Corticostriatal synapse plasticity of medium spiny neurons is regulated by glutamate input from the cortex and dopamine input from the substantia nigra. While cortical stimulation alone results in long-term depression (LTD), the combination with dopamine switches LTD to long-term potentiation (LTP), which is known as dopamine-dependent plasticity. LTP is also induced by cortical stimulation in magnesium-free solution, which leads to massive calcium influx through NMDA-type receptors and is regarded as calcium-dependent plasticity. Signaling cascades in the corticostriatal spines are currently under investigation. However, because of the existence of multiple excitatory and inhibitory pathways with loops, the mechanisms regulating the two types of plasticity remain poorly understood. A signaling pathway model of spines that express D1-type dopamine receptors was constructed to analyze the dynamic mechanisms of dopamine- and calcium-dependent plasticity. The model incorporated all major signaling molecules, including dopamine- and cyclic AMP-regulated phosphoprotein with a molecular weight of 32 kDa (DARPP32), as well as AMPA receptor trafficking in the post-synaptic membrane. Simulations with dopamine and calcium inputs reproduced dopamine- and calcium-dependent plasticity. Further in silico experiments revealed that the positive feedback loop consisted of protein kinase A (PKA), protein phosphatase 2A (PP2A), and the phosphorylation site at threonine 75 of DARPP-32 (Thr75) served as the major switch for inducing LTD and LTP. Calcium input modulated this loop through the PP2B (phosphatase 2B) -CK1 (casein kinase 1) -Cdk5 (cyclin-dependent kinase 5) -Thr75 pathway and PP2A, whereas calcium and dopamine input activated the loop via PKA activation by cyclic AMP (cAMP). The positive feedback loop displayed robust bi-stable responses following changes in the reaction parameters. Increased basal dopamine levels disrupted this dopamine-dependent plasticity. The present model elucidated the mechanisms involved in bidirectional regulation of corticostriatal synapses and will allow for further exploration into causes and therapies for dysfunctions such as drug addiction.
The present study reviews electrophysiological studies on corticostriatal synapse plasticity of medium spiny neurons and molecular biological studies focused on intracellular signaling cascades involved in this plasticity.
All signaling pathway reactions shown in Fig. 2 are represented by binding and enzymatic reactions. Binding reaction of molecule A and molecule B to form molecule AB (1) where and are rate constants for forward and backward reactions, is simulated by the ordinary differential equation: (2) The rate constants and were related to the dissociation constant and the time constant, i. e. , and. An enzymatic reaction of substrate S with enzyme E to produce product P was simulated by a collection of two elementary processes: 1) enzyme E bound to substrate S to form the enzyme-substrate complex ES; and 2) the complex ES dissociated into enzyme E and product P. The chemical equation can be written as (3) The Michaelis-Menten formulation was avoided due to problems with the steady-state assumption [21], [43]. However, many papers and databases have provided only and the Michaelis constant rather than and. In such cases, it was assumed that was four times larger than (i. e. and), based on the default setting in GENESIS/Kinetikit simulator. (Tables S1, S2, S3). Postsynaptic spines receive two presynaptic inputs: glutamatergic terminals from the cerebral cortex and dopaminergic terminals from the substantia nigra pars compacta. Plasticity of corticostriatal synaptic input results from phosphorylation of AMPA-type glutamatergic receptors, which promotes insertion into the postsynaptic membrane [20], [44], [45]. Below, the pathways linking glutamatergic and dopaminergic input to phosphorylation of AMPA receptors are delineated. The above-described signaling cascade, which links glutamatergic and dopaminergic inputs to AMPA receptor regulation, includes multiple excitatory and inhibitory pathways and feedback loops. This makes logical or intuitive inference of network behaviors virtually impossible; the outcomes depend on the strength and delay associated with each arrow in the diagram. However, logical or intuitive inference of network behaviors becomes virtually impossible, because the outcomes depend on strength and delay associated with each arrow in the diagram. This necessitates numerical simulation of a quantitative model of a signaling cascade to understand and prediction the dynamic behavior. Therefore, the present study designed a kinetic model