Control theory is a powerful framework to understand how DBS modulates behavior through perturbation of brain dynamics. Efforts to bridge control theory techniques into DBS are growing, but a complete reframe may be needed to most effectively and ethically move forward with DBS research.
A central part of my PhD work in antidepressant DBS involves building a new framework for inferring and controlling generalized DBS systems.
The brain dynamics immediately following DBS initiation is likely to be a rich source of information about therapeutic mechanism of action. Inferring the effect of DBS on dynamics potentially spanning the whole brain requires a
- Tracing Dynamic Oscillations through brain networks here
Inferring DBS Mechanism
The effort to build mathematical models that accurately reflect the inner workings of the brain is decades, maybe centuries, ongoing. But we don’t need perfect accuracy to glean important insights into the mechanism of DBS. Instead, we can rely on first-order models that capture large scale, high SNR observations in a way that’s predictive of novel perturbations. Towards that effort, I took a dynamical systems +/- neural mass model approach to explaining observations like the DBS-DO.
Limitations of Readouts
AutoLie/AutoDyn Python Libraries
To address the above questions in a way that accelerates the next generation of CT-informed DBS, I developed two FOSS Python libraries: