Dopamine enhances willingness to exert effort for reward in Parkinson’s disease

Accepted Manuscript

Dopamine enhances willingness to exert effort for reward in Parkinson’s disease

Dr. Trevor T-J. Chong, Valerie Bonnelle, Sanjay Manohar, Kai-Riin Veromann, KinanMuhammed, George K. Tofaris, Michele Hu, Masud Husain

PII: S0010-9452(15)00127-6

DOI: 10.1016/j.cortex.2015.04.003

Reference: CORTEX 1444

To appear in: Cortex

Received Date: 8 December 2014

Revised Date: 6 March 2015

Accepted Date: 9 April 2015

Please cite this article as: Chong TT-J, Bonnelle V, Manohar S, Veromann K-R, Muhammed K, TofarisGK, Hu M, Husain M, Dopamine enhances willingness to exert effort for reward in Parkinson’s disease,CORTEX (2015), doi: 10.1016/j.cortex.2015.04.003.

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Dopamine enhances willingness to exert effort for reward

in Parkinson’s disease

Trevor T-J Chong a,b

, Valerie Bonnelle a, Sanjay Manohar

a, Kai-Riin Veromann

a, Kinan

Muhammed a,b

, George K Tofaris b, Michele Hu

b, Masud Husain

a,b

Submitted as a NOTE (3000 words) to Cortex

a Department of Experimental Psychology, University of Oxford, Oxford OX1 3UD

b Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford OX3 9DU

Corresponding Author:

Dr Trevor T-J Chong

Nuffield Department of Clinical Neurosciences

John Radcliffe Hospital

Oxford OX3 9DU

United Kingdom

+44 (0) 1865 618634

[email protected]

Running Title: Dopamine in Effort-Based Decision-Making

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Abstract

Parkinson’s disease (PD) is traditionally conceptualised as a disorder of movement, but

recent data suggest that motivational deficits may be more pervasive than previously thought.

Here, we ask whether subclinical deficits in incentivised decision-making are present in PD

and, if so, whether dopaminergic therapy ameliorates such deficits. We devised a novel

paradigm in which participants decided whether they were willing to squeeze a hand-held

dynamometer at varying levels of force for different magnitudes of reward. For each

participant, we estimated the effort level at which the probability of accepting a reward was

50% – the effort ‘indifference point’. Patients with PD (N = 26) were tested ON and OFF

their usual dopaminergic medication, and their performance compared to those of age-

matched controls (N = 26). No participant was clinically apathetic as defined by the Lille

Apathy Rating Scale. Our data show that, regardless of medication status, patients with PD

chose to engage less effort than controls for the lowest reward. Overall, however, dopamine

had a motivating effect on participants’ choice behaviour – patients with PD chose to invest

more effort for a given reward when they were in the ON relative to OFF dopamine state.

Importantly, this effect could not be attributed to motor facilitation. We conclude that deficits

in incentivised decision-making are present in PD even in the absence of a clinical syndrome

of apathy when rewards are low, but that dopamine acts to eliminate motivational deficits by

promoting the allocation of effort.

Keywords

Dopamine, effort, reward, decision-making, Parkinson’s disease

Abbreviations

PD = Parkinson’s disease; MoCA = Montreal Cognitive Assessment; LARS = Lille Apathy

Rating Scale; DASS = Depression Anxiety Stress Scale; UPDRS = Unified Parkinson’s

Disease Rating Scale; LE = Levodopa equivalence; MVC = Maximal Voluntary Contraction;

ANOVA = Analysis of Variance

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1. Introduction

Parkinson’s disease (PD) is a prototypical model of striatal dysfunction. The accompanying

dopaminergic depletion is traditionally considered one of the underlying mechanisms that

contributes to the cardinal motor symptoms of bradykinesia, rigidity and tremor (Jankovic,

2008). Recently, however, some authors have proposed that at least some Parkinsonian motor

symptoms may represent a deficit in ‘implicit’ motor motivation. For example, one study

reported that patients with PD had similar kinematic parameters to controls, but were more

likely to move slowly when the energetic demands of a movement increased (Mazzoni,

Hristova, & Krakauer, 2007). They therefore conceptualised Parkinsonian bradykinesia as a

shift in the balance between the perceived reward of reaching the target endpoint and the

amount of effort required to achieve a movement of normal speed. Findings such as this

suggest that motivational deficits may be more pervasive in PD than previously thought.