of the cascade with the concentrations of intracellular calcium and extracellular dopamine as the inputs and AMPA receptor concentration in the postsynaptic membrane as the output. However the cascade, which links glutamate stimulation to calcium response was not included in this model but will be addressed in a future study. Similar to most large-scale cascade models, many reactions were adopted from previously published model [21], [22], [55], [66], [68], [69] or deposited the DOQCS database [42]. When available, models of striatal spiny neurons were utilized (e. g. , DARPP-32, D1R, and AC5). Otherwise, Otherwise, hippocampal neuron models were adopted (e. g. , CaM, CaMKII, PP2B, I-1, and AMPA receptor) by assuming that molecular processes are common between different brain areas. If no previous model was available (e. g. , PP2A, PP1, CK1, and Cdk5), a reaction model was designed based on previous literature. Because many of the reactions remain poorly understood, a number of assumptions and simplifications were necessary to design the cascade models. For instance, although DARPP32 contains at least four phosphorylation sites that affect its enzymatic properties, phosphorylation of Ser102 by CK2, which facilitates phosphorylation of Thr34 by PKA, was not modeled [70]. This was because the upstream regulation mechanisms for CK2 are now well known. Therefore, an 8-state model was designed for DARPP-32, with three phosphorylation sites: Thr34, Thr75, and Ser137. CK1 activation is required for Cdk5 activation [64]. Although the cascade linking these two molecules has not yet been identified, a direct pathway from CK1 to Cdk5 has been hyphothesized [23]. Although reports have described PP1 phosphorylation by Cdk5 [71], a simple model was adopted from the DOQCS database, where only inhibition and disinhibition by I-1 and Thr34 were taken into account [72]. AMPA receptor trafficking in the postsynaptic membrane was modeled using the state transition diagram shown in Fig. 3. AMPA receptors contain two phosphorylation sites - Ser845 phosphorylated by PKA and Ser831 phosphorylated by CaMKII. Therefore, a serial phosphorylation model was proposed for hippocampal neurons [73] where Ser831 was phosphorylated after Ser 845 phosphorylation. Initially, the model was tested to determine whether it reproduced known features of calcium- and dopamine-dependent plasticity in medium spiny neurons. Subsequently, the dynamic characteristics of the model were analyzed to predict effects of experimental manipulation. The entire model consisted of 72 reactions, with 132 reaction parameters. Among these, 83 parameters were retrieved from literature and model database. The remaining 49 parameters were hand-tuned to qualitatively reproduce the following properties: Forms and parameters of all reactions are listed in Tables S1, S2, S3. Because many of the parameters affected multiple features of the model behavior, it was difficult to specify which parameter was responsible for the replication of each property. Numerical simulations were implemented by GENESIS/kinetikit (http: //www. genesis-sim. org/GENESIS/). It was assumed that the postsynaptic spine was a homogeneous volume of (cubed), so that each molecular species concentration represented the state variables. The two inputs to the cascade model comprised the concentrations of intracellular calcium, which were evoked by cortical glutamatergic input, and extracellular dopamine, which were evoked by nigral dopaminergic input. The time courses of the concentrations were approximated by the alpha function (4) which takes a maximum value of 1 when. The intracellular calcium concentration induced by a train of cortical spikes, which begin at time with inter-spike interval (ISI), was simulated by (5) where and were the basal level and stimulus amplitude of calcium concentration, respectively (Fig. 4A). The maximum function, rather than temporal summation, of calcium transients was used to replicate calcium response data from D1R-expressing striatal neurons [76]. The time constant of the alpha function was [77], [78]. spikes at ISI (100 Hz) were simulated and repeated six times with 10-sec intervals (Fig. 4B). The concentrations used in the simulation were as follows: and. The extracellular dopamine concentration, which was induced by a single presynaptic spike at time, was simulated by: (6) where and were the basal level and stimulus amplitude of dopamine concentration, respectively (Fig. 4C). The time constant of the alpha function was [78], [79]. Dopamine input simulation was repeated six times with 10-sec intervals (Fig. 4D). The concentrations used in the simulation were as follows: and.
