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. 2013 Sep 3;35(6):2499–2506. doi: 10.1002/hbm.22344

Dopamine‐agonists and impulsivity in Parkinson's disease: Impulsive choices vs. impulsive actions

Francesca Antonelli 1,2, Ji Hyun Ko 1,2, Janis Miyasaki 1, Anthony E Lang 1, Sylvain Houle 2, Franco Valzania 3, Nicola J Ray 1,2, Antonio P Strafella 1,2,4,✉
PMCID: PMC4452224  CAMSID: CAMS4599  PMID: 24038587

Abstract

The control of impulse behavior is a multidimensional concept subdivided into separate subcomponents, which are thought to represent different underlying mechanisms due to either disinhibitory processes or poor decision‐making. In patients with Parkinson's disease (PD), dopamine‐agonist (DA) therapy has been associated with increased impulsive behavior. However, the relationship among these different components in the disease and the role of DA is not well understood. In this imaging study, we investigated in PD patients the effects of DA medication on patterns of brain activation during tasks testing impulsive choices and actions. Following overnight withdrawal of antiparkinsonian medication, PD patients were studied with a H2 (15)O PET before and after administration of DA (1 mg of pramipexole), while they were performing the delay discounting task (DDT) and the GoNoGo Task (GNG). We observed that pramipexole augmented impulsivity during DDT, depending on reward magnitude and activated the medial prefrontal cortex and posterior cingulate cortex and deactivated ventral striatum. In contrast, the effect of pramipexole during the GNG task was not significant on behavioral performance and involved different areas (i.e., lateral prefrontal cortex). A voxel‐based correlation analysis revealed a significant negative correlation between the discounting value (k) and the activation of medial prefrontal cortex and posterior cingulate suggesting that more impulsive patients had less activation in those cortical areas. Here we report how these different subcomponents of inhibition/impulsivity are differentially sensitive to DA treatment with pramipexole influencing mainly the neural network underlying impulsive choices but not impulsive action. Hum Brain Mapp 35:2499–2506, 2014. © 2013 Wiley Periodicals, Inc.

Keywords: Parkinson's disease, impulsivity, dopamine agonists

INTRODUCTION

Impulsivity is a complex dimension, extending beyond the concept of “poorly planned actions” and engaging different cognitive functions. According to current models [Bechara et al., 2000], two major processes linked to different neural networks and activated by different experimental paradigms have been defined as part of impulsivity: motor impulsivity (i.e., impulsive actions) and cognitive impulsivity (i.e., impulsive choices). Motor impulsivity is described in terms of disinhibition of prepotent responses. Go/no‐go task is frequently used to measure the ability to inhibit previously learned motor responses despite signals to the contrary. Thus, inaccurate responses to this task indicate higher degrees of motor impulsivity. Cognitive impulsivity [Harnishfeger, 1995] is a more complex process, and is the result of a suppression of previously activated cognitive contents [Aron, 2007]. Delay discounting task (DDT) is a way to experimentally measure one of these contents, in particular to measure the perception of time during a decision [Kirby et al., 1999]. This task accounts for the temporal discounting behavior, in which a delayed outcome of a choice reduces the subjective value of the reward [Mitchell, 1999]. Thus, impulsive responses to this task discount delayed rewards more strongly than do more self‐controlled responses [Kirby et al., 1999].

Parkinson's disease (PD) has been shown to be associated with different degrees of impulsivity [Voon et al., 2011]. This might be in relation to the disease itself, to the presence of others susceptibility factors and to the effect of therapy. In particular the use of pramipexole and ropinirole, two widely used nonergot dopamine‐agonists (DA) with a great D2/D3 dopamine selectivity, have been shown to favor impulsivity [Voon et al., 2011]. This effect has been related to the abnormal activation of D2/D3 receptors expressed in the ventral striatum [Steeves et al., 2009], which are relatively spared in earlier stages of the disease and considered being part of the cognitive and limbic networks particularly involved in cognitive impulsivity. According to this hypothesis, DA, may have an “overdosing” effect on these networks. On the other hand, in PD patients, motor impulsivity and improper actions seem to be more negatively influenced by STN‐DBS [Ballanger et al., 2009; Frank et al., 2007].

