John Rolston

My research focuses on improving neuromodulation therapies for epilepsy through patient-specific computational modeling, advanced neuroimaging, and brain network analysis. Many epilepsy patients do not respond to medication, and while surgery is an option, its success depends on accurately identifying and targeting seizure-generating brain networks. My work aims to enhance the precision and effectiveness of brain stimulation therapies, such as responsive neurostimulation (RNS) and deep brain stimulation (DBS), by understanding how stimulation interacts with individual patients’ brain circuits.
   
A major component of my research involves using intracranial EEG data to model brain network dynamics, improving our ability to predict the effects of electrical stimulation. My team has demonstrated that RNS is most effective when delivered during low-risk brain states rather than during high-risk seizure periods, a paradigm-shifting finding published in Brain (Anderson et al., 2024). Additionally, we have shown that patient-specific structural connectivity can predict RNS outcomes, offering a roadmap for optimizing stimulation targets (Epilepsia, Charlebois et al., 2022).
   
Another critical aspect of my research involves corticocortical evoked potentials (CCEPs) and their role in brain connectivity. Our findings (Kundu et al., 2023) indicate that CCEPs propagate as traveling waves, which could serve as biomarkers for network-based neuromodulation. We have also developed open-source software for precise electrode localization, making these tools widely accessible to the research community. Ultimately, my work aims to transform neuromodulation into a truly personalized therapy by ensuring the right patient receives the right stimulation at the right time, paving the way for more effective epilepsy treatments.