Computational analysis of subthalamic nucleus and lenticular fasciculus activation during
therapeutic deep brain stimulation
Computational analysis of STN DBS
Svjetlana Miocinovic1,2, Martin Parent3, Christopher R. Butson2, Philip J. Hahn2, Gary S. Russo4,
Jerrold L. Vitek4, Cameron C. McIntyre1,2
1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
2Department of Biomedical Engineering, Cleveland Clinic Foundation, Cleveland, Ohio
3Centre de Recherche Université Laval Robert-Giffard, Beauport, Québec, Canada
4Department of Neuroscience, Cleveland Clinic Foundation, Cleveland, Ohio
Cameron C. McIntyre, Ph.D.
Department of Biomedical Engineering
Cleveland Clinic Foundation
9500 Euclid Avenue ND20
Cleveland, OH, 44195
Phone: (216) 445-3264
Fax: (216) 444-9198
Page 1 of 42
Articles in PresS. J Neurophysiol (May 31, 2006). doi:10.1152/jn.00305.2006
Copyright © 2006 by the American Physiological Society.
The subthalamic nucleus (STN) is the most common target for the treatment of
Parkinson’s disease (PD) with deep brain stimulation (DBS). DBS of the globus pallidus internus
(GPi) is also effective in the treatment of PD. The output fibers of the GPi that form the
lenticular fasciculus pass in close proximity to STN DBS electrodes. In turn, both STN
projection neurons and GPi fibers of passage represent possible therapeutic targets of DBS in the
STN region. We built a comprehensive computational model of STN DBS in parkinsonian
macaques to study the effects of stimulation in a controlled environment. The model consisted of
three fundamental components: 1) a 3D anatomical model of the macaque basal ganglia, 2) a
finite element model of the DBS electrode and electric field transmitted to the tissue medium,
and 3) multi-compartment biophysical models of STN projection neurons, GPi fibers of passage
and internal capsule fibers of passage. Populations of neurons were positioned within the 3D
anatomical model. Neurons were stimulated with electrode positions and stimulation parameters
defined as clinically effective in two parkinsonian monkeys. The model predicted axonal
activation of STN neurons and GPi fibers during STN DBS. Model predictions regarding the
degree of GPi fiber activation matched well with experimental recordings in both monkeys. Only
axonal activation of the STN neurons showed a statistically significant increase in both monkeys
when comparing clinically effective and ineffective stimulation. Nonetheless, both neural targets
may play important roles in the therapeutic mechanisms of STN DBS.
Keywords: Parkinson’s disease; globus pallidus; electric field model; neuron model; clinical
efficacy; parameter selection
Page 2 of 42
Deep brain stimulation (DBS) has become an established clinical therapy for advanced
Parkinson’s disease (PD) (Obeso et al., 2001). Chronic high frequency electrical stimulation of
subcortical structures can provide more than 50% improvement in clinical ratings of motor
symptoms (Walter and Vitek 2004). However, the impressive clinical efficacy achieved by DBS
has occurred without a clear understanding of the therapeutic mechanisms of action. In addition,
a number of adverse effects can be generated by DBS including sensorimotor impairments,
involuntary movements (stimulation-induced dyskinesias), as well as speech, mood and
cognitive disturbances (Okun et al. 2005; Volkmann et al. 2002). Often these side-effects can be
avoided or alleviated with proper adjustment of the stimulation settings (Krack et al. 2003).
However, further improvements in the engineering design and clinical implementation of DBS
technology will rely on addressing a number of questions on the effects of DBS on the nervous
system. The fundamental goal of this project was to enhance our understanding of the target
neural elements of the stimulation.
The subthalamic nucleus (STN) represents the most common anatomical target for DBS
treatment of PD (Limousin et al. 1998). Electrodes placed in the STN are surrounded by several
neural types (local projection neurons and their axons, fibers of passage, afferent inputs, etc.),
but knowledge of the response properties of these different neural types to DBS is limited.
Therapeutic DBS electrode contacts are typically located in the region of the dorsal STN,
lenticular fasciculus (LF or Forel’s field H2) and zona incerta (Voges et al. 2002; Saint-Cyr et al.
2002; Starr et al. 2002; Hamel et al. 2003; Yelnik et al. 2003; Zonenshayn et al. 2004; Nowinski
et al. 2005). STN projection neurons send their highly collateralized axons to the globus pallidus,
striatum and substantia nigra (Sato et al. 2000). The LF courses just dorsal to the STN and is
Page 3 of 42
composed of fibers from the internal segment of the globus pallidus (GPi), which carry output
from the basal ganglia to the thalamus (Parent et al. 2001; Parent and Parent 2004). Given that
GPi DBS provides similar therapeutic benefits as STN DBS (Burchiel et al. 1999; Obeso et al.
2001; Rodriguez-Oroz et al. 2005), both STN projection neurons and pallidothalamic (GPi)
fibers represent viable candidates as the therapeutic target of the stimulation (Parent and Parent
2004). However, the axons of local projection neurons and fibers of passage respond at similar
extracellular stimulation thresholds (Ranck 1975; Nowak and Bullier 1998; McIntyre and Grill
1999) making it difficult to determine which neuron types are activated during STN DBS.
The extent of neural activation generated by extracellular stimulation depends on the
stimulation parameters, electrode and tissue electrical properties, and the position and orientation
of neural elements with respect to the electrode (Ranck 1975; Tehovnik 1996; McIntyre et al.
2004a). To address these issues in the context of STN DBS, we developed a comprehensive
computer model with detailed representation of the 3D neuroanatomy, the time-dependent
electric field generated by DBS electrodes, and the underlying biophysics that regulate the neural
response to stimulation. We tested our hypothesis that both STN projection neurons and GPi
fibers of passage are activated during clinically effective STN DBS. This would imply that both
neural types could play a role in the therapeutic mechanisms of STN DBS, and prompt further
investigation into electrode localization and stimulation parameter selection techniques for
optimizing the stimulation to individual subjects.
We customized our modeling framework to analyze neural activation in two parkinsonian
macaques implanted with chronic scaled clinical DBS systems (Hashimoto et al. 2003). Our goal
was to theoretically reproduce the experimental effects of STN DBS that improved parkinsonian
symptoms (bradykinesia and rigidity) in the two monkeys. Our first aim was to quantify the
Page 4 of 42
proportion of STN projection neurons and GPi fibers of passage that were activated during
clinically effective and ineffective stimulation. We found a significant increase in axonal
activation of STN projection neurons during clinically effective compared to ineffective
stimulation. Considerable GPi fiber activation was observed in only one of the two monkeys.
Single unit extracellular recordings of short latency, presumably antidromic, GPi activation
during STN DBS in both monkeys support our model predictions. The second aim was to
analyze how changes in electrode location affected neural activation. We found that sub-
millimeter shifts can alter neural activation, highlighting the importance of precise electrode
positioning. The final aim was to investigate the effects of stimulation-induced trans-synaptic
inhibition on somatic and axonal firing in STN projection neurons during STN DBS. We found
that somatic firing was reduced during DBS compared to the spontaneous pre-stimulation
activity, as seen experimentally (Welter et al. 2004; Filali et al. 2004; Meissner et al. 2005).
However, axonal activation was largely unaffected by the somatic inhibition and the axon easily
fired at the stimulation frequency (McIntyre et al. 2004a). Preliminary portions of this work have
been presented in abstract form (Miocinovic et al. 2004).
The implementation of chronic scaled clinical DBS systems in parkinsonian macaques
provides the foundation for detailed study of the therapeutic mechanisms of the stimulation
(Hashimoto et al., 2003; Elder et al. 2005). We coupled our experimental results with detailed
computer modeling to provide new insight into the cellular effects of STN DBS. We developed
computational models customized to two parkinsonian macaques implanted with STN DBS
systems. Each model consisted of three fundamental components: 1) a 3D anatomical model of
Page 5 of 42
the monkey basal ganglia, 2) a finite element model of the DBS electrode and electric field
transmitted to the tissue medium, and 3) multi-compartment biophysical models of reconstructed
STN projection neurons, GPi fibers of passage and internal capsule fibers of passage.
Populations of the neuron models were placed within context of the 3D anatomical model and
the histologically defined DBS electrode positions. The DBS electric field model was then
applied to the neuron models, thereby allowing for theoretical prediction of the neural response
to the stimulation.
