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      Distinct roles of dopamine and subthalamic nucleus in learning and probabilistic decision making

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          Abstract

          Even simple behaviour requires us to make decisions based on combining multiple pieces of learned and new information. Making such decisions requires both learning the optimal response to each given stimulus as well as combining probabilistic information from multiple stimuli before selecting a response. Computational theories of decision making predict that learning individual stimulus–response associations and rapid combination of information from multiple stimuli are dependent on different components of basal ganglia circuitry. In particular, learning and retention of memory, required for optimal response choice, are significantly reliant on dopamine, whereas integrating information probabilistically is critically dependent upon functioning of the glutamatergic subthalamic nucleus (computing the ‘normalization term’ in Bayes’ theorem). Here, we test these theories by investigating 22 patients with Parkinson’s disease either treated with deep brain stimulation to the subthalamic nucleus and dopaminergic therapy or managed with dopaminergic therapy alone. We use computerized tasks that probe three cognitive functions—information acquisition (learning), memory over a delay and information integration when multiple pieces of sequentially presented information have to be combined. Patients performed the tasks ON or OFF deep brain stimulation and/or ON or OFF dopaminergic therapy. Consistent with the computational theories, we show that stopping dopaminergic therapy impairs memory for probabilistic information over a delay, whereas deep brain stimulation to the region of the subthalamic nucleus disrupts decision making when multiple pieces of acquired information must be combined. Furthermore, we found that when participants needed to update their decision on the basis of the last piece of information presented in the decision-making task, patients with deep brain stimulation of the subthalamic nucleus region did not slow down appropriately to revise their plan, a pattern of behaviour that mirrors the impulsivity described clinically in some patients with subthalamic nucleus deep brain stimulation. Thus, we demonstrate distinct mechanisms for two important facets of human decision making: first, a role for dopamine in memory consolidation, and second, the critical importance of the subthalamic nucleus in successful decision making when multiple pieces of information must be combined.

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          Most cited references29

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          Hold your horses: impulsivity, deep brain stimulation, and medication in parkinsonism.

          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.
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            Dopamine neurons report an error in the temporal prediction of reward during learning.

            Many behaviors are affected by rewards, undergoing long-term changes when rewards are different than predicted but remaining unchanged when rewards occur exactly as predicted. The discrepancy between reward occurrence and reward prediction is termed an 'error in reward prediction'. Dopamine neurons in the substantia nigra and the ventral tegmental area are believed to be involved in reward-dependent behaviors. Consistent with this role, they are activated by rewards, and because they are activated more strongly by unpredicted than by predicted rewards they may play a role in learning. The present study investigated whether monkey dopamine neurons code an error in reward prediction during the course of learning. Dopamine neuron responses reflected the changes in reward prediction during individual learning episodes; dopamine neurons were activated by rewards during early trials, when errors were frequent and rewards unpredictable, but activation was progressively reduced as performance was consolidated and rewards became more predictable. These neurons were also activated when rewards occurred at unpredicted times and were depressed when rewards were omitted at the predicted times. Thus, dopamine neurons code errors in the prediction of both the occurrence and the time of rewards. In this respect, their responses resemble the teaching signals that have been employed in particularly efficient computational learning models.
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              Enhanced or impaired cognitive function in Parkinson's disease as a function of dopaminergic medication and task demands.

              We investigated how dopamine (DA) systems contribute to cognitive performance in the domain of learning and attentional flexibility by examining effects of withdrawing DA-ergic medication in patients with Parkinson's disease (PD). Medication remediated impairments in switching between two tasks, thought to depend on circuitry connecting the dorsolateral prefrontal cortex and the posterior parietal cortex to the dorsal caudate nucleus, which is profoundly DA-depleted in PD. By contrast, the same medication impaired probabilistic reversal learning that implicates orbitofrontal cortex- ventral striatal circuitry, which is relatively spared of DA loss in PD. Hence, DA-ergic medication improves or impairs cognitive performance depending on the nature of the task and the basal level of DA function in underlying cortico-striatal circuitry.
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                Author and article information

                Journal
                Brain
                Brain
                brainj
                brain
                Brain
                Oxford University Press
                0006-8950
                1460-2156
                December 2012
                31 October 2012
                31 October 2012
                : 135
                : 12
                : 3721-3734
                Affiliations
                1 Department of Neurology, Institute of Clinical Neurosciences, University of Bristol and North Bristol NHS Trust, Bristol, BS16 1LE, UK
                2 Department of Computer Science, University of Bristol, Bristol, BS8 1UB, UK
                3 Department of Neurosurgery, Institute of Clinical Neurosciences, University of Bristol and North Bristol NHS Trust, Bristol, BS16 1LE
                4 Department of Neurosciences, North Bristol NHS Trust, Bristol, UK
                5 Department of Clinical Neuropsychology, North Bristol NHS Trust, Bristol, BS16 1LE, UK
                Author notes
                Correspondence to: Elizabeth J. Coulthard, Consultant Senior Lecturer, Department of Neurology, Institute of Clinical Neurosciences, University of Bristol and North Bristol NHS Trust, Frenchay Hospital, BS16 1LE, Bristol, UK E-mail: elizabeth.coulthard@ 123456bristol.ac.uk
                Article
                aws273
                10.1093/brain/aws273
                3525052
                23114368
                459c1523-2c95-4949-90d1-3618bd41bcac
                © The Author (2012). Published by Oxford University Press on behalf of the Guarantors of Brain.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 February 2012
                : 18 July 2012
                : 14 August 2012
                Page count
                Pages: 14
                Categories
                Original Articles

                Neurosciences
                decision making,parkinson’s disease,mathematical modelling,reasoning,memory
                Neurosciences
                decision making, parkinson’s disease, mathematical modelling, reasoning, memory

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