Multi-Graph Assessment of Temporal and Extratemporal Lobe Epilepsy in Resting-State fMRI

被引:0
|
作者
Amoiridou, Dimitra [1 ]
Gkiatis, Kostakis [1 ,2 ]
Kakkos, Ioannis [1 ]
Garganis, Kyriakos [2 ]
Matsopoulos, George K. [1 ]
机构
[1] Natl Tech Univ Athens, Biomed Engn Lab, Athens 15773, Greece
[2] St Lukes Hosp, Epilepsy Monitoring Dept, Thessaloniki 55236, Greece
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 18期
关键词
epilepsy; network topology; graph theory; fMRI; centrality measures; FOCAL EPILEPSY; FUNCTIONAL NETWORKS; BRAIN CONNECTIVITY; COMPONENT ANALYSIS; ORGANIZATION; CHILDREN; ACCURATE; ANATOMY; ROBUST; HUBS;
D O I
10.3390/app14188336
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
引用
收藏
页数:27
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