The neural noise hypothesis of dyslexia posits an imbalance between excitatory and inhibitory (E/I) brain activity as an underlying mechanism of reading difficulties. This study provides the first direct test of this hypothesis using both electroencephalography (EEG) power spectrum measures in 120 Polish adolescents and young adults (60 with dyslexia, 60 controls) and glutamate (Glu) and gamma-aminobutyric acid (GABA) concentrations from magnetic resonance spectroscopy (MRS) at 7T MRI scanner in half of the sample. Our results, supported by Bayesian statistics, show no evidence of E/I balance differences between groups, challenging the hypothesis that cortical hyperexcitability underlies dyslexia. These findings suggest that alternative mechanisms must be explored and highlight the need for further research into the E/I balance and its role in neurodevelopmental disorders.
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http://dx.doi.org/10.7554/eLife.99920 | DOI Listing |
Biopharm Drug Dispos
March 2025
Department of Information Technology, Panimalar Engineering College, Chennai, India.
Drug-drug interactions (DDIs) are an important concern in the clinical practice and drug development process as these may lead to serious adverse effects on patient safety. Thorough DDI prediction is important for effective medication management and reduced risk factors. This work presents a new technique, namely MV2SAPCNNO: MobileNetV2 with simplicial attention network-based parallel convolutional neural network and narwhal optimiser, for improving the precision of DDI prediction.
View Article and Find Full Text PDFNetwork
March 2025
Department of Biomedical Engineering, Noorul Islam Centre for Higher Education, Kanyakumari, India.
A crucial role in many security and surveillance applications is crowd anomaly detection, where seeing unusual activity helps avert possible threats or interruptions. For precise anomaly identification, current models might not successfully incorporate spatial and temporal features. To overcome these drawbacks, a novel Crowd Anomaly Detection based on Opposition Behavior Learning updated Chimp Optimization Algorithm (CAD-OBLChoA) is proposed in this research to enhance the detection of abnormal crowd behaviours in dynamic environments.
View Article and Find Full Text PDFYi Chuan
March 2025
College of Animal Science and Technology, Yangtze University, Jingzhou 434025, China.
Single-cell transcriptome sequencing (scRNA-seq) is widely used in the fields of animal and plant developmental biology and important trait analysis by obtaining single-cell transcript abundance data in high throughput, which can deeply reveal cell types, subtype composition, specific gene markers and functional differences. However, scRNA-seq data are often accompanied by problems such as high noise, high dimensionality and batch effect, resulting in a large number of low-expressed genes and variants, which seriously affect the accuracy and reliability of data analysis. This not only increases the complexity of data processing, but also limits the effectiveness of feature selection and downstream analysis.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Department of Convergence of Healthcare and Medicine (ALCHeMIST), Graduate School of Ajou University, 164, World Cup-ro, Yeongtong-gu, Suwon, Gyeonggi-do 16499, Republic of Korea; Department of Neurology, Ajou University School of Medicine, 164, World Cup-ro, Yeongtong-gu, Suwon, Gyeonggi-do 16499, Republic of Korea. Electronic address:
Background And Objectives: Reflex syncope (RS) is the most common type of syncope caused by dysregulation of the autonomic nervous system. Diagnosing RS typically involves the head-up tilt test (HUTT), which tracks physiological signals such as blood pressure and electrocardiograms during postural changes. However, the HUTT is time-consuming and may trigger RS symptoms in patients.
View Article and Find Full Text PDFNeural Netw
February 2025
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
Minimum error entropy with fiducial points (MEEF) has gained significant attention due to its excellent performance in mitigating the adverse effects of non-Gaussian noise in the fields of machine learning and signal processing. However, the original MEEF algorithm suffers from high computational complexity due to the double summation of error samples. The quantized MEEF (QMEEF), proposed by Zheng et al.
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