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A network model of basal ganglia for understanding the roles of dopamine and serotonin in reward-punishment-risk based decision making.

Balasubramani PP, Chakravarthy VS, Ravindran B, Moustafa AA - Front Comput Neurosci (2015)

Bottom Line: Our prior model of the BG was an abstract model that did not incorporate anatomical and cellular-level data.Though the existence of MSNs that co-express D1R and D2R are reported by various experimental studies, prior existing computational models did not include them.Starting from the assumption that 5HT modulates all MSNs, our study predicts significant modulatory effects of 5HT on D2R and co-expressing D1R-D2R MSNs which in turn explains the multifarious functions of 5HT in the BG.

View Article: PubMed Central - PubMed

Affiliation: Department of Biotechnology, Indian Institute of Technology Madras Chennai, India.

ABSTRACT
There is significant evidence that in addition to reward-punishment based decision making, the Basal Ganglia (BG) contributes to risk-based decision making (Balasubramani et al., 2014). Despite this evidence, little is known about the computational principles and neural correlates of risk computation in this subcortical system. We have previously proposed a reinforcement learning (RL)-based model of the BG that simulates the interactions between dopamine (DA) and serotonin (5HT) in a diverse set of experimental studies including reward, punishment and risk based decision making (Balasubramani et al., 2014). Starting with the classical idea that the activity of mesencephalic DA represents reward prediction error, the model posits that serotoninergic activity in the striatum controls risk-prediction error. Our prior model of the BG was an abstract model that did not incorporate anatomical and cellular-level data. In this work, we expand the earlier model into a detailed network model of the BG and demonstrate the joint contributions of DA-5HT in risk and reward-punishment sensitivity. At the core of the proposed network model is the following insight regarding cellular correlates of value and risk computation. Just as DA D1 receptor (D1R) expressing medium spiny neurons (MSNs) of the striatum were thought to be the neural substrates for value computation, we propose that DA D1R and D2R co-expressing MSNs are capable of computing risk. Though the existence of MSNs that co-express D1R and D2R are reported by various experimental studies, prior existing computational models did not include them. Ours is the first model that accounts for the computational possibilities of these co-expressing D1R-D2R MSNs, and describes how DA and 5HT mediate activity in these classes of neurons (D1R-, D2R-, D1R-D2R- MSNs). Starting from the assumption that 5HT modulates all MSNs, our study predicts significant modulatory effects of 5HT on D2R and co-expressing D1R-D2R MSNs which in turn explains the multifarious functions of 5HT in the BG. The experiments simulated in the present study relates 5HT to risk sensitivity and reward-punishment learning. Furthermore, our model is shown to capture reward-punishment and risk based decision making impairment in Parkinson's Disease (PD). The model predicts that optimizing 5HT levels along with DA medications might be essential for improving the patients' reward-punishment learning deficits.

No MeSH data available.


Related in: MedlinePlus

Comparison between the experimental and simulated results for the (A) overall choice (B) Unequal EV (C) Equal EV, under Rapid Tryptophan Depletion (RTD) and Baseline (balanced) condition. Error bars represent the Standard Error (SE) with size “N” = 100 (N = number of simulation instances). The experiment (Expt) and the simulation (Sims) results of any condition are not found to be significantly different. Here the experimental results are adapted from Long et al. (2009).
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Figure 3: Comparison between the experimental and simulated results for the (A) overall choice (B) Unequal EV (C) Equal EV, under Rapid Tryptophan Depletion (RTD) and Baseline (balanced) condition. Error bars represent the Standard Error (SE) with size “N” = 100 (N = number of simulation instances). The experiment (Expt) and the simulation (Sims) results of any condition are not found to be significantly different. Here the experimental results are adapted from Long et al. (2009).

Mentions: When the RTD condition is simulated by setting [αD1, αD2, αD1D2] = [1, 1, 0.0012], and the baseline by [αD1, αD2, αD1D2] = [1, 1, 1.32], a decrease in the selection of the safe choices is observed in the simulation as demonstrated in the experiment. The model has shown increased risk seeking behavior for low α condition particularly in the D1R-D2R co-expressing MSNs. Hence, modulating the αD1D2 best captures the baseline (high αD1D2) and RTD (low αD1D2) conditions for explaining risk sensitivity. The performance of the network model shown in this section is consistent with that of the lumped model described earlier (Balasubramani et al., 2014) in depicting the role of 5HT in risk-based action selection (Figure 3). More analysis on the effect of αD1, αD2, αD1D2 in showing risk sensitivity are provided in Supplementary Material B.


