# Influence of Deep Brain Stimulation and Dopaminergic Therapy on Intrinsic Preference for Free Choice in Patients With Parkinson's Disease

David Bendetowicz, Gizem Temiz, Nicolas Tempier, Elodie Hainque, Marie-Laure Welter, Virginie Czernecki, Brian Lau, Carine Karachi, Jérôme Munuera

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To cite this version:

David Bendetowicz, Gizem Temiz, Nicolas Tempier, Elodie Hainque, Marie-Laure Welter, et al. Influence of Deep Brain Stimulation and Dopaminergic Therapy on Intrinsic Preference for Free Choice in Patients With Parkinson's Disease. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, In press, Online ahead of print. 10.1016/j.bpsc.2025.06.008. hal-05298916

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# Influence of Deep Brain Stimulation and Dopaminergic Therapy on Intrinsic Preference for Free Choice in Patients With Parkinson's Disease

David Bendetowicz, Gizem Temiz, Nicolas Tempier, Elodie Hainque, Marie-Laure Welter, Virginie Czernecki, Brian Lau, Carine Karachi, and Jérôme Munuera

## ABSTRACT

**BACKGROUND:** Humans prefer to make choices freely, even when doing so does not maximize future outcomes, which suggests that free choice is intrinsically rewarding. While value-based decision impairments are well documented in Parkinson's disease (PD), the mechanisms that underlie intrinsically motivated behavior remain unclear. In this study, we investigated how the dopaminergic (DAergic) and basal ganglia systems contribute to intrinsic reward in PD.

**METHODS:** We designed a decision-making task to dissociate the intrinsic value of free choice from extrinsic reward. Twenty PD patients with subthalamic nucleus deep brain stimulation (STN-DBS) and 25 on DA therapy completed the task both while ON and OFF treatment. Performance was compared with 20 age-matched healthy control participants. We analyzed DBS electrode contacts, modeled activated tissue volumes, and examined cortico-subthalamic connectivity using high-resolution diffusion magnetic resonance imaging.

**RESULTS:** PD patients OFF STN-DBS showed reduced preference for free choice, which increased when STN-DBS was ON. This effect was associated with recruitment of the right medial prefrontal cortex (mPFC). Acute ON/OFF DA therapy did not alter free-choice preference. However, patients with lower chronic DA doses—comparable to those in the DBS group—exhibited reduced free-choice preference compared with patients with higher chronic intake.

**CONCLUSIONS:** STN-DBS enhances free-choice preference by modulating the right mPFC-STN network, suggesting that this hyperdirect pathway influences intrinsic valuation of choice. These results indicate that STN-DBS promotes self-determined behavior even in risky contexts. Furthermore, chronic DAergic therapy may influence sensitivity to intrinsic reward.

https://doi.org/10.1016/j.bpsc.2025.06.008

Decisions are often described as a balancing act between rewards and punishments. However, rational decision-making models based on this balance frequently fail to accurately predict human behavior (1). Recent suggestions indicate that decisions can be influenced by value attribution that is not solely tied to extrinsic outcomes but also relies on intrinsic motivation. While extrinsic rewards refer to the explicit goal of maximizing the collection of external outcomes (such as money), intrinsic motivation provides an opportunity to enhance control over one's environment, independent of its practical benefits for obtaining extrinsic rewards. This concept is crucial for understanding behaviors driven by incentives that do not conform to economic optimization or that can be easily explained by fulfilling basic needs such as hunger, thirst, sex, or pain avoidance (1-3). For example, multiple species show a preference for opportunities to choose, even when these choices do not provide increased extrinsic rewards (4-6). Similarly, humans tend to prefer having options to collect favorite items rather than being forced to make a selection, even if this choice incurs greater costs (7,8). Although the desire to

choose may seem economically irrational, opportunities for choice can offer greater control over the environment, potentially triggering rewarding signals (3,8).

The dopaminergic (DAergic) networks and the basal ganglia system are implicated in decisions influenced by extrinsic rewards and punishments (9-11). These systems may also play a central role in processing intrinsic rewards (1,12). For example, midbrain DA neurons in nonhuman primates encode the inferred value of cues, providing other forms of intrinsic rewards, such as information about future outcomes, without impacting the rate of external reward consumption (13). In humans, striatal activation increases when participants can freely choose different items, regardless of the magnitude of their associated extrinsic rewards or even when they are aware that free choice is available (14-18).

