• Wei Liao, Zhiqiang Zhang, Dante Mantini, Qiang Xu, Gong-Jun Ji, Han Zhang, Jue Wang, Zhengge Wang, Guanghui Chen, Lei Tian, Qing Jiao, Yu-Feng Zang, and Guangming Lu.
• Center for Cognition and Brain Disorders and the Affiliated Hospital, Hangzhou Normal University, Hangzhou, 310015, China, weiliao.wl@gmail.com.
• Brain Struct Funct. 2014 Nov 1; 219 (6): 2001-15.

AbstractEpilepsy is characterized by recurrent and temporary brain dysfunction due to discharges of interconnected groups of neurons. The brain of epilepsy patients has a dynamic bifurcation that switches between epileptic and normal states. The dysfunctional state involves large-scale brain networks. It is very important to understand the network mechanisms of seizure initiation, maintenance, and termination in epilepsy. Absence epilepsy provides a unique model for neuroimaging investigation on dynamic evolutions of brain networks over seizure repertoire. By using a dynamic functional connectivity and graph theoretical analyses to study absence seizures (AS), we aimed to obtain transition of network properties that account for seizure onset and offset. We measured resting-state functional magnetic resonance imaging and simultaneous electroencephalography (EEG) from children with AS. We used simultaneous EEG to define the preictal, ictal and postictal intervals of seizures. We measured dynamic connectivity maps of the thalamus network and the default mode network (DMN), as well as functional connectome topologies, during the three different seizure intervals. The analysis of dynamic changes of anti-correlation between the thalamus and the DMN is consistent with an inhibitory effect of seizures on the default mode of brain function, which gradually fades out after seizure onset. Also, we observed complex transitions of functional network topology, implicating adaptive reconfiguration of functional brain networks. In conclusion, our work revealed novel insights into modifications in large-scale functional connectome during AS, which may contribute to a better understanding the network mechanisms of state bifurcations in epileptogenesis.

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