# Multi-Graph Assessment of Temporal and Extratemporal Lobe Epilepsy in Resting-State fMRI
###### Abstract
Specifically, since epilepsy often involves abnormal connectivity patterns in the brain, network metrics, such as global efficiency, clustering coefficients, and path lengths, can highlight changes in brain connectivity over time, providing insights into the neural disruptions, aiding in diagnosis, treatment or surgical planning, and understanding the underlying mechanisms of epilepsy [19; 20]. The use of graph-theory fMRI biomarkers is especially important in clinical applications [21]. For instance, the study of [PERSON] et al. [22] displays the value of graph theoretical features in the prediction of neurocognitive outcomes through the presurgical evaluation of TLE patients, proposing a model of three topological features (distance, local efficiency, and participation) capable of explaining more than 68% of the variance observed in the neurocognitive functions. Moreover, longer path lengths (indicating a lower global efficiency) in the graphs of patients with temporal lobe epilepsy (TLE) compared to controls are frequently observed [23; 24; 25; 26; 27; 28]. Most notably, graph-theory metrics help in characterizing the overall architecture of brain networks, identifying hubs (the regions that have a key role in the information flow since they facilitate communication between other regions), and understanding how different regions interact. In fact, hub-related analysis can reflect adaptive changes in network connectivity, demonstrating distinct functional or structural characteristics in individuals with neurological disorders [29]. In this context, numerous studies emphasize the significance of hub reorganization in epilepsy, highlighting its role as a critical biomarker associated with patients' cognitive impairments. A meta-analysis of [PERSON] et al. [30] concludes that the hub regions are prone to turning into lesions in a variety of brain disorders, and their increased biological cost is combined with higher vulnerability compared to the non-hub regions. Interestingly, they report significantly increased values of degree centrality in the lesion voxels compared to the non-lesion voxels in 9 of the 26 disorders analyzed, where 3 of them include different types of epilepsy (right and left TLE, juvenile myoclonic epilepsy). Regarding TLE patients, the study of [PERSON] et al. [23] reports different hub organization in patients compared to controls, with most of the TLE hubs localized in the paralimbic and the temporal lobe. Another relevant study also presented a shift in the hub localization of TLE patients, with the 98% of the hub regions of the right TLE group located in the contralateral hemisphere to the epilepsy onset [31]. Moreover, the study by [PERSON] et al. [32] summarizes the utility of hub-mapping analysis in the diagnosis, the formation of a treatment plan, and the presurgical evaluation of patients with epilepsy, as well as highlighting the necessity for further investigation of the hub-mapping techniques. There is a need for extensive validation and refinement of our understanding of brain reorganization in epilepsy, and for accurately identifying hub nodes and predicting the spread of epileptic discharge in the individual patient's brain. However, most studies employ only degree centrality to identify hub nodes, neglecting other measures that consider the distance between regions or their ability to facilitate connections. Furthermore, most relevant research analyzes average graphs constructed from mean connectivity values within each group, thus disregarding individual-specific brain networks. In this study, we aim to address these constraints by evaluating the importance of the regions of interest (ROIs), employing four centrality measures for the hub definition in three different connectivity techniques. Moreover, instead of analyzing the mean brain network, we analyze the hubs of each subject separately and then create the hub probability based on their presence within each group, minimizing the averaging bias.