Neurology

Predicting gait kinematics in youth with cerebral palsy using clinically informed machine learning algorithms.

BACKGROUND: Gait deviations are common in youth with Cerebral Palsy (CP), with the change in gait pattern during growth/development being influenced by a variety of individual and treatment factors. The goal of this study is to use Machine Learning (ML) to evaluate the relationship between youth and treatment-related factors to predict changes in gait kinematics. METHODS: Kinematic gait data acqui...

The cerebro-cerebellar system: Integrative roles in motor control, cognition, and neuropsychiatric disorders.

The cerebro-cerebellar system, a network of bidirectional loops between the cerebellum and cerebral cortex, is crucial for coordinating motor control, cognition, and emotion. Moving beyond its traditional role in motor coordination, contemporary research underscores the cerebellum's significant involvement in higher-order functions, including executive control, language, and social cognition. This...

Deep Learning-Driven Transmission Electron Microscopy Analysis of Murine Optic Nerve Myelinated Axons.

OBJECTIVE: To develop and validate a deep learning-based method for automated quantification of retinal ganglion cell axons in transmission electron m...

Proteomic profile of CSF obtained at the time of diagnosis determines amyotrophic lateral sclerosis progression and survival: CXCL7 levels in disease prognosis and survival.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease primarily affecting motor neurons. Neurofilament light chain (NfL) is the mos...

Natural language processing of biomedical text to map and prioritize protein-disease associations in HFpEF.

The validation of promising clinical biomarkers, molecular mechanisms, and novel drug targets in cardiovascular disease (CVD) is hindered by a vast an...

The role of ionotropic glutamate receptors in Alzheimer's disease: A scientometric analysis.

BackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-β plaques, neurofibrillary tangles, and synapt...

A CNN-transformer fusion for EEG-based discrimination of Alzheimer's and frontotemporal dementia.

Dementia is a progressive neurodegenerative disorder that severely impacts cognitive functions and daily living, especially in aging populations. Amon...

WGB-GLFI: A Novel Graph-Based Global-Local Feature Interaction Framework for Automated Seizure Detection.

Epilepsy detection faces significant challenges due to unpredictable seizures, ranging from brief awareness lapses to severe convulsions, posing risks...

Impedance Myography Circle Mapping: A Geometry-Based Multi-Frequency EIM Framework for Quantitative Assessment of Post-Stroke Muscle Impairment.

Post-stroke neuromuscular impairment arises from heterogeneous structural and electrophysiological alterations, including disrupted membrane integrity...

ICP-WAVES: Intracranial Pressure Waveform Analysis and Visualization for Enhanced Signal Processing.

OBJECTIVE: Intracranial pressure (ICP) waveform morphology reflects brain compliance and cerebrospinal fluid dynamics. Existing monitoring methods fai...

Multimodal Wearable Sensor-Based Stress Detection: Machine Learning Pipeline with Systematic Feature Selection and Key Biomarker Insights.

The increasing awareness of stress-related health impacts has driven demand for accurate, non-invasive stress detection methods, particularly those le...

Attention-enhanced deep learning model for automated classification of hemorrhagic and ischemic stroke on CT imaging.

BACKGROUND: Stroke is a disease with extremely high mortality and disability rates worldwide. Hemorrhagic stroke and ischemic stroke require completel...

The value of triglyceride-glucose-body shape index (TyG-ABSI) in evaluating stroke: a multi-centre cross-sectional study.

BACKGROUND: Stroke is the second leading cause of disability and mortality worldwide. A body shape index (ABSI) is calculated as waist circumference /...

Elucidating biopsychosocial mechanisms in migraine: an integrative analytics approach combining genetics, neuroimaging, and machine learning.

BACKGROUND: Migraine is a complex and disabling neurological disorder shaped by the interplay of biological, psychological, and behavioral factors. Cu...

A novel framework for cognitive state identification using resting-state EEG.

Recent advancements in cognitive impairment research have led to significant progress. Electroencephalography (EEG)-based cognitive state identificati...

ARIMA-based forecasting of cerebral physiologic signals in acute traumatic brain injury: a CAnadian high-resolution TBI (CAHR-TBI) cohort study.

BACKGROUND: Traumatic brain injury (TBI) remains a major global health issue, with limited progress in reducing morbidity and mortality for TBI patien...

M[Formula: see text]DGAT: Multi-view multi-scale dynamic graph attention network(GAT) based prediction of Parkinson's disease(PD) progression using whole-blood RNA sequencing data.

With emerging single-cell transcriptomics data, deep learning approaches have enabled the diagnosis of neurodegenerative disorders such as Parkinson's...

Integrating attractor dynamics and connectivity features for EEG-based dementia classification.

The research progress of wearable digital health technologies in epilepsy management.

Epilepsy is one of the most common neurological disorders, characterized by recurrent, unpredictable seizures. Due to the unpredictability of seizures...

Integrating multimodal data and deep learning for functional assessment and rehabilitation prediction after cerebral hemorrhage.

BACKGROUND: Intracerebral hemorrhage (ICH) is a leading cause of long-term disability, particularly in China. Post-stroke motor recovery exhibits cons...