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Journal of Integrative Neuroscience 16 (2017) 441–452
DOI 10.3233/JIN-170027
IOS Press
Sensitivity analysis of discharge patterns of
subthalamic nucleus in the model of basal
ganglia in Parkinson disease
Jyotsna Singh a,∗ , Phool Singh b and Vikas Malik c
Department of Computer Science & Engineering and Information Technology, The NorthCap
University, Gurgaon, Haryana, India
Department of Applied Sciences, The NorthCap University, Gurgaon, Haryana, India
Department of Physics, JIIT Noida, Uttar Pradesh, India
Received 21 January 2017
Accepted 23 May 2017
Abstract. Parkinson disease alters the information patterns in movement related pathways in brain. Experimental results performed on rats show that the activity patterns changes from single spike activity to mixed burst mode in Parkinson disease.
However the cause of this change in activity pattern is not yet completely understood. Subthalamic nucleus is one of the main
nuclei involved in the origin of motor dysfunction in Parkinson disease. In this paper, a single compartment conductance based
model is considered which focuses on subthalamic nucleus and synaptic input from globus pallidus (external). This model
shows highly nonlinear behavior with respect to various intrinsic parameters. Behavior of model has been presented with the
help of activity patterns generated in healthy and Parkinson condition. These patterns have been compared by calculating their
correlation coefficient for different values of intrinsic parameters. Results display that the activity patterns are very sensitive to
various intrinsic parameters and calcium shows some promising results which provide insights into the motor dysfunction.
Keywords: Spiking patterns, Parkinson disease, dopaminergic neuron, subthalamic nucleus, globus pallidus, hyper direct
1. Introduction
Brain disorders constitute a major health problem and pose a challenge to the society (Arnulfo et al.
[1]; Dorsey et al. [10]; Dhar et al. [9]). These disorders are among the most puzzling of all the diseases,
and our ignorance of the underlying disease mechanism is a major obstacle to the development of better
diagnostic systems. One of the disease caused by brain disorder is Parkinson disease (PD). PD is the one
of many diseases that are collectively known as movement disorders. PD primarily affects neurons in the
area of the brain called substantia nigra pars compacta (SNc) (Kang and Lowery [17]; Santaniello et al.
* Corresponding
author. E-mail:
0219-6352/17/$35.00 © 2017 – IOS Press and the authors. All rights reserved
J. Singh et al. / Sensitivity analysis of STN in PD
[28]). These dying neurons produce dopamine, a chemical that sends messages to the part of the brain
that controls movement and coordination along with other activities. As PD progresses, the amount of
dopamine produced in the brain decreases, leaving a person normally unable to control movement. The
cause to the depletion of dopamine is not well known, and hence there is presently neither diagnosis no
cure at early stage. There are treatment options at the later stages of the disease, such as medication and
surgery to manage its symptoms (Bergman et al. [3]).
Basal ganglia has four main nuclei, which are (1) substantia nigra, (2) striatum, (3) Subthalamic nucleus (STN) and (4) Globus pallidus (GP). Substantia nigra itself is further divided into two sub regions
pars compacta and pars reticulata and GP is further divided into internal Globus pallidus (GPi) and external Globus pallidus (GPe). It has strong connections with the cerebral cortex, thalamus and other brain
areas. The striatum receives input directly from cortex and delivers neurotransmitter which is inhibitory
in nature to the GP through different pathways (1) direct and (2) indirect. STN directly receives inputs
from cortex through (3) hyper direct pathway (Santaniello et al. [28]; Bergman et al. [3]; Hammond
et al. [14]). The two pathways direct and indirect affect the basal ganglia network in opposite ways and
are simultaneously involved in the control of voluntary movements (Kang and Lowery [17]).
It has been acknowledged by researchers (Jankovic [15]; Gelb et al. [12]; Jellinger [16]; Rivlin-Etzion
et al. [26]; Moran et al. [22]; Wichmann et al. [35]; Weinberger et al. [33]) that there is a central origin to
PD but the localization to the origin is still not clear. There are few hypothesis about oscillation, bursting
and tremor generation as proposed in (Jellinger [16]; Deuschl et al. [8]). Some researchers suggest that
the circuits in basal ganglia are itself oscillating and bursting generating circuits (Llinás [21]; Guehl
et al. [13]; Paré et al. [24]; Brewer et al. [6]; Song et al. [29]; Aur et al. [2]; Wichmann and DeLong
While other researchers (Brewer et al. [6]) suggested that a quantitative measure of motor signs would
enable physicians to strengthen the medication regime more easily for a specific patient. Wen-Jie et al.
