Topographical reorganization of brain functional connectivity during an early period of epileptogenesis

The current study aims to investigate functional brain network representations during the early period of epileptogenesis.

Eighteen rats with the intrahippocampal kainate model of mesial temporal lobe epilepsy were used for this experiment. Functional magnetic resonance imaging (fMRI) measurements were made 1 week after status epilepticus, followed by 2-4-month electrophysiological and video monitoring. Animals were identified as having (1) developed epilepsy (E+, n = 9) or (2) not developed epilepsy (E-, n = 6). Nine additional animals served as controls. Graph theory analysis was performed on the fMRI data to quantify the functional brain networks in all animals prior to the development of epilepsy. Spectrum clustering with the network features was performed to estimate their predictability in epileptogenesis.

Our data indicated that E+ animals showed an overall increase in functional connectivity strength compared to E- and control animals. Global network features and small-worldness of E- rats were similar to controls, whereas E+ rats demonstrated increased small-worldness, including increased reorganization degree, clustering coefficient, and global efficiency, with reduced shortest pathlength. A notable classification of the combined brain network parameters was found in E+ and E- animals. For the local network parameters, the E- rats showed increased hubs in sensorimotor cortex, and decreased hubness in hippocampus. The E+ rats showed a complete loss of hippocampal hubs, and the appearance of new hubs in the prefrontal cortex. We also observed that lesion severity was not related to epileptogenesis.

Our data provide a view of the reorganization of topographical functional brain networks in the early period of epileptogenesis and how it can significantly predict the development of epilepsy. The differences from E- animals offer a potential means for applying noninvasive neuroimaging tools for the early prediction of epilepsy.