Keeping track of various amounts of social cognitive information, including people's mental states, traits, and relationships, is fundamental to navigating social interactions. However, to date, no research has examined which brain regions support variable amounts of social information processing ("social load"). We developed a social working memory paradigm to examine the brain networks sensitive to social load. Two networks showed linear increases in activation as a function of increasing social load: the medial frontoparietal regions implicated in social cognition and the lateral frontoparietal system implicated in nonsocial forms of working memory. Of these networks, only load-dependent medial frontoparietal activity was associated with individual differences in social cognitive ability (trait perspective-taking). Although past studies of nonsocial load have uniformly found medial frontoparietal activity decreases with increasing task demands, the current study demonstrates these regions do support increasing mental effort when such effort engages social cognition. Implications for the etiology of clinical disorders that implicate social functioning and potential interventions are discussed.
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http://dx.doi.org/10.1073/pnas.1121077109 | DOI Listing |
PLoS One
January 2025
School of Electronic Information Engineering, Inner Mongolia University, Hohhot, Inner Mongolia, China.
Cognitive Radio (CR) technology enables wireless devices to learn about their surrounding spectrum environment through sensing capabilities, thereby facilitating efficient spectrum utilization without interfering with the normal operation of licensed users. This study aims to enhance spectrum sensing in multi-user cooperative cognitive radio systems by leveraging a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. A novel multi-user cooperative spectrum sensing model is developed, utilizing CNN's local feature extraction capability and LSTM's advantage in handling sequential data to optimize sensing accuracy and efficiency.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Information Systems and Cybersecurity, University of Bisha, Bisha, KSA.
Accurate energy demand forecasting is critical for efficient energy management and planning. Recent advancements in computing power and the availability of large datasets have fueled the development of machine learning models. However, selecting the most appropriate features to enhance prediction accuracy and robustness remains a key challenge.
View Article and Find Full Text PDFNetwork
January 2025
Computer Science and Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India.
Skin cancer is one of the most prevalent and harmful forms of cancer, with early detection being crucial for successful treatment outcomes. However, current skin cancer detection methods often suffer from limitations such as reliance on manual inspection by clinicians, inconsistency in diagnostic accuracy, and a lack of personalized recommendations based on patient-specific data. In our work, we presented a Personalized Recommendation System to handle Skin Cancer at an early stage based on Hybrid Model (PRSSCHM).
View Article and Find Full Text PDFJ Neurol
January 2025
NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, Faculty of Brain Sciences, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK.
Cognitive impairment (CI) in multiple sclerosis (MS) is only partially explained by whole-brain volume measures, but independent component analysis (ICA) can extract regional patterns of damage in grey matter (GM) or white matter (WM) that have proven more closely associated with CI. Pathology in GM and WM occurs in parallel, and so patterns can span both. This study assessed whether joint-ICA of GM and WM features better explained cognitive function compared to single-tissue ICA.
View Article and Find Full Text PDFMetab Brain Dis
January 2025
School of Natural Product Studies, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700 032, India.
Alzheimer's disease is a complex neurodegenerative disease characterized by progressive decline in cognitive function and behaviour. Ginger is the rhizome of the plant Zingiber officinale Roscoe, has been an important ingredient of many Ayurveda formulations to treat neurological disorders. The present study aims to estimate the variation of 6-gingerol content in nine different ginger samples collected from Manipur, India, investigate the neuroprotective potential of the most potent ginger sample against scopolamine-induced cognitively impaired mice, and validate the therapeutic claim by molecular docking analysis.
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