Abstracts

Rationale: A critical step towards optimizing direct neuromodulation of refractory focal-onset epilepsy is to effectively interface with an epileptogenic circuit with two or more communicating epileptogenic sources using a maximum of two 4-contact depth electrode leads. Our objective was to predict, preoperatively, the maximum extent to which responsive neurostimulation therapy (RNS, NeuroPace) can propagate through an epileptic brain circuit for stabilizing the epileptogenic network by placing virtual electrodes at the grey-white matter interface.A classical approach to determine the volume of brain activation is to simply calculate the electric field (E-field) immediately surrounding the stimulating electrode and select a “magnitude threshold” above which cortical activation occurs. While this model gives information on possible activated tracts when placed in white matter (WM), it fails to account for stimulation effects on the axonal membrane potential. However, an understanding of the membrane biophysics is crucial for predicting activation in axon bundles adjacent to the electrode.Our model can differentiate between regions of depolarization and hyperpolarization produced by the applied stimulus by computing an activation function (AF), derived from the core-conductor model which considers three factors: 1) electric potential (EP), 2) directionality of the E-field, and 3) axon bundle orientation.The model was generated for 6 RNS patients with refractory focal-onset epilepsy implanted at our institution. The AF was computed for each patient and then compared with the classical E-field model.Validation of both models was addressed post-operatively by performing a stimulation activated SPECT (SAS). This technique captured transient blood flow changes during delivery of focused cortical RNS using a relatively high therapeutic charge density without generating an after-discharge. Methods: The workflow consisting of 6 steps was completed for 6 candidate patients for RNS depth lead implantation as follows: 1) strategic virtual electrode lead placement was performed using a patient-specific 3D-model from the structural MRI; 2) computations of the EP and E-Field were completed using the finite element method; 3) the AF was calculated as the second directional derivative of the EP in the direction of axon bundles relative to the electrode; 4) regions with high E-Field depolarization and hyperpolarization were used as seeds for creating modulated circuit tractography (MCT) maps for each model; 5) MCT maps were then used as targets for intraoperative placement of 1-2 RNS depth leads. Finally, 6) SAS was used post-implant to validate the extent of regions influenced by RNS. Results: For the 6 patients, the AF model generated irregular volumes of activation surrounding the depth contacts due to hyperpolarization and depolarization and compared with the spherical shape regions generated by the E-field model. The MCT activated by the AF predicted the extent to which WM-connected epileptic sources were influenced during RNS. MCT was validated by SAS (performed in 4 of the 6 patients) and RNS electrocorticography. Conclusions: The preimplant AF-based model offers the potential to predict optimal implant sites for two 4-contact depth leads influencing up to 3 distant communicating epileptogenic sources in an epileptogenic network. As importantly, the model provides an ability to identify regions of depolarization and hyperpolarization. The AF takes into account patient-specificity such as axon bundle orientation near the electrode contacts. A workflow incorporating such a model can potentially increase the number of patient candidates for RNS therapy.  Funding: Mary Keane Fund, Foglia Family Foundation, CONACYT