The activities of intracellular molecules were simulated in response to four input conditions: i) weak calcium input alone (and); ii) strong calcium input alone (and); iii) dopamine input alone (and). iv) weak calcium input coincident with dopamine input (and); The detailed input forms are explained by Eqs. (4) – (6) in Materials and Methods, and the transient time courses are shown in Fig. 4. Results are shown in Fig. 5. Direct downstream of calcium, CaM (Fig. 5A), PP2B (Fig. 5B), and PP2A (Fig. 5C) were moderately activated by weak calcium input (cyan), but more highly activated by strong calcium input (blue). In contrast, CaMKII (Fig. 5D), which self-phosphorylates, did not respond to weak calcium input (cyan), but responded drastically to strong calcium input (blue). The differential activation profiles of PP2A, which dephosphorylates AMPA receptors, and CaMKII, which phosphorylates AMPA receptors, can be a source of bi-directional plasticity due to calcium input. CK1 (Fig. 5E) was activated by PP2B, but the response to strong calcium input was saturated due to a self-inhibitory mechanism. CK1 subsequently activated Cdk5 (Fig. 5F) and the Ser137 phosphorylation site of DARPP-32 (Fig. 5G). Phosphorylation of Thr75 in DARPP-32 (Fig. 5H) increased with weak calcium input (cyan) via Cdk5 activation (Fig. 5), but decreased with strong calcium input (blue) via PP2A activation (Fig. 5C). This bi-directional calcium effect on Thr75 was consistent with experiments showing phosphorylation of Thr75 with a glutamate receptor agonist [17], [18]. Downstream of the D1Rs, AC5 (Fig. 5I) increased with dopamine input, but decreased with strong calcium input due to calcium inhibition. cAMP concentration (Fig. 5J) increased or decreased depending on AC5 activation level, and subsequently slowly decayed. Phosphorylated PKA (Fig. 5K) decreased with weak calcium input (cyan) and increased with strong calcium input (blue), mirroring the bi-directional changes of Thr75 (Fig. 5H). PKA increased at a slower rate with dopamine input (red), subsequent to increased cAMP. Simultaneous stimulation of weak calcium and dopamine resulted in a bi-phasic response, including an initial dip followed by a sustained elevation. PDE activation (Fig. 5L) was similar to the activation profile of PKA. Dopamine input (red) resulted in increased Thr34 phosphorylation of DARPP-32 (Fig. 5M) via PKA activation. Calcium input (cyan and blue) reduced Thr34 phosphorylation due to stronger inhibition by PP2B. The decreased Thr34 phosphorylation due to calcium input was consistent with experimental results utilizing AMPA and NMDA [17]. Coincident calcium input (magenta) reduced the response of Thr34 to dopamine input (red). These results were consistent with experimental responses to different levels of dopamine and NMDA inputs [74]. Dopamine input alone increased phosphorylation of Inhibitor-1 (I-1) (Fig. 5N) via PKA activation. However, I-1 phosphorylation decreased due to either weak or strong calcium input, or simultaneous calcium and dopamine inputs, via PP2B inhibition. Phosphorylation of PP1 (Fig. 5O) was opposite to that of I-1 by dopamine input (red), but similarly phosphorylated by both strong (blue) and weak (cyan) calcium inputs, even under simultaneous dopamine input (magenta). Finally, via phosphorylation by CaMKII (Fig. 5D) and PKA (Fig. 5K), and dephosphorylation by PP2A (Fig. 5C) and PP1 (Fig. 5O), AMPA receptor phosphorylation at Ser845 decreased due to weak calcium input, but increased due to strong calcium input and simultaneous calcium and dopamine inputs (Fig. 5P). Fig. 6A shows the time course of synaptic efficacy (AMPA receptor concentration in the post-synaptic membrane) induced by different levels of dopamine input coincident with a weak calcium input. While the absence of dopamine input caused depression of the synapse (solid), increased dopamine levels resulted in potentiation. Fig. 6B shows the time course of synaptic efficacy in three different levels of calcium input without dopamine input. While weak calcium input causes depression, increased calcium input resulted in potentiation. Synaptic efficacy was evaluated 10 min after conditioning as an index of long-term synaptic