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The neural basis for long-term behavioral improvements resulting from multi-session transcranial direct current stimulation (tDCS) combined with working memory training (WMT) remains unclear. In this study, we used task-related electroencephalography (EEG) measures to investigate the lasting neurophysiological effects of anodal high-definition (HD)-tDCS applied over the left dorsolateral prefrontal cortex (dlPFC) during a challenging WMT. Thirty-four healthy young adults were randomized to sham or active tDCS groups and underwent ten 30-minute training sessions over ten consecutive days, preceded by a pre-test and followed by post-tests performed one day and three weeks after the last session, respectively, by performing high-load WM tasks along with EEG recording. Multi-session HD-tDCS significantly enhanced the behavioral benefits of WMT. Compared to the sham group, the active group showed facilitated increases in theta, alpha, beta, and gamma task-related oscillations at the end of training and significantly increased P300 response 3 weeks post-training. Our findings suggest that applying anodal tDCS over the left dlPFC during multi-session WMT can enhance the behavioral benefits of WMT and facilitate sustained improvements in WM-related neural efficiency.
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http://dx.doi.org/10.1038/s41539-023-00200-y | DOI Listing |
Sci Justice
September 2025
Department of Multidisciplinary Radiological Science, The Graduate School of Dongseo University, 47 Jurye-ro, Sasang-gu, Busan 47011, Republic of Korea. Electronic address:
The identification of deceased individuals is essential in forensic investigations, particularly when primary identification methods such as odontology, fingerprint, or DNA analysis are unavailable. In such cases, implanted medical devices may serve as supplementary identifiers for positive identification. This study proposes deep learning-based methods for the automatic detection of metallic implants in scout images acquired from computed tomography (CT).
View Article and Find Full Text PDFMethods
September 2025
School of Computer and Information Engineering, Henan University, Kaifeng, Henan, China; Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, Henan, China. Electronic address:
Genomic selection (GS) is a breeding technique that utilizes genomic markers to predict the genetic potential of crops and animals. This approach holds significant promise for accelerating the improvement of agronomic traits and addressing food security challenges. While traditional breeding methods based on statistical or machine learning techniques have been useful in predicting traits for some crops, they often fail to capture the complex interactions between genotypes and phenotypes.
View Article and Find Full Text PDFProg Neurobiol
September 2025
The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States; Elmezzi Graduate School of Molecular Medicine at Northwell Health, Manhasset, NY, United States; Department of Neurosurgery, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States; Tr
Humans live in an environment that contains rich auditory stimuli, which must be processed efficiently. The entrainment of neural oscillations to acoustic inputs may support the processing of simple and complex sounds. However, the characteristics of this entrainment process have been shown to be inconsistent across species and experimental paradigms.
View Article and Find Full Text PDFPhys Med Biol
September 2025
Institute for Instrumentation in Molecular Imaging (i3M), Consejo Superior de Investigaciones Cientificas, Camino de Vera s/n, Valencia, Valencia, 46022, SPAIN.
A key challenge in PET systems is collecting large amount of data with the most accurate information-time, energy, and position-to produce high-resolution images while limiting the number of channels to reduce costs and improve data collection efficiency. The new Ultra-High-performance Brain (UHB) scanner under development aims to tackle this issue, using a semi-monolithic detector that combines pixelated arrays and monolithic designs, along with signal multiplexing techniques. Approach.
View Article and Find Full Text PDFComput Methods Programs Biomed
September 2025
eXiT Research Group, Universitat de Girona (UdG), EPS - Edifici P-IV, Carrer Universitat de Girona, 6, Girona, 17003, Catalunya, Spain.
Background And Objective: Hybrid forecasting methods aim to overcome the limitations of classical statistical approaches and deep learning models. While statistical methods provide interpretability, they often lack predictive power. Conversely, deep learning models achieve high accuracy but act as "black boxes.
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