To examine the effects of bilingualism on cognitive control, we studied monolingual and bilingual young adults performing a flanker task with functional MRI. The trial types of primary interest for this report were incongruent and no-go trials, representing interference suppression and response inhibition, respectively. Response times were similar between groups. Brain data were analyzed using partial least squares (PLS) to identify brain regions where activity covaried across conditions. Monolinguals and bilinguals activated different sets of brain regions for congruent and incongruent trials, but showed activation in the same regions for no-go trials. During the incongruent trials, monolinguals activated the left temporal pole and left superior parietal regions. In contrast, an extensive network including bilateral frontal, temporal and subcortical regions was active in bilinguals during the incongruent trials and in both groups for the no-go trials. Correlations between brain activity and reaction time difference relative to neutral trials revealed that monolinguals and bilinguals showed increased activation in different brain regions to achieve less interference from incongruent flankers. Results indicate that bilingualism selectively affects neural correlates for suppressing interference, but not response inhibition. Moreover, the neural correlates associated with more efficient suppression of interference were different in bilinguals than in monolinguals, suggesting a bilingual-specific network for cognitive control.
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http://dx.doi.org/10.1016/j.bandc.2010.09.004 | DOI Listing |
MAGMA
January 2025
Aix Marseille Univ, CNRS, CRMBM, Marseille, France.
Objective: Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness in this field. However, the impact of reducing neural network complexity remains unexplored in the FF quantification in individual muscles.
View Article and Find Full Text PDFEur J Neurol
January 2025
Department of Neurosurgery, Medical University of Vienna, Vienna, Austria.
Background: Temporal lobe epilepsy (TLE) can lead to structural brain abnormalities, with thalamus atrophy being the most common extratemporal alteration. This study used probabilistic tractography to investigate the structural connectivity between individual thalamic nuclei and the hippocampus in TLE.
Methods: Thirty-six TLE patients who underwent pre-surgical 3 Tesla magnetic resonance imaging (MRI) and 18 healthy controls were enrolled in this study.
Psychiatry Clin Neurosci
January 2025
Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Aim: Autistic traits exhibit neurodiversity with varying behaviors across developmental stages. Brain complexity theory, illustrating the dynamics of neural activity, may elucidate the evolution of autistic traits over time. Our study explored the patterns of brain complexity in autistic individuals from childhood to adulthood.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30-059, Krakow, Poland.
In this study, a predictive maintenance (PdM) system focused on feature selection for the detection and classification of simulated defects in wind turbine blades has been developed. Traditional PdM systems often rely on numerous, broadly chosen diagnostic indicators derived from vibration data, yet many of these features offer little added value and may even degrade model performance. General feature selection methods might not be suitable for PdM solutions, as information regarding observed faults is often misinterpreted or lost.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing 100081, China.
Epilepsy is a group of neurological disorders characterized by epileptic seizures, and it affects tens of millions of people worldwide. Currently, the most effective diagnostic method employs the monitoring of brain activity through electroencephalogram (EEG). However, it is critical to predict epileptic seizures in patients prior to their onset, allowing for the administration of preventive medications before the seizure occurs.
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