Recent functional magnetic resonance (fMRI) imaging studies have revealed that subchronic medication with escitalopram leads to significant reduction in both amygdala and medial frontal gyrus reactivity during processing of emotional faces, suggesting that escitalopram may have a distinguishable modulatory effect on neural activation as compared with other serotonin-selective antidepressants. In this fMRI study we aimed to explore whether short-term medication with escitalopram in healthy volunteers is associated with reduced neural response to emotional processing, and whether this effect is predicted by drug plasma concentration. The neural response to fearful and happy faces was measured before and on day 7 of treatment with escitalopram (10mg) in 15 healthy volunteers and compared with those in a control unmedicated group (n=14). Significantly reduced activation to fearful, but not to happy facial expressions was observed in the bilateral amygdala, cingulate and right medial frontal gyrus following escitalopram medication. This effect was not correlated with plasma drug concentration. In accordance with previous data, we showed that escitalopram exerts its rapid direct effect on emotional processing via attenuation of neural activation in pathways involving medial frontal gyrus and amygdala, an effect that seems to be distinguishable from that of other SSRIs.
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http://dx.doi.org/10.1177/0269881115620462 | DOI Listing |
Sci Rep
December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
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December 2024
Department of Computer Science and Digital Technologies, University of East London, London, UK.
Nursing activity recognition has immense importance in the development of smart healthcare management and is an extremely challenging area of research in human activity recognition. The main reasons are an extreme class-imbalance problem and intra-class variability depending on both the subject and the recipient. In this paper, we apply a unique two-step feature extraction, coupled with an intermediate feature 'Angle' and a new feature called mean min max sum to render the features robust against intra-class variation.
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December 2024
Henan College of Transportation, Zhengzhou, 450000, Henan, China.
Novel Human Activity Recognition (HAR) methodologies, which are built upon learning algorithms and employ ubiquitous sensors, have achieved remarkable precision in the identification of sports activities. Such progress benefits all age groups of humanity, and in the future, AI will be used to address difficult problems in scientific research. A novel approach is introduced in this article to utilize motion sensor data in order to categorize and distinguish various categories of sports activities.
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December 2024
Department of Electrical Engineering, College of Engineering, Taif University, P.O. BOX 11099, 21944, Taif, Saudi Arabia.
Weather recognition is crucial due to its significant impact on various aspects of daily life, such as weather prediction, environmental monitoring, tourism, and energy production. Several studies have already conducted research on image-based weather recognition. However, previous studies have addressed few types of weather phenomena recognition from images with insufficient accuracy.
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December 2024
Creative Robotics Lab, UNSW, Sydney, 2021, Australia.
Unlike the conventional, embodied, and embrained whole-body movements in the sagittal forward and vertical axes, movements in the lateral/transversal axis cannot be unequivocally grounded, embodied, or embrained. When considering motor imagery for left and right directions, it is assumed that participants have underdeveloped representations due to a lack of familiarity with moving along the lateral axis. In the current study, a 32 electroencephalography (EEG) system was used to identify the oscillatory neural signature linked with lateral axis motor imagery.
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