Impairments in value-based decisions are observed in Parkinson's disease (PD), in which DA cell loss leads to profound basal ganglia dysfunction, including subthalamic nucleus (STN) hyperactivity. Patients exhibit impaired value-based learning, which can be enhanced through DA therapy
by shifting outcome value predictions to improve learning from rewards while minimizing the effects of adverse outcomes (19–22). Additionally, deep brain stimulation (DBS) targeting the STN also alters decision making that results in impulsive choices (23–26). This impulsivity may stem from the modulation of the prefrontal hyperdirect pathway, facilitating quick decision processes through response inhibition of suboptimal behaviors (17–20), depending on the anatomical and functional organization of the STN and the positioning of the electrodes (27–32). While the posterior regions of the STN relate to motor functions, more anterior regions are associated with nonmotor processes (28,29,32,33). On the other hand, some patients treated with STN-DBS may display hypo-DAergic states such as apathy that are induced by the significant reduction of DA therapy following surgery. While these complex alterations in value-based decision making have already been related to DA and prefrontal-subthalamic networks, they primarily emphasize external consequences (34,35).

To deepen our understanding of the cortico-subthalamic and DA systems' contributions to intrinsic motivation, we designed an experiment in which patients with PD express their intrinsic preference for collecting extrinsic rewards through either a free- or forced-choice option. We investigated the role of the cortico-subthalamic and DA systems in choice preferences by manipulating STN-DBS on the one hand and DA therapy on the other. By altering the risk of losing rewards associated with free choice, we explored how risk-seeking attitudes and their associated neural systems can modulate the value of intrinsic reward when it competes with the maximization of extrinsic reward intake.

# METHODS AND MATERIALS

## Participants

We determined the sample size based on previous studies (8,16). We recruited 45 patients through the neurology and neurosurgery departments at the Pitié Salpêtrière Hospital (Paris, France). DOPA patients (n = 25, 7 females) received DA therapy only and were assessed during a follow-up visit to evaluate their eligibility for DBS surgery, encompassing a comprehensive neuropsychological evaluation and an acute levodopa challenge test. DBS patients (n = 20, 9 females) were assessed during a routine follow-up visit (delay after surgery: 15 ± 5.3 months). For the comparative analysis, we enrolled 20 age- and sex-matched healthy control participants (HCs).

Inclusion criteria and details of stimulation devices and parameters of DBS patients are provided in Supplemental Methods and Table S1.

The study was approved by the local ethics committee (Ethics Evaluation Committee of the French National Institute of Health and Medical Research/Institutional Review Board No. 21-810), and informed consent was given following the Declaration of Helsinki ethics rules.

## Task Description

We developed a 2-stage task using the Psychophysics Toolbox (http://psychtoolbox.org/) running on MATLAB (version 9.11.0 R2021b; The MathWorks, Inc.) that was described previously (8).

Participants performed multiple trials organized in a 2-stage structure (Figure 1A). During the first stage, 2 image targets were shown, representing the free or forced options, and participants had to make an initial selection about what could occur in the second stage. Choosing the free option led to an opportunity for another choice in the second stage, while selecting the forced option led to an obligatory computer-selected action. In the second stage, participants were presented with 2 new image targets. In the free condition, they could select either target, whereas in the forced condition, they were required to choose the preselected target. The computer consistently selected the same target during the forced condition, disabling the selection of the alternative. The reward outcome was displayed after the second-stage target was selected.

To manipulate the expected reward, the task included 3 blocks of trials, each associated with different reward probabilities (P). In one block, reward delivery was stochastic, with all second-stage targets associated with a P of 0.5. In a second block, the reward probabilities were unbalanced, with one second-stage target set at P = 0.75 and the other at P = 0.25. In the final block, reward delivery was deterministic: One second-stage target was rewarded at P = 1 while the other was never rewarded (P = 0). Each action in the second stage was linked to unique fractal images, with a new set of images for each block. Reward feedback, either positive or negative, as well as the placement of targets during the first and second stages, were pseudorandomly alternated between the right or left side of the screen. Feedback was shown through the presentation (reward) or absence (nonreward) of a large green euro image.

Immediately before each testing phase, participants underwent a preliminary training phase to learn the contingencies between the distinct targets and their associated reward probabilities (Figure 1B, C). For blocks where P < 1, participants performed 48 trials per block in both the training (forced and free) and test phases. For the P = 1 blocks, they completed 24 trials in the training phases (both forced and free) and 48 trials in the test phase.

Participants were informed that the objective of the task was to earn the maximum number of rewards (depicted as a large green euro). They were briefed on the different trial types (free and forced) and told that the extrinsic reward contingencies from the training phases would remain the same during the test phases. Participants were not told that the computer always selected the best rewarding option for the P = 0.75 and P = 1 blocks.

## Procedure

DOPA patients performed the task during an acute levodopa challenge test (36). OFF sessions took place after discontinuation of levodopa treatment for 12 hours and DA agonist treatments for 72 hours. ON sessions took place the same day, 45 minutes after patients took the levodopa equivalent dose of their usual morning treatment plus 50 mg of levodopa.

