Network analysis of prospective brain development in youth with benign epilepsy with centrotemporal spikes and its relationship to cognition

Abstract

Objective: Benign 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.

Methods: GT 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.

Results: Groups 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.

Significance: Children 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.

Keywords: Rolandic epilepsy; benign epilepsy with centrotemporal spikes; brain volume development; cognition; graph theory.

Figure 1:

Modularity in controls (left) and BECTS (right). Node abbreviations are the same as in Table 1S. Same color nodes belong to the same module. The spatial distribution of nodes was calculated using the force-atlas graph algorithm, where nodes that demonstrated stronger connections are located closer in space, while nodes with fewer connections tend to be farther in space. Bigger nodes represent the hubs of the network. Calculated at a hybrid threshold of 25%.

Figure 2:

Transitivity (left), global efficiency (middle), and modularity index (right) in in controls (blue) and BECTS (red). Error bars represent the standard error of the mean. *Statistically significant between groups after Student’s t-test calculations. Each group was statistically significant against random at each density level; corrected for multiple comparisons (Bonferroni correction).

Figure 3:

Local efficiency in controls (blue) and BECTS (red). Filled symbols represent statistical significance against random. *Statistically significantly after Student’s t-test analysis; corrected for multiple comparisons (Bonferroni correction). Calculated at a hybrid density level of 25%.

Figure 4:

Nodes with high BC in controls (top) and BECTS (bottom) at their approximate anatomical location. Nodes with the same color represent the same module (as in Figure 2). Labels are the node abbreviations from Table 1S. Calculated at a hybrid density level of 25%.