scholarly journals Tremor in Parkinson's disease inverts the effect of dopamine on reinforcement

Brain ◽  
2020 ◽  
Vol 143 (11) ◽  
pp. 3178-3180
Author(s):  
Sanjay G Manohar
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Reinforcement Learning ◽  
Motor Phenotype

This scientific commentary refers to ‘Effects of dopamine on reinforcement learning in Parkinson’s disease depend on motor phenotype’ by van Nuland et al. (doi:10.1093/brain/awaa335).

scholarly journals Effects of dopamine on reinforcement learning in Parkinson’s disease depend on motor phenotype

Brain ◽  
2020 ◽  
Vol 143 (11) ◽  
pp. 3422-3434
Author(s):  
Annelies J van Nuland ◽  
Rick C Helmich ◽  
Michiel F Dirkx ◽  
Heidemarie Zach ◽  
Ivan Toni ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Reinforcement Learning ◽  
Learning Task ◽  
Learning Rate ◽  
Dopamine Depletion ◽  
Large Variability ◽  
Dopaminergic Medication ◽  
Improved Learning ◽  
Motor Phenotype

Abstract Parkinson’s disease is clinically defined by bradykinesia, along with rigidity and tremor. However, the severity of these motor signs is greatly variable between individuals, particularly the presence or absence of tremor. This variability in tremor relates to variation in cognitive/motivational impairment, as well as the spatial distribution of neurodegeneration in the midbrain and dopamine depletion in the striatum. Here we ask whether interindividual heterogeneity in tremor symptoms could account for the puzzlingly large variability in the effects of dopaminergic medication on reinforcement learning, a fundamental cognitive function known to rely on dopamine. Given that tremor-dominant and non-tremor Parkinson’s disease patients have different dopaminergic phenotypes, we hypothesized that effects of dopaminergic medication on reinforcement learning differ between tremor-dominant and non-tremor patients. Forty-three tremor-dominant and 20 non-tremor patients with Parkinson’s disease were recruited to be tested both OFF and ON dopaminergic medication (200/50 mg levodopa-benserazide), while 22 age-matched control subjects were recruited to be tested twice OFF medication. Participants performed a reinforcement learning task designed to dissociate effects on learning rate from effects on motivational choice (i.e. the tendency to ‘Go/NoGo’ in the face of reward/threat of punishment). In non-tremor patients, dopaminergic medication improved reward-based choice, replicating previous studies. In contrast, in tremor-dominant patients, dopaminergic medication improved learning from punishment. Formal modelling showed divergent computational effects of dopaminergic medication as a function of Parkinson’s disease motor phenotype, with a modulation of motivational choice bias and learning rate in non-tremor and tremor patients, respectively. This finding establishes a novel cognitive/motivational difference between tremor and non-tremor Parkinson’s disease patients, and highlights the importance of considering motor phenotype in future work.

scholarly journals Executive function network’s white matter alterations relate to Parkinson’s disease motor phenotype

2021 ◽  
Vol 741 ◽  
pp. 135486
Author(s):  
Qinglu Yang ◽  
Shruti Nanivadekar ◽  
Paul A. Taylor ◽  
Zulin Dou ◽  
Codrin I. Lungu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Executive Function ◽  
White Matter ◽  
Motor Phenotype

scholarly journals Computational medication regimen for Parkinson’s disease using reinforcement learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yejin Kim ◽  
Jessika Suescun ◽  
Mya C. Schiess ◽  
Xiaoqian Jiang
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Reinforcement Learning ◽  
Symptom Severity ◽  
Rating Scale ◽  
Motor Symptom ◽  
Sequential Decision ◽  
Medication Regimen ◽  
Severity Scores ◽  
Decision Making Rule

AbstractOur objective is to derive a sequential decision-making rule on the combination of medications to minimize motor symptoms using reinforcement learning (RL). Using an observational longitudinal cohort of Parkinson’s disease patients, the Parkinson’s Progression Markers Initiative database, we derived clinically relevant disease states and an optimal combination of medications for each of them by using policy iteration of the Markov decision process (MDP). We focused on 8 combinations of medications, i.e., Levodopa, a dopamine agonist, and other PD medications, as possible actions and motor symptom severity, based on the Unified Parkinson Disease Rating Scale (UPDRS) section III, as reward/penalty of decision. We analyzed a total of 5077 visits from 431 PD patients with 55.5 months follow-up. We excluded patients without UPDRS III scores or medication records. We derived a medication regimen that is comparable to a clinician’s decision. The RL model achieved a lower level of motor symptom severity scores than what clinicians did, whereas the clinicians’ medication rules were more consistent than the RL model. The RL model followed the clinician’s medication rules in most cases but also suggested some changes, which leads to the difference in lowering symptoms severity. This is the first study to investigate RL to improve the pharmacological approach of PD patients. Our results contribute to the development of an interactive machine-physician ecosystem that relies on evidence-based medicine and can potentially enhance PD management.

scholarly journals Examining the Motor Phenotype of Patients with Both Essential Tremor and Parkinson’s Disease

2012 ◽  
Vol 2 (0) ◽  
pp. 02 ◽  
Author(s):  
Rita M. Simões ◽  
Anne Constantino ◽  
Eliza Gibadullina ◽  
David Houghton ◽  
Elan D. Louis ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Essential Tremor ◽  
Motor Phenotype

scholarly journals Author response: Effects of dopamine on reinforcement learning and consolidation in Parkinson’s disease

2017 ◽  
Author(s):  
John P Grogan ◽  
Demitra Tsivos ◽  
Laura Smith ◽  
Brogan E Knight ◽  
Rafal Bogacz ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Reinforcement Learning ◽  
Author Response

scholarly journals Dopamine and performance in a reinforcement learning task: evidence from Parkinson’s disease

Brain ◽  
2012 ◽  
Vol 135 (6) ◽  
pp. 1871-1883 ◽  
Author(s):  
Tamara Shiner ◽  
Ben Seymour ◽  
Klaus Wunderlich ◽  
Ciaran Hill ◽  
Kailash P. Bhatia ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Reinforcement Learning ◽  
Learning Task ◽  
And Performance
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