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Growing evidence suggests that childhood ADHD is associated with larger impairments in working memory relative to inhibition. However, most studies have not considered the role of co-occurring anxiety on these estimates - a potentially significant confound given prior evidence that anxiety may increase working memory difficulties but decrease inhibition difficulties for these children. The current study extends prior work to examine the extent to which co-occurring anxiety may be systematically affecting recent estimates of the magnitude of working memory/inhibitory control deficits in ADHD. The carefully-phenotyped sample included 197 children with ADHD and 142 children without ADHD between the ages of 8 and 13 years (N = 339; M = 10.31, SD = 1.39; 144 female participants). Results demonstrated that ADHD diagnosis predicted small impairments in inhibitory control (d = 0.31) and large impairments in working memory (d = 0.99). However, child trait anxiety assessed dimensionally across multiple informants (child, parent, teacher) did not uniquely predict either executive function, nor did it moderate estimates of ADHD-related working memory/inhibition deficits. When evaluating anxiety categorically and controlling for ADHD, anxiety diagnosis predicted slightly better working memory (d = 0.19) but not inhibitory control for clinically evaluated children generally. Findings from the current study indicate that trait anxiety, measured dimensionally or categorically, does not differentially affect estimates of executive dysfunction in pediatric ADHD. Further, results suggest that trait anxiety is generally not associated with executive dysfunction above and beyond the impact of co-occurring ADHD. Future research is needed to further assess the role of anxiety in ADHD behavioral symptomatology, neurocognitive functioning, and mechanisms underlying these relations.
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http://dx.doi.org/10.1007/s10802-023-01152-y | DOI Listing |
Comput Methods Biomech Biomed Engin
September 2025
International School of Microelectronics, Dongguan University of Technology, Dongguan, China.
Many traditional classification networks directly use the limb two-lead signal (MLII) ECG signals as input for training. However, this method suffers from reduced accuracy when ECG features are not obvious, especially for premature heartbeats. To solve the issue, this paper proposed a novel network, namely CDLR-Net, that combines a Deep Residual Shrinkage Network (DRSN) with a Long Short-Term Memory (LSTM).
View Article and Find Full Text PDFBiol Psychol
September 2025
Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, 610066, China. Electronic address:
Working memory (WM) regulates information flow through gate mechanisms, consisting of four subprocesses: gate opening, gate closing, updating, and substitution. However, their neural mechanisms remain underexplored. While emotion-cognition interactions are well studied, the effects of negative mood on these subprocesses are unclear.
View Article and Find Full Text PDFAnal Biochem
September 2025
School of Computer Science and Engineering, Southeast University, Nanjing 210000, China.
In the complex process of gene expression and regulation, RNA-binding proteins occupy a pivotal position for RNA. Accurate prediction of RNA-protein binding sites can help researchers better understand RNA-binding proteins and their related mechanisms. And prediction techniques based on machine learning algorithms are both cost-effective and efficient in identifying these binding sites.
View Article and Find Full Text PDFActa Psychol (Amst)
September 2025
Department of Psychology, Chung-Shan Medical University, Taichung, Taiwan; Clinical Psychological Room, Chung-Shan Medical University Hospital, Taichung, Taiwan. Electronic address:
Background: Previous research indicates near transfer effects of working memory (WM) training on updating, shifting, and inhibition tasks, although findings vary. Regarding fluid intelligence (Gf), studies yield conflicting results on the far transfer effects of WM training. The current study investigates whether different styles of adaptive visuospatial N-back WM training produce near and far transfer effects and whether individual differences moderate these effects.
View Article and Find Full Text PDFEnviron Int
September 2025
School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.
Sichuan Basin (SCB) is a critical region in China facing the dual pressures of air pollution and population aging. This study constructed high resolution (1 km) PM datasets for SCB using advanced machine learning approaches - Super Resolution Generative Adversarial Networks (SRGAN) and Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM). Evaluation results demonstrate good performance of the machine learning model (SRGAN: R = 0.
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