical structural covariance near the time of epilepsy and 2 years later.ResultsOver 2 years, normally developing children showed modular cortical development and network integration between cortical and subcortical regions. In contrast, children with JME developed a highly correlated and less modular cortical network, which was atypically dissociated from subcortical structures. Furthermore, the JME group also presented higher clustering and lower modularity indices than controls, indicating weaker modules or communities. The local efficiency in JME was higher than controls across the majority of cortical nodes. Regarding network hubs, controls presented a higher number than youth with JME that were spread across the brain with ample representation from the different modules. In contrast, children with JME showed a lower number of hubs that were mainly from one module and comprised mostly subcortical structures.SignificanceYouth with JME prospectively developed a network of highly correlated cortical regions dissociated from subcortical structures during the first 2 years after epilepsy onset. The cortical-subcortical network dissociation provides converging insights into the disparate literature of cortical and subcortical abnormalities found in previous studies.

Recent neuroimaging and behavioral studies have revealed that children with new onset epilepsy already exhibit brain structural abnormalities and cognitive impairment. How the organization of large-scale brain structural networks is altered near the time of seizure onset and whether network changes are related to cognitive performances remain unclear. Recent studies also suggest that regional brain volume covariance reflects synchronized brain developmental changes. Here, we test the hypothesis that epilepsy during early-life is associated with abnormalities in brain network organization and cognition. We used graph theory to study structural brain networks based on regional volume covariance in 39 children with new-onset seizures and 28 healthy controls. Children with new-onset epilepsy showed a suboptimal topological structural organization with enhanced network segregation and reduced global integration compared with controls. At the regional level, structural reorganization was evident with redistributed nodes from the posterior to more anterior head regions. The epileptic brain network was more vulnerable to targeted but not random attacks. Finally, a subgroup of children with epilepsy, namely those with lower IQ and poorer executive function, had a reduced balance between network segregation and integration. Taken together, the findings suggest that the neurodevelopmental impact of new onset childhood epilepsies alters large-scale brain networks, resulting in greater vulnerability to network failure and cognitive impairment.

The purpose of this project was to characterize brain structure and organization in persons with active and remitted childhood onset epilepsy 50 years after diagnosis compared with healthy controls. Participants from a population-based investigation of uncomplicated childhood onset epilepsy were followed up 5 decades later. Forty-one participants had a history of childhood onset epilepsy (mean age of onset = 5.2 years, current chronological age = 56.0 years) and were compared with 48 population-based controls (mean age = 55.9 years). Of the epilepsy participants, 8 had persisting active epilepsy and in 33 the epilepsy had remitted. All participants underwent 3T MRI with subsequent vertex analysis of cortical volume, thickness, surface area and gyral complexity. In addition, cortical and subcortical volumes, including regions of the frontal, parietal, temporal, and occipital lobes, and subcortical structures including amygdala, thalamus, and hippocampus, were analyzed using graph theory techniques. There were modest group differences in traditional vertex-based analyses of cortical volume, thickness, surface area and gyral index, as well as across volumes of subcortical structures, after correction for multiple comparisons. Graph theory analyses revealed suboptimal topological structural organization with enhanced network segregation and reduced global integration in the epilepsy participants compared with controls, these patterns significantly more extreme in the active epilepsy group. Furthermore, both groups with epilepsy presented a greater number of higher Z-score regions in betweenness centrality (BC) than lower Z-score regions compared with controls. Also, contrary to the group with remitted epilepsy, patients with active epilepsy presented most of their high BC Z-score regions in subcortical areas including the amygdala, thalamus, hippocampus, pallidum, and accumbens. Overall, this population-based investigation of long term outcome (5 decades) of childhood onset epilepsy reveals persisting abnormalities, especially when examined by graph theoretical measurements, and provides new insights into the very long-term outcomes of active and remitted epilepsy. Hum Brain Mapp 38:3289-3299, 2017. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]