We built a three-dimensional (3D) reconstruction of the basal ganglia for monkey R7160
(Macaca mulatta) (Hashimoto et al., 2003). Digital atlas templates of the macaque brain (Martin
and Bowden 2000) were warped in Edgewarp v3.28 (Bookstein 1990) to histological brain slices
to identify borders of nuclei not visible directly in the Nissl stained sections (Fig. 1). Nuclei of
interest were outlined in the 2D warped atlas slices, spaced in 1 mm increments. 3D volumes
were created by interpolating between these contour lines using the graphical modeling program
Rhinoceros v3.0 (McNeal & Associates, Seattle, WA). The resulting 3D brain atlas provided an
anatomically realistic virtual space to position the DBS electrode and the neuron models (Fig. 1).
The DBS electrode trajectory was reconstructed from the histological slices, and it was verified
that it matched the electrode position drawing in the top row of Figure 1 from Hashimoto et al.
(2003). The brain of the second monkey (R370) was not available; therefore, we used the same
3D atlas, but manually adjusted the DBS electrode position until the sagittal cross-section from
the 3D atlas matched the rendering in the bottom row of Figure 1 from Hashimoto et al. (2003).
Page 6 of 42
The 3D geometry of a longtailed macaque’s STN projection neuron (Macaca
fascicularis) was reconstructed using biotin dextran amine labeling and axonal tracing as
described by Sato et al. (2000) (Fig. 2). The neuron geometry was defined using Neurolucida
(MicroBrightField, Inc., Williston, VT) and converted for display in Rhinoceros and our 3D
brain atlas. Axonal tracing experiments (Sato et al. 2000) have revealed that STN projection
neurons course either dorsally along the ventral border of the thalamus or ventrally along the
lateral border of the STN on their way to the globus pallidus. To account for this anatomical
variability, the original neuron reconstruction was used to create two additional STN neuron
geometries with alternative axonal paths (Fig. 3A). The GPi axon geometry was based on the
description of lenticular fasciculus trajectory from Parent and Parent (2004). The LF fibers
emerged dorsomedially from the GPi, crossed the IC at a level dorsal to the STN, turned caudally
to run along the dorsal border of the STN in Forel’s field H2, joined the ansa lenticularis in
Forel’s field H, continued through Forel’s field H1, and terminated in the ventral thalamus (Fig.
The internal capsule defines the lateral border of the STN. Consequently, motor evoked
responses from activation of the corticospinal tract (CST) can be elicited with relatively low
thresholds during STN stimulation. To provide a gross level of model validation and connection
to behavioral measurements we incorporated CST axon trajectories into our anatomical
framework. From the level of dorsal thalamus, the CST fibers coursed ventrally at an
approximately 20 degree anterior-to-posterior angle (Fig. 3C).
The three STN neuron geometries, along with the GPi and CST axon trajectories were
placed within the 3D atlas in their respective anatomically realistic positions and orientations
Page 7 of 42
(Fig. 3). Neural populations of the STN neurons, GPi fibers and CST fibers were created by
copying the five basic geometries and distributing them randomly within the atlas, while still
keeping each within their respective anatomical boundaries. In total, three such populations were
created by randomly shifting neurons by ± 250 µm in any direction, and manually repositioning
those that ended up outside their anatomical boundaries. After the populations were established,
a DBS electrode was placed within the STN. The cells were defined as damaged if any part of
the axon, soma or substantial portion of the dendritic tree intersected with the electrode. We
started with 100 STN neurons, 80 GPi fibers and 70 CST fibers, removed all the damaged cells
and then randomly removed additional cells so that the final count for each of the three
populations was 50 cells.
We built multi-compartment cable models of STN projection neurons, GPi fibers of
passage and CST fibers of passage using NEURON v5.8 (Hines and Carnevale 1997). The STN
soma, initial segment and dendrites contained channel dynamics of the rat subthalamic projection
neuron originally developed by Gillies and Willshaw (2006), where dendritic channel
conductance densities scale linearly with distance from the soma (Fig. 2C). To better fit the
membrane dynamics to our neuron geometry and the firing pattern of STN neurons in a
parkinsonian monkey, we modified the original conductances in the following way: the calcium-
activated potassium channel was increased by 80%, the fast potassium rectifier was increased by
20% (soma and initial segment only), and the fast acting sodium channel was decreased by 25%
in the soma and initial segment, and by 35% in the dendrites. The model temperature was set to
36°C to simulate in-vivo conditions. The STN neuron model had a resting potential of
Page 8 of 42
approximately -54 mV and spontaneous tonic firing of 32 Hz consistent with the rate of
36.5±10.8 Hz recorded by Wichmann et al (2002) in the parkinsonian macaque. The neuron
firing rate increased with increasing amplitude of intracellular depolarizing current. The slope of
the model frequency-intensity (f-I) curve was 0.26 Hz/pA, lower than slopes recorded in rat brain
slices (~0.54 Hz/pA in Bevan and Wilson 1999). Despite this discrepancy the model neuron was
able to fire at more than 200 Hz, well above the range of clinically used DBS frequencies (100-
185 Hz). The input resistance measured as the slope of the I-V curve while neuron was in a
hyperpolarized state (-59.6 mV resting potential) and injected with small currents (-0.2 nA to
0.05 nA) was 35 MΩ, twice the value of resistances recorded in rat STN neurons in-vivo (mean
18 MΩ, range 9-28 MΩ in Kita et al. 1983). This discrepancy could be due to a lack of synaptic
inputs into the dendritic tree, differences between monkey and rat STN neuron morphology, or
imperfect measurement of dendritic diameters during histological 3D reconstruction resulting in
underestimate of the surface area. Average rat in-vitro measurements range from 146-200 MΩ
(Nakanishi et al. 1987; Beurrier et al. 1999) placing the model neuron within the range of in-vivo
and in-vitro recordings. The membrane time constant estimated from the 1/e point of the
membrane potential change induced by a low intensity hyperpolarizing current pulse was 6 ms
consistent with in-vivo rat measurements (6±2 ms in Kita et al. 1983).
The axon of the STN neuron as well as the GPi and CST axons were based on the
myelinated axon model originally described by McIntyre et al. (2002). The fiber diameter was
set to 2 µm and the individual segment dimensions and ion channel conductances were adjusted
as previously described (McIntyre et al. 2004a). The axonal resting potential was set to -65 mV.
The GPi axon was induced to fire tonically at 80 Hz by injecting short current pulses at the GPi
terminal end of the axon to mimic parkinsonian macaque GPi firing rate of 80.1±20.2 Hz
Page 9 of 42
(Wichmann et al. 2002). There were no synaptic connections or interactions between any of the
different neurons in the model system.
In some simulations trans-synaptic GABAa input to the STN neurons was added to
evaluate the influence of stimulation-induced trans-synaptic conductances on somatic activity
during high frequency stimulation. This was simplistically modeled as an inhibitory postsynaptic
current applied only to the central compartment of the cell body. The time course and amplitude
of the GABAa synaptic conductance was modeled with experimentally defined first-order
kinetics of the transmitter binding to postsynaptic receptors (Destexhe et al. 1994a,b). Since
afferent inputs (i.e. axonal terminals) are typically more excitable than the passing axons
(McIntyre et al. 2004a; Anderson et al. 2006), in all applicable simulations we assumed that
synaptic inhibition was always activated by each extracellular stimulus pulse, regardless of the
cell position with respect to the electrode.
Electric Field Model
An axisymmetric finite element model (FEM) of the DBS electrode was created in
FEMLAB 3.1 (COMSOL, Inc.,Burlington, MA) to calculate potentials generated in the tissue
medium by the electrode. The stimulating lead was a scaled-down version of the chronic DBS
electrode used in humans (Model 3387, Medtronic Inc., Minneapolis, MN) and consisted of four
metal contacts each with a diameter of 0.75 mm, height of 0.50 mm, and separation between
contacts of 0.50 mm. The volume conductor was 5 cm x 5 cm in size and grounded at the
boundaries. The bulk conductivity of the tissue medium was 0.2 S/m (Ranck 1963), and a 0.25
mm encapsulation sheath (0.1 S/m) surrounded the electrode shaft (Grill and Mortimer 1994;
Butson et al. 2006). The electrode shaft was modeled as an insulator (1e-6 S/m) and each
Page 10 of 42
electrode contact as a conductor (1e6 S/m). Voltage sources were specified at two electrode
contacts for bipolar stimulation. The model had variable resolution mesh with a total of 32,640
mesh elements that increased in size with increasing distance from the electrode. Voltage values
within the volume were determined from the Poisson equation, which was solved using direct
matrix inversion (UMFPACK solver) (Fig. 4).
