Subject-specific computational modeling of DBS in the PPTg area

Abstract

Deep brain stimulation (DBS) in the pedunculopontine tegmental nucleus (PPTg) has been proposed to alleviate medically intractable gait difficulties associated with Parkinson's disease. Clinical trials have shown somewhat variable outcomes, stemming in part from surgical targeting variability, modulating fiber pathways implicated in side effects, and a general lack of mechanistic understanding of DBS in this brain region. Subject-specific computational models of DBS are a promising tool to investigate the underlying therapy and side effects. In this study, a parkinsonian rhesus macaque was implanted unilaterally with an 8-contact DBS lead in the PPTg region. Fiber tracts adjacent to PPTg, including the oculomotor nerve, central tegmental tract, and superior cerebellar peduncle, were reconstructed from a combination of pre-implant 7T MRI, post-implant CT, and post-mortem histology. These structures were populated with axon models and coupled with a finite element model simulating the voltage distribution in the surrounding neural tissue during stimulation. This study introduces two empirical approaches to evaluate model parameters. First, incremental monopolar cathodic stimulation (20 Hz, 90 μs pulse width) was evaluated for each electrode, during which a right eyelid flutter was observed at the proximal four contacts (−1.0 to −1.4 mA). These current amplitudes followed closely with model predicted activation of the oculomotor nerve when assuming an anisotropic conduction medium. Second, PET imaging was collected OFF-DBS and twice during DBS (two different contacts), which supported the model predicted activation of the central tegmental tract and superior cerebellar peduncle. Together, subject-specific models provide a framework to more precisely predict pathways modulated by DBS.

Multi-objective particle swarm optimization for postoperative deep brain stimulation targeting of subthalamic nucleus pathways

“…This region was used as a seedpoint mask for tractography for both the lenticular fasciculus and for the thalamic fasciculus, with the globus pallidus and the thalamus used as termination masks, respectively. After generating the fiber tract trajectories, the tracts were populated with myelinated axons of 2 μm fiber diameter (FLUT length 10 μm; STIN length 57.7 μm; MYSA length 3 μm; node diameter 1.4 μm; axon diameter 1.6 μm; node length 1 μm; number lamellae 30; passive first and last nodes) using the threshold-contour mapping procedure described in [41]. Axons that overlapped with the DBS lead were removed from subsequent simulations.…”

Particle swarm optimization for programming deep brain stimulation arrays

“…While this study used a simplistic homogeneous and isotropic model of tissue conductance to obtain extracellular potential values at the axonal nodes of Ranvier, one may consider using more complex models of brain tissue with the PSO algorithm. For example, studies have shown that finite element models that incorporate inhomogeneous and anisotropic tissue properties (assigning different conductance values to various tissue types) improve modeling results and may better reflect the physiological properties of the brain [11], [32], [57]. Finally, while the implementation presented here was designed for programming a set of electrodes that have already been implanted, it is conceivable to leverage the efficiency of this approach for pre-surgical planning of DBS lead placement.…”

Model-Based Comparison of Deep Brain Stimulation Array Functionality with Varying Number of Radial Electrodes and Machine Learning Feature Sets

“…The quasistatic finite element models used for predicting tissue voltage in this study were idealized as isotropic and were homogeneous within bulk neural tissue. Increasingly complex models that more precisely model tissue conductivity using diffusion weighted imaging have been introduced in the past decade and have been shown to impact biophysical simulation results (Butson et al, 2007 ; Chaturvedi et al, 2010 ; Zitella et al, 2015 ), particularly for modeling of electrical stimulation near white matter fiber tracts (Butson et al, 2007 ; Schmidt and van Rienen, 2012 ). Further, the conductance values utilized in the tissue models presented here rely on experimentally determined values for conductance that are subject to uncertainty as evident by the range of values reported within the scientific literature (Gabriel C. et al, 1996 ; Faes et al, 1999 ).…”