Subjective Feelings associated with Expectations and Rewards during Risky Decision-making in Impulse Control Disorder

doi:10.21203/rs.3.rs-1942744/v1

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Subjective Feelings associated with Expectations and Rewards during Risky Decision-making in Impulse Control Disorder

https://doi.org/10.21203/rs.3.rs-1942744/v1

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published 04 Mar, 2024

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Impulse Control Disorder (ICD) in Parkinson’s disease is a behavioral addiction induced by dopaminergic therapies, but otherwise unclear cognitive etiology. The current study investigates the influence of objective reward processing variables, dopaminergic therapy, and risky decision-making on ‘subjective feelings’ in patients with versus without ICD. Patients with (n = 18) and without (n = 12) a history of ICD secondary to Parkinson’s disease performed a risky decision-making task (on and off standard-of-care dopaminergic therapies). During the task, participants choose a ‘gamble’ or a ‘certain reward’ and report how they feel about decision outcomes. The subjective feeling of ‘pleasure’ is driven by different aspects of reward processing in patients with, versus without, a history of ICD. While off-medication, expectations about risky-decisions have a negative influence on subjective feelings in patients with a history of ICD versus a positive influence in patients without ICD. While on-medication, the influence of reward processing variables and recent experience on modulating subjective feelings in both patient groups are altered; patients with ICD become more emotionally reactive, whereas patients without ICD become less emotionally reactive. Computational modeling of risky decision-making behavior reveals latent factors that drive subjective feelings and risky behaviors in patients with versus without a history of ICD. Group differences are observed in off-medication states. Changes are induced in both groups with medication suggesting ICD-related decision-making may be driven by mechanisms associated with emotion regulation and that heightened dopaminergic processes may induce more reactive, less stable, emotion regulation in patients predisposed to developing ICD.

Impulse Control Disorder (ICD) is a class of behavioral addictions that occur in Parkinson’s disease (PD) as a side effect of dopaminergic therapies.^1–6 ICD consists of the sudden onset of risky decisions, including excessive gambling, shopping, sexual activity, and eating.^1–5 These ICD symptoms encompass behaviors associated with dynamic emotional states resulting directly from dopaminergic modulation.^1–6 Notably, dopaminergic processes underlie both substance use disorders and behavioral addictions, a general finding led to ‘gambling disorder’ as the first behavioral addiction described in the DSM-V.^6–11 It is unclear whether differences in dopaminergic processes or related behavior can be detected in individuals susceptible to developing ICD prior to administering dopaminergic agonist therapy. The ability to do so may provide an approach to identify patients susceptible to ICD prior to the onset of devastating symptoms and more generally may also provide a model for investigating dopaminergic systems in humans predisposed to addiction disorders.

Dopaminergic processes have previously been implicated in the modulation of subjective feelings and emotional changes induced by stimuli and substances in both behavioral and substance-based addiction disorders.^12–15 Positron Emission Tomography imaging has shown that patients with gambling disorder have increased dopamine release associated with higher levels of excitement while.¹² Increased D2-receptor availability has been shown to be associated with modulations of subjective feelings in Alcohol Use Disorder.¹³ Dopamine Transporter polymorphisms have been associated with changes in subjective responsiveness to amphetamines.¹⁴ In patients with ICD, increased subjective ‘wanting’ was observed when patients were on dopaminergic therapies.¹⁵ These and related findings raise questions regarding how ‘subjective feelings’ in the context of risky – dopamine-system engaging – behavior may differ in individuals at risk for developing ICD, but also the potential utility predictive measures of ‘subjective feelings’ may have in identifying neurobehavioral conditions that predispose patients to developing addiction and substance use disorders more generally.

Rutledge and colleagues developed a computational model that is predictive of subjective feelings (e.g., ‘subjective well-being’ or ‘happiness’) from objective and parametrically probed choice behavior.^16–18 They employed a ‘risky decision-making’ task that required participants to choose (on each trial) between a ‘certain reward’ or a ‘gamble’. Participants also report how they feel, which allowed the development of a computational model that links reward processing to measures of subjective feelings. The task and model have been validated and its utility demonstrated for estimating factors associated with positive subjective-feelings in healthy populations and patients with Major Depression Disorder.^16–18 To our knowledge, this approach has not been applied to investigate risky decision-making and associated subjective feelings in patients with Parkinson’s disease and a history of ICD (nor other behavioral addictions or substance use disorders).

