The present study utilized functional magnetic resonance imaging (fMRI) to examine the neural processing of concurrently presented emotional stimuli under varying explicit and implicit attention demands. Specifically, in separate trials, participants indicated the category of either pictures or words. The words were placed over the center of the pictures and the picture-word compound-stimuli were presented for 1500 ms in a rapid event-related design. The results reveal pronounced main effects of task and emotion: the picture categorization task prompted strong activations in visual, parietal, temporal, frontal, and subcortical regions; the word categorization task evoked increased activation only in left extrastriate cortex. Furthermore, beyond replicating key findings regarding emotional picture and word processing, the results point to a dissociation of semantic-affective and sensory-perceptual processes for words: while emotional words engaged semantic-affective networks of the left hemisphere regardless of task, the increased activity in left extrastriate cortex associated with explicitly attending to words was diminished when the word was overlaid over an erotic image. Finally, we observed a significant interaction between Picture Category and Task within dorsal visual-associative regions, inferior parietal, and dorsolateral, and medial prefrontal cortices: during the word categorization task, activation was increased in these regions when the words were overlaid over erotic as compared to romantic pictures. During the picture categorization task, activity in these areas was relatively decreased when categorizing erotic as compared to romantic pictures. Thus, the emotional intensity of the pictures strongly affected brain regions devoted to the control of task-related word or picture processing. These findings are discussed with respect to the interplay of obligatory stimulus processing with task-related attentional control mechanisms.
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http://dx.doi.org/10.3389/fpsyg.2015.01861 | DOI Listing |
J Gastroenterol
January 2025
Faculty of Information Science and Technology, Hokkaido University, Sapporo, Japan.
Background: The automated classification of Helicobacter pylori infection status is gaining attention, distinguishing among uninfected (no history of H. pylori infection), current infection, and post-eradication. However, this classification has relatively low performance, primarily due to the intricate nature of the task.
View Article and Find Full Text PDFNat Commun
January 2025
School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Neural reuse can drive organisms to generalize knowledge across various tasks during learning. However, existing devices mostly focus on architectures rather than network functions, lacking the mimic capabilities of neural reuse. Here, we demonstrate a rational device designed based on ferroionic CuInPS, to accomplish the neural reuse function, enabled by dynamic allocation of the ferro-ionic phase.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, PR China; Shanghai Yangpu Mental Health Center, Shanghai, 200093, PR China. Electronic address:
Background And Objective: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models.
View Article and Find Full Text PDFComput Biol Chem
January 2025
Department of Computer Application, National Institute of Technology, Raipur, India. Electronic address:
The emergence of infectious disease and antibiotic resistance in bacteria like Escherichia coli (E. coli) shows the necessity for novel computational techniques for identifying essential genes that contribute to resistance. The task of identifying resistant strains and multi-drug patterns in E.
View Article and Find Full Text PDFPLoS One
January 2025
Division of Biological Sciences, US Fish and Wildlife Southwest Regional Office, Albuquerque, New Mexico, United States of America.
There is growing interest in using deep learning models to automate wildlife detection in aerial imaging surveys to increase efficiency, but human-generated annotations remain necessary for model training. However, even skilled observers may diverge in interpreting aerial imagery of complex environments, which may result in downstream instability of models. In this study, we present a framework for assessing annotation reliability by calculating agreement metrics for individual observers against an aggregated set of annotations generated by clustering multiple observers' observations and selecting the mode classification.
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