To determine if an action is worth initiating, one must evaluate the cost of that action – for

example, the effort associated with it – against its potential rewards. Effort is generally

considered aversive and, when given a choice, most animals will usually prefer actions that

are less effortful (Salamone, Correa, Farrar, & Mingote, 2007; Walton, Kennerly,

Bannerman, Phillips, & Rushworth, 2006). Thus, rewards which require less effort are

generally preferred over rewards of identical value which are associated with greater effort

(Hull, 1943). A number of animal studies have implicated dopamine in effort and reward

valuation (Pasquereau & Turner, 2013). In rats, dopamine depletion decreases tolerance for

effort, while drugs enhancing dopamine have the reverse effect (Salamone & Correa, 2002;

Salamone et al., 2007). Human data regarding the involvement of dopamine on effort and

reward integration remain relatively scarce, although there is a growing interest towards

understanding the role of dopamine in cost-benefit integration (Frank, 2005; Wardle,

Treadway, Mayo, Zald, & de Wit, 2011).

The pathognomonic striatal dysfunction in PD makes it an excellent model with which to

study the effect of dopamine on incentivised decision-making in humans. It remains poorly

understood how PD affects the valuation of an action’s costs and benefits, and how that may

subsequently affect choice behaviour. Although several studies in PD have examined

impairments in decision-making and reward (e.g., Bódi et al., 2009; Cools, Barker, Sahakian,

& Robbins, 2003; Czernecki et al., 2002; Frank, Seeberger, & O'Reilly, 2004; Mimura, Oeda,

& Kawamura, 2006; Porat, Hassin-Baer, Cohen, Markus, & Tomer, 2014), relatively few

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have explicitly examined effort-based motivational deficits (e.g., Porat et al., 2014; Schmidt

et al., 2008). Given the large animal literature postulating the role of striatal dopamine in

incentivisation, we hypothesise that motivational deficits are likely present at least

subclinically in PD, and independent of a clinical syndrome of apathy in which amotivation is

a defining characteristic (Pluck & Brown, 2002). Moreover, we predict that dopamine should

ameliorate these motivational deficits by promoting the allocation of effort.

Here, we report the results of a novel paradigm in which participants decided whether to

accept or reject a potential reward based on the effort that would be required to obtain it. An

important feature of our design was that it allowed us to focus on the effects of dopamine on

participants’ choices. This contrasts with many previous studies, especially those in animals,

which have inferred the motivational effects of dopamine on behaviour by examining the

effort manifest in the actions themselves (see Salamone et al., 2007 for review). By analysing

participants’ choices, we were able to calculate for each stake the effort level at which

participants considered an action not worth pursuing – their ‘effort indifference points.’ We

could then quantify the effect of PD and dopaminergic medication on shifting the position of

these indifference points relative to healthy controls.

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2. Material and Methods

2.1. Participants

This study was approved by the local institutional review board, and patients did not receive

financial compensation for their participation in the study. Patients with PD were recruited

through a tertiary hospital and community support groups. All patients were reviewed by at

least two consultant neurologists (TC and one other), and had a confirmed diagnosis of

idiopathic PD. They were excluded if they had a history of stroke, depression, impulse

control disorder, cognitive impairment (Montreal Cognitive Assessment (MoCA) score

<26/30) or musculoskeletal disease that would have interfered with their ability to perform

our task. Patients were on levodopa-containing compounds (n = 10), dopamine agonists (n =

5, including pramipexole, ropinirole, rotigotine), or combinations of both (n = 11). Clinical

severity was assessed with the Unified Parkinson’s Disease Rating Scale (UPDRS) (Fahn,

Elton, & Committee, 1987). We screened for apathy and depression with the Lille Apathy

Rating Scale (LARS) (Sockeel et al., 2006) and Depression Anxiety Stress Scale (DASS)

(Brown, Korotitsch, Chorpita, & Barlow, 1997; Lovibond & Lovibond, 1995), respectively.

Table 1 summarises the characteristics of our final sample of 26 patients. An equal number of

age- and education-matched controls was recruited through the local participant pool. Control

participants were excluded if they had a history of neurological illness, but exclusion criteria

were otherwise identical to those for patients.

2.2. Method

Participants were seated in front of a computer running Psychtoolbox

(http://psychtoolbox.org) implemented in Matlab (MathWorks, USA). They registered their

responses using two hand-held dynamometers (SS25LA, BIOPAC Systems, USA).

