Deep brain stimulation (DBS) of the subthalamic nucleus markedly improves the motor symptoms of Parkinson's disease, but causes cognitive side effects such as impulsivity. We showed that DBS selectively interferes with the normal ability to slow down when faced with decision conflict. While on DBS, patients actually sped up their decisions under high-conflict conditions. This form of impulsivity was not affected by dopaminergic medication status. Instead, medication impaired patients' ability to learn from negative decision outcomes. These findings implicate independent mechanisms leading to impulsivity in treated Parkinson's patients and were predicted by a single neurocomputational model of the basal ganglia.

It takes effort and time to tame one's impulses. Although medial prefrontal cortex (mPFC) is broadly implicated in effortful control over behavior, the subthalamic nucleus (STN) is specifically thought to contribute by acting as a brake on cortico-striatal function during decision conflict, buying time until the right decision can be made. Using the drift diffusion model of decision making, we found that trial-to-trial increases in mPFC activity (EEG theta power, 4–8 Hz) were related to an increased threshold for evidence accumulation (decision threshold) as a function of conflict. Deep brain stimulation of the STN in individuals with Parkinson's disease reversed this relationship, resulting in impulsive choice. In addition, intracranial recordings of the STN area revealed increased activity (2.5–5 Hz) during these same high-conflict decisions. Activity in these slow frequency bands may reflect a neural substrate for cortico–basal ganglia communication regulating decision processes.

Converging evidence implicates striatal dopamine (DA) in reinforcement learning, such that DA increases enhance "Go learning" to pursue actions with rewarding outcomes, whereas DA decreases enhance "NoGo learning" to avoid non-rewarding actions. Here we test whether these effects apply to the response time domain. We employ a novel paradigm which requires the adjustment of response times to a single response. Reward probability varies as a function of response time, whereas reward magnitude changes in the opposite direction. In the control condition, these factors exactly cancel, such that the expected value across time is constant (CEV). In two other conditions, expected value increases (IEV) or decreases (DEV), such that reward maximization requires either speeding up (Go learning) or slowing down (NoGo learning) relative to the CEV condition. We tested patients with Parkinson's disease (depleted striatal DA levels) on and off dopaminergic medication, compared with age-matched controls. While medicated, patients were better at speeding up in the DEV relative to CEV conditions. Conversely, nonmedicated patients were better at slowing down to maximize reward in the IEV condition. These effects of DA manipulation on cumulative Go/NoGo response time adaptation were captured with our a priori computational model of the basal ganglia, previously applied only to forced-choice tasks. There were also robust trial-to-trial changes in response time, but these single trial adaptations were not affected by disease or medication and are posited to rely on extrastriatal, possibly prefrontal, structures.

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