The stimulus waveform produced by the Itrell II (Medtronic Inc., Minneapolis, MN)
implantable pulse generator (IPG), as used in human DBS and the monkey experiments, is a
voltage-controlled biphasic asymmetric square pulse. However, the actual stimulus delivered to
the brain tissue is modified by the electrode capacitance. To account for the effects of electrode
capacitance, a Fourier finite element model (FEM) was utilized as described in Butson and
McIntyre (2005). The four general steps of this method are briefly described here. First, the IPG
stimulus waveform was constructed in the time domain. It was then converted to frequency
domain using discrete Fourier transform (DFT) in Matlab (Mathworks, Natick, MA). Third, the
FEM model was solved at each component frequency of the DFT (1024 frequencies between 0
and 50kHz). The result at each frequency was scaled and phase shifted using the DFT
magnitudes and phases. Finally, an inverse Fourier transform was performed to obtain the
stimulus waveform in the time domain. The electrode was modeled as purely capacitive (0.65
µF), and adjusted to account for the smaller surface area of monkey electrode contacts compared
to the human DBS electrode (Butson and McIntyre 2005).
We coupled the finite element electric field model with the multi-compartment neuron
models to enable quantitative stimulation predictions in the context of the 3D neuroanatomy.
Page 11 of 42
This coupling was accomplished by applying extracellular voltages from the electric field model
to each compartment of each neuron model and simulating the biophysical response (action
potential signaling) of each neuron over time (McIntyre et al. 2004a) (Figs. 4, 5). The magnitude
of the applied extracellular voltage was dependent on the stimulus amplitude, stimulus
waveform, and the compartment’s distance from the electrode. At each time step of the
simulation the extracellular voltage at each neural compartment was updated to a value
determined by the time-dependent stimulus train delivered to the tissue medium (Figs. 4, 5).
Clinical efficacy for various DBS parameter settings was established in two parkinsonian
macaques using behavioral tests (for details see Hashimoto et al. 2003). In both monkeys, R7160
and R370, the electrode was positioned in the posterior STN at a 20 degree angle in the sagittal
plane. In monkey R7160, bipolar stimulation (contact 0 cathode, contact 2 anode) at 136 Hz and
210 µs pulse width produced consistent improvement in rigidity and bradykinesia at 1.8 V
amplitude (clinically effective stimulation), but not at 1.4 V (clinically ineffective stimulation).
In monkey R370 bipolar stimulation (contact 2 cathode, contact 0 anode) at 136 Hz and 90 µs
pulse width was clinically effective at 3 V amplitude and clinically ineffective at 2 V. Thus,
these various stimulation parameters were applied to our model system. The fundamental
differences between the model simulations for the two monkeys were the electrode position and
stimulation parameters. In addition, the two electrode positions resulted in somewhat different
neural populations because different neurons were ‘destroyed’ by the electrodes. The model
neurons were stimulated with a train of 25 pulses. Longer train durations (1 second; 136 pulses)
did not impact neural response to stimulation (Fig. 5B).
We did not observe any model neurons that exhibited a blocking of axonal firing from the
direct application of the DBS electric field; therefore, we concentrated our analysis on the
Page 12 of 42
excitatory response of the stimulation (Fig. 5A). Those that produced orthodromically
propagating action potentials in response to more than 80% of the stimulus pulses were
considered to be activated. This percentage was chosen because most neurons responded either
to none or to 20 or more of the 25 stimulus pulses. Some activated neurons did not respond to all
25 pulses because in certain cases the stimulus pulse was delivered immediately following a
spontaneous action potential (originating in the soma), while the axon was still in the refractory
state. Percentages of activated neurons were averaged over three randomized populations.
Student’s t-test (one-tailed; p<0.05) were performed to compare the averages for clinically
ineffective and effective stimulation conditions.
Experimental Recordings of GPi Activity During STN DBS
The experimental recording procedure and data collection from the two monkeys used in
this study has been described in detail elsewhere (Hashimoto et al., 2002; 2003; Elder et al.,
2005). Briefly, single unit neural activity was recorded extracellularly from GPi identified
neurons using glass-coated platinum–iridium microelectrodes (impedances of 0.4–0.8 MΩ at 2
kHz). Recording penetrations were made in parasagittal planes moving rostral to caudal at an
angle of 70° to the orbitomeatal line. Neurons (n = 12 for monkey R7160 and n=27 for monkey
R370) were recoded for 25-35 sec during clinically effective STN DBS at 136 Hz. Stimulus
artifact template subtraction methods (Hashimoto et al., 2002) and in-house software developed
in MATLAB v7.0 (Mathworks Inc., Natick, MA) were used for the neural signal analysis.
Peristimulus time histograms were constructed with a 0.2 ms bin size. The histograms used
336,373 spikes resulting from 644,929 stimuli in 39 GPi cells. Stimuli for which no spike was
recorded in the interstimulus interval were not considered. Probability distributions for R7160
Page 13 of 42
and R370 were compared using a χ2 goodness of fit test (p<0.05). Cells were further classified
as having an early response if more than 15% of the stimulus responses occurred at a latency of
less than 1.5 ms. Significance of differences in the proportion of early response cells was found
by χ2 test (p<0.05).
We calculated levels of axonal activation for populations of STN projection neuron, GPi
fibers of passage (lenticular fasciculus) and CST fibers of passage during clinically effective and
ineffective STN DBS in two parkinsonian macaques. We evaluated four general aspects of our
model system. First, activation of CST fibers at muscle contraction thresholds were analyzed to
provide a gross degree of model validation. Second, we evaluated the neural response to
therapeutic stimulation using three separate randomized populations of STN neurons and GPi
fibers, and we correlated the model predictions to single-unit microelectrode recordings from the
two monkeys during STN DBS. Third, the sensitivity of neural activation to electrode position
was assessed by moving the electrode by 0.25 mm in four directions in the horizontal plane. And
finally, the effects of DBS on STN somatic firing were evaluated in a model with stimulation-
induced inhibitory trans-synaptic conductances.
Activation of the Internal Capsule with DBS
To address the experimental predictability of our model system we evaluated the
activation of CST fibers. Visually determined muscle contraction thresholds in the two monkeys,
were 3V for R7160 and 3.5V for R370. These stimulation parameters resulted in activation of
11±1% and 9±3% of CST fibers in our R7160 and R370 models, respectively, averaged over
Page 14 of 42
three random populations (mean±SD). In addition, clinically effective stimulation parameters
resulted in minimal activation of CST fibers, 0% and 5±2% in the R7160 and R370 models,
respectively. These results indicate that the model exhibited appreciable increases in CST fiber
activation when comparing stimuli that experimentally resulted in no visible muscle contraction
(clinically effective) and stimuli where muscle contractions were observed (CST threshold).
This provides some evidence that the voltage spread in the tissue around the electrode was
accurately predicted by the finite element model, and that the neuron models fire at realistic
Activation of the STN and LF with STN DBS
In the context of STN DBS, both STN projection neurons and GPi fibers of passage in
the LF represent viable candidates as the therapeutic target of the stimulation. The GPi is an
output nucleus of the basal ganglia and the STN modulates basal ganglia output through
excitatory projections into the GPi. We defined DBS induced activation of STN projection
neurons as the generation of an action potential anywhere in the neuron, resulting from the
applied electric field, which propagated to the axonal terminal. When stimulating STN projection
neurons with DBS, action potential initiation always took place in the myelinated axon, and if an
action potential was induced by the stimulation it always propagated to the axon terminal (Fig.
Our simulations predict activation of both GPi fibers and STN neurons during STN DBS
(Fig. 6). These results were consistent over three randomized populations of neurons.