A network model of basal ganglia for understanding the roles of dopamine and serotonin in reward-punishment-risk based decision making.

Balasubramani PP, Chakravarthy VS, Ravindran B, Moustafa AA - Front Comput Neurosci (2015)

Comparison between the experimental and simulated results for the (A) overall choice (B) Unequal EV (C) Equal EV, under Rapid Tryptophan Depletion (RTD) and Baseline (balanced) condition. Error bars represent the Standard Error (SE) with size “N” = 100 (N = number of simulation instances). The experiment (Expt) and the simulation (Sims) results of any condition are not found to be significantly different. Here the experimental results are adapted from Long et al. (2009).
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4469836&req=5

Figure 3: Comparison between the experimental and simulated results for the (A) overall choice (B) Unequal EV (C) Equal EV, under Rapid Tryptophan Depletion (RTD) and Baseline (balanced) condition. Error bars represent the Standard Error (SE) with size “N” = 100 (N = number of simulation instances). The experiment (Expt) and the simulation (Sims) results of any condition are not found to be significantly different. Here the experimental results are adapted from Long et al. (2009).
Mentions: When the RTD condition is simulated by setting [αD1, αD2, αD1D2] = [1, 1, 0.0012], and the baseline by [αD1, αD2, αD1D2] = [1, 1, 1.32], a decrease in the selection of the safe choices is observed in the simulation as demonstrated in the experiment. The model has shown increased risk seeking behavior for low α condition particularly in the D1R-D2R co-expressing MSNs. Hence, modulating the αD1D2 best captures the baseline (high αD1D2) and RTD (low αD1D2) conditions for explaining risk sensitivity. The performance of the network model shown in this section is consistent with that of the lumped model described earlier (Balasubramani et al., 2014) in depicting the role of 5HT in risk-based action selection (Figure 3). More analysis on the effect of αD1, αD2, αD1D2 in showing risk sensitivity are provided in Supplementary Material B.

Bottom Line: Our prior model of the BG was an abstract model that did not incorporate anatomical and cellular-level data.Though the existence of MSNs that co-express D1R and D2R are reported by various experimental studies, prior existing computational models did not include them.Starting from the assumption that 5HT modulates all MSNs, our study predicts significant modulatory effects of 5HT on D2R and co-expressing D1R-D2R MSNs which in turn explains the multifarious functions of 5HT in the BG.

View Article: PubMed Central - PubMed

Affiliation: Department of Biotechnology, Indian Institute of Technology Madras Chennai, India.

ABSTRACT
There is significant evidence that in addition to reward-punishment based decision making, the Basal Ganglia (BG) contributes to risk-based decision making (Balasubramani et al., 2014). Despite this evidence, little is known about the computational principles and neural correlates of risk computation in this subcortical system. We have previously proposed a reinforcement learning (RL)-based model of the BG that simulates the interactions between dopamine (DA) and serotonin (5HT) in a diverse set of experimental studies including reward, punishment and risk based decision making (Balasubramani et al., 2014). Starting with the classical idea that the activity of mesencephalic DA represents reward prediction error, the model posits that serotoninergic activity in the striatum controls risk-prediction error. Our prior model of the BG was an abstract model that did not incorporate anatomical and cellular-level data. In this work, we expand the earlier model into a detailed network model of the BG and demonstrate the joint contributions of DA-5HT in risk and reward-punishment sensitivity. At the core of the proposed network model is the following insight regarding cellular correlates of value and risk computation. Just as DA D1 receptor (D1R) expressing medium spiny neurons (MSNs) of the striatum were thought to be the neural substrates for value computation, we propose that DA D1R and D2R co-expressing MSNs are capable of computing risk. Though the existence of MSNs that co-express D1R and D2R are reported by various experimental studies, prior existing computational models did not include them. Ours is the first model that accounts for the computational possibilities of these co-expressing D1R-D2R MSNs, and describes how DA and 5HT mediate activity in these classes of neurons (D1R-, D2R-, D1R-D2R- MSNs). Starting from the assumption that 5HT modulates all MSNs, our study predicts significant modulatory effects of 5HT on D2R and co-expressing D1R-D2R MSNs which in turn explains the multifarious functions of 5HT in the BG. The experiments simulated in the present study relates 5HT to risk sensitivity and reward-punishment learning. Furthermore, our model is shown to capture reward-punishment and risk based decision making impairment in Parkinson's Disease (PD). The model predicts that optimizing 5HT levels along with DA medications might be essential for improving the patients' reward-punishment learning deficits.

No MeSH data available.


Related in: MedlinePlus