Here, we apply a computational psychiatric approach^19,20 to investigate risky decision-making behavior and associated subjective feelings in PD patients with versus without a history of ICD. We test the overarching hypotheses that patients with a history of ICD have altered subjective feelings associated with risky decision-making and that dopaminergic modulation induces changes in how objective information drives feelings associated with subjective well-being. We demonstrate that unmedicated patients with a history of ICD are negatively influenced by expectations about the risky decisions they choose, whereas patients without ICD show a positive influence of these expectations. We show that standard-of-care prescribed dopaminergic medications change how dopamine-related decision-variables influence subjective feelings in both ICD and non-ICD patient groups and that the heightened dopaminergic tone seems to cause patients with a history of ICD become more emotionally reactive in response to environmental stimuli. We discuss the implications of these findings in relation to differences in patients at risk for ICD and the role dopamine levels may play in modulating behavior and associated subjective feelings in patients with addiction disorders.

Risk-taking in patients with ICD.

ICD is characterized by a sudden increase in risky decisions caused by dopaminergic therapies. To test the hypothesis that patients with a history of ICD are in a predisposition that may be revealed by the ICD-inducing medications, we compared risk taking behavior in patients with a history of ICD, versus patients without a history of ICD, in an off-medication state. The decision to gamble occurred at the same rate in both groups (Supplemental Table 2) and was also not affected by medication state (off-medication, ICD = 46.98%, non-ICD = 46.04%, p-value = 0.9502; on-medication, ICD = 44.96%, non-ICD = 47.57%, p-value = 0.3048; ICD-on versus ICD-off, p-value = 0.7666; non-ICD-on versus non-ICD-off, p-value = 0.4023). Factors that drive the decision to gamble were similar in the two groups. A logistic regression fit to participants’ decision to gamble revealed that both groups’ decisions to gamble were positively influenced by the value of the “expected value of the gamble” and negatively influenced by the value of the “certain reward” in both on and off medication states, as expected (Table 1).

Table 1

**Gamble choice model parameters.** The “decision to gamble” on each trial was modeled as a dependent binary outcome with the independent variables being the “expected value of the gamble option”, “certain reward value”, and the participants actual or imputed “subjective feeling rating on the previous trial”, and a constant term “Baseline”. 𝜷_x – coefficients for each independent variable were fit in a logistic regression and reported.
OFF Medication	Non-ICD Off Medication				ICD Off Medication
Model Parameter	Parameter Estimate	95% Confidence Interval	P-value	T-Statistic	Parameter Estimate	95% Confidence Interval	P-value	T-Statistic
Baseline (𝜷_𝟎)	0.141	-1.124 to 1.405	0.811	0.245	0.228	-0.644 to 1.099	0.588	0.551
Gamble EV(𝜷₁)	2.068	1.658 to 2.478	0.000	11.089	1.796	1.309 to 2.283	0.000	7.780
Certain Reward EV(𝜷₂)	-1.828	-2.244 to -1.411	0.000	-9.657	-1.715	-2.159 to -1.272	0.000	-8.159
Subjective Feeling on (t-1) (𝜷₃)	0.045	-0.628 to 0.718	0.885	0.148	-0.088	-0.799 to 0.624	0.798	-0.260
ON Medication	Non-ICD On Medication				ICD On Medication
Model Parameter	Parameter Estimate	95% Confidence Interval	P-value	T-Statistic	Parameter Estimate	95% Confidence Interval	P-value	T-Statistic
Baseline (𝜷_𝟎)	0.269	-0.324 to 0.862	0.340	0.998	-0.253	-2.675 to 2.169	0.828	-0.220
Gamble EV(𝜷₁)	1.826	1.448 to 2.204	0.000	10.620	2.012	1.564 to 2.459	0.000	9.492
Certain Reward EV(𝜷₂)	-1.727	-2.142 to -1.311	0.000	-9.141	-1.799	-2.228 to -1.371	0.000	-8.858
Subjective Feeling on (t-1) (𝜷₃)	-0.123	-0.286 to 0.04	0.125	-1.660	0.173	-1.008 to 1.353	0.762	0.308

Predictors of subjective feelings differentiate ICD-history in off-medication state.