On the basis of these premises, by using [15O] H2OPET, we planned to perform, in a group of PD patients, a study focusing on the specific effect of D2/D3 agonist (i.e., pramipexole) on the different aspects of impulsivity. In particular, we investigated DA‐driven effects on regional cerebral blood flow (rCBF) during tasks that tapped into motor impulsivity (i.e., Go/no–go task) and cognitive impulsivity (i.e., DDT). We predicted that DA might have a different influence on the two types of impulsivity, by activating different neural network and by influencing differently the behavior during task performance. In particular, our working hypothesis was DA may influence impulsive choices, without strong effect on impulsive actions.

METHODS

Seven nondemented PD patients (6 men, 1 woman; mean age: 58.6 ± 6 years) meeting UK Brain Bank criteria for the diagnosis of idiopathic PD participated in the H2 15O PET study. Severity of the disease was assessed with the Unified Parkinson's Disease Rating Scale (UPDRS) in OFF‐med and ON‐med condition [MDS task force, 2003]. Patients were screened for cognitive deficits using the Montreal Cognitive Assessment (MoCA: 28.1 ± 1.3) [Nasreddine et al., 2005] and depression using the Beck Depression Inventory (BDI: 7.8 ± 6.5). Patients recruited for the study did not have any impulse control disorder. The demographic and clinical characteristics of the PD patients are described in Table 1. The study was approved by the Research Ethics Committee of our Institution. After complete description of the study to the participants, written informed consent was obtained.

Table 1.

Demographic features of PD participants

Mean ± SD
Age (years) 58.6 ± 6
UPDRS III (OFF) 28.6 ± 6.8
Disease duration (years) 6.8 ± 2.9
DA LEDD (mg) 215.3 ± 115.7
Levodopa dose (mg) 657.1 ± 330.9
MoCA 28.1 ± 1.3
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UPDRS, Unified Parkinson's disease rating scale (section III); LEDD, levodopa equivalent dose, (Total LEDD calculated as l‐dopa dose + pramipexole (mg) × 100, ropinirole (mg) × 20, amantadine × 1, rasagiline × 100) (Tomlinson et al., 2010); MoCA, montreal cognitive assessment; BDI, beck depression inventory.

PET Scanning Procedure

Patients were imaged after overnight withdrawal (12–18 h) of their antiparkinsonian medications with H2 15O PET to measure changes in regional cerebral blood flow (rCBF). Subjects were positioned supine on the PET scanner bed with their head maintained in a fixed position using a thermoformed mask. PET measurements were obtained using a whole body PET camera system, Siemens‐Biograph HiRez XVI (Siemens Molecular Imaging, Knoxville, TN) operating in 3D mode with an in‐plane resolution of ∼4.6 mm full width at half‐maximum (FWHM). To measure rCBF, 10 mCi H2 15O were administered intravenously over 50 s with a semibolus injection using an infusion pump. Scanning of emission data was started 10 s after the injection and lasted for 90 s. Before the first emission scan, a scout view was obtained to determine accurate positioning of the subject and a low dose (0.2 mSv) CT scan was acquired to correct for attenuation. Images were reconstructed by two‐dimensional filtered back‐projection, resulting in 81 slices with a 256 × 256 pixel‐matrix (pixel size, 2 mm). The interval between successive H2 15O administrations was 10 min for adequate decay of radioactivity.

The experiment consisted of 12 emission scans and was divided in 2 sessions. One session was performed without antiparkinsonian medication (OFF) and the other session following administration of 1 mg pramipexole (ON) with an interval of 30 min in between, during which the patients were out of the scanner. Each PET session consisted of 6 PET scans in a block design. Both sessions were performed during the same day and whenever possible, we performed the experiment during the same time of the day for all patients (morning). Before each session, subjects underwent the same procedure of head positioning described above.

Behavioral Tasks

During H2O15 PET, patients played the computerized behavioral tasks wearing video eyewear (VR920; Vuzix Corporation, NY) and providing responses with their right hand on a 4‐button keyboard. The behavioral tasks consisted of the Go‐no‐Go (GNG) and the delay‐discounting task (DDT). The task started at tracer injection and continued for 120 s after injection. The following six conditions were studied: (1) REST OFF medication, (2) DDT OFF medication, (3) GNG OFF medication, (4) REST ON medication, (5) DDT ON medication, (6) GNG ON medication. The scan order was counterbalanced across patients. To minimize the learning effect, patients performed a practicing session of both tasks before scanning, consisting of 50 trials, and were instructed to respond as quickly and as accurately as possible.

