Objective: Social problems are a key area of functional impairment for children with attention deficit hyperactivity disorder (ADHD), and converging evidence points to executive dysfunction as a potential mechanism underlying ADHD-related social dysfunction. The evidence is mixed, however, with regard to which neurocognitive abilities account for these relations.
Method: A well-characterized group of 117 children ages 8-13 (M = 10.45, SD = 1.53; 43 girls; 69.5% Caucasian/Non-Hispanic) with ADHD (n = 77) and without ADHD (n = 40) were administered multiple, counterbalanced tests of neurocognitive functioning and assessed for social skills via multi-informant reports.
Results: Bayesian linear regressions revealed strong support for working memory and cross-informant interfering behaviors (inattention, hyperactivity/impulsivity) as predictors of parent- and teacher-reported social problems. Working memory was also implicated in social skills acquisition deficits, performance deficits, and strengths based on parent and/or teacher report; inattention and/or hyperactivity showed strong correspondence with cross-informant social problems in all models. There was no evidence for, and in most models strong evidence against, effects of inhibitory control and processing speed. The ADHD group was impaired relative to the non-ADHD group on social skills (d = 0.82-0.88), visuospatial working memory (d = 0.89), and phonological working memory (d = 0.58). In contrast, the Bayesian ANOVAs indicated that the ADHD and non-ADHD groups were equivalent on processing speed, IQ, age, gender, and socioeconomic status (SES). There was no support for or against group differences in inhibition.
Conclusions: These findings confirm that ADHD is associated with impaired social performance, and implicate working memory and core ADHD symptoms in the acquisition and performance of socially skilled behavior. (PsycINFO Database Record
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http://dx.doi.org/10.1037/neu0000416 | 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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