Searching across hundreds of databases
Error monitoring is essential for optimizing motor behavior. It has been linked to the medial frontal cortex, in particular to the anterior midcingulate cortex (aMCC). The aMCC subserves its performance-monitoring function in interaction with the basal ganglia (BG) circuits, as has been demonstrated in patients suffering from BG lesions or from Parkinson's disease (PD). The subthalamic nucleus (STN) has been assumed an integrative structure for emotional, cognitive and motor processing. Error-related behavioral adaptation such as post-error slowing has been linked to motor inhibition involving activation of an inhibitory network including the STN. However, direct involvement of the STN in error monitoring and post-error behavioral adjustment has not yet been demonstrated. Here, we used simultaneous scalp electroencephalogram (EEG) and local field potential (LFP) recordings from the BG in 17 patients undergoing deep brain stimulation (DBS) for PD to investigate error-related evoked activity in the human STN, its relation to post-error behavioral adjustment and the influence of dopamine during the performance of a speeded flanker task. We found an error-related positive deflection (STN-Pe) in the STN-LFP 260-450 msec after error commission. Importantly, the STN-Pe amplitude was larger in trials with post-error slowing compared to trials with post-error speeding. There was no overall effect of dopamine on error processing. Subgroup analysis revealed a higher error rate (ER) in younger patients with earlier disease onset ON medication compared to OFF medication (and vice versa in the older patient group), which was associated with modulatory effects of the early cortical error-related negativity (ERN) and late STN-Pe. The late error-related STN-Pe that is associated with post-error reaction time (RT) adjustments supports the notion that post-error slowing is implemented by motor inhibition involving the STN. Further, the modulation of behavioral performance by dopaminergic therapy depending on patients' age may suggest a dopamine overdose effect in patients with earlier onset of PD.
Pubmed ID: 24525245 RIS Download
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
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
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
scicrunch.org