GNG Task

The GNG task involved 2 stimuli: a Go stimulus (a white circle) and a NoGo stimulus (a white X). Patients were instructed to press a button with their right index finger at the occurrence of the circle symbol but to withhold a response when the letter X was presented. Each scan contained 20 trials with 40% of NoGo trials [Ballanger et al., 2009]. Reaction time (RT) along with anticipation and commission errors was recorded. Anticipations errors were defined as failed responses to the Go stimulus; commission errors were defined as a response to the NoGo stimulus. To avoid novelty or learning effect, patients performed a practicing session before scanning, consisting of 50 trials, and were instructed to respond as quickly and as accurately as possible.

DDT Task

The task, based on the Kirby's DD inventory [Kirby et al., 1999], involved an individual preference and assumed low risk and ambiguity conditions. A description of the task has also been reported in Cho et al., 2010. Briefly, participants were asked to choose between a small immediate reward and larger delayed reward across a range of different delays. Individual impulsivity level was determined by k‐value based on hyperbolic function of delay discounting V5A/(11 kD), where V is the value of the delayed outcome (i.e., the indifference value), A is the delayed reward, D is the length of the delay, and k expresses the steepness of the discount function [de Wit et al., 2002; Mitchell, 1999; Richards et al., 1999]. On the basis of this function, higher k‐values are associated with preference for immediate small‐size reward and lower k‐values are expression of delayed larger‐size reward. Thus, the k‐values are an index of cognitive impulsivity. Each scan contained 10 trials; the choice stimulations were presented on the screen for 3 s and the interstimulation interval was 2 s. All reward choices were made by pressing either the 4 or 6/key on keyboard with the subject's right hand. The available time delays were 12 in total (1 week, 2 weeks, 3 weeks, 1 month, 3 months, 6 months, 1 year, 2 years, 3 years, 4 years, 5 years, and 10 years) and there were two categories of delayed reward magnitude: small (1–500 CAD) and large (600–1000 CAD). The predefined k‐values were 0.0005, 0.0028, 0.0050, 0.0275, 0.05, 0.075, 0.1, 0.3, 0.5, and 0.7 for the 120 trials; the same number of trials was assigned for each k‐value and reward magnitude. The trial order was randomized for each session across the subjects.

Image Analysis

Image and statistical analysis were performed in MATLAB version 7.4 [Matlab, 2007] using statistical parametric mapping software [SPM2, 2002]. After the realignment procedure for motion correction among the frames, motion corrected PET frames were summed, coregistered to the corresponding individual T1‐MRI, transformed into standardized stereotaxic space, and smoothed using an isotropic, Gaussian kernel of 12 mm to reduce the variance due to individual anatomical variability and to improve the signal/noise ratio. Variations in global CBF across subjects and scans were removed by proportionally scaling each image to have an arbitrary level of 50 ml/100 ml/min. To identify rCBF changes, a 2 × 2 factorial design (2 [medication conditions] × 2 [task conditions] was used to measure the main effect of task and medication and interaction effect. A secondary post hoc analysis was performed to measure simple effect. We generated t‐statistic subtraction maps of change in rCBF [ON–OFF medication] during GNG and DDT to measure effect of medication during each task. Normalized rCBF were extracted from an 10 mm diameter spherical volume of interest centered at the x, y, and z‐coordinates of the peaks defined by the subtraction maps and correlated with behavioral task to determine whether any relationship exists between those variables and the amount of rCBF changes.

Images were thresholded at a level of P < 0.001 uncorrected with an extent threshold of at least 20 contiguous voxels. Regions were considered significant at the voxel‐level threshold of P < 0.05 after correction for multiple comparisons using false discovery rate (FDR) [Genovese and Wasserman, 2002]. Effect of disease severity and depression scores were included as nuisance variables.

The search region was limited within a mask that is defined according to specific a priori hypothesis [Friston et al., 1996; Worsley et al., 1996]. This mask image was created using the WFU‐Pick Atlas tool (http://www.fmri.wfubmc.edu) [Maldjian et al., 2003] and based on the automated anatomical labeling (AAL) atlas [Tzourio‐Mazoyer, 2002]. On the basis of previous reports on impulsivity investigating GNG and DDT [Barrat, 1983; Wittmann and Paulus, 2008], our mask included the medial prefrontal cortex (mPFC) (BA 10/32), the cingulate cortex (CC) (BA 31–33/24), the presupplementary motor area (pre‐SMA) (BA 6), the ventrolateral and the dorsolateral prefrontal cortex (VL/DLPFC) (BA 9/46/47), the orbitofrontal cortex (OFC) (BA 10‐11‐12), the insula, motor cortical areas (M1/PMC) (BA 4), inferior parietal lobe (BA 39‐40) and striatum. All coordinates reported are based on the Talairach atlas.