Stimulation parameters that failed to improve parkinsonian symptoms in monkey R7160 (1.4 V,
210 µs, 136 Hz), activated 29±2% of STN projection neurons, 9±4% of GPi fibers of passage
Page 15 of 42
and no CST fibers. Clinically effective stimulation (1.8 V) activated 37±4% of STN projection
neurons (30% average increase), 18±6% of GPi fibers of passage (107% increase) and no CST
fibers (Fig. 6A). In the second monkey, R370, clinically ineffective (2 V, 90 µs, 136 Hz) and
clinically effective stimulation (3 V, 90 µs, 136 Hz) activated 31±3% and 49±5% of STN
neurons (54% increase), 66±2% and 82±6% of GPi fibers (24% increase) and 1±1% and 5±2%
of CST fibers, respectively (Fig. 6B). The increase in activation between clinically ineffective
and effective stimulation was statistically significant for STN neurons in both monkeys and for
GPi and CST fibers in monkey R370 (p<0.05) (Fig. 6).
The therapeutic effects of DBS depend critically on the stimulation frequency, with
frequencies over 100 Hz being generally beneficial and those bellow 50 Hz sometimes
worsening the symptoms (Rizzone et al. 2001). However, the output of our model system was
not substantially affected by the stimulation frequency (i.e. approximately the same numbers of
neurons were activated by the stimulus train). We did observe small decreases in the number of
neurons activated as the frequency decreased from 136 Hz to 2 Hz. For example, monkey R7160
exhibited an 8% reduction in STN neurons and a 2% reduction in GPi fibers activated when
comparing 136 Hz and 2 Hz stimulation. More importantly, all neurons activated by a given
stimulus pulse fired in synchrony, and as the stimulation frequency changed the activated
neurons were entrained to fire at the given stimulation frequency. These results suggest that
changes in stimulation frequency do not dramatically affect the volume of tissue activated, but
rather that stimulation frequency exerts its influence on the basal ganglia network functioning
(Montgomery and Baker 2000; Rubin and Terman 2004; Grill et al. 2004), which was not
addressed in this model.
Page 16 of 42
Given the limited quantitative characterization of STN neuron membrane dynamics, we
performed a range of sensitivity analyses to addresses the robustness of our DBS model
predictions. Before finalizing the variant of the Gillies and Willshaw (2006) membrane dynamics
used in this study we evaluated a wide range of models of STN neuron membrane dynamics
(Wilson et al. 2004; Otsuka et al. 2004). The model used in this study best matched the available
in vitro experimental characterization of STN neuron firing. However, nearly every STN neuron
model variant that we evaluated generated nearly identical neural activation results in response to
STN DBS. This consistency in the model output was primarily linked to the fact that action
potential initiation in response to extracellular stimulation takes place in the myelinated axon
(Nowak and Bullier, 1998; McIntyre and Grill, 1999; McIntyre et al., 2004a). In turn, the
description of the somatic and dendritic membrane dynamics had little effect on the axonal
output generated by DBS. For example, we modified the m (activation) gate of the spike
triggering fast sodium channel in the dendrites, soma and initial segment. Large changes in the
time constant (± 50%) and small shifts in the steady-state activation voltage (± 1mV) had no
effect on the number of STN neurons activated by DBS (data not shown). However, it should be
noted that modifications of the steady-state activation voltage strongly affected the spontaneous
firing frequency of the STN neurons.
Experimentally Recorded Short-Latency GPi Activity During STN DBS
The response of GPi neurons to STN DBS was monitored experimentally by single-unit
microelectrode recordings. Utilizing stimulus artifact template subtraction we were able to
construct peristimulus time histograms and analyze activity of GPi neurons immediately
following each DBS pulse (Fig. 7) (Hashimoto et al., 2002; 2003). Experimentally the activation
Page 17 of 42
of GPi fibers resulted in short latency excitation of GPi cell bodies which could be interpreted as
antidromic activation (Bar-Gad et al., 2004). Using the 3D anatomical brain atlas and GPi axon
model we estimated the propagation latency from DBS activation of the LF to antidromic
activation of the GPi to be < 1.5 ms. We calculated the number of experimentally recorded GPi
cells that exhibited short-latency excitation for 15% or more of the DBS pulses, indicative of
reliable antidromic activation (see Discussion). In monkey R370 41% of GPi cells fired within
1.5 ms following a DBS pulse whereas only 8% of GPi cells fired within the same time period in
monkey R7160 (Fig. 7). Consistent with these experimental results, our model predicted a large
degree of GPi fiber activation for monkey R370 (82%), but a much smaller activation for
monkey R7160 (18%).
Sensitivity of Neural Activation to Electrode Position
Electrode location can have a substantial impact on the therapeutic efficacy and side
effects of DBS; however, conflicting opinions exist on the optimal position of STN DBS
electrodes (Voges et al. 2002; Saint-Cyr et al. 2002; Starr et al. 2002; Hamel et al. 2003; Yelnik
et al. 2003; Zonenshayn et al. 2004; Nowinski et al. 2005). Therefore, we examined how small
modifications in electrode position affected our simulation results. We compared neural
activation for the original electrode position in monkey R7160 to models with the electrode
moved either 0.25 mm medial, lateral, anterior or posterior to the original position (Fig. 8). For
clinically ineffective stimulation, the range of activation was 6-16% for GPi fibers, 0-2% for
CST fibers and 12-30% for STN neurons. For clinically effective stimulation, activation
extended between 6-28% for GPi fibers, between 0-10% for CST fibers and between 18-42% for
Page 18 of 42
STN neurons. As a result, electrode location within the STN region can substantially affect
axonal activation of both STN neurons and GPi fibers.
STN Neuron Somatic Firing During STN DBS
In the simulations described above, STN neuron activation was assessed at the distal end
of the axon. Since experimental extracellular STN microelectrode recordings register primarily
somatic firing rather than the axonal output, we also evaluated firing frequency in the STN soma
during STN DBS (Figs. 5 and 9). During extracellular stimulation, action potential initiation
occurs in the axon, and as a result the soma and axon can fire independently (Nowak and Bullier
1998; McIntyre and Grill 1999; McIntyre et al. 2004a). In addition, extracellular stimulation can
activate synaptic inputs impinging on local projection neurons (Baldissera et al. 1972;
Gustafsson and Jankowska 1976; Dostrovsky et al. 2000; Welter et al. 2004; Filali et al. 2004;
Meissner et al. 2005; Anderson et al. 2006). Synaptic inputs to the STN come primarily from the
external part of globus pallidus (GPe) and cerebral cortex. Dominant GPe inhibitory afferents
converge primarily on the cell body and proximal dendrites (Smith et al. 1990), which we
modeled as GABAa trans-synaptic conductance in the central compartment of the soma. The
presence of stimulation induced GABAa synaptic input inhibited somatic firing and thus reduced
the frequency of spontaneous spikes (Figs. 9A and 5B). The magnitude of somatic firing
suppression induced by stimulated GABAa input depended on the neuron position relative to the
electrode and the strength of the inhibitory synaptic conductance. In the absence of extracellular
stimulation or inhibitory synaptic input, the output of the STN neuron was dictated by the rate of
spontaneous somatic spiking (32 Hz). If the STN neuron was too far from the electrode to be
directly activated by the extracellular stimulus, but high frequency inhibitory synaptic inputs
Page 19 of 42
were applied, the spontaneous somatic firing could be entirely suppressed, resulting in no axonal
output. However, if the neuron was close enough to the electrode to be directly activated by the
extracellular stimulus, axonal firing (i.e. neural output) was almost completely dictated by the
stimulation frequency and largely unaffected by inhibitory synaptic currents applied to the soma
(Fig. 9B). Interestingly the inhibition of spontaneous somatic firing also increased the reliability
of stimulus-evoked action potential generation in the axon from extracellular stimulation because
of the elimination of the possible interaction between spontaneous spikes and stimulation
The neural response to STN DBS that is responsible for the therapeutic effects of the
stimulation has been unclear. This limitation in scientific knowledge has hindered our ability to
understand and optimize this medical technology for current and future applications. Using an
anatomically and electrically detailed computer model, we evaluated the neural activation
generated by clinically effective and ineffective DBS in two parkinsonian macaques. Our
simulation results showed axonal activation of both STN projection neurons and GPi fibers of
passage during STN DBS. The relative proportion of neural types activated depended strongly on
the position of the cathodic contact in the STN region. In monkey R7160, the cathode was in the
ventral portion of the STN, which resulted in limited GPi fiber activation (~10-20%). In monkey
R370, the cathode was in the dorsal STN, on the border with LF, resulting in greater GPi fiber
recruitment (~65%) which also significantly increased during therapeutic stimulation conditions
(~80%). These theoretical predictions were supported by the large number of GPi neurons
experimentally recorded with short latency excitation in monkey R370, indicative of LF
Page 20 of 42
antidromic activation, not seen in monkey R7160. Both monkeys had similar levels of STN
neuron activation which showed significant increases with clinically effective stimulation (from
~30% to 40-50%). These results indicate that activation of approximately half of the STN is
sufficient for the behavioral manifestation of the therapeutic effects of STN DBS. The additional
recruitment of GPi fibers may also play an important role in therapeutic outcome, but large-scale
activation of GPi fibers may not be necessary.
