Facial expression is among the most natural methods for human beings to convey their emotional information in daily life. Although the neural mechanisms of facial expression have been extensively studied employing lab-controlled images and a small number of lab-controlled video stimuli, how the human brain processes natural dynamic facial expression videos still needs to be investigated. To our knowledge, this type of data specifically on large-scale natural facial expression videos is currently missing. We describe here the natural Facial Expressions Dataset (NFED), an fMRI dataset including responses to 1,320 short (3-second) natural facial expression video clips. These video clips are annotated with three types of labels: emotion, gender, and ethnicity, along with accompanying metadata. We validate that the dataset has good quality within and across participants and, notably, can capture temporal and spatial stimuli features. NFED provides researchers with fMRI data for understanding of the visual processing of large number of natural facial expression videos.
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http://dx.doi.org/10.1038/s41597-024-04088-0 | DOI Listing |
Alzheimers Dement
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Yonsei University, Wonju, Gangwon-do, Korea, Republic of (South).
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With the advent of multi-layered and 3D scaffolds, the understanding of microbiome composition and pathogenic mechanisms within polymicrobial biofilms is continuously evolving. A fundamental component in mediating the microenvironment and bacterial-host communication within the biofilm are bilayered nanoparticles secreted by bacteria, known as bacterial extracellular vesicles (BEVs), which transport key biomolecules including proteins, nucleic acids, and metabolites. Their characteristics and microbiome profiles are yet to be explored in the context of in vitro salivary polymicrobial biofilm.
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Department of Psychology, University of Wisconsin - Madison, Madison, WI, USA.
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