Neurology

Improving meningitis surveillance and diagnosis with machine learning: Insights from São Paulo.

INTRODUCTION: Meningitis, an inflammatory condition of the membranes surrounding the brain and spinal cord, can be caused by various agents. Bacterial meningitis is particularly severe due to its high morbidity and mortality rates. This study aims to develop machine learning (ML) models to classify the aetiology of bacterial meningitis using data from the Notifiable Diseases Information System (SI...

Utilizing Predictive Analytics to Understand Neurogenic Bladder Symptom Score (NBSS) Variations in Adults With Acquired Spinal Cord Injury.

INTRODUCTION: Individuals with spinal cord injury (SCI) have varying bladder health trajectories after their injury. We explored whether a predictive machine learning model could identify which variables impact urinary symptoms.

Brain region localization: a rapid Parkinson's disease detection method based on EEG signals.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder worldwide, often progressing to mild cognitive impairment (MCI) and dementia. Clini...

Inferring concussion history in athletes using pose and ground reaction force estimation and stability analysis of plyometric exercise videos.

Concussions present a significant risk to athletes, with females exhibiting higher rates and prolonged recovery times than males. Current sideline con...

Detection of focal impaired awareness seizures using a biometric shirt.

OBJECTIVE: In recent years, seizure detection using wearable technology has gained significant attention in research. Most studies, however, have focu...

A Composable Channel-Adaptive Architecture for Seizure Classification.

Multi-variate time-series are one of the primary data modalities involved in large classes of problems, where deep learning models represent the state...

Deep ensemble learning with transformer models for enhanced Alzheimer's disease detection.

The progression of Alzheimer's disease is relentless, leading to a worsening of mental faculties over time. Currently, there is no remedy for this ill...

Enhancing automated detection and classification of dementia in individuals with cognitive impairment using artificial intelligence techniques.

Dementia is a degenerative and chronic disorder, increasingly prevalent among older adults, posing significant challenges in providing appropriate car...

Evolution of CT perfusion software in stroke imaging: from deconvolution to artificial intelligence.

Computed tomography perfusion (CTP) represents one of the main determinants in the decision-making strategy of stroke patients, being very useful in t...

Automated instance segmentation and registration of spinal vertebrae from CT-Scans with an improved 3D U-net neural network and corner point registration.

This paper presents a rapid and robust approach for 3D volumetric segmentation, labelling, and registration of human spinal vertebrae from CT scans us...

Insights into the Diagnosis, Treatment, and Management of Prion Diseases.

The normal cellular prion protein (PrPC) can misfold into an infectious and pathogenic form (PrPSc) to produce prion diseases, also known as transmiss...

Drp1 Proteins Released from Hydrolysis-Driven Scaffold Disassembly Trigger Nucleotide-Dependent Membrane Remodeling to Promote Scission.

Dynamin-related protein (Drp1) drives mitochondrial fission, dysregulation of which leads to neurodegenerative, metabolic, and apoptotic disorders. Th...

Depression EEG classification based on multi-scale convolutional transformer network.

Depression electroencephalograph (EEG) classification based on machine learning is helpful for the auxiliary diagnosis of major depression disorder (M...

Strategies to decipher neuron identity from extracellular recordings in behaving non-human primates.

Identification of neuron type is critical when using extracellular recordings in awake, behaving animal subjects to understand computation in neural c...

Motor imagery EEG signal classification using novel deep learning algorithm.

Electroencephalography (EEG) signal classification plays a critical role in various biomedical and cognitive research applications, including neurolog...

Enhancing stroke risk prediction through class balancing and data augmentation with CBDA-ResNet50.

Accurate prediction of stroke risk at an early stage is essential for timely intervention and prevention, especially given the serious health conseque...

The application and discovery of animal models in enterovirus research.

Enterovirus infection remains a significant global public health challenge, causing severe diseases such as hand, foot, and mouth disease (HFMD) and m...

Electrophysiological-based automatic subgroups diagnosis of patients with chronic dysimmune polyneuropathies.

BACKGROUND: Chronic Dysimmune Polyneuropathies (CDP) encompass a group of conditions characterized by autoimmune etiology targeting myelin and/or axon...

Recognition of Parkinson disease using Kriging Empirical Mode Decomposition via deep learning techniques.

UNLABELLED: Parkinson's disorder (PD) is a chronic, irreversible neurological disorder that is hard to identify and manage.

Assessing changes in whole-brain structural connectivity in the unilateral 6-hydroxydopamine rat model of Parkinson's disease using diffusion imaging and tractography.

Parkinson's disease (PD) is a multifactorial, progressive neurodegenerative disease that has a profound impact on those it afflicts. Its hallmark path...