At the beginning of each session, the dynamometers were calibrated to each participant’s

maximal voluntary contraction (MVC). Participants alternately squeezed the left and right

dynamometers as strongly as possible, and the maximum contraction reached over three trials

was taken as each participant’s MVC for that hand. This procedure normalised subsequent

responses to each participant’s maximum force.

During the experiment, participants were presented with cartoons of apple trees, and were

instructed to accumulate as many apples as possible based on the combinations of stake and

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effort that were presented (Figure 1). Potential rewards were indicated by the number of

apples on the tree (1, 3, 6, 9, 12, 15), while the associated effort was indicated by the height

of a yellow bar positioned on the tree trunk, and ranged over six levels as a function of

participants’ MVCs (60%, 70%, 80%, 90%, 100%, 110%). By referencing the effort levels in

each session to each individual’s maximum force, we were able to normalise the difficulty of

each level across sessions and across individuals. Participants were familiarised with the

effort required for each level prior to commencing the experiment.

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Insert Figure 1 about here

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On each trial, participants had to decide whether they were willing to exert the specified level

of effort for the specified stake. If they judged the particular combination of stake and effort

to be ‘not worth it,’ they selected the ‘No’ response, and the next trial would commence. If,

however, they decided to engage in that trial, they selected the ‘Yes’ option. The tree would

subsequently reappear on the left or right of the screen (selected at random), corresponding to

the hand to be used for response execution. Participants then had five seconds to squeeze the

dynamometer to reach the target effort level. Apples could only be acquired if the target

effort level was reached; if participants failed to do so, no apples were received. If they

rejected a particular combination of effort and reward, they were instructed that a different

tree would subsequently appear and they were to proceed with the same process. At the

conclusion of the trial, they received feedback on their performance. Combinations of stake

and effort were presented according to an adaptive staircase algorithm (see Supplementary

Material).

After an initial practice block of 36 trials, participants completed five experimental blocks of

36 trials, separated by rest breaks. They were tested in two sessions approximately one week

apart. In one (‘ON’) session, patients were tested while taking their usual dopaminergic

medication; and, in the other (‘OFF’), patients were tested after overnight withdrawal of

medication. The order of ON and OFF sessions was counterbalanced across patients. Control

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participants performed two identical sessions to exclude the possibility of strategic changes

across sessions.

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Insert Figure 2 about here

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3. Results

For each stake, we estimated the effort level at which the probability of accepting an offer

was 50% (i.e., the effort ‘indifference point’). For each participant, we fitted a logistic

function to the choice probability data at each effort level (Figure 2). The effort indifference

points thus derived for each participant were then plotted against their corresponding stake

magnitudes. We then compared the effort indifference points for PD ON, PD OFF and

controls with repeated-measures ANOVAs.

3.1. Control Data

First, we ensured that control performance did not differ across testing sessions (Figure 3). A

repeated-measures ANOVA on effort indifference points with the factors of Session (First,

Second) and Stake (Levels 1-6) showed a significant main effect of Stake (F(5, 125) = 47.90,

p < .001), with Bonferroni-corrected contrasts revealing significant differences at each

successive Stake Level (all p < 0.05). Importantly, neither the main effect of Session (F(1,

25) = 0.59) nor its interaction with Stake (F(5, 125) = 1.54) was significant, indicating no

differences in control performance across Sessions 1 and 2. We therefore collapsed the

control data across the two sessions for subsequent analyses.

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3.2. Patient Data – ON vs OFF

To compare the effect of drug on effort indifference points, we performed a similar two-way

repeated-measures ANOVA, with the factors of Drug (ON, OFF) and Stake (1-6) (Figure

4A). This revealed a significant main effect of Drug, F(1, 25) = 25.9, p < .001, such that

patients ON medication were willing to invest more effort than those OFF, as reflected by a

higher mean effort indifference point (M 4.34 ± SE 0.10 vs 3.89 ± 0.13). The main effect of

Stake was also significant, F(5, 125) = 111.2, p < .001, with Bonferroni-corrected contrasts

demonstrating significant differences between all pairings of Stake (p < .001). The interaction

between Drug and Stake was not significant (F(5, 125) = 1.26).

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To determine if maximal force output was modulated by dopamine, we compared MVCs ON

and OFF medication. Importantly, they were not significantly different (OFF 355 ± 24N vs

ON 361 ± 23N, t(25) = -1.34). There was also no significant effect of time-on-task, which we

used to examine the effect of fatigue on motor performance (see Supplementary Material).