DBS patients performed the task after a complete washout of DA therapy for both sessions following the same procedure of treatment discontinuation for DOPA patients. They performed the task ON-DBS, and then OFF sessions started at least an hour after stopping stimulation.
Intrinsic Motivation in Parkinson's Disease

A

<figure><img src="image_3.png" /></figure>

B

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Training phase

free

C

Test phase

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**Figure 1.** Two-stage task structure. (A) State diagram illustrating the 6 possible states (s), actions (a), and associated extrinsic reward probabilities (e.g., P = 0.5, 0.75, or 1 for blocks 1 to 3, respectively); s2 and s3 were represented by 2 different first-stage targets (e.g., colored squares with or without arrows for free and forced trials, respectively), and s4 to s6 were associated with 3 different second-stage targets (fractals). (B) Illustration of typical trials during the training phase. Participants learned the reward contingencies (green euro symbol) associated with the second-stage targets in the forced and free options for each probability block. When training for forced choice, participants had to select the target indicated by a V-shaped gray symbol. Participants could freely select 1 of the 2 targets when training for free choice. The V-shaped red symbol indicates the participant's selection after the keypress. (C) Schematic illustration of a trial during the test phase. In the first stage, participants had to select whether to perform the trial in the free or forced option.

Statistical Analyses

Generalized linear mixed models (GLMMs) were utilized for bi-
nary dependent variables (first- and second-stage choices).
LMMs were used on log-transformed reaction times (RTs). We
removed RT values <150 ms and >5 seconds (5% of the data).

We designed separate models for DBS and DOPA patients. Both models included ON/OFF sessions and the average obtained reward and their interaction as fixed effects together with a random intercept and slopes for session and obtained reward by the participant. An additional term for choice (free/forced) with all interactions was added for the RT models as well as a quadratic term for the obtained reward. We built a fourth model including all patients with HCs for group comparisons, using HCs as a reference level.

We utilized parametric bootstrapping and Kenward-Roger approximation to test the significance of GLMM and LMM terms, respectively. We used Wald's z statistic for post hoc comparisons, with adjustment for multiple comparisons using Tukey's method. Wilcoxon or Kruskal-Wallis tests were used for statistics on demographic variables, and Spearman coefficients were used for correlations. Additional information on model formula, summary estimates, and posterior predictive simulations is provided in the Supplement (Figure S1 and Tables S2-S5). We performed all analyses using R version 4.4.2 (2024-10-31) and the following packages: lme4 (v.1.1-37), afex (v.1.4-1), and emmeans (v.1.11.0).

Tractography Analysis

We assessed structural connectivity between the prefrontal
cortex and the STN area in each patient treated with DBS using
volume of activated tissue (VAT) modeling and a high-definition
normative PD connectome (18,37). This normative con-
nectome was created using multishell diffusion-weighted im-
aging data from a local cohort of 33 patients with PD who were
candidates for DBS (28). We used the Lead-DBS V2 pipeline
for electrode reconstruction, VAT modeling, and coregistration
between the pre- and postsurgery brain imagery. The spatial

normalization was done in the Montreal Neurological Institute
reference space, and the coherence between the individual
and the normative space was verified patient by patient (38).
The full image processing pipeline using Lead-DBS is available
in *Supplemental Methods*.

The cortical connectivity was extracted from the whole-
brain normative tractogram. For each extracted cortico-VAT
connectivity, corresponding track density (TD) maps were
created using spherical informed filtering technique (SIFT2)
weights for statistical analysis. These 3-dimensional density
maps reflect the estimated weight of streamlines within the
voxel (39). Additional information on the processing of trac-
tography analysis is provided in the Supplement.

Voxelwise analysis was performed in the TD maps using
permutation analysis of linear models from FSL (https://fsl.
fmrib.ox.ac.uk/fsl/fslwiki/PALM). We compared the TD maps
of DBS patients with a difference in free-choice preference
between ON and OFF sessions with patients whose preference
remained the same by median splitting the DBS group based
on ON-OFF difference. To focus on the specificity of nonmotor
processes and minimize the number of voxelwise compari-
sons, the analysis was performed on a cortical mask that
included all prefrontal nonmotor areas, including associative
and limbic cortices, as defined in a previous study that
assessed cortico-subthalamic connectivity using the anatomo-
functional YeB atlas (40). Two-sample t tests were performed
for each voxel, with 1000 permutations. p Values were cor-
rected using familywise error with the threshold-free cluster
enhancement approach, which could provide reliable correc-
tion at the voxel level (41).

