Neurology

Parkinson's disease classification using optimized attention-based deep learning from EEG signals with interpretable sub-band topography.

Parkinson's disease (PD) diagnosis remains challenging because subtle neural alterations may be difficult to capture using conventional clinical assessment alone. This study proposes an attention-based deep learning framework for classifying PD from resting-state EEG with minimal preprocessing and leakage-safe evaluation. Raw EEG recordings were first partitioned at the subject level. Within each ...

Screening and Evaluation of Post-stroke Dysphagia: Insights from Neurology, Artificial Intelligence and Data Science-A Scoping Review.

Post-stroke dysphagia (PSD) affects approximately 42% of acute stroke patients, increasing hospitalization costs and length of stay. Early identification improves outcomes, yet many patients-especially in low-resource settings-lack access to gold-standard evaluations. This scoping review explores the integration of artificial intelligence (AI) and data science-defined as the interdisciplinary use ...

Exploring Complex Genetic Mechanisms in Brain Imaging Genetics via a New Multi-task Learning Method.

Brain imaging genetics generally combines genotype data with brain structure and functional measures to investigate the genetic basis of neurological ...

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

Neuron segmentation in complex mouse brain images improves neuron reconstruction and supports studies of brain structure and function, while the exist...

Arterial spin labeling cerebral blood flow quantification from quantitative transport mapping based on multiscale fluid mechanics simulation and deep learning.

OBJECTIVE: The traditional kinetic model (TKM) for perfusion quantification from arterial spin labeling (ASL) assumes a global arterial input function...

EMG-Based Gait Estimation Using Koopman-Inspired Method.

EMG-based state estimation and prediction in human-machine interaction,biomechanics, and robotics applications is an emerging approach offering potent...

Multi-view constrained semi-supervised vertebra detection for 3D ultrasound spine volume.

BACKGROUND: Scoliosis is a spinal disorder characterized by a three-dimensional (3D) deformity of the vertebral column. 3D ultrasound imaging has been...

Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.

The human brain maintains functional stability under changing conditions through interacting processes that include synaptic plasticity, homeostatic r...

NeuroHarm‑Kit: An Open‑Source Toolbox for Benchmarking Deep‑Learning Harmonization of Multi‑Site T1‑Weighted MRI.

Multi‑site magnetic resonance imaging (MRI) studies enable studying brain structure across diverse populations, but scanner‑related variability remain...

A Dataset with Bilingual TV Commands for Silent Speech Interfaces Using Electroencephalographic Signals.

This paper introduces TESSCCo (TV-control EEG-based Silent Speech Command Corpus), a new dataset including electroencephalography (EEG) signals during...

Leveraging Swin Transformer for Enhanced Diagnosis of Alzheimer's Disease Using Multi-Shell Diffusion MRI.

OBJECTIVE: This study aims to support early diagnosis of Alzheimer's disease and detection of amyloid accumulation by leveraging the microstructural i...

Assessing the Robustness of Deep Learning Based Brain Age Prediction Models Across Multiple EEG Datasets.

The increasing availability of large electroencephalography (EEG) datasets enhances the potential clinical utility of deep learning (DL) for cognitive...

Cognitive Process Models Reveal Meaningful Brain-Behavior Associations in the Adolescent Brain Cognitive Development Study Stop-signal Task.

Characterizing associations between individual differences in brain activity and behavior remains a primary challenge in functional neuroimaging resea...

Cloud EEG Privacy Using Red-Billed Blue Magpie Optimized Physics-Penalized Dual-Branch Spectral-Spatial Neural Network for Epileptic Seizure Prediction.

Epileptic seizure prediction is a critical research area that enables timely intervention and prevention of severe neurological complications. With th...

Inpatient Robotic Therapy for Stroke Survivors With Severe Upper Limb Impairments: A Randomized Controlled Trial.

IMPORTANCE: Understanding how upper extremity (UE) robotic therapy (RT) affects efficiency and effectiveness of inpatient rehabilitation is important ...

Time Distributed Classification of Alzheimer's Disease on MRI Scans.

The diagnosis of Alzheimer's disease (AD) has progressively depended on sophisticated neuroimaging methods alongside cognitive assessments. This study...

NToxSEM: Enhancing prediction of neurotoxic peptides and neurotoxins using a stacked ensemble-based multimodal framework.

The safety assessment of therapeutic proteins and genetically modified (GM) organisms relies heavily on the rapid and accurate prediction of peptides,...

An Enhanced Machine Learning-Based Multimodal Framework for Seizure Detection Using EEG and MRI Data.

One of the most common neurological disorders that immediately alters a person's way of life is an epileptic seizure. Accurate seizure detection remai...

An Effective LRSF-DLNN-Based Autism Spectrum Disorder Prediction Using EEG and fMRI.

In today's society, autism spectrum disorder (ASD) is a common neurological disorder that affects a person's behavior and communication. Hence, an ear...

A Fully Automated Pipeline for Vertebral Structural Assessment From Medical Images. Application Under Metastatic Conditions.

Spinal bone metastases often lead to vertebral fractures and other skeletal events that severely affect patients' quality of life. Predicting structur...