Furthermore, there was no correlation between shifts in effort indifference points and

improvements in motor severity on the motor subscale (Part III) of the UPDRS (r = 0.22, p =

.28; see Supplementary Material). Thus, the shift of effort indifference points ON medication

was not simply attributable to a capacity to exert greater force or reductions in motor severity.

Given the association between dopamine and impulse control disorders (Weintraub et al.,

2010), could the incentivising effect of dopamine be mediated by lower risk aversion? We

analysed the proportion of trials in which patients engaged in effort levels beyond their

capacity to perform (i.e., Effort Level 6, or 110% MVC). Importantly, there was no

significant difference in this parameter ON versus OFF medication (t(25) = -1.59).

Furthermore, there was no effect of drug on the proportion of accepted trials in which patients

failed to reach the target effort level (t(25) = 0.17), and no effect of drug on failure rates or

trial history (see Supplementary Material).

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3.3. Patient vs Control Data

Next, we compared patient performance with that of controls (Figure 4B). An ANOVA

showed a significant effect of Stake (F(5, 250) = 106.96, p < .001) but not of Group, which

was qualified by a significant interaction (F(5, 250) = 9.62, p < .001). Patients ON dopamine

invested less effort than controls for the lowest Stake (2.42 ± 0.24 vs 3.19 ± 0.19, p < .05).

However, quite the opposite was found for higher Stakes (levels 4-6), at which controls were

actually willing to exert less effort than patients ON medication (Stake Level 4, ON 4.89 ±

0.11 vs Control 4.40 ± 0.11, p < .005; Level 5, ON 5.05 ± 0.12 vs Control 4.62 ± 0.12, p <

.05; Level 6, ON 5.26 ± 0.13 vs Control 4.75 ± 0.13, p < .01). Notably, there was no

significant difference in MVCs between patients ON medication and controls (Patients 360 ±

23N vs Controls 350 ± 24N, t(50) = 0.31).

For patient performance OFF medication vs controls (Figure 4C), the analogous ANOVA

demonstrated a significant effect of Stake (F(5, 250) = 111.90, p < .001), with a non-

significant main effect of Group (F(1, 50) = 2.70). Again, the two-way interaction was

significant (F(5, 250) = 6.12, p < .001), with Bonferroni-corrected comparisons revealing that

patients OFF medication were willing to expend less effort than controls, but only for the

lowest two stakes (Stake Level 1, OFF 3.19 ± 0.20 vs Control 2.31 ± 0.21, p < .005; Level 2,

3.80 ± 0.16 vs 3.33 ± 0.16, p < .05). MVCs between patients OFF medication and controls

were not significantly different (Patients 354 ± 23N vs Controls 350 ± 24N, t(50) = 0.129).

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4. Discussion

Few studies to date have examined impairments in effort-based decision-making in PD (e.g.,

Porat et al., 2014; Schmidt et al., 2008). Our data reveal two key findings. First, patients with

PD, regardless of medication status, were willing to invest less effort than their healthy

counterparts for the lowest reward. Second, dopamine exerted a motivating influence on

choice behaviour. Specifically, patients with PD chose to invest more effort for a given stake

when they were ON medication relative to OFF. Importantly, the incentivising effect of

dopamine cannot simply be due to motor facilitation, as there were no significant differences

in MVC across drug session, or between patients and controls. Furthermore, the shift in effort

indifference points from OFF to ON was not correlated with improvements in clinical motor

severity as measured by the motor section of the UPDRS.

A notable feature of our paradigm, and one of its significant strengths, is that it allowed us to

dissect out choice behaviour from motor preparation and execution. Many studies, in

particular those in animals, infer the effect of dopamine on effort by observing the effort

manifest in the behaviour itself (see Salamone et al., 2007 for review). A recent study in

healthy adults, for example, reported that dopamine augments response vigour in proportion

to average reward rate (Beierholm et al., 2013). In contrast to these previous studies,

however, our paradigm demonstrates that the incentivising effect of dopamine is evident even

during choice behaviour – i.e., prior to an action being initiated.

The question of how dopamine modulates aberrant cost-benefit integration in PD has not

been extensively explored. The finding that patients ON medication were willing to exert

greater force relative to OFF supports animal data showing that increasing dopaminergic tone

enables high-effort behaviours and increases tolerance of effort expenditure (Cagniard et al.,

2006; Niv, Daw, Joel, & Dayan, 2007; Robbins & Everitt, 1992; Wardle et al., 2011).

