Previous research demonstrates that people with 22q11.2 deletion syndrome (22q11DS) have social and interpersonal skill deficits. However, the basis of this deficit is unknown. This study examined, for the first time, how people with 22q11DS process emotional face stimuli using visual scanpath technology. The visual scanpaths of 17 adolescents and age/gender matched healthy controls were recorded while they viewed face images depicting one of seven basic emotions (happy, sad, surprised, angry, fear, disgust and neutral). Recognition accuracy was measured concurrently. People with 22q11DS differed significantly from controls, displaying visual scanpath patterns that were characterised by fewer fixations and a shorter scanpath length. The 22q11DS group also spent significantly more time gazing at the mouth region and significantly less time looking at eye regions of the faces. Recognition accuracy was correspondingly impaired, with 22q11DS subjects displaying particular deficits for fear and disgust. These findings suggest that 22q11DS is associated with a maladaptive visual information processing strategy that may underlie affect recognition accuracy and social functioning deficits in this group.
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http://dx.doi.org/10.1016/j.psychres.2009.06.007 | DOI Listing |
Cogn Neurodyn
December 2025
Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, TamilNadu India.
Emotion recognition plays a crucial role in brain-computer interfaces (BCI) which helps to identify and classify human emotions as positive, negative, and neutral. Emotion analysis in BCI maintains a substantial perspective in distinct fields such as healthcare, education, gaming, and human-computer interaction. In healthcare, emotion analysis based on electroencephalography (EEG) signals is deployed to provide personalized support for patients with autism or mood disorders.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang China.
The increasing adoption of wearable technologies highlights the potential of electroencephalogram (EEG) signals for biometric recognition. However, the intrinsic variability in cross-session EEG data presents substantial challenges in maintaining model stability and reliability. Moreover, the diversity within single-task protocols complicates achieving consistent and generalized model performance.
View Article and Find Full Text PDFPeerJ
January 2025
Departments of Clinical Pathology, The Second Affiliated Hospital of Medical College of Zhejiang University, Hangzhou, Zhejiang, China.
Objective: Breast cancer stands as the most prevalent form of cancer among women globally. This heterogeneous disease exhibits varying clinical behaviors. The stratification of breast cancer patients into risk groups, determined by their metastasis and survival outcomes, is pivotal for tailoring personalized treatments and therapeutic interventions.
View Article and Find Full Text PDFHeliyon
January 2025
The First Affiliated Hospital of Shantou University Medical College, 57 Changping Road, Shantou City, Guangdong Province, 515000, China.
Background: Due to their young age and limited ability to communicate, pediatric patients in internal medicine wards are at risk of nursing assessment errors, which can lead to adverse events and disputes.
Objective: To explore the application effect of modified pediatric early warning score (PEWS) in the early identification of critically ill children in pediatric general wards.
Design: A single-blind, two-arm randomized controlled trial was conducted using a convenience sampling method.
Front Plant Sci
January 2025
School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang, China.
Introduction: With the advent of technologies such as deep learning in agriculture, a novel approach to classifying wheat seed varieties has emerged. However, some existing deep learning models encounter challenges, including long processing times, high computational demands, and low classification accuracy when analyzing wheat seed images, which can hinder their ability to meet real-time requirements.
Methods: To address these challenges, we propose a lightweight wheat seed classification model called LWheatNet.
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