Activation metrics for structural connectivity recruitment in deep brain stimulation.

Abstract

Comparatively high excitability of myelinated fibres suggests that they represent a major mediator of deep brain stimulation effects. Such effects can be modelled 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 modelling. 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 analysed 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 modelling, 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 to the community, the modelling and statistical framework was implemented in the openly available Lead-DBS toolbox.