We next tested the hypothesis that the influence of objective decision-making variables on participants’ subjective experience would be different in patients with a history of ICD versus those without (Table 2). Subjective feeling (i.e., ‘Happiness’) model parameters for patients in the off-medication state were significantly different across ICD and non-ICD groups (Table 2a). Post-hoc Tukey HSD comparisons of ICD versus non-ICD groups’ subjective feeling parameters are reported in Table 2a. The ICD group had a significantly greater influence of recent events ($\gamma$, MD = 0.2947, p 0.0000, 95% C.I.=[0.197, 0.3924], Table 2a). The non-ICD group was significantly more positively influenced by the collection of ‘certain rewards’ than the ICD group for which the weight of certain rewards was nearly noncontributory (${w}_{1}$, MD=-0.0497, p = 0.0008, 95% C.I.=[ -0.082, -0.0175], Table 2a). The ‘expected value of chosen gambles’ negatively impacted subjective feelings in the ICD group, whereas these expectations positively impacted subjective feelings in the non-ICD group (${w}_{2}$, MD=-0.1166, p = 0.0000, 95% C.I.=[-0.1653,-0.0678], Table 2a). Finally, the reward prediction error associated with the gamble outcome had a significantly greater positive influence on subjective feelings in the non-ICD group compared to the ICD group (${w}_{3}$, MD=-0.2247, p = 0.0001, 95% C.I.=[-0.3489,-0.1006], Table 2a).

Table 2

**Happiness model parameter comparisons: ICD versus non-ICD.** Participants ‘happiness’ with their decision outcomes were modeled as the dependent variables using Eq. 1. Parameter weight estimates and confidence intervals for each group (ICD and non-ICD) and medication state (on and off) were determined using hierarchichal baysiean modeling. Difference in means (MD) and confifdence intervals (95% CI) for comparisons across ICD-off versus non-ICD-off (2a) or ICD-on versus non-ICD-on (2b) with corresponding p-value are reported.
2a. OFF Medication		Baseline (w0)	Certain Reward (w1)	Gamble EV (w2)	RPE (w3)	Recent Experience Weight (g)
ICD Off Medication	Parameter Estimate	1.882	0.018	-0.059	0.085	0.874
	95% Confidence Interval	1.344	0.015	-0.077	0.081	0.856
		to	to	to	to	to
		2.42	0.021	-0.04	0.089	0.892
Non-ICD Off Medication	Parameter Estimate	1.086	0.068	0.058	0.31	0.58
	95% Confidence Interval	0.709	0.04	0.049	0.163	0.485
		to	to	to	to	to
		1.464	0.096	0.067	0.457	0.675
ICD Off : non-ICD Off	Difference in Means (MD)	0.7959	-0.0497	-0.1166	-0.2247	0.2947
	95% Confidence Interval	-0.0776	-0.082	-0.1653	-0.3489	0.197
		to	to	to	to	to
		1.6693	-0.0175	-0.0678	-0.1006	0.3924
	P-value	0.0863	0.0008	0	0.0001	0
2b. ON Medication		Baseline (w0)	Certain Reward (w1)	Gamble EV (w2)	RPE (w3)	Recent Experience Weight (g)
ICD On Medication	Parameter Estimate	1.763	0.041	-0.013	0.101	0.733
	95% Confidence Interval	1.263	0.024	-0.022	0.047	0.673
		to	to	to	to	to
		2.262	0.057	-0.005	0.155	0.793
Non-ICD On Medication	Parameter Estimate	1.928	0.018	-0.166	0.281	0.697
	95% Confidence Interval	1.601	-0.009	-0.228	0.221	0.663
		to	to	to	to	to
		2.255	0.044	-0.104	0.341	0.731
ICD On : non-ICD On	Difference in Means (MD)	-0.1657	0.0229	0.1522	-0.18	0.0359
	95% Confidence Interval	-1.0392	-0.0093	0.1034	-0.3042	-0.0618
		to	to	to	to	to
		0.7078	0.0552	0.2009	-0.0559	0.1335
	P-value	0.9582	0.2467	0	0.0018	0.7658

Dopaminergic medications differentially influence predictors of subjective experience in ICD and non-ICD groups.