Model Development, Analysis, and Limitations
The computer model developed in this study utilized a number of anatomical,
physiological, and electrical improvements over previous attempts to theoretically address the
cellular effects of DBS (McIntyre et al. 2004a). However, when constructing such a
comprehensive model it is necessary to make a number of assumptions and simplifications. The
following section attempts to document these limitations and provide insight into their possible
impact on our results.
We dedicated substantial effort toward the development of accurate neuron models for
our simulations; however, they were unable to capture the full spectrum of experimentally
defined characteristics. We utilized neuron biophysical properties based on rat STN neurons
(Gillies and Willshaw 2006). Rat STN neurons have been extensively characterized with in-vitro
preparations, making it possible to construct faithful model representations with Hodgkin-
Huxley type channel dynamics (Terman et al. 2002; Wilson et al. 2004; Otsuka et al. 2004;
Gillies and Willshaw 2006). In addition, we attempted to approximate in vivo conditions by
adjusting both the STN projection neurons and GPi fibers of passage to match the spontaneous
activity observed in parkinsonian macaques (Wichmann et al. 2002). Nonetheless, our STN DBS
Page 21 of 42
model exhibited a simple on/off activation outcome (Fig. 5B), whereas the therapeutic effects of
DBS typically evolve over seconds, minutes, and even hours of stimulation (Temperli et al.
2003). This discrepancy in the model predictions and clinical observations may be related to our
inability to fully characterize the STN neuron membrane dynamics. Recent in vitro experimental
studies have shown slow inactivation of sodium channels may play an important role in STN
somatic activity during high frequency stimulation (Beurrier et al. 2001; Do and Bean 2003). In
addition, there exists a long list of factors influencing neural plasticity that could be affected by
DBS (e.g. gene expression changes, channel density changes, synaptic strength changes, etc.)
that were not included in our model system. However, our previous experience suggests that the
specific details of the somatic ion channel membrane dynamics are of limited importance in the
global effects predicted by computer models of extracellular stimulation (see sensitivity analyses
in McIntyre and Grill 1999, 2000; McIntyre et al. 2004a). Possibly of greater importance is the
use of realistic neural morphologies which directly interact with the electric field and determine
polarization profiles of each neuron (Figs. 4B and 5A). Although our model included a full 3D
reconstruction of a macaque STN projection neuron (Sato et al. 2000), as well as GPi and CST
axonal trajectories based on documented morphologies (Parent et al. 2001), all neurons in each
population were copies of a generic geometry. To compensate for this limitation we added
diversity in our STN projection neurons by creating two additional axonal trajectories based on
histological axonal tracing experiments (Sato et al. 2000).
The results suggest that electrode position with respect to the surrounding anatomy is an
important factor in the stimulation outcome (Figs. 6 and 8). Using histological brain slices we
developed a 3D basal ganglia reconstruction for one of the monkeys (R7160) and were able to
precisely recreate the electrode position in the tissue. Unfortunately, we did not have access to
Page 22 of 42
R370’s brain, so the same anatomical model was used for both monkeys. However, the
neuroanatomical differences are likely to be small since the two monkeys were both rhesus
macaques of similar size and the same sex. Further, the electrode for R370 was positioned using
the descriptions and drawings from Hashimoto et al. (2003). It should also be noted that the
three-dimensionally complex tissue anisotropy around the STN can affect the neural response to
DBS (McIntyre et al. 2004b). However, in this study we were unable to obtain diffusion tensor
imaging data to estimate the 3D tissue conductivity properties specific to these animals so an
isotropic conductivity was used for the bulk tissue medium. To address some of these issues, a
gross level model validation was attempted by comparing the experimentally defined CST
thresholds in the two monkeys with the corresponding model predictions (Fig. 6). The models
showed minimal CST activation during clinically effective stimulation (~0-5% fibers activated),
but more substantial activation at experimentally determined CST thresholds (~10%). The exact
percentage of CST fibers needed to be activated to generate a noticeable muscle contraction is
unknown. However, we believe that the increase in CST activation demonstrated by the model is
substantial enough to justify the assumption that it would correspond to a perceptible effect
experimentally. This is particularly true for monkey R7160 whose CST activation went from
zero to 11±1%, and whose brain we explicitly reconstructed in 3D and were able to determine
electrode location with a high degree of certainty.
We evaluated somatic firing during high frequency stimulation (HFS) by including a
highly simplistic representation of stimulation-induced trans-synaptic inhibition. STN
microelectrode recording studies in monkey (Meissner et al. 2005) and human (Welter et al.
2004; Filali et al. 2004) have shown that STN HFS reduces the somatic firing rate of STN
neurons, which our results support (Fig. 9A). By selecting the appropriate synaptic conductance
Page 23 of 42
value, our model is in close agreement with Meissner et al. (2005) who documented an ~50%
decrease in somatic firing frequency with STN HFS. Our simulations also show that axonal
output is largely unaffected by the inhibition of somatic firing (Fig. 9B). This disconnect
between axonal and somatic firing has previously been noted in a model of DBS of a
thalamocortical relay neuron (McIntyre et al. 2004a) and we now demonstrate it for a population
of STN projection neurons.
It should also be noted that the STN is surrounded by many other fiber tracts that we
ignored in this study. The reciprocal STN-GPe projections, STN-substantia nigra pars reticulata
connections, and nigrostriatal fibers may also be directly affected by STN DBS (Lee et al.,
2006). While it is possible that these tracts play a role in therapeutic mechanisms of DBS, we
limited the present study to evaluate the two most probable candidates (based on location of
optimal therapeutic contacts in human patients), namely STN projection neurons and
pallidothalamic (GPi) fibers. We also ignored possible physiologic effects that could result from
antidromic activation of afferent inputs projecting to the STN (mostly from GPe and cerebral
cortex), as well as the potential role of glial cells in the regulation of the extracellular
environment (e.g. extracellular ionic concentrations and neurotransmitter levels). In turn, our
model had a number of limitations, but we do not believe they impact our fundamental
conclusions. In addition, the model system used in this study represents one of the most
anatomically and electrically accurate computer models of DBS ever created and never before
has such explicit connection between modeling and experimental DBS results been attempted.
Page 24 of 42
Neural Target of STN DBS
In current clinical practice, the STN is the target of choice for DBS treatment of PD, even
though GPi DBS is similarly effective (Burchiel et al. 1999; Obeso et al. 2001; Rodriguez-Oroz
et al. 2005). Several studies have shown that clinically effective STN DBS electrode contacts are
located in the dorsal STN, or the white matter above the STN formed by the LF and zona incerta
(Voges et al. 2002; Saint-Cyr et al. 2002; Starr et al. 2002; Hamel et al. 2003; Yelnik et al. 2003;
Zonenshayn et al. 2004; Nowinski et al. 2005). This has introduced the hypothesis that activation
of either STN projection neurons and/or pallidothalamic fibers could be responsible for the
therapeutic effects of STN DBS.
The results of this study suggest that both STN neurons and GPi fibers are activated
during clinically effective STN DBS. Recent histological tracing studies have shown that the LF
is composed of axons from the entire GPi (Parent et al. 2001; Parent and Parent 2004), and not
just its medial (non-motor) part as previously believed (Kuo et al. 1973; Kim et al. 1976).
