Recent studies as well as theoretical models of error processing assign fundamental importance to the brain's dopaminergic system. Research about how the electrophysiological correlates of error processing--the error-related negativity (ERN) and the error positivity (Pe)--are influenced by variations of common dopaminergic genes, however, is still relatively scarce. In the present study, we therefore investigated whether polymorphisms in the DAT1 gene and in the DRD4 gene, respectively, lead to interindividual differences in these error processing correlates. One hundred sixty participants completed a version of the Eriksen Flanker Task while a 26-channel EEG was recorded. The task was slightly modified in order to increase error rates. During data analysis, participants were split into two groups depending on their DAT1 and their DRD4 genotypes, respectively. ERN and Pe amplitudes after correct responses and after errors as well as difference amplitudes between errors and correct responses were analyzed. We found a differential effect of DAT1 genotype on the Pe difference amplitude but not on the ERN difference amplitude, while the reverse was true for DRD4 genotype. These findings are in line with predictions from theoretical models of dopaminergic transmission in the brain. They furthermore tie results from clinical investigations of disorders impacting on the dopamine system to genetic variations known to be at-risk genotypes.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3230585PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0028396PLOS

Publication Analysis

Top Keywords

error processing
12
drd4 genotypes
8
electrophysiological correlates
8
correlates error
8
theoretical models
8
correct responses
8
difference amplitude
8
error
6
dopamine transporter
4
dat1
4

Similar Publications

Repeatable process for extracting health data from HL7 CDA documents.

J Biomed Inform

December 2024

Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia; STACC, 51009 Tartu, Estonia.

Objective: This study aims to address the gap in the literature on converting real-world Clinical Document Architecture (CDA) data into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), focusing on the initial steps preceding the mapping phase. We highlight the importance of a repeatable Extract-Transform-Load (ETL) pipeline for health data extraction from HL7 CDA documents in Estonia for research purposes.

Methods: We developed a repeatable ETL pipeline to facilitate the extraction, cleaning, and restructuring of health data from CDA documents to OMOP CDM, ensuring a high-quality and structured data format.

View Article and Find Full Text PDF

The widespread use of pesticides, including diazinon, poses an increased risk of environmental pollution and detrimental effects on biodiversity, food security, and water resources. In this study, we investigated the impact of Potentially Toxic Elements (PTE) including Zn, Cd, V, and Mn on the degradation of diazinon in three different soils. We investigated the capability and performance of four machine learning models to predict residual pesticide concentration, including adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), radial basis function (RBF), and multi-layer perceptron (MLP).

View Article and Find Full Text PDF

Don't Sleep on Sleep: A Case Report from a Division I Heptathlete.

J Athl Train

December 2024

Musculoskeletal Adaptations to Aging and eXercise (MAAX) Laboratory, Oklahoma State University, Stillwater, OK, USA.

A female NCAA Division I track athlete experienced non-localized shin pain midway through her first season, which was diagnosed as medial tibial stress syndrome. Treatments included strengthening and range of motion exercises, reduced training volume, and pain control modalities, but symptoms worsened. It was revealed she had been suffering from severe sleep deprivation (<3 hours/night) contributing to bilateral tibial and fibular stress reactions.

View Article and Find Full Text PDF

Background: INTER- and INTRAmuscular fat (IMF) is elevated in high metabolic states and can promote inflammation. While magnetic resonance imaging (MRI) excels in depicting IMF, the lack of reproducible tools prevents the ability to measure change and track intervention success.

Methods: We detail an open-source fully-automated iterative threshold-seeking algorithm (ITSA) for segmenting IMF from T1-weighted MRI of the calf and thigh within three cohorts (CaMos Hamilton (N = 54), AMBERS (N = 280), OAI (N = 105)) selecting adults 45-85 years of age.

View Article and Find Full Text PDF

Ossification of the ligamentum flavum (OLF) is the main causative factor of spinal stenosis, but how to accurately and efficiently identify the ossification region is a clinical pain point and an urgent problem to be solved. Currently, we can only rely on the doctor's subjective experience for identification, with low efficiency and large error. In this study, a deep learning method is introduced for the first time into the diagnosis of ligamentum flavum ossificans, we proposed a lightweight, automatic and efficient method for identifying ossified regions, called CDUNeXt.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!