Lateralization of epilepsy using intra‐hemispheric brain networks based on resting‐state MEG data

Abstract

Focal epilepsy originates within networks in one hemisphere. However, previous studies have investigated network topologies for the entire brain. In this study, magnetoencephalography (MEG) was used to investigate functional intra‐hemispheric networks of healthy controls (HCs) and patients with left‐ or right‐hemispheric temporal lobe or temporal plus extra‐temporal lobe epilepsy. 22 HCs, 25 left patients (LPs), and 16 right patients (RPs) were enrolled. The debiased weighted phase lag index was used to calculate functional connectivity between 246 brain regions in six frequency bands. Global efficiency, characteristic path length, and transitivity were computed for left and right intra‐hemispheric networks. The right global graph measures (GGMs) in the theta band were significantly different (p < .005) between RPs and both LPs and HCs. Right and left GGMs in higher frequency bands were significantly different (p < .05) between HCs and the patients. Right GGMs were used as input features of a Naïve‐Bayes classifier to classify LPs and RPs (78.0% accuracy) and all three groups (75.5% accuracy). The complete theta band brain networks were compared between LPs and RPs with network‐based statistics (NBS) and with the clustering coefficient (CC), nodal efficiency (NE), betweenness centrality (BC), and eigenvector centrality (EVC). NBS identified a subnetwork primarily composed of right intra‐hemispheric connections. Significantly different (p < .05) nodes were primarily in the right hemisphere for the CC and NE and primarily in the left hemisphere for the BC and EVC. These results indicate that intra‐hemispheric MEG networks may be incorporated in the diagnosis and lateralization of focal epilepsy.

Epilepsy and brain network hubs

“…Although centrality metrics allow the quantification of interrelated aspects of network organization, they each emphasize distinct properties of the graph and may thus yield slightly different hub maps. 40 Relatedly, knowledge about the community structure of brain networks may be used to typify hubs as to whether F I G U R E 1 Identifying network hubs. (A) Network hubs can be characterized using a variety of approaches.…”

Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity

“…Jin et al (2014) further investigated the brain functional network in the EC and EO conditions of the MEG resting state from 39 healthy subjects and found an enhanced functional connectivity in the theta and alpha bands during the EO state relative to the EC state. In a recent study, functional connectivity in the theta band was significantly different between healthy subjects in the EO state and focal epileptic patients in the EC state (Pourmotabbed et al, 2020); however, the role of eye behavior states was uncertain. These significant differences in the resting-state functional connectivity between the EO state and the EC state may support the hypothesis of Marx et al (2003) on mental states, including the "interoceptive" network, which was characterized by imagination and multisensory activity during EC, and the "exteroceptive" network, which can be characterized by the attention and ocular motor activity during the EO state.…”

Reproducibility of graph measures derived from resting‐state MEG functional connectivity metrics in sensor and source spaces

“…The MEG analysis pipeline described in the following sections was adapted from our previous work (Pourmotabbed et al, 2020). An overview of the analysis pipeline is shown in Figure 1.…”

“…Nodal graph measures characterize individual node properties and the influence of the nodes on the network (Rubinov & Sporns, 2010). Many of these measures have been shown to reflect disease-related abnormalities in the brain networks of patients with epilepsy (Garcia-Ramos, Song, Hermann, & Prabhakaran, 2016;Pourmotabbed, Wheless, & Babajani-Feremi, 2020;Quraan, McCormick, Cohn, Valiante, & McAndrews, 2013), Alzheimer's disease (Hojjati, Ebrahimzadeh, & Babajani-Feremi, 2019;Khazaee, Ebrahimzadeh, & Babajani-Feremi, 2015;Stam et al, 2009), schizophrenia (Hadley et al, 2016;Jalili & Knyazeva, 2011), autism (Tsiaras et al, 2011;Zeng et al, 2017), or other disorders. This suggests that graph measures have the potential to be useful as clinical biomarkers.…”