Activation metrics for structural connectivity recruitment in deep brain stimulation

Comparatively high excitability of myelinated fibers suggests that they represent a major mediator of deep brain stimulation effects. Such effects can be modeled using different levels of abstraction, ranging from simple electric field estimates to complex multicompartment axon models. In this study, we explored three metrics to evaluate axonal activation: electric field magnitudes, electric field projections and pathway activation modeling. Furthermore, in order to account for variability in axonal morphology, these metrics were computed in a probabilistic fashion. To showcase and illustrate their relevance, we retrospectively analyzed a dataset of 15 Parkinson’s disease patients, who were stimulated in the subthalamic nucleus in bipolar mode. High similarity of activation patterns was observed for the electric field metrics, but not for pathway activation modeling, which might be attributed to its ability to capture stimulation’s polarity. Nevertheless, all three metrics associated motor improvement with activation of motor pallidosubthalamic and hyperdirect pathways. To make these probabilistic approaches accessible for the community, the modeling and statistical framework was implemented in the openly available Lead-DBS toolbox.