RESULTS

Demographic Data

Descriptive statistics for demographic variables are provided in Table 1. The 2 PD patient groups and the HC group were comparable in age and cognitive performance, as assessed by
<table><caption>Table 1. Statistical Comparison of Demographic Data in the Three Groups of Participants</caption><thead><tr><th></th><th>DBS Group, n = 20</th><th>DOPA Group, n = 25</th><th>Healthy Control Group</th><th>Kruskall-Wallis or Wilcoxon's Test (p)</th></tr></thead><tbody><tr><td>Age, Years</td><td>54.2 (7.8)</td><td>56.8 (8.6)</td><td>54.2 (7.7)</td><td>K = 2.33 (.312)</td></tr><tr><td>MoCA</td><td>27.5 (1.5)</td><td>28.2 (1.4)</td><td>28.1 (1.2)</td><td>K = 3.41 (.182)</td></tr><tr><td>Disease Duration, Years</td><td>12.3 (3.3)</td><td>10.4 (4.3)</td><td>-</td><td>W = 319.5 (.113)</td></tr><tr><td>MDS-UPDRS III, OFF</td><td>47.7 (16.3)</td><td>43.5 (14.5)</td><td>-</td><td>W = 291 (.355)</td></tr><tr><td>MDS-UPDRS III, ON</td><td>16.6 (9.9)<sup>a</sup></td><td>10.0 (7.3)</td><td>-</td><td>W = 360.5 (.012)</td></tr><tr><td>LEDD, mg</td><td>405 (224)</td><td>1355 (525)</td><td>-</td><td>W = 497 (&lt;.0001)</td></tr></tbody></table>

Values are presented as mean (SD). MoCA scores are reported based on a maximum score of 30.

DBS, deep brain stimulation; LEDD, levodopa equivalent daily dose; MDS-UPDRS, Movement Disorder Society-Unified Parkinson's Disease Rating Scale; MoCA, Montreal Cognitive Assessment.

<sup>a</sup>ON-DBS was assessed off dopamine replacement therapy.

the Montreal Cognitive Assessment. The 2 PD patient groups were similar in disease duration and parkinsonian motor disability as assessed by the Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III OFF treatment (42). As expected, DBS patients had a significantly lower levodopa equivalent daily dose (LEDD) than DOPA patients.

## Free-Choice Preference in the HC Group

Consistent with our previous findings with younger healthy volunteers performing the same task (7,8), free-choice preference was higher than expected by chance for HCs matched on sex and age with the patient groups (average free-choice proportion in HCs: 81%, *p* < .001). Importantly, because the patient groups had to repeat the task twice (ON and OFF therapy) and to account for any potential test-retest effect, HCs also performed the task 2 times with a 1-hour interval between the 2 sessions. For HCs, there was no difference in performance between the 2 sessions (*p* = .74), with an intraclass correlation between the 2 sessions of 0.80. There was also no interaction with reward probability (*p* = .79) (Figure 2). When looking at trial-by-trial preferences, behavioral preferences were expressed from the onset of each reward probability block and tended to remain constant during the testing phase (Figure S2).

## Free-Choice Preference in the DBS Group

We found a main effect of session ($\chi^2$ = 4.81, *p* = .04) and obtained reward ($\chi^2$ = 4.81, *p* = .03) but no interaction between the 2 variables ($\chi^2$ = 0.20, *p* = .67). Post hoc analysis showed that patients had higher free-choice preference during ON than during OFF sessions (ON: 63%, OFF: 44%; *p* = .02) (Figure 3A) and that free-choice preference increased as a function of average reward probability (ON: trend = 0.42, *p* = .02; OFF: trend = 0.47, *p* = .01) (Figure 3B). Importantly, patients had lower free-choice preference than HCs during the OFF session and ON sessions (Figure S3).

Examining RTs using LMMs, we found no main effect of choice, session, or reward, but there was a choice × session ($F_{1,1817.23}$ = 18.69, *p* < .01) and a choice × session × reward interaction ($F_{1,806}$ = 9.64, *p* < .01). Overall, patients selected free options faster than forced options while ON stimulation (free vs. forced: *p* < .01) but not during OFF sessions (free vs. forced: *p* = .71) (Figure 3C). Reward influenced RTs,

particularly on the P = 0.75 block, when patients had longer RTs compared with P = 0.5 and P = 1 for both forced and free trials during the ON session (trend for reward: *p* < .001) but only for free trials during OFF sessions (trend for reward: free *p* < .001/forced *p* = .21). We observed different distributions of RTs for free and forced options at the population level (Figure S4) but also that the smallest RTs (25% and 50% quantiles) differed significantly between free and forced options at the individual level (Figure S5).

