Prediction error processing and sharpening of expected information

Prediction error processing and sharpening of expected information

Scientific Report Background Introduction Perception and neuronal processing of sensory information are largely influenced by prior expectations. Perception is not merely passive reception, but an active inferential process by combining existing sensory information with prior information based on past experience and current context. The combination...

Analysis of Reading-Task-Based Brain Connectivity in Dyslexic Children Using EEG Signals

Brain Connectivity Analysis Based on Reading Tasks in Children with Dyslexia (Using EEG Signals) Dyslexia is a neurodevelopmental disorder that affects the normal reading ability, even though children with normal intelligence may still be affected. This paper investigates the differences in brain connectivity between children with dyslexia and norm...

Potential Biomarker for Early Detection of ADHD Using Phase-Based Brain Connectivity and Graph Theory

Research Report on Potential Biomarkers for Early Detection of ADHD: Phase-Based Functional Brain Connectivity and Graph Theory Analysis This is a research report titled “Potential Biomarkers for Early Detection of ADHD: Using Phase-Based Functional Brain Connectivity and Graph Theory Analysis”. This study was conducted by Farhad Abedinzadeh Torgha...

KG4NH: A Comprehensive Knowledge Graph for Question Answering in Dietary Nutrition and Human Health

Background and Research Motivation It is well-known that food nutrition is closely related to human health. Scientific research has shown that improper dietary nutrition is linked to more than 200 diseases. Especially when considering the metabolic processes of gut microbiota, the complex interactions between food nutrients and diseases become diff...

Predicting Drug-Target Affinity by Learning Protein Knowledge from Biological Networks

Predicting Drug-Target Affinity Based on Learning Protein Knowledge from Biological Networks Background The prediction of drug-target affinity (DTA) plays a crucial role in drug discovery. Efficient and accurate DTA prediction can significantly reduce the time and economic costs of new drug development. In recent years, the explosive development of...

Adeno-Associated Virus-Mediated Trastuzumab Delivery to the Central Nervous System for Human Epidermal Growth Factor Receptor 2+ Brain Metastasis

AAV-Mediated Trastuzumab Delivery to the Central Nervous System for EGFR2-Positive Brain Metastases Introduction In the treatment of breast cancer, tumors that are human epidermal growth factor receptor 2 (HER2) positive exhibit more aggressive characteristics, posing significant challenges for clinical treatment. Since the approval of trastuzumab ...

A Wearable Fluorescence Imaging Device for Intraoperative Identification of Human Brain Tumors

Malignant Glioma (MG) Report Malignant Glioma (MG) is the most common type of primary malignant brain tumor. Surgical resection of MG remains the cornerstone of treatment, and the extent of resection is highly correlated with patient survival. However, it is difficult to distinguish tumor tissue from normal tissue during surgery, which greatly limi...

An Explicit Estimated Baseline Model for Robust Estimation of Fluorophores Using Multiple-Wavelength Excitation Fluorescence Spectroscopy

Research Background Fluorescence spectroscopy is a widely used method for identifying and quantifying fluorescent substances (fluorophores). However, quantifying the fluorophores of interest becomes challenging when the material contains other fluorophores (baseline fluorophores), especially when the emission spectrum of the baseline is not well-de...

Multi-view Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classification

Sleep stage classification is crucial for sleep quality assessment and disease diagnosis. However, existing classification methods still face numerous challenges in handling the spatial and temporal features of time-varying multi-channel brain signals, coping with individual differences in biological signals, and model interpretability. Traditional...

A Temporal Dependency Learning CNN with Attention Mechanism for MI-EEG Decoding

MI-EEG Decoding Using a Temporal Dependency Learning Convolutional Neural Network (CNN) Based on Attention Mechanism Research Background and Problem Description Brain-Computer Interface (BCI) systems provide a new way of communicating with computers by real-time translation of brain signals. In recent years, BCI technology has played an important r...