plasticity. Synaptic efficacy increased with increasing dopamine input coincident with calcium input (Fig. 6). In conditions of dopamine depletion, where both and were set at, the calcium input did not alter synaptic efficacy. These results were in accordance with dopamine-dependent synaptic plasticity [27], as characterized in Fig. 1A. Fig. 6D shows synaptic plasticity dependence on calcium input levels in the absence of dopamine input. Weaker calcium input resulted in LTD, but stronger calcium input caused LTP. These results were consistent with previous experimental observations [11], [28], [29], as schematized in Fig. 1B. To further clarify the interactions between calcium and dopamine inputs and the roles of molecules in the signaling cascade, 2D maps of synaptic plasticity were plotted with different levels of calcium and dopamine inputs using standard and modified models. Fig. 7A shows synaptic plasticity after 10 minutes stimulation in the standard model. LTD was induced by weak calcium input in the absence of dopamine (blue area), and LTP was induced by either strong calcium or strong dopamine input (red area). When CaMKII activation was fixed at a steady-state level (Fig. 7B), increased calcium input did not induce LTP. Rather, LTD occurred only at low levels of dopamine input. When PKA was fixed at the steady-state level (Fig. 7C), dopamine-dependent plasticity disappeared. Fixing PP1 produced LTP, regardless of the strength of calcium and dopamine inputs (Fig. 7D). The potentiation induced by strong dopamine alone disappeared, because the disinhibition due to decreased PP1 (corresponding to the red line in Fig. 5O) was removed.
Several studies have modeled signal transduction in medium spiny neurons [21]–[23]. The novelty of the present model is the incorporation of AMPA receptor phosphorylation and membrane trafficking to directly assess the effects of cascade dynamics on striatal synaptic plasticity. This allowed for the reproduction of both LTD and LTP in calcium- and dopamine-dependent plasticity and to predict interactions between calcium and dopamine inputs, as shown in Fig. 7, and effects of various manipulations on striatal synaptic plasticity. Embedding of the present model in a complete neuronal model, or even a neural network model, enables the assessment of the role of calcium- and dopamine-dependent plasticity in cellular and network functions. The model can also serve as the basis for building simplified signaling cascade models for large-scale simulation and theoretical analysis. The present signaling cascade model involving DARPP-32 differs from previous models in several points. The factors incorporated by this model but not by existing models [21]–[23] were inhibition of PDE by PKA, Ser137 effect on Thr34, and inhibition of PP1 by I-1. The CK1-Cdk5 pathway, which has been previously described [23], was critical for reproducing bidirectional phosphorylation of Thr75, which was dependent on calcium input intensity. In addition, the present study performed a rigorous analysis of bistability of positive feedback loop formed by PKA, PP2A, and DARPP-32 on Thr75, which was a source of a threshold-like response of PKA activity to both dopamine and calcium inputs. The model prediction of Thr34 and Thr75 responses to dopamine and calcium input were consistent with the Fernandez model [21] if the calcium input levels from the Fernandez model were regarded as the strong calcium input for the present model. However, simultaneous calcium and dopamine inputs resulted in Thr34 dephosphorylation in the present model, but phosphorylation in the Lindskog model [22]. This discrepancy could be due to inactivation by the calcium-PP2B-Thr34 pathway was stronger than activation by the PKA-Thr34 pathway in present model. DARPP-32 phosphorylation on Thr75 has been shown to because of glutamate, AMPA, or NMDA exposure, but returns to normal level within 10 min [17], [18]. In addition, an mGluR agonist has been shown to potentiate Cdk5 activation and phosphorylation of DARPP-32 on Thr75 and Ser137, and returns to baseline levels after peaking at 2 min [64]. Assuming that an mGluR agonist induced weak calcium levels, and glutamate, AMPA, or NMDA produced