The ‘the expected value of chosen gambles’, collection of ‘certain rewards’, and ‘reward prediction errors’ about chosen gambles are hypothesized to engage or be affected by the dopaminergic system; thus, we hypothesized that the impact of these variables on subjective experience would be modulated by dopaminergic medications used to treat PD symptoms (Table 2b). In patients without a history of ICD, the influence of the ‘certain reward’, ‘expected value of the gamble’, and ‘recent experience’ were significantly different in the on-medication state (Table 3a): the influence of the ‘certain reward’ and ‘expected value of the gamble’ decreased (Table 3a) and the influence of ‘recent experience’ increased (Table 3a). In patients with a history of ICD, the influence of the ‘expected value of the gamble’ remained negative but decreased in magnitude (Table 3b); and the influence of the ‘recent experience’ decreased (Table 3b). The influence of the ‘reward prediction error’ did not change in either patient group (Table 3a and Table 3b).

Table 3

**Happiness model parameter comparisons: On versus Off medication.** Participants ‘happiness’ with their decision outcomes were modeled as the dependent variables using Eq. 1. Parameter weight estimates and confidence intervals for each group (ICD and non-ICD) and medication state (on and off) were determined using hierarchichal baysiean modeling (Table 2). Difference in means (MD) and confifdence intervals (95% CI) for comparisons across non-ICD-on versus non-ICD-off (3a) or ICD-on versus ICD-off (3b) with corresponding p-value are reported.
3a. non-ICD On : non-ICD Off	Difference in Means (MD)	95% Confidence Interval			P-value
Baseline (w0)	0.8418	-0.115	to	1.7987	0.1035
Certain Reward (w1)	-0.0502	-0.086	to	-0.015	0.0022
Gamble EV (w2)	-0.2235	-0.277	to	-0.17	0
RPE (w3)	-0.0286	-0.165	to	0.1074	0.9441
Recent Experience Weight (g)	0.1174	0.0104	to	0.2244	0.0262
3b. ICD On : ICD Off	Difference in Means (MD)	95% Confidence Interval			P-value
Baseline (w0)	-0.1198	-0.901	to	0.6615	0.9772
Certain Reward (w1)	0.0225	-0.006	to	0.0513	0.1767
Gamble EV (w2)	0.0452	0.0016	to	0.0888	0.0391
RPE (w3)	0.0161	-0.095	to	0.1271	0.9807
Recent Experience Weight (g)	-0.1414	-0.229	to	-0.054	0.0004

The addition of dopaminergic medication significantly changed the influence of dopamine-related decision-making variables in both ICD and non-ICD groups. Notably, the impact of these changes was that the influence of these variables for both groups became more similar except for the influence of the ‘expected value of the gamble’ and the ‘reward prediction error’ of those outcomes (Table 2b). ICD and non-ICD showed a negative influence of the ‘expected value of the gamble’ with ICD being influenced significantly less (${w}_{2}$, MD = 0.1522, p = 0.0000, 95% C.I.=[0.1034,0.2009], Table 2b). ICD also showed a significantly reduced influence of the ‘reward prediction error’ (${w}_{3}$, MD=-0.18, p = 0.0018, 95% C.I.=[-0.3042, -0.0559], Table 2b). No difference between groups was observed for the influence of the ‘certain reward’, ‘recent experience’, or ‘baseline’ subjective feelings (Table 2b).