## Learning Performance

Patients' mean performance for selecting the most rewarded target was above 80% in all blocks (P = 0.75: ON 94%, OFF 84%/P = 1: ON 95%, OFF 88%) (Figure S6). We found no significant interaction between session and probability blocks ($\chi^2$ = 1.64, *p* = .20). We did not find any significant difference compared with the HC group in any blocks (P = 0.75: HC 79%; P = 1: HC 91%; *p* > .38). To make sure that learning performance would not affect free-choice preference, we excluded patients whose learning performance was below 90% in all sessions and probability blocks and observed that the DBS effect on free-choice preference remained in this new dataset ($\chi^2$ = 6.67, *p* = .02). The temporal dynamics of learning behavior are provided in Figure S7.

## Tractography Analysis

We found that the connectivity between the right VATs and a subregion including the right medial prefrontal cortex (mPFC), pre-supplementary motor area (pre-SMA), and anterior cingulate cortex (total volume significant voxels: 436 mm³) was significantly higher in DBS patients who selected the free-choice option ON more frequently compared with OFF-DBS session (Figure 4, upper panel). This connectivity was quantitatively associated with the DBS effect on free-choice preference. That is, the higher the TD in that region, the higher the difference for free-choice preference between ON and OFF (Figure 4, lower panel). Precisely, we found that SIFT2 values (representing the amount of connectivity) was correlated with this ON-OFF difference for free choice (Brodmann area [BA] 8 *r* = 0.48, *p* = .03; BA 9 *r* = 0.51, *p* = .02; BA 32 *r* = 0.50, *p* = .03) (Figure 4, right panel).

To provide a visualization of the right prefrontal streamline density distribution in the STN related to free-choice preference, we computed 2 T-maps, one corresponding to the mean
Intrinsic Motivation in Parkinson's Disease

<figure><img src="image_6.png" /></figure>

**Figure 2.** Free-choice preference of healthy control participants during 2 consecutive sessions. (A) Mean free-choice proportions between the first and second sessions. (B) Free-choice proportions plotted against the estimated obtained reward. Plain lines are the mean estimates. Small colored dots represent the mean individual free-choice preference. Large diamonds represent the mean free-choice proportion at the population level for each probability block (error bars: SE). The plain line represents the estimated mean with SE.

streamline density distribution activated by VATs within the STN (Figure 5A) and the other corresponding to the *t* values of the mean difference of streamline density distribution between the 2 groups based on their ON-OFF differences for free option selection (Figure 5B). We observed that the maximal activated streamline density in patients with high ON-DBS free-choice preference compared with OFF-DBS was located in a more anterior position within the STN (anteroposterior coordinates in the anterior commissure [AC]/posterior commissure [PC] referential; y = 11 mm vs. y = 9 mm) (Figure 5C). The right VATs also tended to be larger than the left VATs, but this was not statistically significant (mean VAT for the right and left STN = 95 and 74 mm³, respectively; *p* = .23).

Finally, patients' implantation sites had contacts located more anteriorly in the right than in the left hemisphere (anteroposterior coordinates in the AC/PC referential; left: y = 10.2 mm; right: y = 10.8 mm; W = 431, *p* = .004) (Figure S8).

Free-Choice Preference in the DOPA Group

We found no significant main effect of session ($\chi^2$ = 0.01, *p* = .94) or reward ($\chi^2$ = 0.04, *p* = .83) and no session x reward interaction ($\chi^2$ = 3.40, *p* = .06) in the DOPA group (Figure 6A, B). Similarly, we did not find any significant difference between free and forced-choice RTs or an interaction with session (Figure 6C, D).

Given the absence of an acute levodopa effect, we subsequently examined within the DOPA group whether heterogeneity in chronic pharmacological therapy modulated free-choice preference. To explore this hypothesis, patients within the DOPA group were divided based on their LEDD. A median split of LEDD was used to categorize patients into low-DOPA (<1300 mg; *n* = 12) and high-DOPA (>1300 mg; *n* = 13) LEDD subgroups (Figure 7A). These 2 groups did not differ on disease severity, disease duration, or age (low vs. high DOPA: MDS-UPDRS OFF 44.9 vs. 43, *p* = .93; duration 10.3 vs. 8.9 years, *p* = .42; age 54 vs. 58 years, *p* = .07). Using this approach, we observed a significant main effect of LEDD ($\chi^2$ = 4.94, *p* = .04), with high DOPA having higher free-choice preference than low DOPA (low DOPA vs. high DOPA, *p* = .02) (Figure 7B). We did not find a significant LEDD x session interaction ($\chi^2$ = 0.61, *p* = .48).

