Optimizing Patient-Specific Deep Brain Stimulation Models Using Electrophysiology

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PROJECT SUMMARY/ABSTRACT Patient-specific computational models of deep brain stimulation (DBS) have been used to understand the effects of electrical stimulation on brain structures and pathways in Parkinson's disease (PD) and other neuropsychiatric disorders. These 3-dimensional (3-D), image-based, biophysical models provide a visual representation of neu- ronal activation patterns around the DBS electrode and in connected distal regions. As a result, they are con- ceptually attractive in both clinical practice (for targeting and postoperative programming) and in research (for mechanistic insights and hypotheses development). However, despite their widespread use in research, and recent introduction into clinical practice, the direct assessment of model accuracy is lacking. At present, it is unknown if the spatial extent of stimulation effects predicted by the patient-specific computational DBS models reflect genuine neuronal activations in the human brain. Consequently, recent clinical studies have shown poor correlations between predictions from simple volume of tissue activation (VTA) DBS models and general PD clinical outcomes. Driving force (DF) predictor DBS models incorporate more realistic axonal trajectories into the local anatomy; however, it is unclear if the additional complexity of DF models improves the clinical accuracy of the simulations. To test this, we have developed an experimental paradigm to quantify the degree of axonal pathway activation by subthalamic DBS in PD patients. Intracranial cortical evoked potentials (cEP) and periph- eral motor evoked potentials (mEP) can differentiate activation of several neighboring neural pathways by DBS. Modulation of DBS settings (active contacts, amplitude, pulse width) alters the amplitude of cEP and mEP. Therefore, by changing the DBS settings, pathway recruitment can be quantified, and the activation predictions for different modeling methods can be compared. The goal of this Bioengineering Research Grant proposal is to determine the accuracy of patient-specific DBS models (VTA and DF) compared to in-vivo electrophysiologic measurements in PD patients. We hypothesize that DF models are more biophysically accurate than VTA mod- els. We will test this using two different electrophysiological measurements (cEP in Aim1; mEP in Aim2), and we will identify which model components are the most critical to the model simulations. In Aim 3 we will compare neural pathway activations predicted by the models to clinical DBS side effects (resulting from corticospi- nal/bulbar tract activation that can be experimentally measured and predicted by the models). This will allow us to determine the level of model accuracy that is necessary for clinical use in individual patients. The optimal modeling approach systematically characterized in this proposal will provide the first validated standard for clin- ical and research applications of DBS models.