Critically, this incentivising effect of dopamine is independent of any motor changes which

might have occurred between the OFF and ON sessions. This is an important consideration,

given that a recent study in PD found that the greater number of key-presses that patients

exerted for reward when medicated was related to an improvement in their motor symptoms

(Porat et al., 2014). Our study builds on these previous findings by showing that the

motivational effect of dopamine on effort-based choices can occur independent of motor

facilitation, as measured by either motor strength (MVC) or the clinical severity of motor

signs (UPDRS).

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Studies of disordered motivation in PD often focus on clinically apathetic patients (e.g.,

Dujardin et al., 2007). Here, we show that patients with PD, who were neither clinically

apathetic nor depressed, and regardless of medication status, were less motivated than

controls to invest effort when the rewards were low. This confirms that Parkinsonian striatal

dysfunction is sufficient to cause an imbalance in the estimation of an action’s expected

value, and is consistent with animal studies showing that dopamine antagonism or depletion

reduces willingness to work for reward (Salamone et al., 2007). Although we only found a

reduction in motivation for the lowest levels of reward, any potential differences at higher

stakes in the comparison of PD OFF vs controls could very well have been obscured by a

saturation effect at the highest levels of effort. It should also be noted that our finding of

lower effort indifference points in patients vs controls for low stakes occurred despite the

LARS scores between the two groups being statistically similar and within the normal range.

This result therefore emphasises that motivational deficits may be present subclinically in PD

for low rewards, but that they are detectable with a sufficiently sensitive measure.

Finally, it is worth considering why participants in our task may have been willing to trade

effort for fictive rewards. There is of course a considerable literature that supports the view

that effort carries a value cost, and discounts the subjective value of potential rewards (e.g.,

Botvinick, Huffstetler, & McGuire, 2009). Complementing this literature is a considerable

volume of evidence showing that real and fictive rewards are discounted similarly in

behavioural paradigms (Hinvest & Anderson, 2010; Madden, Begotka, Raiff, & Kastern,

2003; Matusiewicz, Carter, Landes, & Yi, 2013). Furthermore, fMRI studies have shown that

real and fictive rewards recruit overlapping neural regions (Bickel, Pitcock, Yi, & Angtuaco,

2009). In light of these findings, we therefore expected our participants to discount effort

even in the presence of fictive rewards, as they in fact ultimately did.

Together, our findings show that deficits in incentivised decision-making are present in PD

when rewards are low even in the absence of a clinical syndrome of apathy, but that

dopamine acts to ameliorate motivational deficits by promoting the allocation of effort. This

echoes recent reports that Parkinsonian movement shares many attributes with healthy

behaviour (Desmurget et al., 2004), with a reduced motor drive being central to certain

Parkinsonian motor symptoms (Kojovic et al., 2014; Mazzoni et al., 2007). The

pervasiveness of motivational impairments in PD invites reconsideration of the degree to

which Parkinsonian hypokinesia is due simply to motor dysfunction versus a primary

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motivational deficit. These contributions are not mutually exclusive, and both might be

important in determining the surface manifestations of dopaminergic deficits in PD.

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Figure Legends

Figure 1. Summary of a typical trial.

Stakes were indicated by the number of apples on the tree (1, 3, 6, 9, 12, 15), while the

associated effort was indicated by the height of a yellow bar positioned at one of six levels on

the tree trunk (corresponding to MVCs of 60%, 70%, 80%, 90%, 100%, 110%). On each

trial, participants decided whether they were willing to exert the specified level of effort for

the specified stake. If they judged the particular combination of stake and effort to be ‘not

worth it,’ they selected the ‘No’ response. If, however, they decided to engage in that trial,

they selected the ‘Yes’ response, and then had to squeeze a hand-held dynamometer with a

force sufficient to reach the target effort level. Participants received visual feedback of their

performance, as indicated by the height of a red force feedback bar. To reduce the effect of

fatigue, participants were only required to squeeze the dynamometers on 50% of accepted

trials. At the conclusion of each trial, participants were provided with feedback on the

number of apples gathered.

Figure 2. An example of the fitted probability functions for a representative participant.

Logistic functions were used to plot the probability of engaging in a trial as a function of the

effort level for each of the six stakes. Each participant’s effort indifference points – the effort

level at which the probability of engaging in a trial for a given stake is 50% (indicated by the

dashed line) – were then computed.

Figure 3. Effort indifference points plotted as a function of stake for healthy controls in

Sessions 1 and 2.

Effort indifference points divide the stake-effort space into a sector in which participants are

willing to engage in an effortful response (below the curve) from a sector that is judged ‘not

worth the effort’ (above the curve). Control performance was identical between sessions 1

and 2. Error bars indicate ± 1 SEM.