Consequently, stimulating LF through contacts located in the dorsal STN would mechanistically
be equivalent to direct GPi stimulation, at least when considering GPi output to thalamus. Since
the GPi fibers of the LF are running in a tightly packed bundle they can be effectively stimulated
with lower charge injection than with electrodes in the GPi where the neurons are widely
dispersed. However, our simulations show that for monkey R7160, where the cathode was
located in the ventral STN, relatively few GPi fibers were activated, yet the stimulation was
clinically effective. Overall our results suggest that large-scale activation of GPi fibers may not
be necessary for therapeutic benefit, and a recent clinical study found that stimulating in the
white matter above the STN was less effective than stimulation of the dorsolateral STN border
(Herzog et al. 2004).
Page 25 of 42
The large difference in model predicted GPi fiber activation in the two monkeys was
supported by experimental single unit recordings performed during STN DBS (Hashimoto et al.,
2003). The proportion of GPi cells exhibiting short latency, presumably antidromic activation,
during STN DBS was similar to our theoretical predictions in both monkeys (R7160 - 18% vs
R370 - 82% in the model; R7160 - 8% vs R370 - 41% in the experiments). The discrepancies
observed between the model and experimental results could be related to a number of factors.
Our analysis of the experimental data required that at least 15% of the DBS pulses must exhibit
short latency activation in the GPi cell to consider it antidromically activated. This somewhat
arbitrary threshold was necessary to perform our analysis for a number of reasons. First, no cell
will respond in exactly the same manner for all stimulus pulses because of stochastic biological
variability in the neural response and inherent problems with extracellular microelectrode
recordings during DBS (artifact subtraction, signal fluctuations, unit isolation, spike sorting, etc).
Second, GPi neurons fire at relatively high frequencies with a mix of antidromic and
orthodromic action potentials during STN DBS (Hashimoto et al. 2003). Orthodromic activity in
the GPi cells will collide with antidromic signals generated by stimulation of the LF. Therefore,
antidromic excitation will not be recorded for every action potential evoked by the stimulation.
In addition, our experimental sample of GPi cells was limited and approximately half of the GPi
exit fibers would be expected to travel via the ansa lenticularis, a fiber tract rostral to the LF and
outside the reach of the DBS voltage spread. Nonetheless, the experimental results support our
model conclusions that therapeutic STN DBS generated significantly different activation of GPi
fibers in the two monkeys.
The importance of activation of either STN projection neurons or GPi fibers of passage
on the therapeutic outcome of STN DBS may be fundamentally dictated by the precise electrode
Page 26 of 42
location within the STN region. This concept is supported by our simulations where small
changes (±0.25 mm) in electrode position could strongly affect GPi fiber and STN neuron
activation (Fig. 8). Clinical studies that found LF/ZI stimulation to be effective could have
placed DBS electrodes in locations more suitable for maximal GPi fiber activation. However,
post-operative electrode localization techniques used in human studies are not accurate to a sub-
millimeter level making it difficult to determine electrode position with high certainty.
Furthermore, it is possible that more than one ‘optimal’ contact and set of stimulation parameters
exists, but due to time-consuming programming methods currently used it is not possible to
definitively address every clinically beneficial setting. The results of this study lay the
foundation for a prospective and coupled, theoretical and experimental, analysis of STN DBS
where electrode location and stimulation parameter selection can be systematically manipulated
to address the impact of STN neuron and/or GPi fiber activation on therapeutic outcome. Such
future developments in DBS research will enable a more complete understanding of the optimal
electrode location and give better insight into the neural targets of STN DBS.
This project was financially supported by the NIH (T32 GM07250, R01 NS-47388, R01 NS-
37019), IRSC (MOP-5781) and the Ohio BRTT/WCI Partnership. The authors thank Dr. Taka
Hashimoto for his experimental contributions, Dr. Andre Parent for providing the stained STN
neuron histology, Dr. Douglas Bowden for help with the warping of atlas templates in Edgewarp,
and Dr. Andrew Gilles for assistance with the STN neuron membrane dynamics.
Page 27 of 42
Anderson TR, Hu B, Iremonger K, Kiss ZH. Selective attenuation of afferent synaptic
transmission as a mechanism of thalamic deep brain stimulation-induced tremor arrest. J
Neurosci. 26(3):841-50, 2006.
Baldissera F, Lundberg A, Udo M. Stimulation of pre- and postsynaptic elements in the red
nucleus. Exp Brain Res. 15(2):151-67, 1972.
Bar-Gad I, Elias S, Vaadia E, Bergman H. Complex locking rather than complete cessation of
neuronal activity in the globus pallidus of a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-
treated primate in response to pallidal microstimulation. J Neurosci. 24(33):7410-9, 2004.
Beurrier C, Bioulac B, Audin J, Hammond C. High-frequency stimulation produces a transient
blockade of voltage-gated currents in subthalamic neurons. J Neurophysiol. 85(4):1351-6, 2001.
Beurrier C, Congar P, Bioulac B, Hammond C. Subthalamic nucleus neurons switch from single-
spike activity to burst-firing mode. J Neurosci. 19(2):599-609, 1999.
Bevan MD, Wilson CJ. Mechanisms underlying spontaneous oscillation and rhythmic firing in
rat subthalamic neurons. J Neurosci. 19(17):7617-28, 1999.
Bookstein FL. Morphometrics. In: Three-dimensional Neuroimaging, edited by Toga AW.
Raven Press, New York, 1990.
Burchiel KJ, Anderson VC, Favre J, Hammerstad JP. Comparison of pallidal and subthalamic
nucleus deep brain stimulation for advanced Parkinson's disease: results of a randomized,
blinded pilot study. Neurosurgery 45(6):1375-82, 1999.
Butson CR, Maks CB, McIntyre CC. Sources and effects of electrode impedance during deep
brain stimulation. Clin Neurophysiol. 117(2):447-54, 2006.
Butson CR, McIntyre CC. Tissue and electrode capacitance reduce neural activation volumes
during deep brain stimulation. Clin Neurophysiol. 116(10):2490-500, 2005.
Destexhe A, Mainen ZF, and Sejnowski TJ. An efficient method for computing synaptic
conductances based on a kinetic model of receptor binding. Neural Comput 6: 14–18, 1994a.
Destexhe A, Mainen ZF, and Sejnowski TJ. Synthesis of models for excitable membranes,
synaptic transmission and neuromodulation using a common kinetic formalism. J Comput
Neurosci 1: 195–230, 1994b.
Do MT, Bean BP. Subthreshold sodium currents and pacemaking of subthalamic neurons:
modulation by slow inactivation. Neuron. 39(1):109-20, 2003.
Page 28 of 42
Dostrovsky JO, Levy R, Wu JP, Hutchison WD, Tasker RR, Lozano AM. Microstimulation-
induced inhibition of neuronal firing in human globus pallidus. J Neurophysiol. 84(1):570-4,
Elder CM, Hashimoto T, Zhang J, Vitek JL. Chronic implantation of deep brain stimulation leads
in animal models of neurological disorders. J Neurosci Methods. 142(1):11-6, 2005.
Filali M, Hutchison WD, Palter VN, Lozano AM, Dostrovsky JO. Stimulation-induced inhibition
of neuronal firing in human subthalamic nucleus. Exp Brain Res. 156(3):274-81, 2004.
Gillies A, Willshaw D. Membrane channel interactions underlying rat subthalamic projection
neuron rhythmic and bursting activity. J Neurophysiol. 95(4):2352-65, 2006.
Grill WM, Mortimer JT. Electrical properties of implant encapsulation tissue. Ann Biomed Eng.
Grill WM, Snyder AN, Miocinovic S. Deep brain stimulation creates an informational lesion of
the stimulated nucleus. Neuroreport. 15(7):1137-40, 2004.
Gustafsson B, Jankowska E. Direct and indirect activation of nerve cells by electrical pulses
applied extracellularly. J Physiol. 1976 Jun;258(1):33-61, 1976.
Hamel W, Fietzek U, Morsnowski A, Schrader B, Herzog J, Weinert D, Pfister G, Muller D,
Volkmann J, Deuschl G, Mehdorn HM. Deep brain stimulation of the subthalamic nucleus in
Parkinson's disease: evaluation of active electrode contacts. J Neurol Neurosurg Psychiatry.
Hashimoto T, Elder CM, Okun MS, Patrick SK, Vitek JL. Stimulation of the subthalamic
nucleus changes the firing pattern of pallidal neurons. J Neurosci. 23(5):1916-23, 2003.
