Dynamic flexibility and controllability of network communities in juvenile myoclonic epilepsy.

Juvenile myoclonic epilepsy (JME) is the most common syndrome within the idiopathic generalized epilepsy spectrum, manifested by myoclonic and generalized tonic-clonic seizures and spike-and-wave discharges (SWDs) on electroencephalography (EEG). Currently, the pathophysiological concepts addressing SWD generation in JME are still incomplete. In this work, we characterize the temporal and spatial organization of functional networks and their dynamic properties as derived from high-density EEG (hdEEG) recordings and MRI in 40 JME patients (25.4 ± 7.6 years, 25 females). The adopted approach allows for the construction of a precise dynamic model of ictal transformation in JME at the cortical and deep brain nuclei source levels. We implement Louvain algorithm to attribute brain regions with similar topological properties to modules during separate time windows before and during SWD generation. Afterwards, we quantify how modular assignments evolve and steer through different states towards the ictal state by measuring characteristics of flexibility and controllability. We find antagonistic dynamics of flexibility and controllability within network modules as they evolve towards and undergo ictal transformation. Prior to SWD generation, we observe concomitantly increasing flexibility (F(1,39) = 25.3, corrected p < 0.001) and decreasing controllability (F(1,39) = 55.3, p < 0.001) within the fronto-parietal module in γ-band. On a step further, during interictal SWDs as compared to preceding time windows, we notice decreasing flexibility (F(1,39) = 11.9, p < 0.001) and increasing controllability (F(1,39) = 10.1, p < 0.001) within the fronto-temporal module in γ-band. During ictal SWDs as compared to prior time windows, we demonstrate significantly decreasing flexibility (F(1,14) = 31.6; p < 0.001) and increasing controllability (F(1,14) = 44.7, p < 0.001) within the basal ganglia module. Furthermore, we show that flexibility and controllability within the fronto-temporal module of the interictal SWDs relate to seizure frequency and cognitive performance in JME patients. Our results demonstrate that detection of network modules and quantification of their dynamic properties is relevant to track the generation of SWDs. The observed flexibility and controllability dynamics reflect the reorganization of de-/synchronized connections and the ability of evolving network modules to reach a seizure-free state, respectively. These findings may advance the elaboration of network-based biomarkers and more targeted therapeutic neuromodulatory approaches in JME.

Large-scale network architecture and associated structural cortico-subcortical abnormalities in patients with sleep/awake-related seizures.

STUDY OBJECTIVES: In this study, we aimed to estimate the alterations of brain networks and structural integrity linked to seizure occurrence during sleep and awake states. METHODS: Using a graph theory approach to magnetic resonance imaging-derived volumes of cortical and subcortical regions, we investigated the topological organization of structural networks in patients with sleep seizures (n = 13), patients with awake seizures (n = 12), and age- and sex-matched healthy controls (n = 10). Abnormalities in regional structural substrates (cortical volume/surface area, subcortical volumes) associated with sleep seizures and awake seizures were further analyzed. RESULTS: Brain networks in patients with sleep seizures compared to patients with awake seizures displayed a more integrated structural organization coupled with greater networks' stability. When compared to healthy controls, networks in both patients with sleep and awake seizures were analogously compromised, exhibiting a less integrated and preserved organization. Patients with sleep seizures in contrast to awake seizures had larger volumes of bilateral insula, superior temporal, and orbitofrontal cortices but lower volumes of left postcentral and right middle temporal cortices in comparison to healthy controls. Patients with awake seizures compared to healthy controls displayed reduced volumes mainly in frontal, temporal, and parietal regions of right hemisphere. Volumes of hippocampus, amygdala, caudate, pallidum, and putamen were larger in patients with sleep seizures than in patients with awake seizures. CONCLUSIONS: Despite epileptogenesis, patients with sleep and awake seizures had distinct network and structural correlates across different epilepsy types. Identified regional cortical/subcortical abnormalities can endorse the pathophysiological alterations that induce seizures during the sleep or awake states.