Learning Performance

Patients' mean performance for selecting the most rewarded target was above 75% in all blocks (Figure S6). We found a significant interaction between session and probability blocks ($\chi^2$ = 4.38, *p* = .05). In the P = 0.75 block, patients had higher performance OFF (88%) than ON (75%, z = 2.966, *p* = .003). There was no difference between OFF (99%) and ON (98%) in the P = 1 block (z = -0.383, *p* = .70). We did not find any significant difference compared with the HC group in any blocks (P = 0.75: HC 79%; P = 1: HC 91%, *p* > .69). Temporal dynamics of learning behavior are provided in Figure S7.

DISCUSSION

Intrinsic motivation accounts for many human behaviors independent of extrinsic outcomes (3). Here, we used choice opportunities as an intrinsic reward and tested patients with PD. First, we showed that STN-DBS enhanced free-choice preference, and this effect depended on an mPFC-STN network. We also found that levels of chronic DA therapy positively correlated with free-choice preference. These findings suggest that deep hypo-DAergic states such as chronic low levels of DA therapy diminished the preference for choice opportunities, and this preference can be reestablished either with high doses of daily DA therapy or STN-DBS with direct modulation of right mPFC projections.

STN-DBS Increases Preference for Risky Choices

We observed that OFF-DBS patients showed no difference between the free- and forced-choice options, whereas ON-DBS patients showed an increased preference for free choice. This improvement was linked to enhanced connectivity between the right mPFC and individual VATs. One explanation of this unilateral effect may be differences between patients' implantation sites, with therapeutic contacts located more anteriorly in the right hemisphere. The mPFC-STN network, which is part of the hyperdirect pathway, has been classically related to processing conflicting information and response inhibition, which could be modulated directly by DBS (25,30,33,43). Therefore, patients with PD who exhibit increased post-DBS connectivity between these regions demonstrate impulsiveness in both behavioral tasks and
<figure><img src="image_7.png" /><figcaption>**Figure 3.** Free-choice preference at the first stage for deep brain stimulation (DBS) patients. (A) Mean free-choice preference averaged over the 3 blocks. (B) Free-choice preference as a function of the average obtained reward. (C) Mean reaction time (RT) for free (violet) and forced (orange) trials. (D) RT for free and forced trials as a function of the average obtained reward. Small colored dots represent the mean individual free-choice preference/RTs. Large diamonds represent the mean free-choice proportion/RT at the population level for each probability block (error bars: SE). The plain line represents the estimated mean with SE.</figcaption></figure>

neuropsychiatric evaluations (26,30,44). Computational modeling suggests that STN-DBS could create a more permissive decision threshold (24,25), particularly in conflict-laden situations that result in ambiguous or irrational decisions where the ratios of gains to losses are not easily discernible, thereby posing a risk of losing rewards. In our experiment, STN-DBS led to faster responses than OFF-DBS sessions, with those responses being even faster during free-than-forced-choice trials. This suggests that value-based decision making occurs alongside a lowered decision threshold induced by STN-DBS (45,46). One explanation may stem from the risky nature of free choice that contradicts the economically rational decision, which would be to select the

safer option with no choice opportunity. Consequently, DBS patients exhibited a risky attitude by overselecting the free option. STN-DBS, by disrupting the inhibitory responses mediated by the right mPFC-STN pathway, may allow participants to express their preference for immediate intrinsic rewards in the initial phase of the task while disregarding the potential loss of an extrinsic reward in the subsequent phase. Altogether, this result suggests that free choices are intrinsically appealing and that this intrinsic rewarding aspect of choice may stem at least in part from its riskier nature in our task (8).

Here, making a risky decision can be analyzed in a way that enhances the agent's perception of the associated action and
Intrinsic Motivation in Parkinson's Disease

<figure><img src="image_8.png" /><figcaption>**Figure 4.** Voxelwise analysis between low and high ON-OFF free-choice preference in the deep brain stimulation (DBS) group. Upper panel: the blue region corresponds to the statistical map with a threshold of *p* < .05 (familywise error rate corrected with threshold-free cluster enhancement) overlaid on a T1 volume of the template. Yellow, red, and green regions correspond to Brodmann areas [BAs] 8, 9, and 32, respectively. Lower panel: illustrations of connectivity in 3 patients with different effects of DBS on free-choice preference. Left to right represent streamline projections of individual patients with negative (–0.23), null, and positive (0.97) effects of the stimulation on free-choice preference. The images are displayed using radiological orientation. Right panel: regression plots between spherical informed filtering technique (SIFT2) values and ON-OFF difference for BAs 8, 9, and 32.</figcaption></figure>

has a significant impact on the environment. This realization fosters a sense of agency and reinforces the perception of one's ability to exert control over one's life (8,47). The mPFC may be involved in processing intrinsic values, consistent with

work indicating that this region is implicated in the sense of agency associated with voluntary decisions (48,49). Thus, the relationship between the appropriate mPFC activity and free-choice preference may be linked to voluntary actions to