ICD is a behavioral addiction disorder caused by dopaminergic action.^3,21 And, dopaminergic systems are engaged by reward expectation, reward feedback, and associated subjective feelings.^18,22−24 Thus, we sought to understand the impact dopaminergic medications and ICD status may have on decisions to take risks and subjective feelings associated with these actions. In our experimental setting, the rate of risky decisions was not different across groups nor affected by the medication state. However, standard-of-care dopaminergic medications prescribed to manage PD symptoms resulted in significant changes in the influence of reward processing variables on subjective feelings. Altogether, our results suggest that patients with ICD may be in a predisposed state where risky choices and dopaminergic medications express a differential influence on emotional states compared to patients without ICD. Our results are consistent with the hypothesis that the impact of ICD inducing dopamine receptor agonists may reveal an underling predisposition for emotional reactivity leading to increased risky behavior as a form of emotional regulation.

The observation that patients with ICD take an equal number of risks as patients without a history of ICD (independent of medication state) is consistent with our experimental design and prior work,²⁵ though seemingly contradictory to expectation given the ICD phenotype.^3,21,25 On any given trial there is a 50% chance that the ‘gamble option’ is rationally the better option. That participants chose to gamble slightly less than 50% of the time is consistent with the general phenomena of humans being risk averse.^26,27 However, our computational psychiatric approach^19,20 and utilization of Rutledge and colleagues’ computational model of ‘happiness’^16–18 allows a deeper investigation into factors that influence subjective feelings associated with risky choices in the context of a behavioral addiction disorder.

Dopaminergic medications are expected to modulate how reward-related variables are processed by dopaminergic systems including the role those systems may play in the generation of emotion. Indeed, we observed changes in the influence dopamine-system related decision variables have on subjective feelings. While off-medication, ICD and non-ICD groups are significantly different in how reward expectations, reward prediction errors, and recent experiences influence their feelings. Patients with ICD, generally, show less immediate influence of reward-processing variables on their current feelings, but their impact appears to linger longer over more trials. In contrast to the non-ICD group, the ICD group demonstrates an opponent effect of their expectations with positive expectations driving feelings in a negative direction. When patients with ICD are on their prescribed dopaminergic medications, the influence of the expected value of the gamble on their feelings decreases, as does the influence of past trials. In contrast, the non-ICD group shows an increase in the influence of past trials, and they acquire a negative impact of expected value. Together these results are consistent with dopaminergic medications increasing emotional reactivity in the ICD group. Further, in this experimental context, computational modeling reveals subtle differences that would otherwise be difficult to detect. This suggests potential for a behavioral screening approach.¹⁵ The task we use is short, only 30 minutes, and can likely be significantly shortened. Development of such a screening tool would be a valuable addition to other more complex multi-session interactions or neuroimaging based approaches.^28,29 More work is needed before such a tool could be implemented, but our results suggest potential utility in using objective measures of moment-to-moment changes in subjective feelings as expressed through task behavior.

There is a yet unclear and complex relationship between reward processing variables, associated subjective feelings, the dopaminergic system, and disorders like PD and ICD. Through computational modeling of risky decisions, we identify quantitative changes in the influences of reward processing on subjective feelings and how these systems may be altered by dopaminergic medications and ICD. Our results are consistent with risk taking in patients with ICD being a form of emotional regulation that is affected by stimuli and interventions that engage the dopaminergic system. Our results also suggest that a computational psychiatric approach may be able to identify patients at risk for developing ICD or perhaps other addiction disorders, but more work is needed before this conclusion can be reached. The present study was conducted at a single center; it is possible this introduces bias into our study that we cannot measure or detect. Similarly, a single-center study places a limit on sample size; though, our sample is comparable to previous studies.³⁰ Nonetheless, our results highlight the impact computational precision can have in aiding our understanding of complex, dynamic behaviors associated with risk taking, reward processing, and subjective experience in psychiatric conditions like ICD.

The Institutional Review Board at Wake Forest University Health Sciences approved all procedures described (protocol #:IRB00051643). All research was performed in accordance with relevant guidelines including those outlined in the Declaration of Helsinki. Informed consent was obtained from all participants.

