  Whole-brain models offer a promising method of predicting seizure spread,
which is critical for successful surgery treatment of focal epilepsy. Existing
methods are largely based on structural connectome, which ignores the effects
of heterogeneity in regional excitability of brains. In this study, we used a
whole-brain model to show that heterogeneity in nodal excitability had a
significant impact on seizure propagation in the networks, and compromised the
prediction accuracy with structural connections. We then addressed this problem
with an algorithm based on random walk with restart on graphs. We demonstrated
that by establishing a relationship between the restarting probability and the
excitability for each node, this algorithm could significantly improve the
seizure spread prediction accuracy in heterogeneous networks, and was more
robust against the extent of heterogeneity. We also strategized surgical
seizure control as a process to identify and remove the key nodes (connections)
responsible for the early spread of seizures from the focal region. Compared to
strategies based on structural connections, virtual surgery with a strategy
based on mRWER generated outcomes with a high success rate while maintaining
low damage to the brain by removing fewer anatomical connections. These
findings may have potential applications in developing personalized surgery
strategies for epilepsy.