<figure><img src="image_9.png" /><figcaption>**Figure 5.** T-maps of mean streamlines density distribution overlapping with the volumes of activated tissue (VATs) in the whole right subthalamic nucleus (STN) (A) and the mean difference between patients with a deep brain stimulation (DBS) choice effect (B) (median split on the ON minus OFF free-choice selection). T-maps are superimposed on the STN limbic (yellow), associative (purple), and sensorimotor (green) schematic anatomo-functional territories from the YeB atlas (41). Color scales represent the *t* values. (C) Maximal activated streamlines density in the right STN overlapping with the VATs (blue dot) and the mean difference between patients with a DBS choice (median split on the ON minus OFF free-choice selection; red dot). Coordinates in millimeters in the anterior commissure/posterior commissure (AC/PC) referential system relative to the PC: x = 13.71 mm, y = 9 mm, z = 2 mm (blue dot) vs. x = 11 mm, y = 11 mm, z = 3 mm (red dot). A, anterior; R, right; S, superior.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Figure 6.** Free-choice preference at the first stage for the DOPA patients. (A) Mean free-choice preference averaged over the 3 blocks. (B) Free-choice preference as a function of the average obtained reward. (C) Mean reaction time (RT) for free (violet) and forced (orange) trials. (D) RT for free and forced trials as a function of the average obtained reward. Small colored dots represent the mean individual free-choice preference/RTs. Large diamonds represent the mean free-choice proportion/RT at the population level for each probability block (error bars: SE). The plain line represents the estimated mean with SE.</figcaption></figure>

control the environment. The mPFC is part of a network that computes action-outcome associations, enabling more flexible motor plans to achieve internal goals (49). This function is supported by the fact that impairments in this area, as well as pre-SMA dysfunctions in PD, are connected to deficits in flexible behaviors that could be alleviated by STN-DBS (50–52). In our study, OFF-DBS patients expressed no preference between free and forced options, suggesting that STN-DBS may promote the emergence of free-choice behaviors as a self-driven incentive by restoring a flexible decision-making process.

However, while these results indicate that modulation of the hyperdirect pathway can enhance intrinsic motivation for

choice opportunities by modulating response inhibition, the role of DA in promoting a preference for intrinsic rewards is also further indicated by the behaviors of DBS patients.

## A Low Dose of DA Therapy Diminished Free-Choice Preference

In the OFF-DBS patients, we observed the lowest preference for free choice, which could be a consequence of the most substantial reduction of DA medication in this group. Moreover, we found that patients in the DOPA group preferred free-choice options independently of the acute levodopa challenge. We hypothesized that chronic, rather than acute, dopamine therapy had a greater impact on behavior. Patients with lower
Intrinsic Motivation in Parkinson's Disease

A

<figure><img src="image_11.png" /><figcaption>**Figure 7.** (A) Distribution of levodopa equivalent daily dose (LEDD) between the deep brain stimulation (DBS), low-DOPA, and high-DOPA groups after splitting the DOPA group by the median (dashed vertical line). (B) Free-choice preference between the DBS, low-DOPA, and high-DOPA groups. Small colored dots represent the mean individual free-choice preference/reaction times. Large diamonds represent mean free-choice proportion at the population level (error bars: SE). The plain line joins the mean between ON and OFF sessions in the 2 groups.</figcaption></figure>

B

<figure><img src="image_12.png" /></figure>

LEDDs showed reduced preference for free choice than patients with higher LEDDs. This median-split approach, previously used in a large PD cohort, helped identify a low-LEDD subgroup whose daily DA dose was comparable—although slightly higher—than that of chronic STN-DBS patients (53). The long-term effects of DA therapy on enhancing value-based decisions have been consistently reported in impulsive and compulsive behaviors in which the incidence is correlated with the long-term exposition of DA therapy (53–55). The mechanism that underlies such enhanced appetitive behavior depending on chronic dopamine treatment remains a topic of discussion and is not yet fully understood (54,56–58), yet the relationship between DA and intrinsic motivation suggests that the same network could encode both intrinsic and the classic extrinsic rewards. Free choices may act as a premium or bonus when selecting rewarding items, thus enhancing their subjective value and reinforcing free-choice options in the long term. Such a bonus has previously been modeled in reinforcement learning paradigms in healthy humans (8,59,60). It has also been recorded through specific phasic midbrain DA activity related to intrinsic rewards in an electrophysiological study of nonhuman primates (13). Polymorphism of DARPP-32, a gene associated with DA signal transduction in the basal ganglia, is also linked to an increased preference for freely chosen over forcibly sampled items (59). Following this hypothesis, enhanced free-choice preference due to chronic DA exposure may relate to the potentiation of appetitive signals related to intrinsic rewards in the same way as extrinsic ones.