(Song et al. [29]) suggested that STN plays an important role in motor function and also suggested the
role of calcium current in the regulation of STN Neuron. Dorian (Aur et al. [2]) suggested that non-linear
propagation of density discharge contains meaningful information that is only revealed during spiking
To identify the cause to the origin of tremor and bursting generation and further to narrow down,
we are considering a model of basal Ganglia as shown in Fig. 1. In this model, we focussed on the
network of subthalmic nucleus (STN) and globus pallidus external (GPe). Connections are disrupted
between different nuclei in PD condition (Santaniello et al. [28]). D1, the dopamine receptor, is excited
by dopamine in healthy primate. It produces inhibitory neurotransmitter to GPi, which in turn suppresses
the activity of GPi and increases the activation in thalamus. D2, the other dopamine receptor, when
activated by dopamine, produces GABA to GPe. This suppresses GPe activities and excites the activities
in STN and GPi. This result in the inhibitory activities in thalamus neuron.
A possible viewpoint is that the bursting and oscillation generator circuits are basal ganglia thalamuscortical circuits (Deuschl et al. [8]). The basal ganglia circuits do not produce tremor oscillations in
healthy basal ganglia circuits (Zirh et al. [36]; Raethjen et al. [25]; Lenz et al. [19]; Volkmann et al.
[32]; Surmeier et al. [30]; Bevan et al. [5]).
On the contrary, the tremor oscillations related activity is observed in STN in PD condition (Levy
et al. [20]). However, the reason underlying tremor oscillations generation is still to be understood.
Above studies suggest that STN could be considered as the target region to analyze the motor symptoms and the origin of PD. Therefore, in this paper we have analyzed the activity patterns generated in
STN Model and compared the behavior of STN Model in healthy primate and in PD condition to explore
J. Singh et al. / Sensitivity analysis of STN in PD
Fig. 1. Modified Basal ganglia-thalamo-cortical circuit (Dovzhenok and Rubchinsky [11]). Bold lines show the connection in
healthy primate and dotted lines show the connection in Parkinson disease.
the dynamics of the STN-GPe loop subjected to various currents. Work has been distributed in various
sections. Section 2 explains the mathematical model and underlying methods. Section 3 is dedicated to
the sensitivity based study of the model for various currents and their results. Section 4 is the conclusion
of the work.
2. Methods and models
The base of this study is a single-compartment conductance-based model of basal ganglia (Santaniello
et al. [28]) in which STN receives excitatory input from cortex through hyper-direct pathway (Kang and
Lowery [17]) and GPe receives inhibitory input from the striatum and excitatory input from STN. In
addition, there are inhibitory synaptic connections within GPe cells. The STN receives inhibitory input
from the GPe and excitatory neurotransmitters through cortex. It increases the frequency of the discharge
patterns in GPi neurons (Kitai and Deniau [18]; Nambu et al. [23]). This result leads to the importance
of interaction between STN and GP neurons in direct and hyper-direct pathway in the basal ganglia
network (Kang and Lowery [17]; Lenz et al. [19]).
To observe the behavior of activity patterns STN-GPe network in PD and healthy primate, mathematical model, conductance and time scales are taken from (Dovzhenok and Rubchinsky [11]). Analysis and
simulation work of STN-GPe network is performed using MATLAB 7.14 (i7 Intel processor, 4GB RAM
machine) using ODE45 for time period from 0 to 250 ms for calcium and from 0 to 500 ms for the other
parameters under observation. The objective of this analysis was to provide insights into major causes
for sensitivity of the model in PD.