Participants:

Patients with PD were recruited from the Movement Disorders Clinic at Atrium Health Wake Forest Baptist. The Questionnaire for Impulsive-Compulsive Disorders in Parkinson’s Disease-Rating Scale (QUIP-RS) was used to determine ICD status.⁴ A cutoff of 10 or higher on the ICD categories of the QUIP (gambling, sex, buying, and eating) was used to sort patients into the ICD cohort (N = 18), and PD patients scoring less than 10 were in the non-ICD cohort (N = 12).^4,5 Patients were matched for gender and age and standard of care medications recorded (Supplemental Table 1).

Procedures:

Participants were scheduled for two research study visits. On both visits, the participants performed the risky decision-making task (Fig. 1). Participants were instructed to come to the study visit in either an on- or off-medication state – the order randomized across participants. For the on-medication state, participants were instructed to take their medications as usual. For the off-medication state, participants were instructed to withhold from taking medications for at least 8 hours prior to their scheduled session.

Risky Decision-Making Task (Fig. 1):

Participants complete a risky decision-making task delivered through a computer-controlled interface (Fig. 1). This task is based on Rutledge and colleagues’ prior work: ^16–18 On each trail, participants are presented with two options: a “certain reward” or a “gamble”. “Certain reward” represents a value equal to a shown dollar amount ranging $1 to $6, and “gamble” represents two independent values ranging $0 to $6 each with a 0.5 probability of payout. The side for each option is randomized for each trial. Decisions must be made ‘as quickly as possible’ with a time limit set according to random draws from a Poisson distribution with mean = variance = $\lambda =6s$ . If participants failed to respond within the time limit, they receive a screen with the message “too late” (this rarely happened). After a choice is made, participants receive feedback about the outcomes of their choice. If a “certain reward” was chosen, they “win” the amount shown. If the “gamble” was chosen, the outcome of that gamble is shown. Participants are instructed, at the outset of the task, that one trial will be randomly selected at the end of the task and the outcome of that trial will contribute to their “bonus payment” of actual money. On one one-third of randomly selected trials (via computer random number generator), participants are shown a screen that asks, “How do you feel about the last outcome?” and are to enter their answer using a slider bar labeled from “very bad” to “very good”. The slider bar entry is encoded as a number between − 4 to 0 to + 4 in whole units.

Statistical Analysis and Computational Modeling of Behavior:

All statistical analyses were completed using RStudio.³¹ Model parameters were determined using the RStan, rstanarm, and hBayesDM packages to perform hierarchical Bayesian modeling using task parameters and participant behvaior.^32–34 The difference in gambling rate was compared using the Wilcoxon Rank Sum Test. Individual participant behavior was fit using hierarchical Bayesian methods.

Two models were fit to participant behavior in our analyses. First, we fit a model (Eq. 1) following Rutledge et al.,^16,17,32 to determine how elements of each trial contributed to each participants’ rating of subjective ‘happiness’ with the outcome of their decisions:

$Happiness\left(t\right)={w}_{0}+{w}_{1}\sum _{j=1}^{t}{\gamma }^{t-j}C{R}_{j}+{w}_{2}\sum _{j=1}^{t}{\gamma }^{t-j}E{V}_{j}+{w}_{3}\sum _{j=1}^{t}{\gamma }^{t-j}RP{E}_{j}$ (Eq. 1)

Where on each trial $t$, the subjective happiness with the outcome is fit to a linear combination of the current $t$ trials’ values: $CR$ = Value of chosen Certain Rewards, $EV$ = Expected Value of Chosen Gambles, and $RPE$ = Reward Prediction Error. ${w}_{0}$ ${w}_{1}$, ${w}_{2}$, ${w}_{3}$ are weights capturing the influence of each factor. $\gamma$ is a ‘forgetting factor’^16,17,32 that increases the weight of recent events over events that occur further back in time. ${w}_{0}$ ${w}_{1}$, ${w}_{2}$, ${w}_{3}$, and $\gamma$ are fit to each participants’ subjective rating behavior. Actual values and predictions of participants’ subjective rating were used in Eq. 2 to determine the influence of feelings and objective parameters on participants’ decision to ‘gamble’ or take the ‘certain reward’ on each trial $t$:

$\text{ln}\left(\frac{P\left(Gamble\left(t\right)=1\right)}{P\left(Gamble\left(t\right)=0\right)}\right)={\beta }_{0}+ {\beta }_{1}EV\_G\left(t\right)+{\beta }_{2}EV\_CR \left(t\right)+ {\beta }_{3} SR\_H(t-1)$ (Eq. 2)

Here, we included the objective values: the expected value of the certain reward (“$EV\_CR$”) and the expected value of the gamble (“$EV\_G$”), and the reported (or imputed) subjective rating of happiness (“$SR\_H$”) with the outcome of the prior trial ($t-1$). ${\beta }_{0}$, ${\beta }_{1}$, ${\beta }_{2}$, and ${\beta }_{3}$ are weights capturing the influence of each factor. Additional details of the modeling approach are provided in the Supplemental Methods.

Comparison of determined model parameters across groups (+/- history of ICD) or conditions (+/- medication) were performed using one-way analysis of variance (ANOVA) with post-hoc Tukey's Honest Significant Difference Tests (Tukey’s HSD), which incorporated adjustments for multiple comparisons. Post hoc tests were only performed when initial one-way ANOVA tests comparing the parameter values across the 4 conditions (ICD-off, ICD-on, non-ICD-off, non-ICD-on) were significant by the following criteria: F(4, 3), p < 0.05. Post hoc test results including confidence intervals are reported in tables and text and were deemed significant when p < 0.05.

Acknowledgements:

This work is supported by Dr. Kenneth T. Kishida’s funding from the NIH: R01DA048096, R01MH121099, R01NS092701, R01MH124115, and KL2TR001421. This work is supported by Brittany Liebenow’s funding from the NIH: F30DA053176.

Author Contributions:

BL and KTK made substantial contributions to the conception and design of the work and interpretation of data. BL additionally made substantial contributions to the acquisition and analysis of data. AJ made substantial contributions to the acquisition and analysis of data. ED and MMM made substantial contributions to the acquisition of data. LPS made substantial contributions to the analysis of data. AWL, MSS, and IH made substantial contributions to the interpretation of data. MSS, and IH additionally made substantial contributions to the acquisition of data. All authors reviewed and edited this manuscript in its entirety for intellectual content, provided final approval of the version to be submitted for review and publication, and agree to be accountable for all aspects of the work including ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Data Availability:

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing Interests Statement:

Dr. Ihtsham ul Haq has received salary support for research from Allergan, Boston Scientific, Great Lakes Neurotechnology, and Pfizer. He has consulted for compensation for Boston Scientific and Medtronics. Dr. Mustafa Siddiqui has received research support as a site Principal Investigator for clinical trials from Neuraly, University of Rochester, Michael J. Fox-Neuropoint Alliance, Theravance Biopharma, Impax Laboratories, Sun Pharma, Abbvie, Boston Scientific Neuromodulation, Biogen and Sunovion. He has received honoraria as a scientific advisor from Abbvie and Boston Scientific Neuromodulation. Dr. Adrian Laxton is a consultant and member of the safety committee for Monteris Medical. All other authors have nothing to disclose.

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Competing interest reported. Dr. Ihtsham ul Haq has received salary support for research from Allergan, Boston Scientific, Great Lakes Neurotechnology, and Pfizer. He has consulted for compensation for Boston Scientific and Medtronics. Dr. Mustafa Siddiqui has received research support as a site Principal Investigator for clinical trials from Neuraly, University of Rochester, Michael J. Fox-Neuropoint Alliance, Theravance Biopharma, Impax Laboratories, Sun Pharma, Abbvie, Boston Scientific Neuromodulation, Biogen and Sunovion. He has received honoraria as a scientific advisor from Abbvie and Boston Scientific Neuromodulation. Dr. Adrian Laxton is a consultant and member of the safety committee for Monteris Medical. All other authors have nothing to disclose.

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Subjective Feelings associated with Expectations and Rewards during Risky Decision-making in Impulse Control Disorder

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