strong calcium input, those experimental results were consistent with the present results, as shown in Fig. 5H. In present model, phosphorylation of DARPP-32 on Thr75, as a result of weak calcium input, takes place through the CK1-Cdk5 pathway. Although CK1 activation is required for Cdk5 activation through signaling from mGluR [64], it is not known whether the pathway from CK1 to Cdk5 is direct. Similar to a previous model, the present study assumed direct activation of Cdk5 by CK1 for simplicity [23]. Alternative mechanisms for inhibition of PP2A dephosphorylation on Thr75 exist - either through the calcium-AC5-cAMP-PKA pathway or the calcium-CaM-PDE-cAMP-PKA pathways. More quantitative data on the strengths of these pathways and additional in silico experiments are necessary to definitely determine the role of the CK1-Cdk5 pathway in calcium-dependent LTD. AMPA receptor trafficking in the present model was derived from Hayer' s model [82]. The primary modification comprised sequential phosphorylation of Ser845 by PKA followed by Ser831 phosphorylation by CaMKII, as proposed by Lee et. al. [83]. However, the LTP mechanism in the present striatal model differed from the hippocampal LTP by Lee et. al. [83]. Previous results demonstrated that the phosphorylation of Ser845 did not increase during LTP [83], and the present model showed that the phosphorylation of Ser845 increased during dopamine-dependent LTP, but did not increase during calcium-dependent LTP. In addition, PKA was required for striatal LTP [75] To address this feature in the present striatal model, most of the AMPA receptors were dephosphorylated at the baseline. This prediction was consistent with the lower phosphorylation level of Ser845 by reduced PKA levels due to inhibition by DARPP-32 in the striatum [69]. It should be noted, however, that the observation of sequential AMPA receptor phosphorylation by Lee et al. [83] in the hippocampus did not exclude a parallel phosphorylation model. It could be interpreted as a result of high PKA and low PPI concentration at the baseline in the hippocampus. It is a subject of future study whether a parallel phosphorylation model can also reproduce the striatal synaptic plasticity. D1-type neurons express GluR1 and GluR2/3 in the spines [84], [85]. A previous hippocampal study [86] showed that GluR1 subunit trafficking was a result of stimulation, but that GluR2 subunit trafficking was constitutive. In addition, chronic treatment with the antidepressant maprotiline increases GluR1, but not GluR2 [87]. Moreover, GluR2-lacking AMPA receptors exhibit larger single-channel currents than GluR2-expressing AMPA receptors [88]. For these reasons, trafficking of GluR1, but not GluR2, was modeled in the present study to ascertain whether synaptic plasticity responded to stimulus. Some theoretical studies [89], [90] have predicted that NMDA receptor-mediated calcium influx results in bidirectional synaptic change. However, these studies modeled only AMPA receptor phosphorylation, but not trafficking, and also did not consider striatal synaptic plasticity. Although the present model considered the number of AMPA receptors in the postsynaptic membrane as a measure of synaptic efficacy, previous studies have suggested that the conductance of AMPA receptor varies according to the phosphorylation state. For example, Ser831 phosphorylation increases conductance [91] and Ser845 phosphorylation increases open probability [92], [93]. If these effects are taken into consideration, the amplitude of LTP could be larger, as observed in experiments [8]–[11]. Threshold dynamics due to the bistability of the positive feedback loop of PKA, PP2A, and Thr75 on DARPP-32 played an important role in reverting the LTD to LTP in dopamine-dependent plasticity. However, when embedded into the entire system, the loop did not exhibit complete bistability, as demonstrated by gradual conversion of synaptic conductance to baseline levels (Fig. S2). The possible mechanisms for longer-lasting synaptic plasticity are described below. First, bistability of some proteins in the cascade has been reported, such as the bistability of CaMKII phosphorylation [82]. However, CaMKII activity did not