Limitations and Future Directions

We did not find an acute effect of DA therapy on free-choice
proportions in the DOPA group when we compared ON and
OFF states. The lack of difference in free-choice proportions may
stem from our population, characterized by patients with
advanced PD. These characteristics include prolonged exposure

to high-dose DA therapy, which may limit behavioral changes after
a 12-hour cessation. Altogether, the mechanism that underlies
the enhanced appetitive behavior associated specifically with
chronic dopamine treatment remains debated, is not fully un-
derstood, and therefore warrants substantial future investigation.

Similarly, an interesting avenue for future research would be
the application of computational modeling, such as reinforce-
ment learning frameworks [e.g., similar to those used in our
previous study (8)], to provide a more granular understanding
of the observed choice behaviors. Such quantitative models
could help to more precisely delineate the underlying decision-
making mechanisms and individual differences in choice
preference and would also benefit from larger patient cohorts
to ensure robust parameter estimation.

Additionally, task performance order raises the question of
test-retest effects, but no significant differences were found
between successive HC sessions. Moreover, for DBS patients,
learning performance showed no significant differences be-
tween ON and OFF states. In contrast, DOPA patients per-
formed better OFF stimulation than ON (although accuracy
remained >75% in both states), while free-choice proportions
were identical during ON and OFF sessions.

Finally, we used a normative connectome instead of indi-
vidual acquisitions for tractography analysis. While this
approach may minimize signal-to-noise ratio, it also reduces
individual variability. Notably, this approach has been found to
yield comparable results to individual acquisitions in patients
with PD (18). Furthermore, our connectome was derived from a
population that closely matched our study cohort.

Conclusions

Our study demonstrates that dopamine depletion diminishes
intrinsic motivation for making choices, particularly in risky
scenarios. However, this can be restored to normal levels by
modulating the prefrontal-subthalamic pathway via DBS or
chronic DA therapy. Future research should explore whether
intrinsic motivation and extrinsic reward-processing deficits
underlie broader motivational deficits in patients.

ACKNOWLEDGMENTS AND DISCLOSURES

This work was supported by a Fondation Recherche Médicale fellowship (Fellowship No. FDM201906008526 [to DB]), the Agence Nationale de la Recherche (Grant Nos. ANR-19-CE37-0014-01 [to CK and BL] and ANR-19-CE37-0014-01 [to JM]), and the European Commission Project H2020-MSCA-IF-2018 (Grant No. 845176 [to JM]).

We thank the staff of the neurology and neurosurgery departments of the
Pitié Salpêtrière Hospital for their excellent patient care during the
investigation.

Anonymized data used for the analyses are available upon reasonable
request.

The authors report no biomedical financial interests or potential conflicts
of interest.

ARTICLE INFORMATION

From the Sorbonne Université, Institut du Cerveau, Paris Brain Institute, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Assistance Publique – Hôpitaux de Paris, Paris, France (DB, GT, NT, M-LW, VC, BL, CK, JM); Neurology Department, Assistance Publique – Hôpitaux de Paris, Hôpital de la Pitié Salpêtrière, Paris, France (DB, EH, VC); Department de Neurophysiology, Centre Hospitalier Universitaire Rouen, Université de Rouen Normandie, Rouen, France (M-LW); Neurosurgery Department, Assistance Publique – Hôpitaux de Paris, Hôpital de la Pitié Salpêtrière, Paris, France (CK); and Institut Jean Nicod, Département d'études Cognitives, School for Advanced Studies in the Social Sciences, Centre National de la Recherche Scientifique, Paris Sciences et Lettres University, Paris, France (JM).

DB is currently affiliated with the University of Bordeaux, Centre National de la Recherche Scientifique, Institut des Maladies Neurodégénératives, UMR5293, Centre Hospitalier Universitaire Bordeaux, Service de Neurologie des Maladies Neurodégénératives, Centre de Recherche en Neurosciences de Montpellier Advanced Medical Sciences, NS-Park/FCRIN Network, Bordeaux, France.

BL, CK, and JM are co-senior authors.

Address correspondence to David Bendetowicz, M.D., Ph.D., at david.bendetowicz@u-bordeaux.fr, or Jérôme Munuera, Ph.D., at jerome.munuera@icm-institute.org.

Received Feb 4, 2025; revised Jun 25, 2025; accepted Jun 28, 2025.

Supplementary material cited in this article is available online at https://doi.org/10.1016/j.bpsc.2025.06.008.

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