Figure 4. Effort indifference points plotted as a function of stake for patients and

controls.

(A) Regardless of medication status, patients had significantly lower effort indifference

points than controls for the lowest reward. However, for high rewards, effort indifference

points were significantly higher for patients when they were ON medication, relative not only

to when they were OFF medication, but even compared to healthy controls. Inset: For clarity,

PD data are replotted against control performance for patients (B) ON medication and (C)

OFF medication. Shading denotes effort indifference points being greater for patients than

controls (orange), or less for patients than controls (yellow). Error bars indicate ± 1 SEM.

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Table 1.

Summary of participant demographics (means ± SD).

Patients with PD Healthy Controls Group Difference

N 26 26 -

Age (years) 66.6 (± 6.8) 66.2 (± 6.4) t(50) = 0.23, p = .82

Gender (M:F) 17:9 15:11 χ2 = 0.08, p = .78

LARS a

-28 (± 4.2) -29 (± 5.7) U = 277, p = .23

Depression Score on DASS b

2.00 (± 2.23) 1.5 (± 1.84) U = 295, p = .41

MoCA Scores c 28.2 (± 1.3) 28.2 (± 1.7) t(50) = 0.09, p = .93

UPDRS III (ON, OFF) d

ON: 21.6 (± 11.7)

OFF: 31.9 (± 13.6)

N/A -

Hoehn & Yahr Stage d 1.85 (± 0.54) N/A -

Disease Duration (years) 5.1 (± 3.1) N/A -

Levodopa Equivalence (mg) e 538 (± 275) N/A -

Interval between sessions

(days)

7.8 (± 1.7) 7.2 (± 0.8) t(50) = 1.51, p = .14

Average time since last dose

(hours)

ON: 2.28 (± 0.97)

OFF: 13.4 (± 3.4)

N/A -

a Normal range < -16 (Sockeel et al., 2006)

b Normal range = 0-9 (Lovibond & Lovibond, 1995)

c MoCA normal range 26-30.

d Clinical severity was assessed with the motor section (Part III, items 18-31) of the Unified

Parkinson’s Disease Rating Scale (UPDRS) (Fahn et al., 1987) and the modified Hoehn and

Yahr scale. See Supplementary Table 1 for a full summary of patients’ UPDRS data.

e Levodopa equivalence (LE) scores were calculated based on standard formulae (Tomlinson

et al., 2010). Patients were on levodopa-containing compounds (n = 10), dopamine agonists

(n = 5), or combinations of both (n = 11).

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References

This study was funded by a grant from The Wellcome Trust to MH and by a Neil Hamilton

Fairley Fellowship, National Health and Medical Research Council, Australia to TC.

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Supplementary Material

S1. Supplementary Method

For the main experiment, participants were instructed that the aim of this task was to gather

as many apples as they could based on the combinations of effort and reward presented on

each trial. Prior to commencing the task, participants were first familiarised with the amount

of force required to achieve each effort level. During these preliminary trials, images of trees

without apples were presented. They were told that the height of the horizontal bar on the tree

trunk was proportional to the amount of force they would need to exert in the main

experiment. However, they were not explicitly informed about the percentage MVC

corresponding to each level. In this preliminary phase, participants had the opportunity to

familiarise themselves with the amount of force required for each effort level by squeezing

the dynamometers on separate trials to attempt to achieve each target effort level (two

familiarisation trials per effort level).

Trials were presented according to an adaptive staircase algorithm, in which combinations

of stake and effort were presented depending on participants’ previous choices

(Christopoulos, Tobler, Bossaerts, Dolan, & Schultz, 2009). If a particular combination of

stake and effort was declined on one trial, a higher stake or lower effort level was presented

on a subsequent trial (stake and effort levels were adjusted alternately). The opposite would

occur if a combination was accepted. Three randomly interleaved staircases were used so that

participants were unaware of the algorithm.

The advantage of such a design, in contrast to the approach of randomly sampling the entire

stake-effort space, is that it substantially reduced any effect of learning on task performance –

an important consideration given that dopamine is thought to be involved in reinforcement

and associative learning (Wise, 2004). By using a staircase algorithm, we were able to

converge efficiently on participants’ indifference points, which were therefore derived

independently of any associative learning between stimulus and reward, or force and effort.