last for an extended period of time in the present model. This was consistent with a previous study [94], which reported that CaMKII activity returns to baseline within 2–5 min. Hayer et. al. observed bistability of AMPA receptor phosphorylation and Catellani et. al. [73] mathematically determined bistability in the sequential AMPA receptor phosphorylation model. These bistable mechanisms were not incorporated in the present model, but may contribute to synaptic changes over longer periods of time. Second, the present model did not consider increased levels of AMPA receptors and other proteins as a result of gene transcription. A possible link from the current model to longer-term synaptic plasticity is cAMP-response element binding protein (CREB), which controls gene transcription for longer-term synaptic plasticity in the striatum [95]. CaMKII, PKA, and PP1 directly activate CREB, but also indirectly via extracellular signal-regulated kinase (ERK), which activates CREB [35], [96]. In addition, calcium activates mitogen-activated protein kinase kinase (MEK), which activates ERK [97]. PP1 activates striatal enriched phosphatase (STEP) [98], which inhibits ERK, and PKA inactivates STEP. As a result, CREB is inhibited by PP1 and activated by CaMKII and PKA. Therefore, activation of CaMKII and PKA, as well as inhibition of PP1, which results in AMPA receptor phosphorylation, can also trigger gene transcription through CREB activation. Approximately half of the model parameters were based on previous reports and databases [21], [22], [42], [55], [66], [68], [69], and the remaining half were designed to reproduce experimental findings [15]–[18], [39], [46], [64], [74]. Model behavior robustness was determined by altering the kinetic parameters of the PKA-PP2A-Thr75 loop up to ten-fold (Fig. 13). Persistence of nonlinear threshold behavior, despite shifts in thresholds, was also verified. Although the present model parameters reflected some uncertainty, the model served as a useful starting point for exploring the mechanisms influencing corticostriatal synaptic plasticity by testing alternative parameter values or incorporating additional pathways. The present model did not include a number of known pathways such as the effect of DARPP-32 Ser102 on phosphorylation of Thr75 [99]. Membrane potential of striatal medium spiny neurons shifts between up- and down-states, depending on cortical inputs [100]. During the up-state, LTP is induced by cortical stimuli, even without dopamine input [31]–[34]. LTP is also induced by cortical stimulation in a magnesium-free solution [11], [28], [29]. Both cases reflect calcium-dependent plasticity because of the large calcium influx through NMDA receptors. Two types of medium spiny neurons exist: D1 receptor-expressing neurons that project to the direct pathway, and D2 receptor-expressing neurons that project to the indirect pathway [101], [102]. In D1 neurons, dopamine increases cAMP via G-proteins and AC5, similar to the present model. However, in D2 neurons dopamine inhibits AC5 and decreases cAMP so the effect of dopamine input is opposite to that in D1 neurons. Schultz et. al. recorded the activities of dopamine neurons in the substantia nigra in monkeys and found that dopamine neurons encode error signals of reward prediction [103]. The reinforcement learning model of the basal ganglia posits that striatal neurons learn to compute expected reward based on the reward prediction error signal carried by dopamine neuron firing [103]. Dopamine-dependent synaptic plasticity plays a major role in learning. The medium spiny neurons are depolarized by glutamatergic inputs from the cortex that represent a sensory or a contextual state. When the acquired reward is more than expected, phasic dopamine neuron firing would induce LTP of the activated cortico-striatal synapses. On the other hand, if the reward is less than expected, a pause in dopamine neuron firing would cause LTD of those synapses. The glutamatergic input would not only cause depolarization and firing, but also induce changes in molecular states, such as the phosphorylation level of DARPP-32 and/or shift the threshold of the positive feedback loop, which would serve