Subtle emotional and cognitive dysfunctions may already be apparent in individuals at risk for psychosis. However, there is a paucity of research on the neural correlates of the interaction of both domains. It remains unclear whether those correlates are already dysfunctional before a transition to psychosis. We used functional magnetic resonance imaging to examine the interaction of working memory and emotion in 12 persons clinically at high risk for psychosis (CHR) and 12 healthy subjects individually matched for age, gender and parental education. Participants performed an n-back task while negative or neutral emotion was induced by olfactory stimulation. Although healthy and psychosis-prone subjects did not differ in their working memory performance or the evaluation of the induced emotion, decreased activations were found in CHR subjects in the superior parietal lobe and the precuneus during working memory and in the insula during emotion induction. Looking at the interaction, CHR subjects, showed decreased activation in the right superior temporal gyrus, which correlated negatively with psychopathological scores. Decreased activation was also found in the thalamus. However, an increase of activation emerged in several cerebellar regions. Dysfunctions in areas associated with controlling whether incoming information is linked to emotional content and in the integration of multimodal information might lead to compensatory activations of cerebellar regions known to be involved in olfactory and working memory processes. Our study underlines that cerebral dysfunctions related to cognitive and emotional processes, as well as their interaction, can emerge in persons with CHR, even in absence of behavioral differences.
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http://dx.doi.org/10.1016/j.schres.2009.12.008 | DOI Listing |
Sci Rep
January 2025
North Carolina School of Science and Mathematics, Durham, NC, 27705, USA.
Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs.
View Article and Find Full Text PDFMed Phys
January 2025
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Respiratory motion during radiotherapy (RT) may reduce the therapeutic effect and increase the dose received by organs at risk. This can be addressed by real-time tracking, where respiration motion prediction is currently required to compensate for system latency in RT systems. Notably, for the prediction of future images in image-guided adaptive RT systems, the use of deep learning has been considered.
View Article and Find Full Text PDFSeizure
December 2024
University College Hospital, London, UK; UCL Queen Square Institute of Neurology: Department of Clinical and Experimental Epilepsy, London WC1N 3BG, UK. Electronic address:
Objective: Professional bodies recommend the use of performance validity tests (PVTs) to aid the interpretation of scores obtained in neuropsychological assessments, but base rates of failure differ according to neurological diagnosis and the associated impairments. This review summarises the PVT literature in people with epilepsy with the aim of establishing base rates of PVT failure and the factors associated with PVT performance in this population.
Methods: Ovid and PubMed databases were searched for studies reporting PVT test performance in people with epilepsy.
Comput Biol Med
January 2025
LMA Laboratory, University of Bejaia, Bejaia 06000, Algeria. Electronic address:
Social networks are increasingly taking over daily life, creating a volume of unsecured data and making it very difficult to capture safe data, especially in times of crisis. This study aims to use a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM)-based hybrid model for health monitoring and health crisis forecasting. It consists of efficiently retrieving safe content from multiple social media sources.
View Article and Find Full Text PDFInt J Eat Disord
January 2025
School of Psychological Sciences, University of Haifa, Haifa, Israel.
Objective: Difficulty updating information in working memory has been proposed to underlie ruminative thinking in individuals with anorexia nervosa (AN). However, evidence regarding updating difficulties in AN remains inconclusive, particularly among adolescents. It has been proposed that exposure to negative emotion and disorder-salient stimuli may uniquely influence updating in AN.
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