Neurology

Are patient-derived models of amyotrophic lateral sclerosis a game changer for novel drug discovery?

INTRODUCTION: ALS drug discovery has long depended on model systems that incompletely capture human disease heterogeneity, aging, and TDP-43 proteinopathy. Patient-derived platforms have therefore emerged as increasingly important human-relevant complements to animal and molecular models. AREAS COVERED: This Critical Perspective examines when patient-derived ALS models genuinely change therapeutic...

Aggression in epilepsy and sleep: from historical accounts to brain networks and human behaviour.

For a long time, epilepsy has been associated with violent behaviour, acquiring a highly stigmatising reputation, shaped mainly by 19th-century medical theories that postulated a direct connection between epilepsy and the criminal personality. Such stigma exerts a negative effect on the quality of life of people with epilepsy and remains a significant concern in forensic settings. This narrative r...

CTE has multiple pathologic variants that might relate to different clinical symptom presentation.

Chronic traumatic encephalopathy (CTE) is a progressive neurodegenerative disease found in individuals with a history of repetitive head injury (RHI) ...

GaitNet: Transfer Learning-Enhanced CNN-GRU Architecture for Intention Detection in Healthy and Post-Stroke Participants.

Highly accurate, data-efficient, and real-time detection of human motion intention is essential for the effective control of assistive and rehabilitat...

Noninvasive Electrophysiological Biomarkers of Olfactory Responses Across Cognitive States in Alzheimer Dementia: Cross-Sectional Study.

BACKGROUND: Alzheimer disease (AD) is characterized by progressive cognitive decline, with olfactory dysfunction emerging among its earliest symptoms....

A fully inductive inference protocol for population GNNs in single-subject brain disorder diagnosis.

Population graph-based Graph Neural Networks (GNNs) have demonstrated superior performance in brain disease diagnosis by modeling inter-subject relati...

Online decoding of rat self-paced locomotion speed from EEG using recurrent neural networks.

OBJECTIVE: Accurate and reliable neural decoding of locomotion holds promise for advancing clinical applications such as rehabilitation and prosthetic...

From the Brain Cell Atlas to Precision Neurology: A review of the application of AI-driven multi-omics in brain science.

Recent advances in multi-omics technologies have catalyzed the construction of comprehensive brain cell atlases, providing essential data foundations ...

Machine Learning-Based Prognostic Models for Functional Outcomes in Spinal Cord Injury: Systematic Review.

BACKGROUND: Machine learning is increasingly used to develop prognostic prediction models for spinal cord injury. Nevertheless, current studies exhibi...

Hypertuned boosting approach with Local Binary Pattern and Pivot Distribution Count method feature extractor for glaucoma identification.

Glaucoma, the second largest cause of irreversible blindness worldwide, causes significant damage to the optic nerve. Early diagnosis of glaucoma is c...

A machine learning framework for multimodal temporal prediction of neurological outcome after out-of-hospital cardiac arrest.

Neurological prognostication after out-of-hospital cardiac arrest (OHCA) remains challenging. Existing clinical scores rely on static, single-timepoin...

An interpretable machine learning model for diabetic foot risk classification in patients with diabetes.

Diabetic foot is a severe chronic complication of diabetes, mainly resulting from peripheral neuropathy and vasculopathy, and may progress to ulcers, ...

Applying ensemble machine learning techniques to MRI scans to predict Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia globally. Early prediction, prior to the onset ...

Bayesian Uncertainty-aware Deep Learning with noisy labels: Tackling annotation ambiguity in EEG seizure detection.

Deep learning is advancing EEG processing for automated epileptic seizure detection and onset zone localization, yet its performance relies heavily on...

Bobcat-optimized hybrid quantum-classical spike-driven network for MRI-based Alzheimer's stage prediction.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely affects memory, cognition, and behavioral functions, making early a...

Epilepsy-IEDs: An automated machine learning model for detecting interictal epileptiform discharges from scalp electroencephalograms.

Interictal epileptiform discharges (IEDs) are essential for epilepsy diagnosis, yet visual electroencephalogram (EEG) analysis remains subjective and ...

Prediction of lower limb compensation for adult degenerative spine patients using spinopelvic parameters: a machine learning approach.

STUDY DESIGN: Retrospective study. OBJECTIVE: This work aims to estimate using machine learning the occurrence of knee flexion in relation to spinopel...

Machine learning based prediction models for first stroke in community primary care: a systematic review and meta-analysis.

BACKGROUND: Stroke is the third leading cause of death and the fourth leading cause of disability globally, particularly in low- and middle-income cou...

Archetypal analysis quantifies changes in patterns of visual field loss in eyes with acute non-arteritic anterior ischaemic optic neuropathy.

AIMS: We characterised visual field (VF) spatial loss patterns in acute non-arteritic anterior ischaemic optic neuropathy (NAION) using archetypal ana...

Predicting persistent pain after total knee arthroplasty using different machine learning algorithms.

BACKGROUND AND PURPOSE:  After total knee arthroplasty (TKA), 10-20% of patients remain unsatisfied. Well-performing clinical prediction models can pr...