Objective: The white matter structural network changes remain poorly understood in patients with temporal lobe epilepsy and comorbid headache (PWH). This study aimed at exploring topological changes in the structural network.
Methods: Twenty-five PWH, 32 patients with temporal lobe epilepsy without headache, and 22 healthy controls were recruited in this study. High-resolution structural MRI and diffusion tensor imaging data were acquired from these participants. A graph theory-based approach was employed to characterize the topological properties of the structural network. A network-based statistical analysis was employed to explore abnormal connectivity alterations in PWH.
Results: Compared with healthy controls, PWH exhibited significantly decreased small-world index, shortest path length, increased clustering coefficient, global efficiency, and local efficiency. Patients with temporal lobe epilepsy and comorbid headache displayed a significantly reduced small-world index, shortest path length, and increased global efficiency when compared with patients with temporal lobe epilepsy without headache. In addition, PWH exhibited abnormal local network parameters, mainly located in the prefrontal, temporal, occipital, and parietal regions. Furthermore, network-based statistical analysis revealed that PWH had abnormal structural connections between the temporoparietal lobe, occipital lobe, insula, cingulate gyrus, and thalamus.
Conclusion: This study reveals the abnormal white matter structural network alterations in PWH, allowing a better insight into the neuroanatomical mechanisms that predispose epileptic patients to comorbid headaches from the network levels.
Keywords: Graph theory; Headache; Network-based statistical analysis; Structural network; Temporal lobe epilepsy.
Copyright © 2023 Elsevier Inc. All rights reserved.