Brain functional connectivity in sleep-related hypermotor epilepsy

Abstract

Objectives: To evaluate functional connectivity (FC) in patients with sleep-related hypermotor epilepsy (SHE) compared to healthy controls.

Methods: Resting state fMRI was performed in 13 patients with a clinical diagnosis of SHE (age = 38.3 ± 11.8 years, 6 M) and 13 matched healthy controls (age = 38.5 ± 10.8 years, 6 M).Data were first analysed using probabilistic independent component analysis (ICA), then a graph theoretical approach was applied to assess topological and organizational properties at the whole brain level. We evaluated node degree (ND), betweenness centrality (BC), clustering coefficient (CC), local efficiency (LE) and global efficiency (GE). The differences between the two groups were evaluated non-parametrically.

Results: At the group level, we distinguished 16 RSNs (Resting State Networks). Patients showed a significantly higher FC in sensorimotor and thalamic regions (p < 0.05 corrected). Compared to controls, SHE patients showed no significant differences in network global efficiency, while ND and BC were higher in regions of the limbic system and lower in the occipital cortex, while CC and LE were higher in regions of basal ganglia and lower in limbic areas (p < 0.05 uncorrected).

Discussion and conclusions: The higher FC of the sensorimotor cortex and thalamus might be in agreement with the hypothesis of a peculiar excitability of the motor cortex during thalamic K-complexes. This sensorimotor-thalamic hyperconnection might be regarded as a consequence of an alteration of the arousal regulatory system in SHE. An altered topology has been found in structures like basal ganglia and limbic system, hypothesized to be involved in the pathophysiology of the disease as suggested by the dystonic-dyskinetic features and primitive behaviours observed during the seizures.

Keywords: Functional connectivity; Graph theory; Independent component analysis; Nocturnal frontal lobe epilepsy; Sleep-related hypermotor epilepsy.

Fig. 1

Results of group level ICA: 16 RSN components; 1: visual network, 2: executive control network (left), 3: mesolimbic network, 4: dorsal attention network, 5: DMN, default mode network, 6: executive control network (right), 7: salience network, 8: visual network, 9: DMN (posterior portion), 10: sensorimotor network, 11: DMN (posterior portion), 12: sensorimotor network, 13: language network, 14: DMN, 15: visual network, 16: cerebellum and deep grey matter.

Fig. 2

ICA results: areas of increased FC in patients compared to controls within sensorimotor network (A), DMN (B) and visual network (C). Statistical maps (p < 0.05 corrected at the voxel level) are overlaid on the MNI-152 T1 template and shown in radiological convention.

Fig. 3

Whole brain graph analysis measures results: ND, node degree; BC, betweenness centrality; CC, clustering coefficient; LE, local efficiency. Nodes where the parameter showed any differences are represented with bigger dots, in red if the parameter was higher in SHE compared to healthy control, in blue if it was lower in SHE. Left and right hemispheres are shown on the left and on the right respectively.

Fig. 4

Whole brain graph analysis: hub evaluation. Nodes coloured in green are hubs for both healthy controls and SHE patients, nodes in red are hubs exclusively for SHE patients, while nodes in blue are hubs only for the healthy subjects.