A study about the topological influence of Hubs and Authorities nodes on the synchronous dynamics of a cat's cerebral cortex.

Epilepsy is a neurological condition resulting from a surge of synchronous neural activities caused by increased cerebral cortex blood flow, leading to seizures and loss of consciousness. While anti-epileptic drugs can help prevent seizures and disrupt the formation of synchronous neural networks, they are not always effective, and approximately one-third of patients still experience seizures even when medicated. As a result, there is a growing need to study brain dynamics and find ways to improve the quality of life for individuals with epilepsy. In this study, we modeled the cerebral cortex of a feline as a complex network to investigate the most influential areas of the cortex and their impact on epilepsy-associated synchronization dynamics. The Kuramoto model was used to study phase synchronization between the different cortex areas, and the Hypertext Induced Topic Search (HITS) algorithm was employed to identify the most influential nodes in the feline cerebral cortex network.

We investigated the dynamics and measures of global, mesoscopic, and microscopic synchrony, with one scenario involving a disturbance to simulate the action of an anti-epileptic drug. The disruption reduced the intensity of connections of a group containing random nodes and the group with nodes chosen by the HITS algorithm by 50%. However, the disturbance also led to a lag in the network's global, microscopic, and mesoscopic levels. For future works, we aim to investigate the neural network of the cat using more sophisticated and realistic dynamics provided by a Hodgkin-Huxley-type neuron.