Structural covariance networks relate to the severity of epilepsy with focal-onset seizures

Abstract

Purpose: The brains of patients with epilepsy may exhibit various morphological abnormalities, which are often not directly visible on structural MR images, as they may be focally subtle or related to a more large-scale inconspicuous disorganization of brain structures. To explore the relation between structural brain organization and epilepsy characteristics, including severity and cognitive co-morbidity, we determined structural covariance networks (SCNs). SCNs represent interregional correlations of morphologic measures, for instance in terms of cortical thickness, between various large-scale distributed brain regions.

Methods: Thirty-eight patients with focal seizures of all subtypes and 21 healthy controls underwent structural MRI, neurological, and IQ assessment. Cortical thickness was derived from the structural MRIs using FreeSurfer. Subsequently, SCNs were constructed on a group-level based on correlations of the cortical thicknesses between various brain regions. Individual SCNs for the epilepsy patients were extracted by adding the respective patient to the control group prior to the SCN construction (i.e. add-one-patient approach). Calculated network measures, i.e. path length, clustering coefficient and betweenness centrality were correlated with characteristics related to the severity of epilepsy, including seizure history and age at onset of epilepsy, and cognitive performance.

Results: Stronger clustering in the individual SCN was associated with a higher number of focal to bilateral tonic-clonic seizures during life time, a younger age at onset, and lower cognitive performance. The path length of the individual SCN was not related to the severity of epilepsy or cognitive performance. Higher betweenness centrality of the left cuneus and lower betweenness centrality of the right rostral middle frontal gyrus were associated with increased drug load and younger age at onset, respectively.

Conclusions: These results indicate that the correlations between interregional variations of cortical thickness reflect disease characteristics or responses to the disease and deficits in patients with epilepsy with focal seizures.

Keywords: Cognition; Cortical thickness; Epilepsy; Magnetic resonance imaging; Seizures; Structural covarience networks.

Fig. 1

A) SCN estimation procedure for healthy controls, from the control subjects an adjacency matrix is obtained using Pearson's correlation. The adjacency matrix represents a graph. B) by adding a specific patient to the control group, the resulting adjacency matrix and graph exhibit some patient-specific alterations.

Fig. 2

The three cortical regions, the postcentral gyrus and posterior cingulate in the left hemisphere, and the insula in the right hemisphere, that show a significantly decreased cortical thickness in patients compared to healthy controls.

Fig. 3

The mean and standard deviation of the network measures (A) γ and (B) λ are shown as a function of sparsity. Significant correlations between network measures and epilepsy characteristics: †positive correlation with number of focal to bilateral tonic-clonic seizures during lifetime ‡negative correlation with onset age *negative correlation with FSIQ.

Fig. 4

Scatter plot of γ vs (A) age at onset and (B) FSIQ for a sparsity level of 72% (see Fig. 3). Linear least squares lines are fitted through the data points for visualization.

Fig. 5

The brain regions that show a significant (positive/negative) relation between BC and either drugload (A) or age at onset (B) are highlighted. The BC of the left cuneus and right rostral middle frontal gyrus are related to both drug load and age at onset.