This study used a series of DBS computational models, customized to an individual human patient, following and expanding upon methodology previously described in Butson et al. (12
). Our fundamental goal was to evaluate the quantitative importance of four components of voltage-controlled DBS electric field models: 1) electrode interface voltage drop, 2) electrode interface capacitance, 3) tissue encapsulation of the electrode, and 4) tissue anisotropy/inhomogeneity. The modeling system combined both anatomical and diffusion tensor magnetic resonance imaging (MRI) data. Pre- and post-operative T1 images were used to position and align 3D surfaces representing anatomical nuclei of interest (i.e. thalamus and STN), as well as to determine the position of the DBS electrode within the patient’s brain. Diffusion tensor imaging (DTI) data (36
) was used to both define axonal trajectories of the internal capsule (IC), and to estimate 3D tissue anisotropy and inhomogeneity in the tissue region surrounding the DBS electrode (12
The electric field generated by DBS was calculated with finite element models (FEMs). We created five variants of the DBS FEM to address differences associated with the degree of model complexity. Model I was the most simplistic, an electrostatic model that ignored the interface voltage drop, electrode capacitance, encapsulation, and tissues anisotropy/inhomogeneity. Models II-V incrementally added explicit representations of the electrode interface with the brain, and the tissue anisotropy/inhomogeneity ().
Characteristics of the DBS electric field models.
The electric field generated by each DBS FEM was applied to detailed multi-compartment cable models of myelinated axons which had trajectories defined by DTI tractography (). Stimulation thresholds were calculated for each IC axon by applying the extracellular voltage distribution generated along the axon trajectory for each of the five variants of the FEM. These model results were then compared to experimentally defined CST thresholds acquired using electromyogram (EMG) recordings from the patient.
Patient-specific DBS model
The study received prior approval from the Cleveland Clinic Institutional Review Board, and the patient provided informed written consent. The subject was a 63-year old male patient with Parkinson’s disease, previously implanted with a Soletra pulse generator and 3387 DBS electrode (Medtronic Inc, Minneapolis, MN) in the STN region, who exhibited good therapeutic benefit from the device based on the Unified Parkinson’s Disease Rating Scale. Our models used the patient’s pre-operative and post-operative high-resolution T1-weighted MRIs, acquired on a Siemens Symphony 1.5 T scanner and on a Siemens 1.5 T Magnetom Vision, respectively. Both images were acquired with a 256 mm × 256 mm field of view and were interpolated to have a 1 mm3
isotropic voxel resolution. The post-operative MRI was performed with imaging parameters previously defined as being safe by extensive phantom testing in the specific scanner used to acquire the images (37
The clinical experiments were conducted at a time point greater than one year post-surgery. Differential EMG recordings were made with electrode pairs placed over the biceps, triceps, flexor carpi ulnaris, extensor carpi radialis, quadriceps, tibialis anterior, and lateral gastrocnemius. During these experiments, 20-second recording epochs were gathered while the patient experienced unilateral low frequency, monopolar stimulation (5 Hz, 60 µsec pulse width, 0 to −10 V in −1 V increments). These recordings were individually performed with stimulation applied through each of the four DBS electrode contacts. The results reported in this paper are for the right-side DBS electrode (measurements and recordings were made on the left arm and leg).
EMG activity was recorded with a Biotop 6R12 amplifier with the following settings: low frequency filter at 5 Hz and a high frequency filter at 1500 Hz, with a 1 mV/full-scale, where the full-scale was 6 V. Signals were subsequently filtered with a ninth-order Butterworth high-pass filter with a cutoff frequency of 50 Hz to remove any low frequency baseline drift. The stimulus artifact was recorded using a surface electrode from the connecting lead on the patient’s neck. Using this as the trigger event, time-triggered average EMG signals were computed from the other channels. These signals were analyzed for a threshold response indicating a muscle twitch, which we interpreted as stimulation spillover into the IC and activation of CST fibers. To simplify the data presentation, EMG thresholds are reported with muscles divided into two distinct groups: arm (bicep, tricep, flexor carpi ulnaris, extensor carpi radialis) and leg (quadriceps, tibialis anterior, lateral gastrocnemius).
