# Multi-Graph Assessment of Temporal and Extratemporal Lobe Epilepsy in Resting-State fMRI
###### Abstract  
Epilepsy is a common neurological disorder that affects millions of people worldwide, disrupting brain networks and causing recurrent seizures. In this regard, investigating the distinctive characteristics of brain connectivity is crucial to understanding the underlying neural processes of epilepsy. However, the various graph-theory frameworks and different estimation measures may yield significant variability among the results of different studies. On this premise, this study investigates the brain network topological variations between patients with temporal lobe epilepsy (TLE) and extratemporal lobe epilepsy (ETLE) using both directed and undirected network connectivity methods as well as different graph-theory metrics. Our results reveal distinct topological differences in connectivity graphs between the two epilepsy groups, with TLE patients displaying more disassortative graphs at lower density levels compared to ETLE patients. Moreover, we highlight the variations in the hub regions across different network metrics, underscoring the importance of considering various centrality measures for a comprehensive understanding of brain network dynamics in epilepsy. Our findings suggest that the differences in brain network organization between TLE and ETLE patients could be attributed to the unique characteristics of each epilepsy type, offering insights into potential biomarkers for type-specific epilepsy diagnosis and treatment. epilepsy; network topology; graph theory; fMRI; centrality measures +
Footnote †: journal: Medical  
The recent review by [PERSON] et al. [12] presents a comprehensive overview of biomarkers related to pediatric epilepsy, including 62 studies that exclusively utilized fMRI data. According to the review, integrating fMRI analysis with graph theory provides a robust framework for enhancing predictions related to cognitive performance, assessing surgical outcomes, and evaluating symptom severity in focal epilepsy. In fact, neural plasticity can elucidate the brain's ability to adapt and reorganize itself, both functionally and structurally [13]. Furthermore, chronic seizures can lead to changes in functional neural networks, making certain regions more prone to generating and spreading seizures. On this premise, functional connectivity (FC) analysis allows the brain to be represented as a graph, where edges can represent the temporal dependencies of signals from the nodes, which can be the anatomical brain regions [14]. In this network, different brain regions have specialized functions and work together to perform higher cognitive functions [15]. Epilepsy has been characterized as a brain network disorder since the seizures not only affect the epileptogenic regions but can also spread to anatomically distant brain regions [16]. From this standpoint, multiple studies underline the importance of the resting-state fMRI (rs-fMRI) FC analysis in decision-making about epilepsy surgery [17; 18]. [PERSON] et al. [18] suggested that connectivity-based biomarkers could enhance preoperative evaluation, emphasizing the role of FC in predicting cognitive outcomes post-epilepsy surgery and in localizing the epileptogenic zone. [PERSON] et al. [17] demonstrated that incorporating rs-fMRI connectivity into the presurgical evaluation resulted in a 50% increase in positive decisions for surgery. In this regard, the incorporation of graph-theory metrics can increase our comprehension of how distant brain regions contribute to information processing, introducing new biomarkers associated with brain network disorders such as epilepsy [19]. This aspect has sprung an increase in research attention towards the topological features that distinguish the epileptic brain network from the healthy one.