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Dopamine systems mediate key aspects of reward learning. Parkinson's disease (PD) represents a valuable model to study reward mechanisms because both the disease process and the anti-Parkinson medications influence dopamine neurotransmission. The aim of this pilot study was to investigate whether the level of levodopa differently modulates learning from positive and negative feedback and its electrophysiological correlate, the error related negativity (ERN), in PD. Ten PD patients and ten healthy participants performed a two-stage reinforcement learning task. In the Learning Phase, they had to learn the correct stimulus within a stimulus pair on the basis of a probabilistic positive or negative feedback. Three sets of stimulus pairs were used. In the Testing Phase, the participants were tested with novel combinations of the stimuli previously experienced to evaluate whether they learned more from positive or negative feedback. PD patients performed the task both ON- and OFF-levodopa in two separate sessions while they remained on stable therapy with dopamine agonists. The electroencephalogram (EEG) was recorded during the task. PD patients were less accurate in negative than positive learning both OFF- and ON-levodopa. In the OFF-levodopa state they were less accurate than controls in negative learning. PD patients had a smaller ERN amplitude OFF- than ON-levodopa only in negative learning. In the OFF-levodopa state they had a smaller ERN amplitude than controls in negative learning. We hypothesize that high tonic dopaminergic stimulation due to the dopamine agonist medication, combined to the low level of phasic dopamine due to the OFF-levodopa state, could prevent phasic "dopamine dips" indicated by the ERN needed for learning from negative feedback.
Pubmed ID: 27822182 RIS Download
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Interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. First developed on Matlab 5.3 under Linux, EEGLAB runs on Matlab v5 and higher under Linux, Unix, Windows, and Mac OS X (Matlab 7+ recommended). EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. EEGLAB also incorporates extensive tutorial and help windows, plus a command history function that eases users'' transition from GUI-based data exploration to building and running batch or custom data analysis scripts. EEGLAB offers a wealth of methods for visualizing and modeling event-related brain dynamics, both at the level of individual EEGLAB ''datasets'' and/or across a collection of datasets brought together in an EEGLAB ''studyset.'' For experienced Matlab users, EEGLAB offers a structured programming environment for storing, accessing, measuring, manipulating and visualizing event-related EEG data. For creative research programmers and methods developers, EEGLAB offers an extensible, open-source platform through which they can share new methods with the world research community by publishing EEGLAB ''plug-in'' functions that appear automatically in the EEGLAB menu of users who download them. For example, novel EEGLAB plug-ins might be built and released to ''pick peaks'' in ERP or time/frequency results, or to perform specialized import/export, data visualization, or inverse source modeling of EEG, MEG, and/or ECOG data. EEGLAB Features * Graphic user interface * Multiformat data importing * High-density data scrolling * Defined EEG data structure * Open source plug-in facility * Interactive plotting functions * Semi-automated artifact removal * ICA & time/frequency transforms * Many advanced plug-in toolboxes * Event & channel location handling * Forward/inverse head/source modeling
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