Driving transitions between brain states using virtual deep brain stimulation for Parkinson’s disease patients

Deep Brain Stimulation (DBS) is a successful symptom-relieving treatment for Parkinson’s disease (PD). However, the introduction of advanced directional DBS electrodes significantly expands the programming parameter space, rendering the traditional trial-and-error approach for DBS optimization impractical and demonstrating the need for computational tools. From a dynamical systems perspective, DBS can be modeled as a strategy to shift patients’ brain dynamics closer to the healthy state. Our recently developed DBS model using The Virtual Brain simulation tool was able to reproduce multiple biologically plausible effects of DBS in PD (Meier et al., 2022, Experimental Neurology). In the current work, we extend our virtual DBS model toward higher resolution for the stimulus input, now sensitive to the exact 3D location of the activated contact, incorporating streamline activations and the electric field.

We leverage empirical DBS data from N=14 Parkinson’s patients with a total of N=392 contact activations of different electrode settings and corresponding motor task outcome. We then use our Virtual Brain simulation engine to model DBS in these patients. A model based on the principal component involvement of the simulated dynamics demonstrated a correlation between predicted and empirically observed motor task improvements due to DBS of r=0.386 (p<10-4) in a leave-one-out cross-validation. Benchmarking revealed better predictions with our computational dynamics than imaging-based static methods such as the sweet spot (r=0.16, p<0.05) and its recently introduced generalisation, termed the “sweet streamline” (r=0.26, p<10-4) approaches (Hollunder et al., 2024, Nature Neuroscience). Furthermore, our model outperforms the traditional trial-and-error method in predicting optimal clinical settings for individual patients, e.g. achieving over a 60% likelihood of identifying the optimal contact within the first two suggested contacts. Thus, our work enables to transition the brain dynamics of individual DBS-implanted PD patients toward a “healthier” state by identifying a sweet-spot analogue for dynamics, i.e. the target state, and linking these dynamics directly to clinical improvement.

In the future, the identified sweet-spot dynamics can be used to optimize the electrode placement and settings in silico in individual patients, showcasing the potential benefit of whole-brain simulations for improving clinical routine.