Hashimoto T, Elder CM, Vitek JL. A template subtraction method for stimulus artifact removal
in high-frequency deep brain stimulation. J Neurosci Methods. 113(2):181-6, 2002.
Herzog J, Fietzek U, Hamel W, Morsnowski A, Steigerwald F, Schrader B, Weinert D, Pfister G,
Muller D, Mehdorn HM, Deuschl G, Volkmann J. Most effective stimulation site in subthalamic
deep brain stimulation for Parkinson's disease. Mov Disord. 19(9):1050-4, 2004.
Hines ML, Carnevale NT. The NEURON simulation environment. Neural Comput. 9(6):1179-
Kim R, Nakano K, Jayaraman A, Carpenter MB. Projections of the globus pallidus and adjacent
structures: an autoradiographic study in the monkey. J Comp Neurol. 169(3):263-90, 1976.
Kita H, Chang HT, Kitai ST. Pallidal inputs to subthalamus: intracellular analysis. Brain Res.
Page 29 of 42
Krack P, Batir A, Van Blercom N, Chabardes S, Fraix V, Ardouin C, Koudsie A, Limousin PD,
Benazzouz A, LeBas JF, Benabid AL, Pollak P. Five-year follow-up of bilateral stimulation of
the subthalamic nucleus in advanced Parkinson's disease. N Engl J Med. 349(20):1925-34, 2003.
Kuo JS, Carpenter MB. Organization of pallidothalamic projections in the rhesus monkey. J
Comp Neurol. 151(3):201-36, 1973.
Lee KH, Blaha CD, Harris BT, Cooper S, Hitti FL, Leiter JC, Roberts DW, Kim U. Dopamine
efflux in the rat striatum evoked by electrical stimulation of the subthalamic nucleus: potential
mechanism of action in Parkinson's disease. Eur J Neurosci. 23(4):1005-14, 2006.
Limousin P, Krack P, Pollak P, Benazzouz A, Ardouin C, Hoffmann D, Benabid AL. Electrical
stimulation of the subthalamic nucleus in advanced Parkinson's disease. N Engl J Med.
Martin RF, Bowden DM. Primate Brain Maps: Structure of the Macaque Brain. Elsevier:
McIntyre CC, Grill WM. Excitation of central nervous system neurons by nonuniform electric
fields. Biophys J. 76(2):878-88, 1999.
McIntyre CC, Grill WM, Sherman DL, Thakor NV. Cellular effects of deep brain stimulation:
model-based analysis of activation and inhibition. J Neurophysiol. 91(4):1457-69, 2004a.
McIntyre CC, Mori S, Sherman DL, Thakor NV, Vitek JL. Electric field and stimulating
influence generated by deep brain stimulation of the subthalamic nucleus. Clin Neurophysiol.
McIntyre CC, Richardson AG, Grill WM. Modeling the excitability of mammalian nerve fibers:
influence of afterpotentials on the recovery cycle. J Neurophysiol. 87(2):995-1006, 2002.
Meissner W, Leblois A, Hansel D, Bioulac B, Gross CE, Benazzouz A, Boraud T. Subthalamic
high frequency stimulation resets subthalamic firing and reduces abnormal oscillations. Brain.
128(Pt 10):2372-82, 2005.
Miocinovic S, Parent M, Parent A, McIntyre CC. Electrical stimulation of the subthalamic
nucleus: Model-based analysis of a 3D reconstructed neuron to intracellular and extracellular
stimulation. Abstract Program No. 70.27. Society for Neuroscience, 2004.
Montgomery EB Jr, Baker KB. Mechanisms of deep brain stimulation and future technical
developments. Neurol Res. 22(3):259-66, 2000.
Nakanishi H, Kita H, Kitai ST. Electrical membrane properties of rat subthalamic neurons in an
in vitro slice preparation. Brain Res. 437(1):35-44, 1987.
Page 30 of 42
Nowak LG, Bullier J. Axons, but not cell bodies, are activated by electrical stimulation in
cortical gray matter. II. Evidence from selective inactivation of cell bodies and axon initial
segments. Exp Brain Res. 118(4):489-500, 1998.
Nowinski WL, Belov D, Pollak P, Benabid AL. Statistical analysis of 168 bilateral subthalamic
nucleus implantations by means of the probabilistic functional atlas. Neurosurgery. 57(4
Obeso JA, Olanow CW, Rodriguez-Oroz MC, Krack P, Kumar R, Lang AE. Deep-brain
stimulation of the subthalamic nucleus or the pars interna of the globus pallidus in Parkinson's
disease. N Engl J Med. 345:956-963, 2001.
Okun MS, Tagliati M, Pourfar M, Fernandez HH, Rodriguez RL, Alterman RL, Foote KD.
Management of referred deep brain stimulation failures: a retrospective analysis from 2
movement disorders centers. Arch Neurol. 62(8):1250-5, 2005.
Otsuka T, Abe T, Tsukagawa T, Song WJ. Conductance-based model of the voltage-dependent
generation of a plateau potential in subthalamic neurons. J Neurophysiol. 92(1):255-64, 2004.
Parent M, Lévesque M, Parent A. Two types of projection neurons in the internal pallidum of
primates: single-axon tracing and three-dimensional reconstruction. J Comp Neurol. 439(2):162-
Parent M, Parent A. The pallidofugal motor fiber system in primates. Parkinsonism Relat
Ranck JB Jr. Specific impedance of rabbit cerebral cortex. Exp Neurol. 7:144-52, 1963.
Ranck JB Jr. Which elements are excited in electrical stimulation of mammalian central nervous
system: a review. Brain Res. 98(3):417-40, 1975.
Rizzone M, Lanotte M, Bergamasco B, Tavella A, Torre E, Faccani G, Melcarne A, Lopiano L.
Deep brain stimulation of the subthalamic nucleus in Parkinson's disease: effects of variation in
stimulation parameters. J Neurol Neurosurg Psychiatry. 71(2):215-9, 2001.
Rodriguez-Oroz MC et al. Bilateral deep brain stimulation in Parkinson's disease: a multicentre
study with 4 years follow-up. Brain. 128(Pt 10):2240-9, 2005.
Rubin JE, Terman D. High frequency stimulation of the subthalamic nucleus eliminates
pathological thalamic rhythmicity in a computational model. J Comput Neurosci. 16(3):211-35,
Saint-Cyr JA, Hoque T, Pereira LC, Dostrovsky JO, Hutchison WD, Mikulis DJ, Abosch A,
Sime E, Lang AE, Lozano AM. Localization of clinically effective stimulating electrodes in the
human subthalamic nucleus on magnetic resonance imaging. J Neurosurg. 97(5):1152-66, 2002.
Page 31 of 42
Sato F, Parent M, Lévesque M, Parent A. Axonal branching pattern of neurons of the
subthalamic nucleus in primates. J Comp Neurol. 424(1):142-52, 2000.
Smith Y, Bolam JP, Von Krosigk M. Topographical and Synaptic Organization of the GABA-
Containing Pallidosubthalamic Projection in the Rat. Eur J Neurosci. 2(6) :500-511, 1990.
Starr PA, Christine CW, Theodosopoulos PV, Lindsey N, Byrd D, Mosley A, Marks WJ Jr.
Implantation of deep brain stimulators into the subthalamic nucleus: technical approach and
magnetic resonance imaging-verified lead locations. J Neurosurg. 97(2):370-87, 2002.
Tehovnik EJ. Electrical stimulation of neural tissue to evoke behavioral responses. J Neurosci
Methods. 65(1):1-17, 1996.
Temperli P, Ghika J, Villemure JG, Burkhard PR, Bogousslavsky J, Vingerhoets FJ. How do
parkinsonian signs return after discontinuation of subthalamic DBS? Neurology. 60(1):78-81,
Terman D, Rubin JE, Yew AC, Wilson CJ. Activity patterns in a model for the
subthalamopallidal network of the basal ganglia. J Neurosci. 22(7):2963-76, 2002.
Voges J, Volkmann J, Allert N, Lehrke R, Koulousakis A, Freund HJ, Sturm V. Bilateral high-
frequency stimulation in the subthalamic nucleus for the treatment of Parkinson disease:
correlation of therapeutic effect with anatomical electrode position. J Neurosurg. 96(2):269-79,
Volkmann J, Herzog J, Kopper F, Deuschl G. Introduction to the programming of deep brain
stimulators. Mov Disord. 17 Suppl 3:S181-7, 2002.