Behavioral Analysis

The outcome measures for the DDT analysis were the following: (1) individual k discounting value, (2) RT, and (3) consistency of choice, a percentage of the same option choice for a given k‐value. The k‐values were estimated separately in small and large reward magnitude as the geometric mean between the lowest implied indifference k‐value in which subjects chose the delayed option, and the highest implied indifference k‐value in which subjects chose the immediate option [Kirby and Santiesteban, 2003; Monterosso et al., 2007]. The geometric mean indicated the central tendency of a set of numbers and was used because the task required subjects to express preferences [Monterosso et al., 2008]. We expected that changes in the individual k‐value would vary in a relative fashion of reward amount and delay period. If the response consistency was more than 66% for one response (small‐immediate or large‐delay option) within the given k‐value, then that k‐value was assigned to immediate choice or delay choice preference.

The outcome measures for GNG analysis were: (1) RT for choice decision, (2) commission errors, and (3) anticipation errors.

Data were analyzed using a 2 × 2 factorial design (2 [medication conditions] × 2 [task conditions]. Where a significant group condition interaction was identified, a post hoc analysis was conducted using a nonparametric Wilcoxon signed rank test. The analysis was performed by using SPSS version 13.0 for Windows software (SPSS, Chicago, IL) and the significance level for all statistical analysis was set at P < 0.05.

RESULTS

The imaging analysis revealed a main effect of medication with changes in rCBF in cortical areas involved in reward processes defined in our a priori hypothesis encompassing the right medial frontal gyrus (BA 10) (T = 5.78; P < 0.001 FDR‐corrected), left lateral prefrontal cortex (BA 46) (T = 5.23, P < 0.001 FDR‐corrected), posterior cingulate (BA 30) (T = 6.18; P < 0.001 FDR‐corrected) and left precentral gyrus (BA 6) (T = 5.09; P < 0.001 FDR‐corrected). There was a significant “medication × task” interaction suggesting that the effect of medication was different depending on the task at hand.

In particular during DDT, DA significantly increased activity in the right medial frontal gyrus (T = 5.13; P < 0.001 FDR‐corrected) and left posterior cingulate (T = 5.63; P = 0.001 FDR‐corrected) whereas during GNG, DA influenced to a lesser degree a different network encompassing the left lateral prefrontal cortex (T = 4.71; P = 0.002 FDR‐corrected). The location, coordinates, and peak P scores of activated areas are detailed in Fig. 1. A deactivation was observed in the left ventral striatum (T = 4.56; P = 0.013) during the DDT, which was not detected during the GNG task (Fig. 2).

Figure 1.

Figure 1

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rCBF activation: Pramipexole activated two different networks depending on the task at hand: during DDT, DA strongly increased rCBF in right medial prefrontal cortex (mPFC) and left posterior cingulate (PCC); during GNG, DA had a minor role by activating the left lateral prefrontal cortex (lPFC). The image shows areas activated in the contrast DDT ON‐DDT OFF and GNG ON‐GNG OFF and individual DA‐rCBF changes extracted by 10 mm sphere from the statistical peak.

Figure 2.

Figure 2

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rCBF deactivation: Pramipexole during DDT de‐activated the left ventral striatum. No changes were observed during the GNG. The image shows areas de‐activated in the contrast DDT ON‐DDT OFF and GNG ON‐GNG OFF.

The behavioral analysis of DDT showed significant differences in k‐values between “large reward choices (600–1000 CAD)” and “small‐reward choices (1‐500 CAD)” depending on medication condition (F 1,5 = 5.65; P = 0.006). The Wilcoxon post hoc test revealed that medication increased k‐values only in choices with large reward (P = 0.003) but not with small reward (P > 0.05). In other words, pramipexole augmented impulsivity in delay discounting task, depending on reward magnitude (Fig. 3). A significant negative correlation was observed between k‐values for larger reward and rCBF in the left PCC (r = 0.73, P = 0.02) and right medial frontal gyrus (r = 0.71, P = 0.03) implying that more impulsive patients had less activation in those areas.

Figure 3.

Figure 3

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Behavioral results: (a) Pramipexole increased impulsivity during DDT depending on reward's amplitude (F 1,5 = 5.65; P=0.006): in large rewards choices, patients ON medications made more impulsive choices; (b) Pramipexole did not affect the performances to GNG, in terms of number of commission errors (CE) and anticipation errors (AE).

