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Introduction: Children with ADHD demonstrate difficulties on many different neuropsychological tests. However, it remains unclear whether this pattern reflects a large number of distinct deficits or a small number of deficit(s) that broadly impact test performance. The current study is among the first experiments to systematically manipulate demands on both working memory and inhibition, with implications for competing conceptual models of ADHD pathogenesis.
Method: A clinically evaluated, carefully phenotyped sample of 110 children with ADHD, anxiety disorders, or co-occurring ADHD+anxiety (=10.35, 44 girls; 69% White Not Hispanic/Latino) completed a counterbalanced, double dissociation experiment, with two tasks each per inhibition (low vs. high) x working memory (low vs. high) condition.
Results: Bayesian and frequentist models converged in indicating that both manipulations successfully increased demands on their target executive function (BF>5.33x10, <.001). Importantly, occupying children's limited capacity working memory system produced slower response times and reduced accuracy on inhibition tasks (BF>317.42, <.001, =0.67-1.53). It also appeared to differentially reduce inhibition (and non-inhibition) accuracy for children with ADHD relative to children with anxiety (BF=2.03, =.02, =0.50). In contrast, there was strong evidence models that view working memory deficits as secondary outcomes of underlying inhibition deficits in ADHD (BF=18.52, =.85).
Discussion: This pattern indicates that working memory broadly affects children's ability to inhibit prepotent tendencies and maintain fast/accurate performance, and may explain the errors that children with ADHD make on inhibition tests. These findings are broadly consistent with models describing working memory as a causal mechanism that gives rise to secondary impairments. In contrast, these findings provide evidence models that view disinhibition as a cause of working memory difficulties or view working memory as a non-causal correlate or epiphenomenon in ADHD.
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http://dx.doi.org/10.3389/fpsyt.2024.1277583 | 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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