In estimating participants’ effort indifference points, choice data were fitted using the

Palemedes toolbox (www.palamedestoolbox.org), a set of routines implemented in Matlab for

analysing psychophysical data (Kingdom & Prins, 2010; Prins & Kingdom, 2009). These

routines were used to fit a logistic function to choice data, characterised by 4 parameters (α, β,

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γ, λ).

We used free parameters for threshold (α) and slope (β). Fixed parameters were used for the

guess and lapse rates, based on the veridical distribution of choice responses for each

participant, with γ corresponding to the minimum, and λ being 1 minus the maximum. Thus,

the logistic functions fitted for each participant correspond to their actual responses.

S2. Supplementary Analyses on Patient Data

S2.1. Comparison of Indifference Point Slopes

In the principal analyses described in the main text, the interaction between Drug (ON, OFF)

and Stake (1-6) was not statistically significant (F(5,125) = 1.26). Graphically, however,

there appears to be a potential interaction between the two variables. As suggested by an

anonymous reviewer, a more sensitive test for an interaction might be to determine the slope

of indifference point curves for individual subjects by regressing them against a non-linear

(sigmoidal) function, and to then compare these slopes in the OFF and ON sessions with a

paired t-test. Doing so revealed that the slopes between the OFF and ON sessions were not

significantly different (t(25) = 1.39). This is in keeping with the absent statistical interaction

between Drug and Stake, and suggests that the difference between drug sessions is best

described as an upward shift of indifference point curves from OFF to ON.

S2.2. Failure Rates

As described in the Method section of the manuscript, participants were rewarded only on

trials in which they successfully achieved the target effort level. To determine if failure rates

differed as a function of Group or Effort, we compared failure rates in patients with a two-

way ANOVA on the factors of Drug (ON, OFF) and Effort level (1-6). This analysis revealed

a main effect of Effort, such that failure rates increased as a function of effort level (Effort

level 1, 1.4 ± 1.1%; Effort level 2, 0.6 ± 0.4%; Effort level 3, 7.2 ± 4.3%; Effort level 4, 32.6

± 6.1 %; Effort level 5, 60.2 ± 8.6%; Effort level 6, 98.0 ± 2.0%, p < .001). Critically,

however, neither the main effect of Drug nor its interaction with Effort was significant (both

p > .90), demonstrating that failure rates did not differ across the ON and OFF sessions.

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We also asked whether failure rates may have been correlated with motor severity as

measured on the motor section (Part III) of the UPDRS. We therefore performed a correlation

analysis between failure rates and scores on the motor section of the UPDRS in each of the

ON and OFF sessions. However, there was no correlation between failure rates and motor

scores in either of the two sessions (ON, r = 0.07, p = .74; OFF, r = -0.07, p = .75), nor was

there a correlation between changes in failure rates (ON > OFF) and improvements in motor

scores (ON > OFF) across the two drug sessions (r = 0.12, p = .56).

S2.3. Trial History

To determine if performance on one trial (i.e., failure or success to reach the required target

force) influenced decisions on subsequent trials, we first determined the probability of a

participant accepting a given offer in the trial immediately following failed versus successful

attempts at gathering a reward. We then conducted a 2 x 2 ANOVA on the factors of Drug

(ON, OFF) and Trial History (preceding trial failed vs successful). This analysis

demonstrated no significant main effects or interactions – specifically, the probability of a

patient accepting an offer was the same, regardless of whether they failed or succeeded on the

preceding trial (Drug, F(1,25) = 0.84, p = .37; Trial History, (F(1,25) = 0.28, p = .60; Drug x

Trial History, F(1,25) = 0.50, p = .49). This shows that the effect of trial history did not

significantly differ between patients ON and OFF medication.

S2.4. Effect of Motor Improvement on Effort Indifference Points

It is conceivable that the amount of effort patients were willing to exert was correlated with

the severity of their motor impairment. To address this question, we examined whether effort

indifference points were correlated with the motor section (Part III) of the UPDRS, for each

of the ON and OFF sessions separately. Importantly, there was no correlation between effort

indifference points and the severity of motor symptoms in either session (ON, r = -0.14, p =

.50; OFF, r = 0.03, p = .89).

On a related point, could the increase in effort indifference points from the OFF to ON

sessions be accounted for by a corresponding reduction in the severity of motor symptoms?

This is an important consideration, given that a recent study in PD found that patients

performed a higher number of keyboard presses for reward while ON medication relative to

OFF, but that this change was related to improvements in motor symptoms (Porat, Hassin-

Baer, Cohen, Markus, & Tomer, 2014). To address this issue, we performed a correlation

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analysis examining changes in effort indifference points from the OFF to ON sessions against

changes in the motor section (Part III) of the UPDRS across those two sessions. Importantly,

however, this correlation was not significant (r = 0.22, p = .28).

