Neurology

Uncovering Image-Driven Subtypes with Distinct Pathology and Clinical Course in Autopsy-Confirmed Four Repeat Tauopathies.

OBJECTIVES: The four-repeat (4R) tauopathies are a group of neurodegenerative diseases, including progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and globular glial tauopathy (GGT). This study aimed to characterize spatiotemporal atrophy progression using structural magnetic resonance imaging (MRI) and to examine its relationship with clinical course and neuropathology in a ...

A library of lineage-specific driver lines connects developing neuronal circuits to behavior in the ventral nerve cord.

Understanding developmental changes in neuronal lineages is crucial to elucidate how they assemble into functional neural networks. Studies investigating nervous system development in model systems have only focused on select regions of the CNS due to the limited availability of genetic drivers that target specific neuronal lineages throughout development and adult life. This has hindered our unde...

Detecting label noise in longitudinal Alzheimer's data with explainable artificial intelligence.

Reliable classification of cognitive states in longitudinal Alzheimer's Disease (AD) studies is critical for early diagnosis and intervention. However...

A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems.

This study aims to enhance brain-computer interface (BCI) applications for individuals with motor impairments by comparing the effectiveness of noninv...

Deep-learning-based Partial Volume Correction in 99mTc-TRODAT-1 SPECT for Parkinson's Disease: A Preliminary Study on Clinical Translation.

Tc-TRODAT-1 SPECT is effective for the early detection of Parkinson's disease (PD). However, SPECT images suffer from severe partial volume effect, wh...

Thalamic neural activity and epileptic network analysis using stereoelectroencephalography: a prospective study protocol.

INTRODUCTION: Epilepsy is a prevalent chronic neurological disorder, with approximately one-third of patients experiencing intractable epilepsy, often...

Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone.

BACKGROUND: The evolution in peri-ictal period (from pre-ictal to ictal phase) of seizures contains abundant epileptogenic information, which aids in ...

Artificial Intelligence Deep Learning Models to Predict Spaceflight Associated Neuro-ocular Syndrome (SANS).

PURPOSE: To create deep learning artificial intelligence (AI) models for predicting the development of Spaceflight Associated Neuro-ocular Syndrome (S...

Enhancing differentiation between unipolar and bipolar depression through integration of machine learning and electroencephalogram analysis.

To enhance the differentiation between unipolar depression (UPD) and bipolar depression (BPD), this study integrates machine learning and deep learnin...

Artery fragment guided approach for enhancing cerebral aneurysm detection in TOF-MRA imaging.

BACKGROUND: Cerebral aneurysms are a type of cerebrovascular disease that poses a severe threat to life and health. Early screening using Time-of-Flig...

Early Alzheimer's Detection Through Voice Analysis: Harnessing Locally Deployable LLMs via , a privacy-preserving diagnostic system.

Diagnosing Alzheimer's Disease (AD) early and cost-effectively is crucial. Recent advancements in Large Language Models (LLMs) like ChatGPT have made ...

Comparison of Machine Learning Models in Predicting Mental Health Sequelae Following Concussion in Youth.

Youth who experience concussions may be at greater risk for subsequent mental health challenges, making early detection crucial for timely interventio...

Determining the Importance of Clinical Modalities for NeuroDegenerative Disorders and Risk of Patient Injury Using Machine Learning and Survival Analysis.

Falls among the elderly and especially those with NeuroDegenerative Disorders (NDD) reduces life expectancy. The purpose of this study is to explore t...

Prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum UCH-L1, S100β, and NSE: a multicenter prospective cohort study employing machine learning methods.

BACKGROUND: Acute ischemic stroke (AIS) is a leading cause of mortality and disability worldwide. Intravenous thrombolysis (IVT) improves recovery, bu...

A Machine Learning Model to Predict Treatment Effect Associated with Targeted Temperature Management After Cardiac Arrest.

BACKGROUND: Targeted temperature management (TTM) has been associated with neurological recovery among comatose survivors of cardiac arrest. The aim o...

Comparison of lesion segmentation performance in diffusion-weighted imaging and apparent diffusion coefficient images of stroke by artificial neural networks.

Stroke is the second leading cause of death, accounting for 11% of deaths worldwide. Comparing diffusion-weighted imaging (DWI) and apparent diffusion...

Robotic-assisted gait training for spinal cord injury neuropathic pain: A systematic review.

CONTEXT: Neuropathic pain (NP), can be a debilitating consequence of spinal cord injury. Robotic-assisted gait training (RAGT) is an effective rehabil...

Emotion recognition with multiple physiological parameters based on ensemble learning.

Emotion recognition is a key research area in artificial intelligence, playing a critical role in enhancing human-computer interaction and optimizing ...

Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases.

Gut microbiota has been implicated in the pathogenesis of multiple gastrointestinal (GI) and systemic metabolic and inflammatory disorders where disru...

Development of machine learning models for gait-based classification of incomplete spinal cord injuries and cauda equina syndrome.

Incomplete tetraplegia, incomplete paraplegia, and cauda equina syndrome are major neurological disorders that significantly reduce patients' quality ...