This model (Eq. (2.1)) (Dovzhenok and Rubchinsky [11]), includes a leak current (Il ), fast spikeproducing potassium (Ik ) and sodium currents (INa ), low threshold T-type (IT ) and high-threshold Ca2+
currents (ICa ), Ca2+ activated voltage independent after-hyperpolarization K + current (IAHP ), synaptic
current (Isyn ) and applied current (Iapp ), so that the equation governing the membrane potential V takes
the form:
= −Il − Ik − INa − IT − ICa − IAHP − Isyn + Iapp ,
J. Singh et al. / Sensitivity analysis of STN in PD
where different membrane currents are given by (Dovzhenok and Rubchinsky [11])
Il = gl · [V − Vl ],
Ik = gk · n4 · [V − Vk ],
INa = gNa · m3∞ (V ) · h · [V − VNa ],
· (V ) · r · [V − VCa ],
IT = gT · a∞
ICa = gCa ·
· (V ) · [V − VCa ],
· [V − Vk ],
[Ca] + k1
where k1 is the dissociation constant of Ca2+ dependent AHP current. Iapp current is used to adjust the
membrane resting potential with the experimental data (Terman et al. [31]; Rubin and Terman [27]).
Calcium current is described by the equation:
= −ICa − IT − kCa[Ca] ,
where characterizes the calcium influx and kCa is calcium pump rate. The equation for gating variable
m, n, h, s and a is given as under
φx [x∞ (V ) − x]
τx (V )
where φ is the constant in the equation for gating variables. The time constant function τx is given by
τx (V ) = τx0 +
[1 + exp[−[V − θxτ ]/σxτ ]]
where θ is half activation/inactivation variable and τ is time constant functions. Synaptic current Isyn in
the STN neuron is computed as a sum of synaptic currents from the GPe and other neighboring neurons
(Dovzhenok and Rubchinsky [11]). Synaptic current Isyn is defined by considering the synaptic inputs
from GP to STN and from feedback neurons to STN.
Isyn = ggs · sg [V − Vgs ] + gf s · sf [V − Vf s ],
where ggs and gf s is the synaptic conductance and sg and sf are synaptic variables. As suggested in
(Dovzhenok and Rubchinsky [11]), dopamine degeneration is one of the main cause to the Parkinson
Disease. Synaptic inputs play a major role to show the degeneration of dopamine. To present this, we
have considered two synaptic variables, sg and sf , to modulate the strength of synaptic input currents.
sg and sf vary in (Gelb et al. [12]) range, so that the lower values of sg and sf correspond to lower
dopamine levels and stronger conductance and higher values represent the opposite. All the synaptic
J. Singh et al. / Sensitivity analysis of STN in PD
variables in the model circuit are modeled by the following 1st order kinetic equation, which describes
the fraction of activation channels
= α · H∞ (Vpresyn − g ) · [1 − s] − β · s,
H∞ (V ) =
1 + exp
−[V −θgH ]
is a sigmoid function, Vpresyn is the synaptic potential from neighboring neurons. The values of synaptic
variables α, β, θ and σ are taken from (Terman et al. [31]). The values of synaptic strengths in the
“normal” state (high dopamine level) are ggs = 0.695 and gf s = 0.215, and the maximal conductance
of the AHP current in STN neuron was set to gAHP = 4.23 nS/mm2 . The values of synaptic strengths
corresponding to the Parkinsonian (low dopamine level) state are ggs = 1.39, gf s = 0.43, with STN
cell’s AHP conductance set to gAHP = 8.46 nS/mm2 .
3. Analysis of electrophysiological properties
This section presents the analysis of the electrophysiological properties of the STN-GPe neurons
within the STN neuron. STN is an oval-shaped small nucleus which receives inhibitory and excitatory inputs from other neurons within Basal ganglia as shown in Fig. 1 (Kang and Lowery [17]). In our
study, we have considered STN and GPe as single neuron and also that STN received input directly from
cortex in hyperdirect pathway. The analysis of electrophysiological properties compares the spiking patterns generated in healthy primate and PD, and a distinction is made between the two types. Different
studies (Beurrier et al. [4]; Terman et al. [31]) reveals that electrophysiological experiments results may
differ from case to case. The spiking patterns generated in healthy primate are termed as STN and those
generated for PD are termed as STNP. Comparison between STN and STNP is performed by computing
correlation coefficient (CCF) between these two. CCF is measure of similarity of two series as a function
of the lag of one relative to the other. Correlation coefficient can be computed as described below: The
CCF between STNt and STNPt+i is called the ith order cross correlation of STN and STNP. The sample
estimate of this correlation coefficient, called CCFk , is calculated using the formula:
− (STN))((STNP)j +k − (STNP))
2 n
1 ((STN)j − (STN))
1 ((STNP)j − (STNP))
j =1 ((STN)j
CCFk = n
To analyze and quantify the sensitivity of STN Model, the comparison between STN and STNP has
been performed for various ionic and synaptic currents. To investigate the model assumptions about
physiological properties and connectivity patterns that lead to the best explanation of (i.e. closest fit to)
our electrophysiological data, we studied different activity patterns generated by the mathematical descriptions presented above. All the discharge patterns were generated using Eq. (2.1). These discharge
J. Singh et al. / Sensitivity analysis of STN in PD
patterns are then simulated for selected ionic current. The simulations are run for 500 ms and 250 ms respectively. We are displaying the results for parameters which are very sensitive for the model. These parameters are applied current Iapp , feedback membrane potential Vf s , presynaptic potential Vpresyn and VCa .