In summary, the severity of motor symptoms cannot account for the effort indifference points

in either the ON or OFF sessions, nor could improvement in patient symptoms account for

increases in indifference points from the OFF to ON sessions.

S2.5. Patient Force Output Data

The following analyses were conducted in order to verify that the shifts in indifference points

reported in the main text could not be attributable simply to changes in force output.

S2.5.1. Initial MVCs

To recapitulate, the analyses reported in the main text showed that the MVC at the beginning

of each session did not differ between patients ON and OFF medication, nor did they differ

between the patient and control groups overall. In addition, the time-to-peak contraction did

not differ between the ON and OFF sessions (OFF 2.56 ± 0.1s vs ON 2.53 ± 0.1s, t(25) =

1.04, n.s.). This suggested that differences in indifference points ON and OFF medication,

and between patients and controls, could not simply be due to differences in motor strength at

the beginning of each session.

In addition, we tested for any correlations between changes in patients’ MVCs and shifts in

their indifference points. For each patient, we calculated the difference in MVC between the

ON and OFF sessions. In addition, we calculated differences in their mean effort indifference

point ON vs OFF medication. If increases in MVC accounted for increases in patients’ effort

indifference points, we would expect a correlation between these two variables. However, no

such correlation was found (Pearson’s correlation coefficient -0.004, p = .984).

S2.5.2. Time-on-task Analyses

In order to verify that there were no changes in force output during the experiment, we

compared motor output between the first and second halves of each session (90 trials per

half). Importantly, dopamine did not differentially affect patients’ maximal grip force across

the first and second halves of each session (Drug, F(1, 25) = 2.36; Session Half, F(1, 25) =

0.19; Drug × Session Half, F(1, 25) = 2.03). Similarly, patients’ time-to-peak contraction did

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not differ over the course of the experiment as a function of drug (Drug, F(1, 25) = 1.24;

Session Half, F(1, 25) = 0.66; Drug × Session Half, F(1, 25) = 1.55).

In summary, these analyses show that there were no significant differences in patients’ motor

output at the beginning of each experimental session, nor during the sessions themselves.

This therefore excludes the possibility that any shifts of effort indifference points were due to

changes in force output.

S2.6. Effect of Medication Class

Because our study was aimed at examining the effect of dopaminergic medication in general

on decision-making, patients in our study were on different therapeutic regimens (levodopa-

containing compounds alone (n = 10), dopamine agonists alone (n = 5), or combinations of

both (n = 11)). An interesting question is whether performance on our task differed according

to medication subgroup. An ANOVA comparing the between-subjects effect of Medication

Subgroup (levodopa only, dopamine agonists only, both) on the within-subjects factors of

Drug Session (ON, OFF) and Stake Level (1-6) showed that Medication Subgroup was not

involved in any significant main effects or interactions (all F < 1.21). Obviously, however,

this null result should be interpreted with caution given the small and uneven sample sizes in

each subgroup, and it would be useful for future studies to pursue whether drug class has a

differential effect on motivation.

S2.7. Effect of Total Levodopa Equivalence Dose

In addition, we also asked whether dopamine had a dose-dependent effect on effort

indifference points. However, the correlations between mean effort indifference points and

levodopa equivalent dose was not significant, even after performing a partial correlation

controlling for disease duration (r = -0.22, p = .29).

S2.8. Effect of Apathy or Depressive Ratings

One might predict that patients who were towards the more ‘apathetic’ or ‘depressed’ range

on the LARS or DASS respectively may have had lower effort indifference points due to

their subjectively lower motivation. We therefore performed correlation analyses between

participants’ mean effort indifference points and their scores on the LARS and the

Depression subscale of the DASS. However, all Spearman correlation coefficients were not

significant (all p > .05). One potential reason for this is that our participants all scored within

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the normal range on these measures, and therefore did not demonstrate the variability that

might be required to reveal such correlations.

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Supplementary Table 1.

Summary of UPDRS scores for patients with PD (means ± SD).

UPDRS Patient Scores

Part I 7.0 (± 4.4)

Part II 11.2 (± 4.7)

Part III (ON) 21.6 (± 11.7)

Part III (OFF)

31.9 (± 13.6)

Part IV

2.08 (± 3.5)

Hoehn & Yahr Stage 1.85 (± 0.54)

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