Low functional robustness in mesial temporal lobe epilepsy

Abstract

Objectives Brain functional topology was investigated in patients with mesial temporal lobe epilepsy (mTLE) by means of graph theory measures in two differentially defined graphs. Measures of segregation, integration, and centrality were compared between subjects with mTLE and healthy controls (HC). Methods Eleven subjects with mTLE (age 36.5 ± 10.9 years) and 15 age-matched HC (age 36.8 ± 14.0 years) participated in this study. Both anatomically and functionally defined adjacency matrices were used to investigate the measures. Binary undirected graphs were constructed to study network segregation by calculating global clustering and modularity, and network integration by calculating local and global efficiency. Node degree and participation coefficient were also computed in order to investigate network hubs and their classification into provincial or connector hubs. Measures were investigated in a range of low to medium graph density. Results The group of patients presented lower global segregation than HC while showing higher global but lower local integration. They also failed to engage regions that comprise the default-mode network (DMN) as hubs such as bilateral medial frontal regions, PCC/precuneus complex, and right inferior parietal lobule, which were present in controls. Furthermore, the cerebellum in subjects with mTLE seemed to be playing a major role in the integration of their functional networks, which was evident through the engagement of cerebellar regions as connector hubs. Conclusions Functional networks in subjects with mTLE presented both global and local abnormalities compared to healthy subjects. Specifically, there was significant separation between groups, with lower global segregation and slightly higher global integration observed in patients. This could be indicative of a network that is working as a whole instead of in segregated or specialized communities, which could translate into a less robust network and more prone to disruption in the group with epilepsy. Furthermore, functional irregularities were also observed in the group of patients in terms of the engagement of cerebellar regions as hubs while failing to engage DMN-related areas as major hubs in the network. The use of two differentially defined graphs synergistically contributed to findings.

Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology

“…Our findings are consistent with previous studies reporting functional abnormalities in brain networks of patients with epilepsy compared to healthy control subjects. For instance, Garcia-Ramos et al observed that the functional brain network in patients with mesial temporal lobe (mTLE) epilepsy presented lower segregation than healthy control subjects, which may be an indication of decreasing robustness and increasing disruption in brain networks of patients with epilepsy ( Garcia-Ramos et al, 2016 ). Another study reported a lower modularity and higher global efficiency in TLE patients compared to healthy controls ( Doucet et al, 2015 ).…”

“…Graph measures can extract useful features from the brain network ( Bullmore and Sporns, 2009 ). Network topology has been used to characterize structural and functional properties of the brain networks in healthy subjects and patients ( Ridley et al, 2015 ; Garcia-Ramos et al, 2016 ; Rubinov and Sporns, 2010 ). The brain of a healthy subject is a complex network and has properties such as balanced integration and segregation; however, the brain of patients with epilepsy is deviated toward regular or random networks, with alterations in integration and segregation characteristics.…”

“…The brain of a healthy subject is a complex network and has properties such as balanced integration and segregation; however, the brain of patients with epilepsy is deviated toward regular or random networks, with alterations in integration and segregation characteristics. Previous studies have demonstrated alteration of graph measures in patients with epilepsy compared to healthy control subjects using electroencephalography (EEG) ( van Diessen et al, 2016 ), MEG ( Niso et al, 2015 ), electrocorticography (ECoG) ( Vega-Zelaya et al, 2014 ), and functional MRI (fMRI) ( Ridley et al, 2015 ; Garcia-Ramos et al, 2016 ; Doucet et al, 2015 ). Despite the growing applications of network topology in patients with epilepsy, there is only one study that used EEG-based graph measures and investigated the difference in these measures in responders and non-responders to VNS treatment ( Fraschini et al, 2014 ).…”

Progressive topological disorganization of brain network in focal epilepsy

“…11 The wide range of studies of genetic, signaling, communications, computation, and neural networks indicated that the structural organization of the human brain has a small-world topology that maximizes simultaneous local and global information processing. 34 The strengths of this study include its relatively large sample size (total of 150 subjects) and comparison according to the duration of epilepsy. The patients with focal epilepsy exhibited a decrease in mean clustering coefficient, characteristic path length, small-worldness, and global efficiency compared with healthy subjects.…”

“…The considerably lower segregation could translate into a network that lacks the capacity to contain functional processes into a specific community, such as memory or language, within the brain making it less specialized, whereas high integration may be interpreted by epileptic networks that are more tightly connected, which can be related to enhanced neural synchronization and seizure propagation. 34 The strengths of this study include its relatively large sample size (total of 150 subjects) and comparison according to the duration of epilepsy. In addition, we did graph theoretical analysis based on DTI, which directly reveled the structural connections and had high reproducibility.…”