In the GNG performance, the medication did not affect RT (OFF= 509.8 ± 69 ms; ON = 487 ± 50 ms; P > 0.05) nor frequency of commission error and errors of anticipation (P > 0.05). No correlation was observed between GNG task and rCBF.

DISCUSSION

In this study we showed that in PD patients, DA influenced different neural networks depending on the task at hand. In particular, we observed that during the DDT while DA increased impulsive choices (i.e., higher k‐values), it activated the medial prefrontal cortex and posterior cingulate, generally involved in decision‐making and impulsive choices. These results are consistent with our previous PET studies [Ray et al., 2012; van Eimeren et al., 2010] conducted in PD patients with and without pathological gambling behavior where DA influenced rCBF and D2 receptor binding in medial prefrontal areas during a computerized gambling card game. Similarly, the importance of the medial prefrontal areas in the temporal judgment during choices has been shown in previous lesion studies [Cardinal et al., 2001] as well as in fMRI studies [MacKillop et al., 2012]. Microdialysis studies, used to investigate neurochemical correlates of impulsive decision‐making, suggested that dopamine in mPFC is the neurochemical involved during delay discounting paradigm [Martin‐Soelch et al., 2001]. The posterior cingulate cortex is also consistently reported to be involved in cognitive tasks involving the choice of delayed rewards [McClure et al., 2004; Wittmann et al., 2007]. Animal studies suggested that posterior cingulate cortex might have a specific role in the integration of the reward expectation and reward outcome [McCoy et al., 2003].

On the other hand, during the GNG task, the effect of pramipexole did not modify behavioral performance and involved (at lower statistical level) a different brain‐network (i.e., lateral prefrontal cortex), generally associated with impulsive action (rather than choices) [Konishi et al., 1999; Sasaki et al., 1989, 1993]. These findings seem to support our recent observations showing how motor impulsivity in PD patients as measured by GNG task may be more influenced by stimulation of the subthalamic nucleus (STN) compromising the activation of the cortical areas underlying reactive and proactive response inhibition (i.e., impulsive action) [Ballanger et al., 2009]. Taken together these observations seem to suggest that DAs and STN stimulation may well influence different aspects of impulsivity, the first one by acting essentially on impulsive choices (cognitive impulsivity) and the latter by promoting more disinhibitory processes allowing impulsive actions (motor impulsivity).

Impulsive choices commonly demonstrate a magnitude effect, with higher impulsive choices accompanying increasing reward magnitude. In our PD patients, DA made more impulsive choices with higher reward magnitudes. Other studies reporting PD patients with ICDs showed greater impulsivity choice to larger reward magnitude [Voon et al., 2001] in part supporting our findings. In these patients, the correlation analysis revealed that our medicated patients with higher k‐values (i.e., more impulsive) had less rCBF changes in “reward‐areas” as compared to the less impulsive patients (i.e., lower k‐value). Thus, from our observations, it appears obvious that failure of inhibition and impulsivity incorporates a number of dissociable components [Antonelli et al., 2011; Chamberlain and Sahakian, 2007]. One component is related to the ability of processing decision‐making information useful to make a proper choice. The second aspect, “motor inhibition” is related to the ability of inhibiting prepotent responses preventing improper action. With this study, we observed how these different components of inhibition/impulsivity are differently sensitive to DA treatment increasing mainly impulsive choices (but not impulsive action) in PD patients by acting essentially on decision making process and in particular by provoking a subjective devaluation of the reward, which might be related to a reduced activation of the mPFC and PCC. Interestingly, while we did not observe any activation in the ventral striatum, we did notice instead during the DDT a deactivation in the left ventral striatum, which was not detected with the GNG. We could only speculate that in our PD patients, pramipexole modulated the top‐down control (with increase activation in the cortical areas and deactivation in the ventral striatum) only during the DDT task but not with the GNG. Thus, it would be extremely interesting to investigate this effect in PD with actual ICDs in whom top‐down control is generally impaired.

With the limitation associated with our small sample size, another potential limitation is the inclusion criteria of this study which may have a number of consequences. However, we felt that our inclusion criteria were justified as the most prudent way of generating meaningful results with a modest sample size. For example, limiting the study to patients who lacked severe motor—cognitive deficits optimized the practicality of the experiment at the expense of their generalizability.

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