Walter BL, Vitek JL. Surgical treatment for Parkinson's disease. Lancet Neurol. 3(12):719-28,
Welter ML, Houeto JL, Bonnet AM, Bejjani PB, Mesnage V, Dormont D, Navarro S, Cornu P,
Agid Y, Pidoux B. Effects of high-frequency stimulation on subthalamic neuronal activity in
parkinsonian patients. Arch Neurol. 61(1):89-96, 2004.
Wichmann T, Kliem MA, Soares J. Slow oscillatory discharge in the primate basal ganglia. J
Neurophysiol. 87(2):1145-8, 2002.
Wilson CJ, Weyrick A, Terman D, Hallworth NE, Bevan MD. A model of reverse spike
frequency adaptation and repetitive firing of subthalamic nucleus neurons. J Neurophysiol.
Yelnik J, Damier P, Demeret S, Gervais D, Bardinet E, Bejjani BP, Francois C, Houeto JL,
Arnule I, Dormont D, Galanaud D, Pidoux B, Cornu P, Agid Y. Localization of stimulating
electrodes in patients with Parkinson disease by using a three-dimensional atlas-magnetic
resonance imaging coregistration method. J Neurosurg. 99(1):89-99, 2003.
Page 32 of 42
Zonenshayn M, Sterio D, Kelly PJ, Rezai AR, Beric A. Location of the active contact within the
subthalamic nucleus (STN) in the treatment of idiopathic Parkinson's disease. Surg Neurol.
Page 33 of 42
Figure 1. Three-dimensional reconstruction of the basal ganglia for monkey R7160. Atlas
templates (top right image; Martin and Bowden, 2000) were warped to Nissl stained brain slices
(top left image) using Edgewarp (bottom image; Bookstein, 1990). The bottom right image
shows a warped atlas template that now matches the histological slice, allowing definition of the
nuclear borders. Outlines of nuclei from a series of warped atlas templates were interpolated into
3D volumes (right image). A four-contact DBS electrode was positioned in the posterior STN of
the customized brain atlas based on the histologically defined electrode location. Each leg of the
3D scale bar is 5 mm (D = dorsal, P = posterior, M = medial). Put = putamen, Caud = caudate
nucleus, GPe = globus pallidus pars externa, GPi = globus pallidus pars interna, OT = optic tract,
STN = subthalamic nucleus, Th = thalamus including thalamic reticular nucleus.
Page 34 of 42
Figure 2. Multi-compartment cable model of an STN projection neuron. A. 3D reconstruction
of a macaque STN neuron generated in Neurolucida. B. The neuron geometry was imported into
the NEURON simulation environment. The soma and neuronal processes were divided into
compartments and modeled as a series of resistors and capacitors. C. The membrane lipid bilayer
was represented as a capacitor and ion channels as variable resistors. Na = fast sodium channel,
NaP = persistent sodium channel, KDR = potassium delayed rectifier, Kv31 = potassium fast
rectifier, sKCa= small conductance calcium activated potassium channel, Ih
hyperpolarization-activated cation channel, CaT = low voltage activated calcium channel, CaN
and CaL = high voltage activated calcium channel, L = leak channel, Cm = membrane
capacitance (see Gillies and Willshaw (2006) for details on the neuron membrane dynamics).
Page 35 of 42
Figure 3. Neural populations and DBS electrode in the context of the 3D neuroanatomy. A.
Three types of STN projection neurons share the same soma and dendritic tree but have different
axonal trajectories. B. GPi fibers form the lenticular fasciculus on their way to the ventral
thalamus. C. Population of internal capsule fibers. Diameters of neuronal processes were
increased ten times for figure rendering. Each leg of 3D scale bar represents 1 mm (D = dorsal, P
= posterior, M = medial).
Page 36 of 42
Figure 4. Field-neuron model of STN DBS. A. FEM voltage solution for 1 V bipolar stimulus
overlaid with a sagittal brain cross-section from monkey R7160. A 250 µm thick encapsulation
layer surrounds the DBS electrode. Str = striatum, GPe = globus pallidus pars externa, GPi =
globus pallidus pars interna, OT = optic tract, Th = thalamus including reticular thalamic
nucleus, STN = subthalamic nucleus, Sn = substantia nigra. B. Extracellular potentials generated
by the electrode create transmembrane polarization along the STN projection neuron. Neural
compartments are colored according to their transmembrane potential at the onset of a
subthreshold stimulus pulse. Arrows indicate depolarized nodes of Ranvier. Diameters of
neuronal processes were thickened for figure rendering.
Page 37 of 42
Figure 5. STN neuron firing in response to extracellular stimulation. Lowercase letters indicate
location in the STN neuron where the transmembrane voltage was recorded. a = soma, b = first
node of Ranvier, c = 30th node of Ranvier, d = 50th node of Ranvier. A. The action potential
initiated in the axon and propagated toward the cell body and axonal terminals in the globus
pallidus. The traces in the top row represent the stimulus voltage waveforms applied to the
neuron. The four traces bellow show the response to a subthreshold DBS pulse (1.4 V), a
suprathreshold DBS pulse (1.8 V) and a suprathreshold DBS pulse (1.8 V) in a model with
inhibitory somatic synapses. B. STN neuron spontaneous firing and response to stimulation are
stable over time. The four traces show neuronal firing before, during and after 1-second, 136-Hz
DBS train in the soma and the distal axon of the same neuron. Results are displayed for models
with and without GABAa stimulation induced synaptic input (0.013 µS). Somatic firing rate was
lower than the stimulation frequency because action potentials initiated in the axon did not
invade the soma for every stimulus pulse. The GABAergic synaptic inputs reduce somatic firing,
but axonal output was largely unaffected (see also Fig. 9).
Page 38 of 42
Figure 6. Neural activation during clinically effective and ineffective DBS. A. STN neuron
axons (top) and GPi fibers (bottom) activated by clinically effective DBS in monkey R7160 (1.8
V, 136 Hz, 210 µs, contact 0 cathode, contact 2 anode) are shown in red. Axons that did not
respond to at least 80% of the stimulus pulses are shown in gray. Each leg of the 3D scale bar
represents 1 mm. B. Percent of neurons activated for monkeys R7160 (top) and R370 (bottom)
during clinically ineffective, clinically effective and corticospinal tract threshold DBS, averaged
over three randomized populations. Only CST fiber activation was evaluated at CST threshold
amplitudes. Asterisks indicate significant difference between clinically ineffective and effective
stimulation (p<0.05; t-test).
Page 39 of 42
Figure 7. Experimentally recorded GPi firing during STN DBS. Histograms of monkeys R7160
(A) and R370 (B) show the percentage of total GPi spikes (all spikes recorded from all cells in
each animal) during clinically effective stimulation at 136 Hz separated into 0.2 ms bins over the
interstimulus interval. Monkey R370 exhibited an increased number of spikes in the first seven
bins (< 1.5 ms), indicting a higher degree of short latency excitation, compared to monkey
Page 40 of 42
Figure 8. Sensitivity of neural activation to electrode position. Percentage of STN neurons (top)
and GPi fibers (bottom) activated during DBS for monkey R7160 with the electrode moved from
the original position (O) to a location 0.25 mm medial (M), lateral (L), anterior (A) or posterior
(P). The original electrode position was in the sagittal plane, 6.2 mm from the midline, at a 20
degree anterior-to-posterior angle. The center of the bottom contact (cathode) was 3.3 mm
ventral to the AC-PC plane (horizontal plane defined by the anterior and posterior commissures)
placing it in the posterior-medial-ventral border of the STN. The second from the top contact
(anode) was 1.4 mm bellow AC-PC plane corresponding to the area of LF just dorsal to
posterior-lateral border of the STN.
Page 41 of 42
42 Download full-text
Figure 9. STN firing frequency under the influence of stimulation-induced trans-synaptic
GABAa inhibitory inputs. Firing frequency was measured in the soma (A) and distal axon (B) of
the same cell and averaged for a population of STN neurons (monkey R7160; clinically effective
stimulation). Somatic firing, with respect to the no stimulation condition (‘No DBS’), decreased
with increasing strength of the GABAa synapse. Distal axon firing, with respect to the zero
conductance condition, was relatively unaffected by somatic inhibition.
Page 42 of 42