The discharge patterns showing sensitivity tradeoffs as compared to the discharge patterns obtained from
(Dovzhenok and Rubchinsky [11]), are presented in the following sections. All the figures have been
plotted as a function of time. Detailed sensitivity analysis and results are given in the following sections.
While simulating the above model we have used parameters as given in (Dovzhenok and Rubchinsky
3.1. Sensitivity analysis for applied current
Activity patterns displayed within STN-GP network in basal ganglia are typically irregular and are
correlated with the activity inside these cells. Firstly we have analyzed the activity patterns with respect
to applied current Iapp . Initial reference value of Iapp = 32 pA/mm2 is taken from (Dovzhenok and
Rubchinsky [11]). To analyze the sensitivity of these patterns against applied current Iapp , we varied the
value of Iapp in the range 30 to 37 pA/mm2 . These patterns have shown the sensitivity for Iapp for both
STN and STNP. We have displayed the discharge pattern for Iapp = 30 in Fig. 2. Figure 2 shows that the
patterns of variation between two time series of STN and STNP is linear in nature, except that the time
series of STNP is slightly delayed. But the correlation coefficient computed for Iapp between Iapp = 30
to 37 pA/mm2 shows non-linearity.
Minor change in the value of applied current shows sensitivity tradeoff in the correlation coefficient of
STN and STNP computed as shown in Fig. 3. It can be observed from Fig. 3 that maximum correlation
attained is 0.0666 at Iapp = 34 pA/mm2 between Iapp = 30 to 37 pA/mm2 . Increasing the value of
Iapp gradually from 30 to 37 pA/mm2 does not improve the correlation coefficient between the two time
series of STN and STNP.
3.2. Sensitivity analysis for pre-synaptic membrane potential
Vpresyn is the membrane potential of a pre-synaptic neuron. We have analyzed the effect of Vpresyn on
synaptic variables which ultimately affect STN model in normal and PD condition. The reference value
Fig. 2. Spiking patterns in STN-GPe Model in healthy and PD condition at Iapp = 30 pA/mm2 .
J. Singh et al. / Sensitivity analysis of STN in PD
Fig. 3. Correlation coefficient for healthy and PD spiking patterns for Iapp = 30 to 37 pA/mm2 .
Fig. 4. Discharge patterns generated in STN and STNP at Vpresyn = 30.5 mV.
of Vpresyn was considered as 31 mV by (Dovzhenok and Rubchinsky [11]). Pre-synaptic potential may
increase or decrease depending upon the connections between neighboring neurons. If the connections
are disrupted, it may decrease and if the connections are strong, it may increase (de Lau and Breteler
[7]). Hence, we have analyzed it for disrupted and strong connection. Change in the pre-synaptic value
does not affect the correlation coefficient for STN model in healthy and PD condition. Activity patterns
have been shown for Vpresyn = 30.5 mV in Fig. 4. Correlation coefficient of STN and STNP for reference
range has been tabulated in Table 1.
Table 1 shows non-linearity in different values of correlation coefficient when we compare them with
activity patterns showing linear behavior. At lag = 0, correlation coefficient is maximum for Vpresyn =
31.5 mV. Correlation coefficient is improving at Vpresyn = 31 mV at lag = 3. It is observed that in STN
model there is time lag in activity patterns in normal and PD condition. This information reveals a linear
shift between STN and STNP and the cause is not yet identified.
J. Singh et al. / Sensitivity analysis of STN in PD
Table 1
Cross correlation coefficient for Vpresyn
CCF at Vpresyn = 30.5 mV
CCF at Vpresyn = 31 mV
CCF at Vpresyn = 31.5 mV
Fig. 5. Discharge patterns generated in STN and STNP at Vf s = 1–3.