Image Registration and Anatomical Nuclei
MRI data formed the basis for the patient-specific DBS computer models. The patient’s pre- and post-operative MRI datasets were co-registered with the Wakana et al. (36
) diffusion tensor atlas brain using Analyze 6.0 (Lenexa, KS). The 3D co-registration algorithm was used within the Insight Toolkit feature of Analyze, and it implemented an intensity-based stochastic approach (38
). The DTI atlas brain was acquired with a 2-mm3
isotropic voxel size with a diffusion gradient weighting of 700 mm2
The general structure of the patient-specific DBS computer model was created following the methodology of Butson et al (12
) (). Graphical representations of relevant anatomical nuclei (STN and thalamus) were defined by warping 3D surfaces to fit the patient’s pre-operative MRI data using a non-linear algorithm (39
), originally developed by Surgical Navigation Technologies (now Medtronic Navigation, Louisville, CO). The electrode tip location and insertion trajectory were determined by segmenting the electrode from the post-operative MRI. This procedure used an image thresholding method to extract out the dark, hollowed cavity artifact created in the MR by the electrode. A virtual replica of the Medtronic 3387 DBS electrode, linked to a multi-resolution finite element mesh, was then placed at that location within the image volume. The anatomical and diffusion tensor MRI data, along with the anatomical nuclei and patient-specific electrode position were all loaded into a common 3D visualization and simulation environment SCIRun/BioPSE (Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT). ().
Internal Capsule Tractography
Individual tensors from the DTI atlas brain, along with a fiber tractography algorithm within SCIRun/BioPSE (40
), were used to extract individual model axon trajectories within the internal capsule. Two hundred forty seed points were placed equidistantly from one another within a 6 × 0.5 × 1 mm rectangular region just lateral to the STN. The resulting 240 trajectories were used to represent a population of IC axons in the patient-specific DBS model ().
Multi-compartment cable models of myelinated axons were created for each of the 240 IC fiber trajectories. These cable models, 10 µm in diameter, included detailed representations of the nodes of Ranvier, paranodal, and internodal sections of the individual axons (41
). Each axon had 51 nodes of Ranvier and 551 total compartments along its 50 mm path length.
A multi-resolution finite element mesh of the DBS electrode and surrounding tissue medium was constructed using FEMLAB 3.1 (Comsol Inc., Burlington, MA). This 3D mesh consisted of over 4.2 million nodes, most of which were located circumferentially around the electrode to provide for greater resolution near the stimulating contacts. The same mesh was used for all variants of the DBS FEM (). The Poisson equation was solved in 3D to determine the voltage distribution generated in the tissue medium by the DBS electrode (). The FEM solutions were performed on an 8-processor 32 GB shared-memory SGI Prism (Silicon Graphics Inc., Mountain View, CA).
The voltage solutions from each variant of the DBS FEM were linearly interpolated onto the center of every compartment of each axon trajectory (). Simulations of the neural response to the applied field were preformed in NEURON 6.1.2 (42
). The stimulus waveform (60 µsec pulse width) applied to the axon models mimicked the output of the Soletra pulse generator implanted in the patient (43
). Each of the 240 model axons had an activation threshold for each model variant that was defined as the minimum stimulus voltage necessary to generate a propagating action potential.
Internal capsule axon activation during monopolar DBS was evaluated at the patient’s clinically defined therapeutic electrode contact (contact 3), as well as each of the other three contacts. Five models with increasing levels of complexity were examined for each of the four contacts (). The simplest model (Model I) consisted of a homogeneous and isotropic tissue medium (0.3 S/m) with no electrode encapsulation, and stimulation was applied under electrostatic conditions with no voltage drop at the electrode interface (). Model II was identical to Model I, but included the 42% voltage drop at the electrode interface (see Appendix
) () (). A slightly more complex model (Model III) integrated electrode capacitance (3.3 µF), producing a more realistic simulation waveform in the tissue medium (14
) (). The fourth model (Model IV) incorporated a 0.5 mm tissue encapsulation layer (0.18 S/m) around the electrode, to account for the chronic electrode impedance (~900 Ω) estimated by the patient’s implanted pulse generator (13
) (). Finally, the most complex model (Model V) added the diffusion tensor based tissue conductivities to represent the anisotropic and inhomogenous bulk tissue medium (12
) (). A simple linear transform (0.8 (S-s)/mm2
scaling factor) was used to convert the diffusion tensors into conductivity tensors (19
Characterizing the electrode-electrolyte interface voltage drop