Evidence map›Paper›PMID 28408878›Full record

ArticleFrontiers in computational neuroscience2017

Reward Based Motor Adaptation Mediated by Basal Ganglia.

Taegyo Kim, Khaldoun C Hamade, Dmitry Todorov, William H Barnett, Robert A Capps, Elizaveta M Latash, Sergey N Markin, Ilya A Rybak, Yaroslav I Molkov

Abstract read
In one paragraph

Article in Frontiers in computational neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Taegyo KimDepartment of Neurobiology and Anatomy, Drexel University College of MedicinePhiladelphia, PA, USA.
Khaldoun C HamadeDepartment of Neurobiology and Anatomy, Drexel University College of MedicinePhiladelphia, PA, USA.
Dmitry TodorovDepartment of Mathematics and Statistics, Georgia State UniversityAtlanta, GA, USA.
William H BarnettDepartment of Mathematics and Statistics, Georgia State UniversityAtlanta, GA, USA.
Robert A CappsDepartment of Mathematics and Statistics, Georgia State UniversityAtlanta, GA, USA.
Elizaveta M LatashDepartment of Mathematics and Statistics, Georgia State UniversityAtlanta, GA, USA.
Sergey N MarkinDepartment of Neurobiology and Anatomy, Drexel University College of MedicinePhiladelphia, PA, USA.
Ilya A RybakDepartment of Neurobiology and Anatomy, Drexel University College of MedicinePhiladelphia, PA, USA.
Yaroslav I MolkovDepartment of Mathematics and Statistics, Georgia State UniversityAtlanta, GA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is widely accepted that the basal ganglia (BG) play a key role in action selection and reinforcement learning. However, despite considerable number of studies, the BG architecture and function are not completely understood. Action selection and reinforcement learning are facilitated by the activity of dopaminergic neurons, which encode reward prediction errors when reward outcomes are higher or lower than expected. The BG are thought to select proper motor responses by gating appropriate actions, and suppressing inappropriate ones. The direct striato-nigral (GO) and the indirect striato-pallidal (NOGO) pathways have been suggested to provide the functions of BG in the two-pathway concept. Previous models confirmed the idea that these two pathways can mediate the behavioral choice, but only for a relatively small number of potential behaviors. Recent studies have provided new evidence of BG involvement in motor adaptation tasks, in which adaptation occurs in a non-error-based manner. In such tasks, there is a continuum of possible actions, each represented by a complex neuronal activity pattern. We extended the classical concept of the two-pathway BG by creating a model of BG interacting with a movement execution system, which allows for an arbitrary number of possible actions. The model includes sensory and premotor cortices, BG, a spinal cord network, and a virtual mechanical arm performing 2D reaching movements. The arm is composed of 2 joints (shoulder and elbow) controlled by 6 muscles (4 mono-articular and 2 bi-articular). The spinal cord network contains motoneurons, controlling the muscles, and sensory interneurons that receive afferent feedback and mediate basic reflexes. Given a specific goal-oriented motor task, the BG network through reinforcement learning constructs a behavior from an arbitrary number of basic actions represented by cortical activity patterns. Our study confirms that, with slight modifications, the classical two-pathway BG concept is consistent with results of previous studies, including non-error based motor adaptation experiments, pharmacological manipulations with BG nuclei, and functional deficits observed in BG-related motor disorders.

Indexed as

basal gangliadopaminergic rewardmotor adaptationreaching movementreinforcement learning

Identifiers

PMID28408878
PMCPMC5374212

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.