3.3. Sensitivity analysis for feedback membrane potential Vf s
In this sub-section we have studied the effects on STN model by varying feedback membrane potential
Vf s . However, the study of this model presents the occurrence of oscillations when STN-GPe loop
becomes stronger. This phenomenon remains vigorous with respect to variations of various parameter
which are dopamine dependent. It also remains vigorous for delays in the feedback loop. Whereas the
delays remain unknown, which does not change in Parkinson disease (Dovzhenok and Rubchinsky [11]).
The model is somewhat stable for various values of Vf s . This has been shown in Fig. 5 and Fig. 6.
Figure 6 shows that correlation coefficient is constant for different values of Vf s i.e. CCF = 0.60 for
Vf s = 1–3 mV, CCF = 0.61 for Vf s = 4–7 mV and CCF = 0.62 for Vf s = 8–15 mV. Correlation
coefficient increases along with the value of Vf s .
3.4. Effect of calcium current on STN model
After simulating the behavior of STN model for various intrinsic parameters, it has been observed that
there was a linear shift in the Parkinson discharge patterns. To identify the cause of linear shift in the
discharge patterns in PD condition, the model was simulated for VCa . It has been found out that calcium
current greatly affected STN Model in PD condition. This can be clearly seen in the results displayed in
J. Singh et al. / Sensitivity analysis of STN in PD
Fig. 6. Correlation coefficient for Vf s = 1–3, 4–7, 8–15.
Fig. 7 for calcium current that shows the sensitivity of Parkinson discharge pattern to calcium current.
Results displayed in Fig. 7(b) targeted towards the possibility of calcium to be the cause of linear shift
in the Parkinson discharge patterns in comparison with patterns generated in healthy primate.
In Fig. 7(a) we can observe the difference between activity patterns in STN and STNP at VCa =
140 mV. Initially the patterns are somewhat overlapping but gradually with time the process of generation of Parkinson discharge patterns is slowed down. Slowing down of discharge pattern might be
due to the deficiency of various chemicals such as calcium, sodium and potassium. When we further
analyzed the effect of VCa in the range from 100 mV to 350 mV for STNP. Results are displayed in
Fig. 7(b). In this figure, we can see that the discharge patterns in Parkinson condition are overlapping
with the discharge patterns in healthy primate. Correlation coefficient between these two patterns is then
plotted for STN at VCa = 140 mV versus STNP at VCa = 235 mV in Fig. 7(c), which gives maximum
correlation between the two. The correlation coefficient is approx. equal to 0.92. Therefore the higher
the correlation coefficient, the better the results are for the parameter under observation. From this result
we can conclude that increasing the concentration of calcium current improves the discharge patterns in
PD. So calcium could be the major parameter under consideration which highly affects the STN model
in PD.
4. Conclusion
The inter-connection between STN-GPe neuron is strong and it is found that they are not engaged
in rhythmic activity all the time. The activity patterns generated inside STN neuron and input from
GPe neuron in healthy primate and Parkinson condition have been investigated in search of finding
the parameters which alter the dynamics in Parkinson disease. We compared the discharge patterns
by analyzing the sensitivity in STN model due to applied current, pre-synaptic membrane potential,
feedback synaptic potential and calcium currents. The results in the above study show that STN model
in Parkinson condition is sensitive to various parameters, but the model seems highly sensitive to calcium
current. Results reveal the importance of observing the calcium current closely in order to identify the
cause to this disease. These results can further be used to study the effect of calcium membrane potential
J. Singh et al. / Sensitivity analysis of STN in PD
Fig. 7. Spiking patterns generated for calcium membrane potential, (a) at VCa = 140 mV for STN and STNP, (b) at
VCa = 140 mV for STN and VCa = 235 mV for STNP. (c) Plot of correlation coefficient for for STN at VCa = 140 mV
versus for STNP at VCa = 235 mV.
on the model with respect to other intrinsic parameters like sodium and potassium to check the stability
of the model.
We are highly thankful to the reviewers for their constructive and insightful comments and suggestions
which helped us to improve the manuscript in a better way.
We thank Department of Science and Technology, Government of India for financial support vide
Reference No. SR/CSRI/166/2014(G) under Cognitive Science Research Initiative(CSRI) to carry out
this work. We are also thankful to Prof. Karmeshu for providing valuable inputs in this work.
J. Singh et al. / Sensitivity analysis of STN in PD
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