as the short-term memory of preceding states. To support this scenario, the temporal order of calcium and dopamine input is a critical factor. Assuming that calcium flux by glutamatergic input is a fast process, the synaptic efficacy should be potentiated when calcium input (associated with a sensory or contextual state) precedes dopamine input (associated with a reward prediction error signal). Our model is consistent with this point (Fig. S1). On the other hand, our model also predicts that the effect of the temporal order on synaptic plasticity is not strong enough. This suggests additional interactions between dopamine and calcium signaling. For example, dopamine facilitates L-type calcium channels, which affect the calcium influx through the interaction of glutamate receptor activation and and back-propagating action potentials. To more precisely simulate calcium dynamics, we have to construct a whole neuron model [104] and combine it with the signaling cascade model. There are several interaction pathways between calcium and dopamine signaling. In the upstream of PKA, calcium directly inhibits AC5 and indirectly cAMP through CaM and PDE. While calcium inhibition of AC5 depended on the timing between calcium and dopamine, PDE inhibition of cAMP did not depend on this timing very much. The stronger interaction of dopamine and calcium on PKA was through DARPP-32. Weak calcium input inhibited PKA through the phosphorylation of Thr75 by Cdk5, but strong calcium input activated PKA through the dephosphorylation of Thr75 by PP2A. While dopamine input reduced the increase of Thr75 by a weak calcium input, it did not affect the decrease of Thr75 by a strong calcium input. Furthermore, the subsystem around the PKA-PP2A-DARPP-32 positive feedback loop showed bistability while PKA activity showed a threshold like response to cAMP activation by dopamine input. However, this loop became mono-stable with both activation of Cdk5 by a weak calcium input, leading to a low level of PKA, and by activation of PP2A by a strong calcium input, leading to a high level of PKA. Addictive drugs (e. g. cocaine and amphetamine) increase the basal level of dopamine by inhibiting the reuptake of dopamine and facilitating the release of presynaptic dopamine [5]. They ultimately decrease DARPP-32 phosphorylation on Thr75 and increase it on Thr34 [105]. In our model, increased basal dopamine levels caused LTD with the calcium and dopamine inputs which caused LTP under control conditions (Fig. 14). This result is consistent with the theory that the value of everything except for drugs decreases because of the impairment of appropriate learning in drug addiction [106].
Recent brain imaging and neurophysiological studies suggest that the striatum, the start of the basal ganglia circuit, plays a major role in value-based decision making and behavioral disorders such as drug addiction. The plasticity of synaptic input from the cerebral cortex to output neurons of the striatum, which are medium spiny neurons, depends on interactions between glutamate input from the cortex and dopaminergic input from the midbrain. It also links sensory and cognitive states in the cortex with reward-oriented action outputs. The mechanisms involved in molecular cascades that transmit glutamate and dopamine inputs to changes in postsynaptic glutamate receptors are very complex and it is difficult to intuitively understand the mechanism. Therefore, a biochemical network model was constructed, and computer simulations were performed. The model reproduced dopamine-dependent and calcium-dependent forms of long-term depression (LTD) and potentiation (LTP) of corticostriatal synapses. Further in silico experiments revealed that a positive feedback loop formed by proteins, the protein specifically expressed in the striatum, served as the major switch for inducing LTD and LTP. This model could allow us to understand dynamic constraints in reward-dependent learning, as well as causes and therapies of dopamine-related disorders such as drug addiction.
computational biology/computational neuroscience
computational biology/signaling networks
A Kinetic Model of Dopamine- and Calcium-Dependent Striatal Synaptic Plasticity