Neurology

Estimating global phase synchronization by quantifying multivariate mutual information and detecting network structure.

In neuroscience, phase synchronization (PS) is a crucial mechanism that facilitates information processing and transmission between different brain regions. Specifically, global phase synchronization (GPS) characterizes the degree of PS among multivariate neural signals. In recent years, several GPS methods have been proposed. However, they primarily focus on the collective synchronization behavio...

Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations.

Stroke is a neurological complication that can occur in patients admitted to the intensive care unit (ICU) for non-neurological conditions, leading to increased mortality and prolonged hospital stays. The incidence of stroke in ICU settings is notably higher compared to the general population, and delays in diagnosis can lead to irreversible neurological damage. Early diagnosis of stroke is critic...

Enhancement and evaluation for deep learning-based classification of volumetric neuroimaging with 3D-to-2D knowledge distillation.

The application of deep learning techniques for the analysis of neuroimaging has been increasing recently. The 3D Convolutional Neural Network (CNN) t...

MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification.

Decoding lower-limb motor imagery (MI) is highly important in brain-computer interfaces (BCIs) and rehabilitation engineering. However, it is challeng...

STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data.

: Early diagnosis of depression is crucial for effective treatment and suicide prevention. Traditional methods rely on self-report questionnaires and ...

CC-TransXNet: a hybrid CNN-transformer network for automatic segmentation of optic cup and optic disk from fundus images.

Accurate segmentation of the optic disk (OD) and optic cup (OC) regions of the optic nerve head is a critical step in glaucoma diagnosis. Existing arc...

MRI classification and discrimination of spinal schwannoma and meningioma based on deep learning.

BACKGROUD: Schwannoma (SCH) and meningiomas (MEN) are the two most common primary spinal cord tumors. Differentiating between them preoperatively rema...

Distinguishing among standing postures with machine learning-based classification algorithms.

The purpose of our study was to evaluate the accuracy with which classification algorithms could distinguish among standing postures based on center-o...

Predicting executive functioning from walking features in Parkinson's disease using machine learning.

Parkinson's disease is characterized by motor and cognitive deficits. While previous work suggests a relationship between both, direct empirical evide...

Use of Artificial Intelligence in Imaging Dementia.

Alzheimer's disease is the most common cause of dementia in the elderly population (aged 65 years and over), followed by vascular dementia, Lewy body ...

Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI.

Early detection and precise characterization of brain tumors play a crucial role in improving patient outcomes and extending survival rates. Among neu...

Current update on the neurological manifestations of long COVID: more questions than answers.

Since the outbreak of the COVID-19 pandemic, there has been a global surge in patients presenting with prolonged or late-onset debilitating sequelae o...

BPEN: Brain Posterior Evidential Network for trustworthy brain imaging analysis.

The application of deep learning techniques to analyze brain functional magnetic resonance imaging (fMRI) data has led to significant advancements in ...

Effective Alzheimer's disease detection using enhanced Xception blending with snapshot ensemble.

Alzheimer's disease (AD), a prevalent neurodegenerative disorder, leads to progressive dementia, which impairs decision-making, problem-solving, and c...

Deep learning-enhanced automated mitochondrial segmentation in FIB-SEM images using an entropy-weighted ensemble approach.

Mitochondria are intracellular organelles that act as powerhouses by breaking down nutrition molecules to produce adenosine triphosphate (ATP) as cell...

Improving care for amyotrophic lateral sclerosis with artificial intelligence and affective computing.

BACKGROUND: Patients with ALS often face difficulties expressing emotions due to impairments in facial expression, speech, body language, and cognitiv...

PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets.

Acute stroke demands prompt diagnosis and treatment to achieve optimal patient outcomes. However, the intricate and irregular nature of clinical data ...

Deep-learning assessment of hippocampal magnetic susceptibility in Alzheimer's disease.

BACKGROUND: Quantitative susceptibility mapping (QSM) is pivotal for analyzing neurodegenerative diseases. However, accurate hippocampal segmentation ...

Identification and cognitive function prediction of Alzheimer's disease based on multivariate pattern analysis of hippocampal volumes.

BACKGROUND: Alzheimer's disease (AD) is strongly associated with slowly progressive hippocampal atrophy. Elucidating the relationships between local m...

Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer's disease and normal aging.

OBJECTIVE: To apply machine learning approaches on EEG event-related oscillations (ERO) to discriminate preclinical Alzheimer's disease (AD) from age-...